Artificial intelligence already supports a wide range of applications, from medical diagnostics and financial systems to logistics, manufacturing, defence and public service delivery. Now, it is starting to move closer to public office.

In January 2025, Albania introduced Diella, an AI-powered virtual assistant developed by the National Agency for Information Society, known as AKSHI, with support from Microsoft. Launched on the e-Albania platform, the government’s digital services portal, Diella helps citizens and businesses access official documents and services through voice assistance. She can also issue electronically stamped documents, which helps speed up administrative processes.

Then, in September 2025, Prime Minister Edi Rama announced that Diella would join his cabinet as the “Minister of State for Artificial Intelligence”. This move drew global attention. It also raised a simple question: what does it actually mean for a government to appoint an AI minister?  

The case raises bigger questions for governments everywhere. Can an AI minister make public services faster and cleaner? Or does it create new risks around transparency, accountability and control?

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Who is Diella, Albania’s AI minister?

Diella is not a humanoid robot sitting in a cabinet room. On screen, she appears as a digitally rendered woman wearing traditional-style Albanian clothing. Her name means “sun” in Albanian, a deliberate choice for a system meant to bring more light into public administration.  

Her face and voice have become part of the controversy. Albanian actor Anila Bisha has said she agreed for her likeness to be used for the e-Albania public services platform, but not for a cabinet-level political role. In 2026, she took legal action to stop the government from using her image and voice for Diella. For now, the government has denied wrongdoing.

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What does Diella actually do?

Diella began as a digital assistant on e-Albania. In that role, she helps users find services, request documents and navigate government processes online. For citizens, that can make public services feel less confusing. Businesses may also spend less time dealing with paperwork.

Her cabinet role is more political. The government wants Diella to support public procurement, where companies compete for government contracts. This is one of the most important areas of public spending. It is also one of the easiest places for corruption, favouritism and hidden influence to enter. The goal is to use AI to process information, check documents, support tender procedures and make the system more traceable.  

That said, the government has emphasized that Diella is not replacing elected officials or civil servants. As per Enio Kaso, director of AI at AKSHI, each stage will be monitored and approved by human experts.

In May 2026, the Albanian government said it had completed the technical groundwork for the AI-powered public procurement system under the Diella project. The planned system would pull data from more than 40 digital public registries, reduce paperwork for businesses and support parts of the tender process. Earlier reports said the government hoped to have the full system ready by the end of 2026.

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Why Albania wants AI in public procurement

The government’s case for Diella is built around anti-corruption reform. Rama has said the goal is to “wipe out every potential influence on public biddings” and thus make public tenders “100% free of corruption”. That is a bold promise, especially in a country where procurement scandals have long damaged public confidence and complicated Albania’s path toward European Union membership.  

At first glance, the logic is easy to understand. AI does not ask for bribes or favour a cousin—a big problem in the country, according to Rama—a friend or a political ally. It can apply the same rules across a large number of applications. Moreover, it can also leave a digital trail, which should make later review easier.

Some anti-corruption and governance experts see real potential in that approach. Dr. Andi Hoxhaj of King’s College London has said that if used well and programmed properly, AI could help procurement officials spot missing documents, check whether companies meet eligibility requirements and flag unusual patterns in bids. In practice, that could make the process more consistent and make it harder for individual officials to quietly bend rules.

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The risks behind AI in government

Diella’s appeal is speed and consistency. Her weakness is dependence.  

Like any AI system, Diella relies on the quality of the data, rules and models behind her. Erjon Curraj, an expert in digital transformation and cybersecurity, has warned that incomplete, outdated or biased data can lead to flawed results. Poor design could also cause the system to reject a valid supplier, miss signs of collusion or treat similar cases differently for reasons that are hard to explain.

In public procurement, those mistakes can have serious consequences. A wrongly flagged company could lose a major contract, and a corrupt bidder could slip through. Government agencies could hide behind the AI and say the system made the recommendation.

That leads to the biggest question: who is accountable when something goes wrong?

The answer cannot be “the AI” because Diella cannot resign. She cannot face voters. Nor can she be cross-examined in any meaningful human sense. Accountability has to sit with ministers, agencies, auditors and courts.

There is also the issue of transparency. If Diella is helping screen tenders, businesses need to know what criteria are being used. They also need a way to challenge incorrect decisions. Citizens should be told whether the AI is making recommendations or merely organizing information. Independent auditors need access to logs, data sources and decision pathways.

Without those safeguards, AI in government can become a black box. It may look modern from the outside, while making power harder to question.

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Diella, politics and public trust

Diella has also become a political symbol. Supporters see her as proof that a small country can move quickly and experiment with new forms of digital government. Critics see her as a distraction from deeper problems in Albania’s institutions.

Both readings can be true at the same time: Diella may help modernize public services, but she may also be used to project reform while older problems continue in the background.

That tension became clearer after the recent procurement investigations involving senior officials since Diella’s appointment. Deputy Prime Minister Belinda Balluku has been accused by prosecutors of alleged misconduct linked to infrastructure tenders, which she denies. Senior figures at AKSHI, the agency behind Diella and e-Albania, have also been placed under house arrest as part of a separate public procurement investigation.  

While these developments do not automatically discredit Diella, they may strengthen the argument for better digital oversight. More importantly, they also show that technology cannot carry the whole burden of reform.

If the institutions around an AI system are weak, the AI will not magically make them strong. Unclear procurement rules will still cause problems, and the process will still be compromised when political pressure shapes the data, the model or the final decision.

After all, AI can support integrity; it cannot replace it.

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Finding the right balance for AI in government

While Diella is already a public symbol of AI in government, her most important procurement role is still taking shape. This makes Albania’s experiment both ambitious and unfinished.

The more realistic model is simple: let AI handle repetitive, data-heavy administrative work. Let humans retain authority where judgment, context and public accountability matter.  

That means AI can help draft tender criteria, check documents, summarise bids and flag risks. Human officials should still make final decisions, explain those decisions and take responsibility for them. Meanwhile, independent bodies should be able to audit the process, and businesses should have a clear appeal route when they believe the system has made a mistake.

Diella once said she felt “hurt” while responding in parliament to claims that her role was unconstitutional. While this made for a memorable moment, it is important to remember simulated emotion is not consciousness, speed is not wisdom, and pattern recognition is not moral judgment.

Albania’s AI minister is therefore neither a triumph nor a failure at this stage. She is a live test case. Other governments will be watching closely, especially as public services become more digital and more automated.

The lesson is not that AI should stay out of government, but that AI must enter government carefully. The technology needs clear limits, public oversight and human accountability.

Diella may help Albania build a faster and cleaner procurement system—or she may become a warning about giving too much symbolic power to systems people do not fully understand. The final judgment will not come from the title “AI minister”. It will come from what the system does, who controls it and whether citizens can trust the results.

As AI companies continue scaling larger models and data centers, the pressure is no longer falling only on chip design. Manufacturing capacity, advanced packaging and infrastructure deployment are becoming equally important parts of the AI race. AMD’s latest investment announcement reflects how quickly that shift is accelerating.

The US chipmaker announced plans to invest more than US$10 billion across Taiwan’s semiconductor and manufacturing ecosystem to support next-generation AI infrastructure. The investment focuses on expanding partnerships and increasing advanced packaging capacity needed for future AI systems.

The announcement highlights a growing reality across the AI industry. Building powerful AI chips is no longer enough on its own. Companies now also need the manufacturing networks, packaging technologies and supply chain coordination required to deploy AI infrastructure at global scale.

AMD’s investments center heavily around advanced chip packaging, an area becoming increasingly critical as AI systems demand higher performance and greater power efficiency. Traditional chip architectures are struggling to keep pace with the size and complexity of modern AI workloads. Advanced packaging helps connect processors, memory and computing systems more efficiently while managing power and cooling limitations inside large-scale AI environments.

The company said it is working with Taiwan-based partners including ASE, SPIL and PTI to develop next-generation packaging technologies for its upcoming 6th Gen AMD EPYC processors, codenamed “Venice.” AMD also said it had qualified what it described as the industry’s first 2.5D panel-based EFB interconnect technology alongside PTI.

At the center of the broader strategy is AMD Helios, the company’s rack-scale AI infrastructure platform scheduled for deployment beginning in the second half of 2026. The platform combines AMD Instinct MI450X GPUs, 6th Gen EPYC CPUs, networking systems and AMD’s ROCm software stack into integrated AI infrastructure systems designed for hyperscale deployment.

Rather than selling individual processors alone, companies are increasingly building complete AI infrastructure platforms that combine hardware, software, cooling systems and power management into unified deployments. That transition is reshaping how AI infrastructure is designed, manufactured and delivered.

Taiwan is also becoming more deeply embedded in that process. AMD’s investment spans not only semiconductor packaging companies but also manufacturing and system integration partners including Sanmina, Wiwynn, Wistron and Inventec. The partnerships reflect Taiwan’s growing role as one of the operational centers of the global AI infrastructure economy.

Dr. Lisa Su, Chair and CEO of AMD, said: “As AI adoption accelerates, our global customers are rapidly scaling AI infrastructure to meet growing compute demand. By combining AMD leadership in high-performance computing with the Taiwan ecosystem and our strategic global partners, we are enabling integrated, rack-scale AI infrastructure that helps customers accelerate deployment of next-generation AI systems”.

Power efficiency is becoming another major challenge shaping AI infrastructure decisions. As AI workloads consume more electricity and generate more heat, infrastructure providers are increasingly being forced to rethink cooling systems, interconnect technologies and deployment economics.

AMD’s announcement signals how the AI competition is evolving beyond model development and raw computing power. The next stage may depend just as heavily on who can manufacture, package and deploy AI infrastructure fast enough to support global demand.

As AI companies build larger models and deploy more AI agents, the industry is running into a new constraint: electricity. The challenge is no longer just about computing power. It is increasingly about how to supply, manage and sustain the energy needed to run AI infrastructure at scale.

That was the central argument behind Huawei’s latest AI data center strategy unveiled at its Global AIDC Industry Summit in Dongguan.

The company introduced what it calls a grid-interactive AIDC strategy, focused on redesigning AI data centers around power supply, cooling systems and energy management. AIDC refers to AI data centers built specifically for large-scale AI computing workloads.

The announcement reflects a broader shift happening across the industry. As AI systems grow larger, data centers are consuming more electricity and generating more heat than traditional computing infrastructure was designed to handle. Companies are now being forced to rethink not just chips and servers, but the physical systems supporting them.

Huawei argues that future AI infrastructure will need closer coordination between computing systems and energy grids. The company says traditional data center designs are struggling to keep up with fluctuating AI workloads, rising power density and the growing use of renewable energy sources.

Hou Jinlong, Director of the Board of Huawei and President of Huawei Digital Power, said: "The booming AI industry, widely adopted large models, and numerous AI agents are creating huge energy demands, set to boost the global AIDC capacity. Electricity is essential for computing; energy is the foundation for AI long-term development. Computing and electricity will deeply synergize and empower each other, progressively building an integrated framework that brings together new power systems and AI infrastructure."

A large part of Huawei’s strategy focuses on power architecture. AI workloads can create sudden spikes in electricity demand, especially in high-density computing environments. To manage that, Huawei says it plans to develop new power systems that combine grid-friendly UPS infrastructure with energy storage technologies.

Cooling is becoming another major pressure point. AI servers generate significantly more heat than traditional enterprise systems and Huawei says liquid cooling is now becoming essential for large-scale AI deployments. The company introduced a liquid cooling system designed to improve long-term thermal management inside high-density AI environments.

Huawei is also pushing modular construction methods to reduce deployment times for AI data centers. Instead of building infrastructure entirely onsite, parts of the system can be prefabricated and tested in factories before installation.

Bob He, Vice President of Huawei Digital Power, said: "The global AI industry is booming, and the token demand surges. As such, the AIDC industry is entering the Token era."

As part of that shift, Huawei introduced a proposed measurement system called the TokEnergy Index. The company says the metric is designed to measure the relationship between energy consumption and AI computing output, rather than relying only on traditional data center efficiency metrics such as PUE.

The broader message behind the strategy is that AI infrastructure is becoming an energy engineering problem as much as a computing problem. As global demand for AI continues to rise, companies across the sector are beginning to realise that the future of AI may depend not only on better models, but also on whether power grids and data centers can keep up with them.

Investor interest in humanoid robotics is continuing to grow as startups race to build systems capable of working alongside humans in real-world environments. That momentum was reflected after WIRobotics announced a KRW 95 billion (USD 68 million) Series B funding round to accelerate development of its humanoid robotics platform, ALLEX.

The Seoul-based startup said the funding comes roughly two years after its KRW 13 billion Series A round in 2024. JB Investment led the financing alongside investors including InterVest, Hana Ventures, Smilegate Investment, SBVA, NH Investment & Securities, Company K Partners, GU Investment and FuturePlay.

WIRobotics has spent the past several years building wearable robotics systems designed to assist human movement. The startup is now using that foundation to expand deeper into humanoid robotics and Physical AI, a category focused on AI systems that can interact with the physical world through movement, perception and manipulation.

Its humanoid platform, ALLEX, is being developed to support human-level object manipulation and interaction capabilities. The startup was recently selected for NVIDIA’s Physical AI Fellowship, a global robotics and AI development initiative aimed at supporting next-generation robotics research.

Rather than building humanoid systems entirely from scratch, WIRobotics is drawing on movement data collected through its wearable walking-assist robot, WIM. Over the past three years, the startup says it has built large real-world datasets around gait patterns, mobility and human movement control.

That wearable robotics business has also started showing commercial traction. WIM has sold more than 3,000 cumulative units and expanded into overseas markets including Europe, China, Türkiye and Japan. Revenue grew from KRW 560 million in 2023 to KRW 1.3 billion in 2024, then to KRW 2.79 billion in 2025. According to the startup, first-quarter 2026 revenue has already surpassed its full-year 2024 total.

The startup believes that real-world movement data collected through wearable robotics could become a competitive advantage as humanoid systems move closer to commercial deployment. WIRobotics is also expanding its global footprint alongside its robotics development efforts. The startup said it is establishing a North American entity in California while growing partnerships with overseas distributors and healthcare networks.

Its humanoid ambitions are moving into a more operational phase as well. Beginning later this year, WIRobotics plans to supply a research-focused version of its Mobile ALLEX platform to global research institutions and international partners for testing and collaborative development. The startup is also in discussions with a global automotive manufacturer around manufacturing-focused platform validation projects.

Yeonbaek Lee said: "This investment represents global recognition that the real-world movement data and control technologies accumulated through wearable robotics can evolve into next-generation humanoid robotics. We aim to accelerate the arrival of humanoid robots capable of interacting naturally with people".

Yongjae Kim added: "All investors from our previous Series A round participated again in this Series B financing, demonstrating strong confidence in WIRobotics' technological capabilities and growth potential amid intensifying global humanoid competition. Our mission is to realize humanoids capable of fundamentally human-like interaction and force control, driving a paradigm shift in high-performance manipulation technologies".

As competition intensifies across humanoid robotics, startups are increasingly trying to differentiate themselves through real-world deployment data rather than simulation alone. WIRobotics is positioning its wearable robotics business as the foundation for that transition, betting that years of human movement data could help shape the next generation of humanoid systems.

As copyright disputes continue to grow around AI-generated music, Sonilo, the world’s first professionally licensed video-to-music AI platform, has partnered with Shutterstock to train its models on licensed music catalogs.

The agreement gives Sonilo access to Shutterstock’s music library for AI model training. According to the companies, it is Shutterstock’s first partnership with a video-to-music AI platform and the timing is significant. AI music companies are facing growing pressure over how their systems are trained. Artists and record labels have increasingly challenged the use of copyrighted music in AI datasets, especially when licensing agreements or compensation structures are unclear.

That tension has created a divide across the industry. Some companies have continued building models around scraped or disputed data. Others are trying to position licensing as part of the product itself.

Sonilo falls into the second group. The company says its models are trained only on licensed material where artists and rights holders have agreed to participate and receive compensation. The Shutterstock partnership strengthens that position while giving Sonilo access to a larger pool of commercially cleared music.

The collaboration also points to a broader change happening inside generative AI. As AI tools move into commercial production, companies are being pushed to show not just what their models can generate, but also where their training data comes from.

Sonilo’s platform is built around video rather than text prompts. The system analyses footage directly, studies pacing and emotional tone, then generates an original soundtrack to match the content. The company says this removes the need for manual music searches, syncing or editing workflows. The generated tracks are cleared for commercial use across social media, branded content and broadcast production.

Shawn Song, CEO of Sonilo, said: "Music has always been the last unsolved layer of video creation, and video has always carried its own soundtrack. We built Sonilo to hear it and compose from it, without a single text prompt. But how we build matters as much as what we build. While others have chosen to take artists' work without permission and charge creators for the privilege, we've chosen a different path—one where artists are compensated from day one. Partnering with Shutterstock reflects that standard. Every model we train meets a bar the music industry can stand behind, because the most innovative AI platforms don't have to come at the expense of the artists who make all of these possible."

For Shutterstock, the deal expands the company’s growing role in generative AI infrastructure. The company has increasingly focused on licensing content for AI systems across images, video and music.

Jessica April, Vice President of Data Licensing & AI Services at Shutterstock, said: "AI innovation depends on access to high-quality, rights-cleared content and trusted licensing partnerships. Sonilo's approach reflects the growing demand for responsibly sourced training data and commercially safe AI workflows. We're pleased to support companies building generative AI products with licensed content and scalable data solutions that help accelerate innovation while respecting creators and rights holders."

The partnership also comes as Sonilo expands into creator and developer ecosystems. Earlier this month, the company launched as a native node inside ComfyUI, an open-source AI workflow platform used by millions of creators. Sonilo also offers API access for integration into creator tools, video platforms, game engines and other AI systems.

As AI-generated music becomes more common across advertising, creator platforms and digital media, the industry’s focus is shifting beyond generation alone. Questions around licensing, ownership and compensation are increasingly shaping how AI music companies position themselves and build trust with creators.

Votee AI, an enterprise AI company headquartered in Hong Kong, has partnered with its Toronto-based research lab Beever AI to launch Beever Atlas. The new platform is designed to turn workplace chats into searchable knowledge that AI systems can retrieve and understand.

The release focuses on a growing issue inside organisations. Much of today’s workplace knowledge now exists inside chat platforms such as Slack, Microsoft Teams, Discord and Telegram. Important discussions, project decisions and technical information often disappear into long message histories that are difficult to search later.

Beever AI developed the platform to organise those conversations into a structured system for AI assistants. The software connects with Telegram, Discord, Mattermost, Microsoft Teams and Slack, then converts conversations into linked records of people, projects, files and decisions.

The collaboration combines Votee AI’s enterprise infrastructure work with Beever AI’s research around AI memory systems. The companies are releasing two versions of the product. The open-source edition is aimed at individual developers, researchers and creators. The enterprise edition is designed for banks, government agencies and larger organisations with stricter security requirements.

The release also reflects a broader shift happening across the AI industry. Companies are increasingly looking at how AI systems store and retrieve long-term knowledge, rather than relying solely on large context windows or search-based retrieval.

Earlier this year, OpenAI founding member and former director of AI at Tesla  Andrej Karpathy discussed the growing need for what he described as “LLM Knowledge Bases.” He argued that AI systems need structured and evolving memory rather than depending only on context windows and vector search.

Beever Atlas approaches that problem through workplace communication. Instead of focusing mainly on uploaded files, the system is designed around conversations that happen daily across team chat platforms. It can also process images, PDFs, voice notes and video files within the same searchable system.

The companies say the software is designed to work directly with AI assistants and coding tools such as Cursor, AWS Kiro and Qwen Code. Integrations for OpenClaw and Hermes Agent are expected later in 2026.

Pak-Sun Ting, Co-Founder and CEO of Votee AI  said: "Hong Kong has always been known for property and finance. Beever Atlas is proof that world-class AI infrastructure can emerge from an HK-headquartered company and be shared openly with the world. Every growing organization faces the same silent liability: conversational knowledge loss. Beever Atlas turns this perishable resource into a compounding organizational asset."

A large part of the enterprise version focuses on privacy and access control. The system mirrors permissions from Slack and Microsoft Teams so users can only retrieve information they are already authorised to access. Permission updates are reflected automatically when access changes inside company systems.

The enterprise edition also includes audit logs, encryption controls and data retention settings for organisations handling sensitive internal data. Companies can run the software entirely inside their own infrastructure using Docker and connect it to their preferred AI models through LiteLLM.

The companies argue that organising information is more useful than simply storing chat archives. Jacky Chan Co-Founder and CTO of Votee AI said: "The key technical decision was to treat agent memory as a knowledge engineering problem, not a retrieval problem. Structure beats similarity — a typed graph of who works on what is more useful to an AI than vector search over a Slack archive."

The software also includes protections against prompt injection attacks and systems designed to reduce hallucinated responses. According to the companies, the AI is designed to return “I don't know” with citations when confidence is low instead of generating unsupported answers.

As workplace communication becomes increasingly fragmented across chat platforms, companies are beginning to treat internal conversations as information that AI systems can organise, retrieve and build on. Beever Atlas reflects a broader push to turn everyday workplace communication into long-term organisational memory.

A new survey from Povaddo, a policy research firm, suggests that concern about artificial intelligence is no longer limited to industry or academia. It is now firmly present within the policy community.

The survey draws on responses from 301 public policy professionals across the United States and Europe, including lawmakers, staffers and analysts involved in shaping and evaluating public policy. A majority of respondents—61%—say governments are falling short in addressing the negative impacts of AI.

There is also broad agreement that regulation needs to increase. In the United States, 92% of respondents support stronger AI regulation, compared to 70% in Europe. At a time when consensus is often difficult, the findings point to a shared view across policy circles that current frameworks are not keeping pace with technological development.

Differences emerge when looking at how AI is affecting national contexts. In the U.S., 57% of policy experts believe AI is already harming the labor market. In Europe, 34% say the same. U.S. respondents are also more likely to see AI as a greater threat to jobs than immigration, with 63% holding that view compared to 47% in Europe.

On misinformation, responses are closely aligned. A large majority of policy experts in both regions expect an AI-driven misinformation crisis within the next one to two years—87% in the U.S. and 82% in Europe. Many also believe that AI-generated or AI-amplified misinformation could affect elections and public health information.

Some respondents frame the risks in more fundamental terms. In the United States, 41% of policy experts say AI poses an existential threat to humanity. In Europe, 29% share that view. U.S. respondents are also more likely to believe that advances in AI could harm global security and stability.

The findings come as policymakers begin to respond more actively. In the U.S., Senators Josh Hawley, Richard Blumenthal and Mark Warner have introduced bipartisan legislation focused on AI accountability, including measures aimed at protecting workers and children.

In Europe, the introduction of the EU AI Act marks a more advanced regulatory approach. The framework sets out rules based on levels of risk and is widely seen as the first comprehensive attempt to govern AI at scale.

William Stewart, President and Founder of Povaddo, said: "What makes these findings so significant is who is saying it. These are the practitioners who work inside the policy process every day, spanning every corner of the policy world from defense to healthcare to finance, not activists or everyday citizens. These findings foreshadow real action. The current path of governments accelerating AI deployment while falling short on governance is not sustainable, and the people who know that best are the ones in this survey. You cannot have nine-in-ten policy insiders demanding more regulation and four-in-ten calling AI an existential threat without that eventually moving the needle in Washington and Brussels in terms of legislative or regulatory action".

Taken together, the survey reflects a shift in how AI is being discussed within policymaking circles. Concern is no longer limited to future risks. It is increasingly tied to current gaps in governance and the pace of deployment.

MagicLab Robotics is a Chinese startup that describes itself as an embodied AI company. At an event in Silicon Valley this week, it outlined its global ambitions and introduced new products designed for real-world use. The company said its international business now spans more than 50 countries and regions, with overseas markets accounting for 60% of total sales in 2025. That gives some indication of how quickly Chinese robotics firms are expanding beyond their home market.

At the centre of the announcement was MagicLab’s latest product line-up. It included Magic-Mix, described as a foundational world model for robots, the H01 dexterous robotic hand and its humanoid robot, MagicBot X1. In practical terms, the company is trying to build robots that can better understand their surroundings and perform physical tasks with greater precision. That is the core idea behind embodied AI, where intelligence is combined with movement and interaction in the real world rather than limited to software alone.

MagicLab says it develops both hardware and software internally. Its product range includes humanoid robots and four-legged machines, with systems designed for factories, commercial services and home use. The company also outlined where it sees demand emerging. It listed sectors such as healthcare, manufacturing, logistics, security, public safety, education and household assistance.

That wide spread of target markets reflects a broader challenge in robotics. Building capable machines is only one part of the equation. The harder task is finding enough practical uses where customers are willing to pay for them.

MagicLab also used the summit to set out a long-term commercial goal. It projected a path toward US$14 billion in annual revenue by 2036 through wider adoption of embodied AI systems. It also announced what it calls the “Co-Create 1000 Initiative”, a plan to work with external developers and partner companies.

As part of that effort, the startup said it plans to invest US$1 billion over the next five years to build a developer ecosystem that would allow third parties to create new applications for its robots. The strategy mirrors what happened in smartphones and cloud software, where ecosystems often mattered as much as the original hardware. If robotics follows a similar path, companies that attract developers could gain an advantage over those selling machines alone.

For now, MagicLab’s announcement is less about immediate breakthroughs and more about positioning. The company is presenting itself not simply as a robot maker, but as a platform business seeking a role in the next phase of intelligent machines.

Flexcompute, a startup that builds software to simulate real-world physics, is working with Northrop Grumman to change how space missions are prepared. Together, they have developed an AI-based system that can predict how spacecraft respond during critical manoeuvres such as docking—when one spacecraft moves in and connects with another in orbit. These steps have traditionally taken months of preparation.

At the centre of this work is a long-standing problem in space operations. When a spacecraft fires its thrusters, the exhaust plume interacts with nearby surfaces. These interactions can affect movement, temperature and stability. Because these effects are difficult to test in real conditions, engineers have relied on large volumes of computer simulations to estimate outcomes before a mission. That process is slow and resource-intensive.

The new system replaces much of that workflow with a trained AI model. Instead of running millions of simulations, the model learns patterns from physics-based data and can make predictions in seconds. It also provides a measure of uncertainty, which helps engineers understand how reliable those predictions are when making decisions.

"At Northrop Grumman, we're pioneering physics AI to accelerate design and solve complex simulation and modelling problems like plume impingement—critical for station keeping, rendezvous and space robotics. Simply put: we're pushing the boundaries of advanced space operations", said Fahad Khan, Director of AI Foundations at Northrop Grumman. "Partnering with Flexcompute and NVIDIA, we're accelerating innovation and mission timelines to deliver superior space capabilities for customers at the speed they need".

The system is built using technology from NVIDIA, which provides the computing framework behind the model. Flexcompute has adapted it to handle the specific challenges of spaceflight, including how gases expand and interact in a vacuum. The result is a tool that can simulate complex scenarios much faster while maintaining the level of accuracy needed for mission planning.

By shortening preparation time, the model changes how engineers approach spacecraft design and operations. Faster predictions mean teams can test more scenarios and adjust plans more quickly. It also helps improve fuel use and extend the lifespan of spacecraft.

"Northrop Grumman's confidence reflects what sets Flexcompute apart", said Vera Yang, President and Co-Founder of Flexcompute. "We are able to take the most accurate and scalable physics foundations and evolve them into highly trained, customized Physics AI solutions that engineers can rely on. This work shows how we are transforming the role of simulation, not just speeding it up, but expanding what engineers can confidently solve and how quickly they can act".

The collaboration points to a broader shift in how engineering problems are being handled. Instead of relying only on detailed simulations that take time to run, companies are beginning to use AI systems that can approximate those results quickly while still reflecting the underlying physics.

"The industry's most ambitious space missions now demand a level of speed and precision that traditional engineering cycles can no longer sustain", said Tim Costa, vice president and general manager of computational engineering at NVIDIA. "By integrating NVIDIA PhysicsNeMo, Northrop Grumman and Flexcompute are transforming complex simulations like plume impingement from days of compute into seconds of insight, drastically accelerating the path from mission concept to orbit".

What emerges from this work is a shift in how missions are prepared. When prediction cycles move from months to seconds, testing and decision-making can happen faster. For space operations, where timing and precision are closely linked, that change could reshape how systems are built and run.

Vizrt, a media technology company, has introduced a new AI-powered tool to simplify the creation of virtual scenes in live production. Its latest release, the AI Keyer, is built around a simple idea: remove the need for green screens and make virtual production possible in almost any environment.

Traditionally, creating virtual backgrounds or augmented reality (AR) scenes requires controlled studio setups, green screens, precise lighting and skilled operators. That makes high-end visual production expensive and difficult to scale, especially for smaller teams or live, on-the-ground reporting.

The AI Keyer is designed to address that gap. It uses AI trained on real-world footage to identify people in a frame and separate them from the background in real time. This allows production teams to replace backgrounds, insert AR graphics or place presenters into virtual environments—whether they are indoors, outdoors or on location.

"Creating XR environments typically demands large infrastructure investments and requires specialized skills for daily operations. The Vizrt AI Keyer removes all these constraints, so high-quality virtual scenes and AR graphics become a reality for live productions of every size", says Edouard Griveaud, Senior Product Manager at Vizrt.

In practical terms, this means a presenter can appear in a different location without moving, a remote speaker can be placed inside a virtual event space or branded graphics can be added to live interviews without a complex setup. The system works without chroma keying, reducing both preparation time and production overhead.

This shift also reflects how the company is approaching AI more broadly. Instead of treating it as a background feature, Vizrt is positioning AI as a core part of the content creation and delivery process.

"AI is transforming the world, and the creative industries are no exception. At Vizrt, we have been on this journey for years, embedding intelligence into our solutions, empowering storytellers and delivering real, measurable impact for our customers", says Rohit Nagarajan, CEO of Vizrt. "That is not a vision for tomorrow. That is happening today. The Vizrt AI Keyer is the latest proof point of our relentless commitment to innovation. Putting breakthrough technology in the hands of every creative, at every level, everywhere in the world".

Beyond the product itself, the direction is clear. By removing the need for green screens and complex setups, tools like the AI Keyer make it easier to produce high-quality visual content in more flexible settings. The result is a production model that is less tied to physical studios and more adaptable to real-world environments, where content can be created and adjusted in real time.

DeepCyte, a startup in the drug development space, is focusing on a long-standing problem: why drugs that appear safe in early testing still fail in clinical trials or are withdrawn later due to toxicity. DeepCyte has launched with US$1.5 million in seed funding to build tools that detect and explain the harmful effects of drugs at much earlier stages.

The startup’s approach focuses on how individual cells respond to a drug. Instead of analysing cells in bulk, it studies them one by one. This helps capture differences in how cells react, which are often missed in traditional testing methods.

Drug toxicity remains one of the main reasons for failure in drug development. Methods such as animal testing and bulk cell analysis do not always reflect how human cells behave. This gap has pushed the industry to look for more reliable and human-relevant ways to test drug safety.

DeepCyte combines cell-level data with artificial intelligence. Its platform, MetaCore, studies what is happening inside individual cells by capturing detailed molecular information. This data is used to build large datasets that can train AI models.

Additionally, the company has developed an AI system called DeeImmuno. It is designed to predict whether a drug could be toxic and identify the biological reasons behind it. In internal testing on 100 drugs, the system identified different types of toxicity and their underlying mechanisms with a reported accuracy of 94 percent.

The focus on explaining why a drug is toxic, not just whether it is, reflects a broader shift in the industry. Regulators such as the U.S. Food and Drug Administration and the European Medicines Agency have been encouraging methods that rely more on human cell data and clearer biological evidence. The seed funding will be used to develop and scale these tools. The company aims to help drug developers make earlier decisions, which could reduce costly failures in later stages. Whether tools like this become widely used will depend on how they perform in real-world settings. For now, DeepCyte’s approach highlights a growing effort to make drug testing more precise by focusing on how drugs affect cells at the most detailed level.

A newly built district in northern China is being used to test how cities function when infrastructure, data and automation are integrated from the ground up. In Xiong'an New Area, traffic systems, public monitoring and urban services are designed to respond in real time rather than operate on fixed rules.

At the centre of this is a traffic management system powered by more than 20,000 roadside sensors. These track traffic flow, vehicle types and congestion levels, feeding data into an AI system that adjusts signals in milliseconds. Official figures show this has reduced the average number of stops per vehicle by half. The system also detects equipment faults, sends alerts and generates maintenance requests without manual input.

Automation extends beyond roads. Drones are deployed across the city for routine monitoring. In the Rongdong district, roadside units release drones that follow fixed patrol routes of around 1.27 kilometres, completing each run in about five minutes. They are used to monitor traffic, detect illegal parking and inspect public spaces. Similar systems operate in parks to track water levels and issue flood alerts, while in some work zones, drones transport packages of up to five kilograms between buildings.

These applications reflect a broader approach: integrating multiple systems into a single, connected urban framework. Unlike older cities where infrastructure evolves in layers, Xiong’an has been built with coordinated digital systems from the outset. This allows transport, maintenance and public services to operate through shared data systems rather than in isolation.

Alongside this, the area is being developed as a technology and innovation hub. Since its establishment in 2017, it has attracted more than 400 branches of state-owned enterprises and over 200 companies working in sectors such as artificial intelligence, aerospace information and digital technology.

This ecosystem supports projects like the “Xiong’an-1” satellite, which completed research, design, production and testing within eight months of regulatory approval in 2025. The satellite is currently undergoing testing, with a planned launch expected in the second quarter of 2026. It forms part of a broader push to build an aerospace information industry in the region.

The area is also structured to bring companies, research and production closer together. At the Zhongguancun Science Park in Xiong’an, which spans 207,000 square metres, 269 technology companies operate across sectors including AI, robotics and biotechnology. The park hosts more than 2,700 researchers and industry professionals, with companies organised into sector-specific clusters.

Policy support continues to shape this development. In early 2026, the State Council approved the upgrade of Xiong’an’s high-tech industrial development zone to national level status, with a focus on attracting high-end research and strengthening links between scientific development and industrial output.  

Xiong’an is positioned as a testing ground for how smart city systems can be deployed at scale. The model depends on coordinated planning, integrated infrastructure and sustained policy support. Whether these systems can be adapted to existing cities, where infrastructure and governance are more fragmented, remains an open question.

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As artificial intelligence makes it easier to write code, a different problem is starting to surface. Building software is no longer limited by technical skill alone. Increasingly, the challenge lies in deciding what to build, how to structure it, and how to turn an idea into something that actually works.

That shift sits at the centre of BrainGrid, a startup that has raised $1 million in pre-seed funding led by Menlo Ventures, with participation from Next Tier Ventures and Brainstorm Ventures. The company is building what it describes as an AI-powered planning layer for people who want to create software but may not have a technical background.

The timing reflects a broader change in how products are being built. Tools like Claude Code and Cursor have made it possible to generate working code through simple prompts. For many first-time founders, this has lowered the barrier to entry. But writing code is only one part of the process. Turning that code into a reliable product requires structure, sequencing and clarity—areas where many projects begin to fall apart.

In traditional teams, this responsibility sits with product managers who define what needs to be built and in what order. Without that layer, even well-written code can lead to products that feel disjointed or incomplete. Features may not work together, integrations can break and the final product often does not match the original idea.

BrainGrid is designed to address that gap. Instead of focusing on generating code, it helps users map out the structure of a product before development begins. The aim is to give builders a clearer starting point so that the tools they use—whether human or AI—can produce more consistent results.

The company says more than 500 builders have already used it to create software products across areas like fitness, healthcare and productivity. These range from first-time founders experimenting with new ideas to experienced developers working independently. In many cases, the products are already live and generating revenue, suggesting that the demand is not just for experimentation but for building something that can scale.

For investors, the appeal lies in the evolving role of software development. As AI takes on more of the technical work, the value shifts toward defining the problem and structuring the solution. In that sense, planning becomes less of a background task and more of a core capability.

The US$1 million raise is relatively modest, but it points to a larger trend. As more people gain access to AI tools, the number of potential builders expands. What remains limited is the ability to organise ideas into products that work in the real world. If that shift continues, the next wave of software may not be defined by who can code, but by who can plan.

As global tech ecosystems become more interconnected, the ability to move innovation across borders is becoming just as important as building it. A new partnership between MTR Lab, the investment arm of MTR Corporation and ZGC Science City Ltd, a government-backed technology ecosystem based in Beijing’s Haidian district, reflects this shift.

At its core, the collaboration is designed to connect high-potential Chinese startups with global capital, real-world deployment opportunities and international markets. It focuses on sectors like AI, robotics, smart mobility and sustainable urban development—areas where China already has strong technical depth but where scaling beyond domestic markets can be more complex.

This is where the partnership begins to matter. ZGC Science City sits at the center of one of China’s most concentrated innovation clusters, with thousands of AI companies and a growing base of specialised and high-growth firms. MTR Lab, on the other hand, brings access to international markets, industry networks and practical deployment environments tied to infrastructure, transport and urban systems. Together, they are attempting to bridge a familiar gap: turning local innovation into globally relevant products.

In practice, the model is straightforward. ZGC Science City will introduce MTR Lab to startups working in priority sectors, creating a pipeline for potential investment and collaboration. From there, MTR Lab can support these companies through funding, pilot projects and access to overseas markets. The idea is not just to invest, but to help startups test and apply their technologies in real-world settings, particularly in complex urban environments.

The timing is notable. China’s AI and deep tech ecosystem has expanded rapidly, with thousands of companies contributing to advancements in automation, smart infrastructure and sustainability. At the same time, global demand for these technologies is rising, especially as cities look for more efficient and scalable solutions. Yet, moving from innovation to adoption often requires cross-border coordination—something individual startups may struggle to navigate alone.

This partnership also builds on a broader pattern. Corporate venture arms like MTR Lab are increasingly positioning themselves not just as investors, but as connectors between markets. By combining capital with access to infrastructure and deployment scenarios, they offer startups a way to move faster from development to real-world use. For ZGC Science City, the collaboration adds an international layer to its ecosystem, helping local companies extend beyond domestic growth.

What emerges is a model that goes beyond a typical investment announcement. It reflects a growing recognition that innovation today is rarely confined to one geography. Technologies may be developed in one ecosystem, refined in another and scaled globally through partnerships like this.

As cross-border collaboration becomes more central to how startups grow, partnerships like the one between MTR Lab and ZGC Science City point to a more connected innovation landscape—one where access, not just invention, defines success.

As Hollywood prepares for this weekend’s Oscars, a different kind of performer is stepping into the spotlight — one that doesn’t physically exist.

Tilly Norwood, described as the world’s first AI actor, has released her debut musical comedy video, Take the Lead. The project arrives at a moment when artificial intelligence has become one of the most contentious topics in the film industry.

The message of the song is simple. AI should not be seen as a threat to actors. Instead, it can become another creative tool. The release also offers a first look at what Norwood’s creators call the “Tillyverse”. It is envisioned as a cloud-based entertainment world where AI characters can live, interact and perform.

Behind the character is actor and producer Eline van der Velden. She is the CEO of production company Particle6 and AI talent studio Xicoia. Van der Velden created Tilly as a way to experiment with how artificial intelligence could be used in storytelling.

The timing is not accidental. The entertainment industry has spent the past few years debating the role AI should play in filmmaking and acting. Questions about digital replicas, automated performances and creative ownership continue to divide artists and studios.

Norwood’s musical video enters that debate with a different tone. Instead of warning about AI replacing actors, the project suggests that the technology could expand what performers are able to do.

The video itself also serves as a technical experiment. The song Take the Lead was generated using the AI music platform Suno. The video was then produced using a combination of widely available AI tools and Particle6’s own creative process.

One of the newer techniques used in the project is performance capture. Van der Velden physically acted out Tilly’s movements and expressions so the digital character could mirror a human performance. But the production was far from automated. According to Particle6, a team of 18 people worked on the video. The group included a director, editor, production designer, costume designer, comedy writer and creative technologist. In other words, the project still relied heavily on human creativity.

“Tilly has always been a vehicle to test the creative capabilities and boundaries of AI,” van der Velden said. “It’s not about taking anyone’s job”. She added that even with powerful tools, good AI content still takes time, taste and creative direction.

The project also reflects how quickly production technology is evolving. Tools that once required large studios are now accessible to smaller creative teams experimenting with AI-driven storytelling.

For Particle6, the character of Tilly Norwood acts as a testing ground. Each project explores how AI performers might be developed, directed and integrated into entertainment. Whether audiences embrace digital actors remains an open question. Many in the industry are still wary of how AI could reshape creative work.

But projects like Take the Lead show another possibility. Instead of replacing performers, artificial intelligence could become part of the creative process itself. In that sense, Tilly Norwood may represent something more than a virtual performer. She is also an experiment in how humans and machines might collaborate in the future of entertainment.

Sandbar, a New York–based interface startup, has raised US$23 million in Series A funding to develop a wearable device that lets people interact with artificial intelligence via voice rather than screens.

Adjacent and Kindred Ventures led the round; both venture firms focused on early-stage technology startups. The investment brings Sandbar’s total funding to us$36 million. Earlier backing included a US$10 million seed round led by True Ventures, a venture capital firm, as well as a US$3 million pre-seed round supported by Upfront Ventures, a venture firm and Betaworks, a startup studio and investment firm.

Sandbar was founded by Mina Fahmi and Kirak Hong, who previously worked together at CTRL-labs, a neural interface startup acquired by Meta in 2019. Their earlier work explored how computers could respond more directly to human intent — an idea that continues to shape Sandbar’s approach to AI interfaces.

The new funding will help the company expand its team across machine learning, interaction design and software engineering as it prepares to launch its first product. That product, called Stream, combines a wearable ring with a conversational AI interface. The system allows users to speak to an AI assistant without unlocking a phone or opening an app.

The concept is simple. Instead of typing into a screen, users press a button on the ring and talk. The system can capture notes, organize ideas, retrieve information from the web or trigger actions through connected applications.

The ring includes a microphone, a touchpad and subtle haptic feedback. These elements allow the device to respond through gentle vibrations rather than visual alerts. According to the company, the ring only listens when the user presses the button — a design meant to address common concerns around always-on microphones.

That design reflects a larger shift Sandbar believes is underway. As AI assistants become more capable, many startups are experimenting with new ways to interact with them. The focus is moving away from screens and keyboards toward interfaces that feel more natural and immediate.

Stream uses multiple AI models working together to process requests, search the web and structure information in real time. The company says users remain in control of their data and can choose whether to share information with other apps.

Sandbar is also developing a feature called Inner Voice, which responds using a voice customized to the user. The feature will debut during a closed beta planned for this spring, giving the company time to refine how the software behaves in everyday use.

The startup currently employs a team of 15 people. Many have worked on well-known consumer devices including the iPhone, Fitbit, Kindle and Vision Pro. Recent hires include Sam Bowen, formerly of Amazon and Fitbit, who joined as vice president of hardware and Brooke Travis, previously at Equinox, Dior and Gap, who now leads marketing.

Sandbar plans to begin shipping Stream in summer 2026 after completing early testing. As artificial intelligence tools become more integrated into daily life, the company is betting that the next shift in computing will not come from another app — but from new ways for people to interact with AI itself.

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As hiring pressures mount across global markets, ManpowerGroup is turning to technology to strengthen how it connects people to work. The workforce solutions major has announced a global partnership with Hubert, a startup focused on AI-driven structured interviews. The aim is simple: make hiring faster and fairer, without removing the human touch.

ManpowerGroup has spent decades operating at the center of the global labor market. The company works with employers across industries to fill roles, manage workforce planning and build talent pipelines. With millions of placements each year, it has a clear view of how strained hiring has become. A large share of employers today report difficulty finding skilled talent. At the same time, candidates expect more transparency, quicker feedback and flexibility in how they engage with employers.

Hubert enters this picture as a specialist in structured digital interviewing. The startup has built tools that allow candidates to complete interviews online, at any time, while being assessed against consistent criteria. Instead of relying on informal screening calls or resume filters, its system focuses on standardized questions tied directly to job requirements. The idea is to bring more consistency to early-stage hiring.

The partnership brings these capabilities into ManpowerGroup’s global operations. AI-powered interviews will now support the first stage of screening, helping recruiters identify qualified candidates earlier in the process. This does not replace recruiters. Final decisions and contextual judgment remain with experienced hiring professionals. What changes is the speed and structure of the initial assessment.

For employers, this could mean earlier visibility into job-ready talent and less time spent on manual screening. For candidates, it offers more flexibility. A significant portion of interviews on Hubert’s platform are completed outside regular office hours, allowing applicants to engage when it suits them. That flexibility can make a difference in competitive labor markets where timing matters.

The collaboration is also positioned as a step toward reducing bias. By evaluating each candidate against the same transparent standards, the process becomes more consistent. While no system can remove bias entirely, structured assessments can reduce the variability that often comes with unstructured interviews.

At its core, the partnership addresses a gap many large organizations are facing. They need scale and speed, but they cannot afford to lose the human judgment that good hiring depends on. Manual processes are too slow. Fully automated systems can feel impersonal and risky. ManpowerGroup’s approach suggests a middle path, where technology handles repetition and structure and recruiters focus on potential and fit.

The move also reflects a broader shift in the workforce industry. AI is no longer being tested on the sidelines. It is being built into the foundation of hiring operations. For established players like ManpowerGroup, the challenge is not whether to adopt AI, but how to do so responsibly and at scale.

By working with Hubert, the company is signaling that the future of recruitment will likely blend structured digital tools with human expertise. In a market defined by talent shortages and rising expectations, that balance may prove critical.

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Rokid, a Chinese company specializing in AI-powered smart eyewear and human–computer interaction, has rolled out a major software update for the international version of its Rokid Glasses. This update makes it the first smart glasses manufacturer to natively support Google’s Gemini, alongside three other leading large language models: OpenAI’s ChatGPT, Alibaba’s Qwen and DeepSeek.

The integration is powered by Rokid’s device-to-cloud architecture, which enables users to switch between AI models on the fly. In practice, this means a traveler can receive a real-time translation in Japanese using one AI model, then quickly switch to ChatGPT to answer a technical query—without noticeable delay. The system also supports multi-modal inputs like voice and gestures, making interactions more intuitive for everyday use.

This is more than a routine software update. By combining AI models from both U.S. and Chinese developers, Rokid is making its smart glasses relevant to global users, with features that adapt to local languages and preferences while maintaining high performance.  

These technological advancements have directly fueled Rokid’s international growth. Between November 2024 and October 2025, Shangpu Group data shows Rokid Glasses ranked No.1 in global sales for AI glasses with display functionality. Crowdfunding milestones further reflect this momentum: the product became the fastest smart glasses to raise over 100 million Japanese Yen on Japan’s MAKUAKE platform and broke Kickstarter records for smart eyewear.

Taken together, Rokid’s update highlights a shift in the smart glasses space: success increasingly comes from openness, flexibility and localized AI experiences rather than closed, single-platform ecosystems. By giving users choice, integrating global AI capabilities and bridging cultural and linguistic gaps, Rokid is positioning itself as a serious contender in the international AR and AI wearable market.

As AI tools improve, creating 3D content is becoming faster and easier. However, building that content into interactive experiences still requires time, structure and technical work. That difference between generation and execution is where HTC VIVERSE and World Labs are focusing their new collaboration.

HTC VIVERSE is a 3D content platform developed by HTC. It provides creators with tools to build, refine and publish interactive virtual environments. Meanwhile, World Labs is an AI startup founded by researcher Fei-Fei Li and a team of machine learning specialists. The company recently introduced Marble, a tool that generates full 3D environments from simple text, image or video prompts.

While Marble can quickly create a digital world, that world on its own is not yet a finished experience. It still needs structure, navigation and interaction. This is where VIVERSE fits in. By combining Marble’s world generation with VIVERSE’s building tools, creators can move from an AI-generated scene to a usable, interactive product.

In practice, the workflow works in two steps. First, Marble produces the base 3D environment. Then, creators bring that environment into VIVERSE, where they add game mechanics, scenes and interactive elements. In this model, AI handles the early visual creation, while the human creator defines how users explore and interact with the world.

To demonstrate this process, the companies developed three example projects. Whiskerhill turns a Marble-generated world into a simple quest-based experience. Whiskerport connects multiple AI-generated scenes into a multi-level environment that users navigate through portals. Clockwork Conspiracy, built by VIVERSE, uses Marble’s generation system to create a more structured, multi-scene game. These projects are not just demos. They serve as proof that AI-generated worlds can evolve beyond static visuals and become interactive environments.

This matters because generative AI is often judged by how quickly it produces content. However, speed alone does not create usable products. Digital experiences still require sequencing, design decisions and user interaction. As a result, the real challenge is not generation, but integration — connecting AI output to tools that make it functional.

Seen in this context, the collaboration is less about a single product and more about workflow. VIVERSE provides a system that allows AI-generated environments to be edited and structured. World Labs provides the engine that creates those environments in the first place. Together, they are testing whether AI can fit directly into a full production pipeline rather than remain a standalone tool.

Ultimately, the collaboration reflects a broader change in creative technology. AI is no longer only producing isolated assets. It is beginning to plug into the larger process of building complete experiences. The key question is no longer how quickly a world can be generated, but how easily that world can be turned into something people can actually use and explore.

Cyber risk is increasingly treated as a financial issue. Boards want to know how much a cyber incident could cost the company, how it could affect earnings, and whether current security spending is justified.

Yet many organizations still measure cyber risk through periodic reviews. These assessments are often conducted once or twice a year, supported by consultants and spreadsheet models. By the time the report reaches senior leadership, the company’s systems may have changed and new threats may have emerged. The way risk is measured does not always match how quickly it evolves.

This gap is where Quantara AI is positioning its new platform. Quantara AI, a Boise-based cybersecurity startup, has introduced what it describes as the industry’s first persistent AI-powered cyber risk solution. The system is designed to run continuously rather than rely on occasional assessments.

The company’s core argument is straightforward: not every security weakness carries the same financial consequence. Instead of ranking issues only by technical severity, the platform analyzes active threats, identifies which company systems are exposed, and estimates how much money a successful attack could cost. It uses statistical models, including Value at Risk (VaR), to calculate potential losses. It also estimates how specific security improvements could reduce that projected loss.

The timing aligns with a broader market shift. International Data Corporation (IDC) projects that by 2028, 40% of enterprises will adopt AI-based cyber risk quantification platforms. These tools convert security data into financial estimates that can guide budgeting and investment decisions. The forecast reflects growing pressure on security leaders to present risk in terms that boards and regulators understand.

Traditional compliance and risk management systems often focus on meeting regulatory standards. Vulnerability management programs typically score weaknesses based on technical characteristics. Consultant-led risk studies provide detailed analysis, but they are usually performed at set intervals. In fast-changing threat environments, that model can leave decision-makers working with outdated information.

Quantara’s platform attempts to replace that periodic process with continuous measurement. It brings together threat data, internal system information and financial modeling in one system. The goal is to show, at any given time, which specific weaknesses could lead to the largest financial losses.

Cyber risk quantification as a concept is not new. What is changing is the expectation that these calculations be updated regularly and tied directly to financial decision-making. As cyber incidents carry clearer monetary consequences, companies are looking for ways to measure exposure with greater precision.

The broader question is whether enterprises will shift fully toward continuous, AI-driven risk analysis or continue relying on periodic external assessments. What is clear is that cybersecurity discussions are moving closer to financial reporting — and tools that estimate potential loss in dollar terms are becoming central to that shift.

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Enterprise interest in AI has moved quickly from experimentation to execution. Many organizations have tested generative tools, but turning those tools into systems that can run inside daily operations remains a separate challenge. Cognizant, an IT services firm, is expanding its partnership with Google Cloud to help enterprises move from AI pilots to fully deployed, production-ready systems.

Cognizant and Google Cloud are deepening their collaboration around Google’s Gemini Enterprise and Google Workspace. Cognizant is deploying these tools across its own workforce first, using them to support internal productivity and collaboration. The idea is simple: test and refine the systems internally, then package similar capabilities for clients.

The focus of the partnership is what Cognizant calls “agentic AI.” In practical terms, this refers to AI systems that can plan, act and complete tasks with limited human input. Instead of generating isolated outputs, these systems are designed to fit into business workflows and carry out structured tasks.

To make that workable at scale, Cognizant is building delivery infrastructure around the technology. The company is setting up a dedicated Gemini Enterprise Center of Excellence and formalizing an Agent Development Lifecycle. This framework covers the full process, from early design and blueprinting to validation and production rollout. The aim is to give enterprises a clearer path from the AI concept to a deployed system.

Cognizant also plans to introduce a bundled productivity offering that combines Gemini Enterprise with Google Workspace. The targeted use cases are operational rather than experimental. These include collaborative content creation, supplier communications and other workflow-heavy processes that can be standardized and automated.

Beyond productivity tools, Cognizant is integrating Gemini into its broader service platforms. Through Cognizant Ignition, enabled by Gemini, the company supports early-stage discovery and prototyping while helping clients strengthen their data foundations. Its Agent Foundry platform provides pre-configured and no-code capabilities for specific use cases such as AI-powered contact centers and intelligent order management. These tools are designed to reduce the amount of custom development required for each deployment.

Scaling is another element of the strategy. Cognizant, a multi-year Google Cloud Data Partner of the Year award winner, says it will rely on a global network of Gemini-trained specialists to deliver these systems. The company is also expanding work tied to Google Distributed Cloud and showcasing capabilities through its Google Experience Zones and Gen AI Studios.

For Google Cloud, the partnership reinforces its enterprise AI ecosystem. Cloud providers can offer models and infrastructure, but enterprise adoption often depends on service partners that can integrate tools into existing systems and manage ongoing operations. By aligning closely with Cognizant, Google strengthens its ability to move Gemini from platform capability to production deployment.

The announcement does not introduce a new AI model. Instead, it reflects a shift in emphasis. The core question is no longer whether AI tools exist, but how they are implemented, governed and scaled across large organizations. Cognizant’s expanded role suggests that execution frameworks, internal deployment and structured delivery models are becoming central to how enterprises approach AI.

In that sense, the partnership is less about new technology and more about operational maturity. It highlights how AI is moving from isolated pilots to managed systems embedded in business processes — a transition that will likely define the next phase of enterprise adoption.

Humanoid robotics has advanced quickly in recent years. Machines can now walk, balance, and interact with their surroundings in ways that once seemed out of reach. Yet most deployments remain limited. Many robots perform well in controlled settings but struggle in real-world environments. Integration is often complex, hardware interfaces are closed, software tools are fragmented, and scaling across industries remains difficult.

Against this backdrop, X-Humanoid has introduced its latest general-purpose platform, Embodied Tien Kung 3.0. The company positions it not simply as another humanoid robot, but as a system designed to address the practical barriers that have slowed adoption, with a focus on openness and usability.

At the hardware level, Embodied Tien Kung 3.0 is built for mobility, strength, and stability. It is equipped with high-torque integrated joints that provide strong limb force for high-load applications. The company says it is the first full-size humanoid robot to achieve whole-body, high-dynamic motion control integrated with tactile interaction. In practice, this means the robot is designed to maintain balance and execute dynamic movements even in uneven or cluttered environments. It can clear one-meter obstacles, perform consecutive high-dynamic maneuvers, and carry out actions such as kneeling, bending, and turning with coordinated whole-body control.

Precision is also a focus. Through multi-degree-of-freedom limb coordination and calibrated joint linkage, the system is designed to achieve millimeter-level operational accuracy. This level of control is intended to support industrial-grade tasks that require consistent performance and minimal error across changing conditions.

But hardware is only part of the equation. The company pairs the robot with its proprietary Wise KaiWu general-purpose embodied AI platform. This system supports perception, reasoning, and real-time control through what the company describes as a coordinated “brain–cerebellum” architecture. It establishes a continuous perception–decision–execution loop, allowing the robot to operate with greater autonomy and reduced reliance on remote control.

For higher-level cognition, Wise KaiWu incorporates components such as a world model and vision-language models (VLM) to interpret visual scenes, understand language instructions, and break complex objectives into structured steps. For real-time execution, a vision-language-action (VLA) model and full autonomous navigation system manage obstacle avoidance and precise motion under variable conditions. The platform also supports multi-agent collaboration, enabling cross-platform compatibility, asynchronous task coordination, and centralized scheduling across multiple robots.

A central part of the platform is openness. The company states that the system is designed to address compatibility and adaptation challenges across both development and deployment layers. On the hardware side, Embodied Tien Kung 3.0 includes multiple expansion interfaces that support different end-effectors and tools, allowing faster adaptation to industrial manufacturing, specialized operations, and commercial service scenarios. On the software side, the Wise KaiWu ecosystem provides documentation, toolchains, and a low-code development environment. It supports widely adopted communication standards, including ROS2, MQTT, and TCP/IP, enabling partners to customize applications without rebuilding core systems.

The company also highlights its open-source approach. X-Humanoid has open-sourced key components from the Embodied Tien Kung and Wise KaiWu platforms, including the robot body architecture, motion control framework, world model, embodied VLM and cross-ontology VLA models, training toolchains, the RoboMIND dataset, and the ArtVIP simulation asset library. By opening access to these elements, the company aims to reduce development costs, lower technical barriers, and encourage broader participation from researchers, universities, and enterprises.

Embodied Tien Kung 3.0 enters a market where technical progress is visible but large-scale adoption remains uneven. The gap is not only about movement or strength. It is about integration, interoperability, and the ability to operate reliably and autonomously in everyday industrial and commercial settings. If platforms can reduce fragmentation and simplify deployment, humanoid robots may move beyond demonstrations and into sustained commercial use.

In that sense, the significance of Embodied Tien Kung 3.0 lies less in isolated technical claims and more in how its high-dynamic hardware, embodied AI system, open interfaces, and collaborative architecture are structured to work together. Whether that integrated approach can close the deployment gap will shape how quickly humanoid robotics becomes part of real-world operations.

Algorized has raised US$13 million in a Series A round to advance its AI-powered safety and sensing technology for factories and warehouses. The California- and Switzerland-based robotics startup says the funding will help expand a system designed to transform how robots interact with people. The round was led by Run Ventures, with participation from the Amazon Industrial Innovation Fund and Acrobator Ventures, alongside continued backing from existing investors.

At its core, Algorized is building what it calls an intelligence layer for “physical AI” — industrial robots and autonomous machines that function in real-world settings such as factories and warehouses. While generative AI has transformed software and digital workflows, bringing AI into physical environments presents a different challenge. In these settings, machines must not only complete tasks efficiently but also move safely around human workers.

This is where a clear gap exists. Today, most industrial robots rely on camera-based monitoring systems or predefined safety zones. For instance, when a worker steps into a marked area near a robotic arm, the system is programmed to slow down or stop the machine completely. This approach reduces the risk of accidents. However, it also means production lines can pause frequently, even when there is no immediate danger. In high-speed manufacturing environments, those repeated slowdowns can add up to significant productivity losses.

Algorized’s technology is designed to reduce that trade-off between safety and efficiency. Instead of relying solely on cameras, the company utilizes wireless signals — including Ultra-Wideband (UWB), mmWave, and Wi-Fi — to detect movement and human presence. By analysing small changes in these radio signals, the system can detect motion and breathing patterns in a space. This helps machines determine where people are and how they are moving, even in conditions where cameras may struggle, such as poor lighting, dust or visual obstruction.

Importantly, this data is processed locally at the facility itself — not sent to a remote cloud server for analysis. In practical terms, this means decisions are made on-site, within milliseconds. Reducing this delay, or latency, allows robots to adjust their movements immediately instead of defaulting to a full stop. The aim is to create machines that can respond smoothly and continuously, rather than reacting in a binary stop-or-go manner.

With the new funding, Algorized plans to scale commercial deployments of its platform, known as the Predictive Safety Engine. The company will also invest in refining its intent-recognition models, which are designed to anticipate how humans are likely to move within a workspace. In parallel, it intends to expand its engineering and support teams across Europe and the United States. These efforts build on earlier public demonstrations and ongoing collaborations with manufacturing partners, particularly in the automotive and industrial sectors.

For investors, the appeal goes beyond safety compliance. As factories become more automated, even small improvements in uptime and workflow continuity can translate into meaningful financial gains. Because Algorized’s system works with existing wireless infrastructure, manufacturers may be able to upgrade machine awareness without overhauling their entire hardware setup.

More broadly, the company is addressing a structural limitation in industrial automation. Robotics has advanced rapidly in precision and power, yet human-robot collaboration is still governed by rigid safety systems that prioritise stopping over adapting. By combining wireless sensing with edge-based AI models, Algorized is attempting to give machines a more continuous awareness of their surroundings from the start.

Spielwarenmesse in Nuremberg is the global meeting point for the toy industry, where brands and designers preview what will shape how children play and learn next. At this year’s fair, one message stood out clearly: toys are no longer built just to entertain, but to listen, respond and grow with children. Tuya Smart, a global AI cloud platform company, used the event to show how AI-powered toys are turning familiar formats into interactive companions that can talk, react emotionally and adapt over time.

The company’s central argument was simple but far-reaching. The next generation of artificial intelligence toys will not be defined by motors, sensors or screens alone, but by how well they understand human behavior. Instead of being single-function objects, smart toys for children are becoming systems that combine language models, emotion recognition and memory to support ongoing interaction.

One of the most talked-about examples was Tuya Smart’s Nebula Plush AI Toy. At first glance, it looks like a soft, expressive plush figure. Inside, it uses emotional recognition to change its LED facial expressions in real time. If a child sounds sad or excited, the toy’s eyes respond visually. It supports natural conversation, reacts to hugs and touch and combines storytelling, news-style updates and interactive games. Over time, it builds memory, allowing it to behave less like a gadget and more like an interactive AI toy that recalls past interactions.

Another example was Walulu, also developed using Tuya’s AI toy platform. Walulu is an AI pet built around personalization. It can detect up to 19 emotional states and speak more than 60 languages. It connects to major large language models such as ChatGPT, Gemini, DeepSeek, Qwen and Doubao. Through simple app-based controls, users choose traits like cheerful, quiet, curious or thoughtful. Those choices shape how Walulu talks and reacts. Instead of repeating scripts, it adjusts its tone and behavior over time. The result is not a novelty item, but an emotionally responsive AI toy that feels consistent in daily use.

Tuya also showed how educational AI toys can extend into learning and exploration. Its AI Learning Camera blends computer vision with interactive content. When it recognizes an object, it links it to cultural and learning material. If a child points it at a foreign word, it offers real-time pronunciation and translation. It can also turn drawings into digital artwork, encouraging active creativity rather than passive screen time. In this sense, AI toys for kids are becoming tools for learning as much as play.

These products point to a larger strategy. Tuya is not just making toys — it is building the AI toy development platform behind them. Through its AI Toy Solution, developers can design a toy’s personality, memory logic and behavior without training models from scratch. The system integrates with leading AI models and supports multi-turn conversation and emotional feedback, turning standard hardware into responsive AI companions.

The platform supports multiple development paths. Brands can use ready-to-market OEM solutions, add AI to existing products or build custom toys around their own characters. Plush toys, robots, educational tools and wearables can all become AI-powered toys without changing their physical design.

Because these products are made for children and families, safety is built in. Tuya’s system includes parental controls, conversation history review and content management. It supports standards such as GDPR and CCPA with encryption and data localization.

From a business standpoint, Tuya’s pitch is speed and scale. The company says its AI toy infrastructure can cut development time by more than half and reduce R&D costs by up to 50 percent. Its AIoT network spans over 200 countries and supports more than 60 languages, making global deployment of AI toys easier.

What emerged at Spielwarenmesse 2026 was not just a lineup of smart gadgets, but a clear shift in the category. AI toys are evolving into emotionally aware systems that talk, listen, remember and adapt. Their value lies not in sounding clever, but in fitting naturally into everyday life.

The fair did not present AI toys as a distant future. It showed them as products already entering the mainstream. The real question now is not whether toys will use AI, but how carefully that intelligence is designed for children.

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AI has transformed how we shop—predicting trends, powering virtual try-ons and streamlining fashion logistics. Yet some of the biggest pain points remain: endless scrolling, too many tabs and never knowing if you’ve overpaid. That’s the gap Phia aims to close.

Co-founded by Phoebe Gates, daughter of Bill Gates, and climate activist Sophia Kianni, Phia was born in a Stanford dorm room and launched in April 2025. The app, available on mobile and as a browser extension, compares prices across over 40,000 retailers and thrift platforms to show what an item really costs. Its hallmark feature, “Should I Buy This?”, instantly flags whether something is overpriced, fair or a genuine deal.

The mission is simple: make shopping smarter, fairer and more sustainable. In just five months, Phia has attracted more than 500,000 users, indexed billions of products and built over 5,000 brand partnerships. It also secured a US$8 million seed round led by Kleiner Perkins, joined by Hailey Bieber, Kris Jenner, Sara Blakely and Sheryl Sandberg—investors who bridge tech, retail and culture. “Phia is redefining how people make purchase decisions,” said Annie Case, partner at Kleiner Perkins.  

Phia’s AI engine scans real-time data from more than 250 million products across its network, including Vestiaire Collective, StockX, eBay and Poshmark. Beyond comparing prices, the app helps users discover cheaper or more sustainable options by displaying pre-owned items next to new ones—helping users see the full spectrum of choices before they buy. It also evaluates how different brands perform over time, analysing how well their products hold resale value. This insight helps shoppers judge whether a purchase is likely to last in value or if opting for a second-hand version makes more sense. The result is a platform that naturally encourages circular shopping—keeping items in use longer through resale, repair or recycling—and resonates strongly with Gen Z and millennial values of sustainability and mindful spending.  

By encouraging transparency and smarter choices, Phia signals a broader shift in consumer technology: one where AI doesn’t just automate decisions but empowers users to understand them. Instead of merely digitizing the act of shopping, Phia embodies data-driven accountability—using intelligent search to help consumers make informed and ethical choices in markets long clouded by complexity. Retail analysts believe this level of visibility could push brands to maintain accurate and competitive pricing. Skeptics, however, argue that Phia must evolve beyond comparison to create emotional connection and loyalty. Still, one fact stands out: algorithms are no longer just recommending what we buy—they’re rewriting how we decide.  

With new funding powering GPU expansion and advanced personalization tools, Phia’s next step is to build a true AI shopping agent—one that helps people buy better, live smarter and rethink what it means to shop with purpose.

In an era where pixels and intelligence converge, few companies bridge art and science as seamlessly as Digital Domain. Founded three decades ago by visionary filmmaker James Cameron, the company built its name through cinematic wizardry—bringing to life the impossible worlds of Titanic, The Curious Case of Benjamin Button and the Marvel universe. But today, its focus has evolved far beyond Hollywood: Digital Domain is reimagining the future of AI-driven virtual humans—and it’s doing so from right here in Hong Kong.

“AI and visual technology are merging faster than anyone imagined,” says William Wong, Chairman and CEO of Digital Domain. “For us, the question is not whether AI will reshape entertainment—it already has. The question is how we can extend that power into everyday life.”

Though globally recognized for its work on blockbuster films and AAA games, Digital Domain’s story is also deeply connected to Asia. A Hong Kong–listed company, it operates a network of production and research centers across North America, China and India. In 2024, it announced a major milestone—setting up a new R&D hub at Hong Kong Science Park focused on advancing artificial intelligence and virtual human technologies. “Our roots are in visual storytelling, but AI is unlocking a new frontier,” Wong says. “Hong Kong has been very proactive in promoting innovation and research, and with the right partnerships, we see real potential to make this a global R&D base.”

Building on that commitment, the company plans to invest about HK$200 million over five years, assembling a team of more than 40 professional talents specializing in computer vision, machine learning and digital production. For now, the team is still growing and has room to expand. “Talent is everything,” says Wong. “We want to grow local expertise while bringing in global experience to accelerate the learning curve.”

Digital Domain’s latest chapter revolves around one of AI’s most fascinating frontiers: the creation of virtual humans.  

These are hyperrealistic, AI-powered virtual humans capable of speaking, moving and responding in real time. Using the advanced motion-capture and rendering techniques that transformed Hollywood visual effects, the company now builds digital personalities that appear on screens and in physical environments—serving in media, education, retail and even public services.

One of its most visible projects is “Aida”, the AI-powered presenter who delivers nightly weather reports on the Radio Television Hong Kong (RTHK). Another initiative, now in testing, will soon feature AI-powered concierges greeting travelers at airports, able to communicate in multiple languages and provide real-time personalized services. Similar collaborations are under way in healthcare, customer service and education.  

“What’s exciting,” says Wong, “is that our technologies amplify human capability, helping to deliver better experiences, greater efficiency and higher capacity. AI-powered virtual humans can interact naturally, emotionally and in any language. They can help scale creativity and service, not replace it.”

To make that possible, Digital Domain has designed its system for compatibility and flexibility. It can connect to major AI models—from OpenAI and Google to Baidu—and operate across cloud platforms like AWS, Alibaba Cloud and Microsoft Azure. “It’s about openness,” says Wong. “Our clients can choose the AI brain that best fits their business.”

Establishing a permanent R&D base in Hong Kong marks a turning point for the company—and, in a broader sense, for the city’s technology ecosystem. With the support of the Office for Attracting Strategic Enterprises (OASES) in Hong Kong, Digital Domain hopes to make the city a creative hub where AI meets visual arts. “Hong Kong is the perfect meeting point,” Wong says. “It combines international exposure with a growing innovation ecosystem. We want to make it a hub for creative AI.”

As part of this effort, the company is also collaborating with universities such as the University of Hong Kong, City University of Hong Kong and Hong Kong Baptist University to co-develop new AI solutions and nurture the next generation of engineers. “The goal,” Wong notes, “is not just R&D for the sake of research—but R&D that translates into real-world impact.”

The collaboration with OASES underscores how both the company and the city share a vision for innovation-led growth. As Peter Yan King-shun, Director-General of OASES, notes, the initiative reflects Hong Kong’s growing strength as a global innovation and technology hub. “OASES was set up to attract high-potential enterprises from around the world across key sectors such as AI, data science, and cultural and creative technology,” he says. “Digital Domain’s new R&D center is a strong example of how Hong Kong can combine world-class talent, technology and creativity to drive innovation and global competitiveness.”

Digital Domain’s story mirrors the evolution of Hong Kong’s own innovation landscape—where creativity, technology and global ambition converge. From the big screen to the next generation of intelligent avatars, the company continues to prove that imagination is not bound by borders, but powered by the courage to reinvent what’s possible.

In the past, “educational toys” usually meant flashcards, prerecorded stories or apps that asked children to tap a screen. ChooChoo takes a different approach. It is designed not to instruct children at them, but to talk with them.

ChooChoo is an AI-powered interactive reading companion built for children aged three to six. Instead of playing stories passively, it engages kids in conversation while reading. It asks questions, reacts to answers, introduces new words in context and adjusts the story flow based on how the child responds. The goal is not entertainment alone, but language development through dialogue.

That idea is rooted in research, not novelty. ChooChoo is inspired by dialogic reading methods from Yale’s early childhood language development work, which show that children learn language faster when stories become two-way conversations rather than one-way narration. Used consistently, this approach has been shown to improve vocabulary, comprehension and confidence within weeks.

The project was created by Dr. Diana Zhu, who holds a PhD from Yale and focused her work on how children acquire language. Her aim with ChooChoo was to turn academic insight into something practical and warm enough to live in a child’s room. The result is a device that listens, responds and adapts instead of simply playing content on command.

What makes this possible is not just AI, but where that AI runs.

Unlike many smart toys that rely heavily on the cloud, ChooChoo is built on RiseLink’s edge AI platform. That means much of the intelligence happens directly on the device itself rather than being sent back and forth to remote servers. This design choice has three major implications.

First, it reduces delay. Conversations feel natural because the toy can respond almost instantly. Second, it lowers power consumption, allowing the device to stay “always on” without draining the battery quickly. Third, it improves privacy. Sensitive interactions are processed locally instead of being continuously streamed online.

RiseLink’s hardware, including its ultra-low-power AI system-on-chip designs, is already used at large scale in consumer electronics. The company ships hundreds of millions of connected chips every year and works with global brands like LG, Samsung, Midea and Hisense. In ChooChoo’s case, that same industrial-grade reliability is being applied to a child’s learning environment.

The result is a toy that behaves less like a gadget and more like a conversational partner. It engages children in back-and-forth discussion during stories, introduces new vocabulary in natural context, pays attention to comprehension and emotional language and adjusts its pace and tone based on each child’s interests and progress. Parents can also view progress through an optional app that shows what words their child has learned and how the system is adjusting over time.

What matters here is not that ChooChoo is “smart,” but that it reflects a shift in how technology enters early education. Instead of replacing teachers or parents, tools like this are designed to support human interaction by modeling it. The emphasis is on listening, responding and encouraging curiosity rather than testing or drilling.

That same philosophy is starting to shape the future of companion robots more broadly. As edge AI improves and hardware becomes smaller and more energy efficient, we are likely to see more devices that live alongside people instead of in front of them. Not just toys, but helpers, tutors and assistants that operate quietly in the background, responding when needed and staying out of the way when not.

In that sense, ChooChoo is less about novelty and more about direction. It shows what happens when AI is designed not for spectacle, but for presence. Not for control, but for conversation.

If companion robots become part of daily life in the coming years, their success may depend less on how powerful they are and more on how well they understand when to speak, when to listen and how to grow with the people who use them.

South Korea–based water technology company ECOPEACE is working on a practical challenge many cities face today: keeping urban water bodies clean as pollution and algae growth become more frequent. Rather than relying on periodic cleanup drives, the company focuses on systems that can monitor and manage water conditions on an ongoing basis.

At the core of ECOPEACE’s work are autonomous water-cleanup robots known as ECOBOT. These machines operate directly on lakes, reservoirs and rivers, removing algae and surface waste while also collecting information about water quality. The idea is to combine cleaning with constant observation so changes in water conditions do not go unnoticed.

Alongside the robots, ECOPEACE uses a filtration and treatment system designed to process polluted water continuously. This system filters out contaminants using fine metal filters and treats the water using electrical processes. It also cleans itself automatically, which allows it to run for long periods without frequent manual maintenance.

The role of AI in this setup is largely about decision-making rather than direct control. Sensors placed across the water body collect data such as pollution levels and water quality indicators. The software then analyses this data to spot early signs of issues like algae growth. Based on these patterns, the system adjusts how the robots and filtration units operate, such as changing treatment intensity or water flow. In simple terms, the technology helps the system respond sooner instead of waiting for visible problems to appear.

ECOPEACE has already deployed these systems across several reservoirs, rivers and urban waterways in South Korea. Those projects have helped refine how the robots, sensors and software work together in real environments rather than controlled test sites.

Building on that experience, the company has begun expanding beyond Korea. It is currently running pilot and proof-of-concept projects in Singapore and the United Arab Emirates. These deployments are testing how the technology performs in dense urban settings where waterways are closely linked to public health, infrastructure and daily city life.

Both regions have invested heavily in smart city initiatives and water management, making them suitable test beds for automated monitoring and cleanup systems. The pilots focus on algae control, surface cleaning and real-time tracking of water quality rather than large-scale rollout.

As cities continue to grow and climate-related pressures on water systems increase, managing waterways is becoming less about occasional intervention and more about continuous oversight. ECOPEACE’s approach reflects that shift by using automation and data to address problems early and reduce the need for reactive cleanup later.

SK Telecom, South Korea’s largest mobile operator, has unveiled A.X K1, a hyperscale artificial intelligence model with 519 billion parameters. The model sits at the center of a government-backed effort to build advanced AI systems and domestic AI infrastructure within Korea. This comes at a time when companies in the United States and China largely dominate the development of the most powerful large language models.

Rather than framing A.X K1 as just another large language model, SK Telecom is positioning it as part of a broader push to build sovereign AI capacity from the ground up. The model is being developed as part of the Korean government’s Sovereign AI Foundation Model project, which aims to ensure that core AI systems are built, trained and operated within the country. In simple terms, the initiative focuses on reducing reliance on foreign AI platforms and cloud-based AI infrastructure, while giving Korea more control over how artificial intelligence is developed and deployed at scale.

One of the gaps this approach is trying to address is how AI knowledge flows across a national ecosystem. Today, the most powerful AI foundation models are often closed, expensive and concentrated within a small number of global technology companies. A.X K1 is designed to function as a “teacher model,” meaning it can transfer its capabilities to smaller, more specialized AI systems. This allows developers, enterprises and public institutions to build tailored AI tools without starting from scratch or depending entirely on overseas AI providers.

That distinction matters because most real-world applications of artificial intelligence do not require massive models operating independently. They require focused, reliable AI systems designed for specific use cases such as customer service, enterprise search, manufacturing automation or mobility. By anchoring those systems to a large, domestically developed foundation model, SK Telecom and its partners are aiming to create a more resilient and self-sustaining AI ecosystem.

The effort also reflects a shift in how AI is being positioned for everyday use. SK Telecom plans to connect A.X K1 to services that already reach millions of users, including its AI assistant platform A., which operates across phone calls, messaging, web services and mobile applications. The broader goal is to make advanced AI feel less like a distant research asset and more like an embedded digital infrastructure that supports daily interactions.

This approach extends beyond consumer-facing services. Members of the SKT consortium are testing how the hyperscale AI model can support industrial and enterprise applications, including manufacturing systems, game development, robotics and autonomous technologies. The underlying logic is that national competitiveness in artificial intelligence now depends not only on model performance, but on whether those models can be deployed, adapted and validated in real-world environments.

There is also a hardware dimension to the project. Operating an AI model at the 500-billion-parameter scale places heavy demands on computing infrastructure, particularly memory performance and communication between processors. A.X K1 is being used to test and validate Korea’s semiconductor and AI chip capabilities under real workloads, linking large-scale AI software development directly to domestic semiconductor innovation.

The initiative brings together technology companies, universities and research institutions, including Krafton, KAIST and Seoul National University. Each contributes specialized expertise ranging from data validation and multimodal AI research to system scalability. More than 20 institutions have already expressed interest in testing and deploying the model, reinforcing the idea that A.X K1 is being treated as shared national AI infrastructure rather than a closed commercial product.

Looking ahead, SK Telecom plans to release A.X K1 as open-source AI software, alongside APIs and portions of the training data. If fully implemented, the move could lower barriers for developers, startups and researchers across Korea’s AI ecosystem, enabling them to build on top of a large-scale foundation model without incurring the cost and complexity of developing one independently.

As artificial intelligence models grow larger and more demanding, the quiet pressure point isn’t the algorithms themselves—it’s the AI infrastructure that has to run them. Training and deploying modern AI models now requires enormous amounts of computing power, which creates a different kind of challenge: heat, energy use and space inside data centers. This is the context in which Supermicro and NVIDIA’s collaboration on AI infrastructure begins to matter.

Supermicro designs and builds large-scale computing systems for data centers. It has now expanded its support for NVIDIA’s Blackwell generation of AI chips with new liquid-cooled server platforms built around the NVIDIA HGX B300. The announcement isn’t just about faster hardware. It reflects a broader effort to rethink how AI data center infrastructure is built as facilities strain under rising power and cooling demands.

At a basic level, the systems are designed to pack more AI chips into less space while using less energy to keep them running. Instead of relying mainly on air cooling—fans, chillers and large amounts of electricity, these liquid-cooled AI servers circulate liquid directly across critical components. That approach removes heat more efficiently, allowing servers to run denser AI workloads without overheating or wasting energy.

Why does that matter outside a data center? Because AI doesn’t scale in isolation. As models become more complex, the cost of running them rises quickly, not just in hardware budgets, but in electricity use, water consumption and physical footprint. Traditional air-cooling methods are increasingly becoming a bottleneck, limiting how far AI systems can grow before energy and infrastructure costs spiral.

This is where the Supermicro–NVIDIA partnership fits in. NVIDIA supplies the computing engines—the Blackwell-based GPUs designed to handle massive AI workloads. Supermicro focuses on how those chips are deployed in the real world: how many GPUs can fit in a rack, how they are cooled, how quickly systems can be assembled and how reliably they can operate at scale in modern data centers. Together, the goal is to make high-density AI computing more practical, not just more powerful.

The new liquid-cooled designs are aimed at hyperscale data centers and so-called AI factories—facilities built specifically to train and run large AI models continuously. By increasing GPU density per rack and removing most of the heat through liquid cooling, these systems aim to ease a growing tension in the AI boom: the need for more computers without an equally dramatic rise in energy waste.

Just as important is speed. Large organizations don’t want to spend months stitching together custom AI infrastructure. Supermicro’s approach packages compute, networking and cooling into pre-validated data center building blocks that can be deployed faster. In a world where AI capabilities are advancing rapidly, time to deployment can matter as much as raw performance.

Stepping back, this development says less about one product launch and more about a shift in priorities across the AI industry. The next phase of AI growth isn’t only about smarter models—it’s about whether the physical infrastructure powering AI can scale responsibly. Efficiency, power use and sustainability are becoming as critical as speed.

Humanoid robots are shifting from sci-fi speculation to engineering reality, and the pace of progress is prompting investors to reassess how the next decade of physical automation will unfold.  ALM Ventures has launched a new US$100 million early-stage fund aimed squarely at this moment—one where advances in robot control, embodied AI and spatial intelligence are beginning to converge into something commercially meaningful.

ALM Ventures Fund I, is designed for the earliest stages of company formation, targeting seed and pre-seed teams building the foundations of humanoid deployment. It’s a concentrated fund that seeks to take early ownership in a sector that many now consider the next major technological frontier.

For Founder and General Partner Modar Alaoui, the timing is not accidental. “After years of research, humanoids are finally entering a phase where performance, reliability and cost are converging toward commercial viability”, he said. “What the category needs now is focused capital and deep technical diligence to turn prototypes into scalable, enduring companies”.

That framing captures a shift happening across robotics: the field is moving out of the lab and into early commercial readiness. Improvements in perception systems, model-based reasoning and motion control are accelerating the transition. Advances in simulation are also lowering the complexity and cost of integrating humanoid platforms into real environments. As these systems become more capable, the gap between research prototypes and market-ready products is narrowing.

ALM Ventures is positioning itself at this inflection point. Fund I’s thesis centers on the core technologies required to scale humanoids safely and economically. This includes next-generation robot platforms, spatial reasoning engines, embodied intelligence models, world-modeling systems and the infrastructure needed for early deployment. Rather than chasing every robotics trend, the fund is concentrating on the essential layers that will determine whether humanoids can work reliably outside controlled settings.

The firm isn’t starting from zero. During the fund’s formation, ALM Ventures made ten early investments that directly align with its investment focus. The portfolio includes companies building at different layers of the humanoid stack, such as Sanctuary AI, Weave Robotics, Emancro, High Torque Robotics, MicroFactory, Mbodi, Adamo, Haptica Robotics, UMA and O-ID. The list reflects a broad but intentional spread, from hardware to intelligence to manufacturing approaches, all oriented toward enabling scalable physical AI.

Beyond capital, ALM Ventures has been shaping the ecosystem through its global Humanoids Summit series in Silicon Valley, London and Tokyo. The series gives the firm early visibility into emerging technologies, pre-incorporation teams and the senior leaders steering the global robotics landscape. That vantage point has helped the firm identify where commercialization is truly taking root and where bottlenecks still exist.

The rise of humanoids is often compared to the early days of self-driving cars: a long arc of research suddenly meeting an acceleration point. What separates this moment is that advances in embodied AI and spatial intelligence are giving robots a more intuitive understanding of the physical world, making them easier to deploy, teach and scale. ALM Ventures’ Fund I is an attempt to capture that transition while shaping the companies that could define the next technological era.

With US$100 million dedicated to the earliest builders in the space, ALM Ventures is signaling its belief that humanoids are not just another robotics cycle—they may be the next major platform shift in AI.

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MicroCloud Hologram Inc. (NASDAQ: HOLO), a technology service provider recognized for its holography and imaging systems, is now expanding into a more advanced realm: a quantum-driven 3D intelligent model. The goal is to generate detailed 3D models and images with far less manual effort — a need that has only grown as industries flood the world with more visual data every year.

The concept is straightforward, even if the technology behind it isn’t. Traditional 3D modeling workflows are slow, fragmented and depend on large teams to clean datasets, train models, adjust parameters and fine-tune every output. HOLO is trying to close that gap by combining quantum computing with AI-powered 3D modeling, enabling the system to process massive datasets quickly and automatically produce high-precision 3D assets with much less human involvement.

To achieve this, the company developed a distributed architecture comprising of several specialized subsystems. One subsystem collects and cleans raw visual data from different sources. Another uses quantum deep learning to understand patterns in that data. A third converts the trained model into ready-to-use 3D assets based on user inputs. Additional modules manage visualization, secure data storage and system-wide protection — all supported by quantum-level encryption. Each subsystem runs in its own container and communicates through encrypted interfaces, allowing flexible upgrades and scaling without disrupting the entire system.

Why this matters: Industries ranging from gaming and film to manufacturing, simulation and digital twins are rapidly increasing their reliance on 3D content. The real bottleneck isn’t creativity — it’s time. Producing accurate, high-quality 3D assets still requires a huge amount of manual processing. HOLO’s approach attempts to lighten that workload by utilizing quantum tools to speed up data processing, model training, generation and scaling, while keeping user data secure.

According to the company, the system’s biggest advantages include its ability to handle massive datasets more efficiently, generate precise 3D models with fewer manual steps, and scale easily thanks to its modular, quantum-optimized design. Whether quantum computing will become a mainstream part of 3D production remains an open question. Still, the model shows how companies are beginning to rethink traditional 3D workflows as demand for high-quality digital content continues to surge.

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E-commerce keeps growing and with it, the number of products moving through warehouses every day. Items vary more than ever — different shapes, seasonal packaging, limited editions and constantly updated designs. At the same time, many logistics centers are dealing with labour shortages and rising pressure to automate.

But today’s image-recognition AI isn’t built for this level of change. Most systems rely on deep-learning models that need to be adjusted or retrained whenever new products appear. Every update — whether it’s a new item or a packaging change — adds extra time, energy use and operational cost. And for warehouses handling huge product catalogs, these retraining cycles can slow everything down.

KIOXIA, a company known for its memory and storage technologies, is working on a different approach. In a new collaboration with Tsubakimoto Chain and EAGLYS, the team has developed an AI-based image recognition system that is designed to adapt more easily as product lines grow and shift. The idea is to help logistics sites automatically identify items moving through their workflows without constantly reworking the core AI model.

At the center of the system is KIOXIA’s AiSAQ software paired with its Memory-Centric AI technology. Instead of retraining the model each time new products appear, the system stores new product data — images, labels and feature information — directly in high-capacity storage. This allows warehouses to add new items quickly without altering the original AI model.

Because storing more data can lead to longer search times, the system also indexes the stored product information and transfers the index into SSD storage. This makes it easier for the AI to retrieve relevant features fast, using a Retrieval-Augmented Generation–style method adapted for image recognition.

The collaboration will be showcased at the 2025 International Robot Exhibition in Tokyo. Visitors will see the system classify items in real time as they move along a conveyor, drawing on stored product features to identify them instantly. The demonstration aims to illustrate how logistics sites can handle continuously changing inventories with greater accuracy and reduced friction.

Overall, as logistics networks become increasingly busy and product lines evolve faster than ever, this memory-driven approach provides a practical way to keep automation adaptable and less fragile.

When British-Canadian cognitive psychologist and computer scientist Geoffrey Hinton joked that his ex-girlfriend once used ChatGPT to help her break up with him, he wasn’t exaggerating.  The father of deep learning was pointing to something stranger: how machines built to mimic language have begun to mimic thought — and how even their creators no longer agree on what that means.

In that one quip — part humor, part unease — Hinton captured the paradox at the center of the world’s most important scientific divide. Artificial intelligence has moved beyond code and circuits into the realm of psychology, economics and even philosophy. Yet among those who know it best, the question has turned unexpectedly existential: what, if anything, do large language models truly understand?  

Across the world’s AI labs, that question has split the community into two camps — believers and skeptics, prophets and heretics. One side sees systems like ChatGPT, Claude, and Gemini as the dawn of a new cognitive age. The other insists they’re clever parrots with no grasp of meaning, destined to plateau as soon as the data runs out. Between them stands a trillion-dollar industry built on both conviction and uncertainty.

Hinton, who spent a decade at Google refining the very neural networks that now power generative AI, has lately sounded like a man haunted by his own invention. Speaking to Scott Pelley on the CBS 60 Minutes interview aired October 8, 2023, Hinton said, “I think we're moving into a period when for the first time ever we may have things more intelligent than us.” . He said it not with triumph, but with visible worry.

Yoshua Bengio, his longtime collaborator, sees it differently. Speaking at the All In conference in Montreal, he told TIME that future AI systems "will have stronger and stronger reasoning abilities, more and more knowledge," while cautioning about ensuring they "act according to our norms". And then there’s Gary Marcus, the cognitive scientist and enduring critic, who dismisses the hype outright: “These systems don’t understand the world. They just predict the next word.”    

It’s a rare moment in science when three pioneers of the same field disagree so completely — not about ethics or funding, but about the very nature of progress. And yet that disagreement now shapes how the future of AI will unfold.

In the span of just two years, large language models have gone from research curiosities to corporate cornerstones. Banks use them to summarize reports. Lawyers draft contracts with them. Pharmaceutical firms explore protein structures through them. Silicon Valley is betting that scaling these models — training them on ever-larger datasets with ever-denser computers — will eventually yield something approaching reasoning, maybe even intelligence.

It’s the “bigger is smarter” philosophy, and it has worked — so far. OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini have grown exponentially in capability  . They can write code, explain math, outline business plans, even simulate empathy. For most users, the line between prediction and understanding has already blurred beyond meaning. Kelvin So, who is now conducting AI research in PolyU SPEED, commented  , “AI scientists today are inclined to believe we have learnt a bitter lesson in the advancement from the traditional AI to the current LLM paradigm. That said, scaling law, instead of human-crafted complicated rules, is the ultimate law governing AI.”  

But inside the labs, cracks are showing. Scaling models have become staggeringly expensive, and the returns are diminishing. A growing number of researchers suspect that raw scale alone cannot unlock true comprehension — that these systems are learning syntax, not semantics; imitation, not insight.  

That belief fuels a quiet counter-revolution. Instead of simply piling on data and GPUs, some researchers are pursuing hybrid intelligence   — systems that combine statistical learning with symbolic reasoning, causal inference, or embodied interaction with the physical world. The idea is that intelligence requires grounding — an understanding of cause, consequence, and context that no amount of text prediction can supply.

Yet the results speak for themselves.  In practice, language models are already transforming industries faster than regulation can keep up. Marketing departments run on them. Customer support, logistics and finance teams depend on them. Even scientists now use them to generate hypotheses, debug code and summarize literature. For every cautionary voice, there are a dozen entrepreneurs who see this technology as a force reshaping every industry. That gap — between what these models actually are and what we hope they might become — defines this moment. It’s a time of awe and unease, where progress races ahead even as understanding lags behind.  

Part of the confusion stems from how these systems work. A large language model doesn’t store facts like a database. It predicts what word is most likely to come next in a sequence, based on patterns in vast amounts of text. Behind this seemingly simple prediction mechanism lies a sophisticated architecture. The tokenizer is one of the key innovations behind modern language models. It takes text and chops it into smaller, manageable pieces the AI can understand. These pieces are then turned into numbers, giving the model a way to “read” human language. By doing this, the system can spot context and relationships between words — the building blocks of comprehension.  

Inside the model, mechanisms such as multi-head attention enable the system to examine many aspects of information simultaneously, much as a human reader might track several storylines at once.

Reinforcement learning, pioneered by Richard Sutton, a professor of computing science at the University of Alberta, and Andrew Barto, Professor Emeritus at the University of Massachusetts, mimics human trial-and-error learning. The AI develops “value functions” that predict the long-term rewards of its actions.  Together, these technologies enable machines to recognize patterns, make predictions and generate text that feels strikingly human — yet beneath this technical progress lies the very divide that cuts to the heart of how intelligence itself is defined.

This placement works well because it elaborates on the technical foundations after the article introduces the basic concept of how language models work, and before it transitions to discussing the emergent behaviors and the “black box problem.”

Yet at scale, that simple process begins to yield emergent behavior — reasoning, problem-solving, even flashes of creativity that surprise their creators. The result is something that looks, sounds and increasingly acts intelligent — even if no one can explain exactly why.

That opacity worries not just philosophers, but engineers. The “black box problem” — our inability to interpret how neural networks make decisions — has turned into a scientific and safety concern. If we can’t explain a model’s reasoning, can we trust it in critical systems like healthcare or defense?

Companies like Anthropic are trying to address that with “constitutional AI,” embedding human-written principles into model training to guide behavior. Others, like OpenAI, are experimenting with internal oversight teams and adversarial testing to catch dangerous or misleading outputs. But no approach yet offers real transparency. We’re effectively steering a ship whose navigation system we don’t fully understand.  “We need governance frameworks that evolve as quickly as AI itself,” says Felix Cheung, Founding Chairman of RegTech Association of Hong Kong (RTAHK). “Technical safeguards alone aren't enough — transparent monitoring and clear accountability must become industry standards.”

Meanwhile, the commercial race is accelerating. Venture capital is flowing into AI startups at record speed. OpenAI’s valuation reportedly exceeds US$150 billion; Anthropic, backed by Amazon and Google, isn’t far behind.   The bet is simple: that generative AI will become as indispensable to modern life as the internet itself.

And yet, not everyone is buying into that vision. The open-source movement — championed by players like Meta’s Llama, Mistral in France, and a fast-growing constellation of independent labs — argues that democratizing access is the only way to ensure both innovation and accountability.   If powerful AI remains locked behind corporate walls, they warn, progress will narrow to the priorities of a few firms.

But openness cuts both ways. Publicly available models are harder to police, and their misuse — from disinformation to deepfakes — grows as easily as innovation does. Regulators are scrambling to balance risk and reward. The European Union’s AI Act is the world’s most comprehensive attempt at governance, but even it struggles to define where to draw the line between creativity and control.

This isn’t just a scientific argument anymore. It’s a geopolitical one. The United States, China, and Europe are each pursuing distinct AI strategies: Washington betting on private-sector dominance, Beijing on state-led scaling, Brussels on regulation and ethics. Behind the headlines, compute power is becoming a form of soft power. Whoever controls access to the chips, data, and infrastructure that fuel AI will control much of the digital economy.  

That reality is forcing some uncomfortable math. Training frontier models already consumes energy on the scale of small nations. Data centers now rise next to hydroelectric dams and nuclear plants. Efficiency — once a technical concern — has become an economic and environmental one. As demand grows, so does the incentive to build smaller, smarter, more efficient systems. The industry’s next leap may not come from scale at all, but from constraint.

For all the noise, one truth keeps resurfacing: large language models are tools, not oracles. Their intelligence — if we can call it that — is borrowed from ours. They are trained on human text, human logic, human error. Every time a model surprises us with insight, it is, in a sense, holding up a mirror to collective intelligence.

That’s what makes this schism so fascinating. It’s not really about machines. It’s about what we believe intelligence is — pattern or principle, simulation or soul. For believers like Bengio, intelligence may simply be prediction done right. For critics like Marcus, that’s a category mistake: true understanding requires grounding in the real world, something no model trained on text can ever achieve.

The public, meanwhile, is less interested in metaphysics. To most users, these systems work — and that’s enough. They write emails, plan trips, debug spreadsheets, summarize meetings. Whether they “understand” or not feels academic. But for the scientists, that distinction remains critical, because it determines where AI might ultimately lead.

Even inside the companies building them, that tension shows OpenAI’s Sam Altman has hinted that scaling can’t continue forever. At some point, new architectures — possibly combining logic, memory, or embodied data — will be needed. DeepMind’s Demis Hassabis says something similar: intelligence, he argues, will come not just from prediction, but from interaction with the world.  

It’s possible both are right. The future of AI may belong to hybrid systems — part statistical, part symbolic — that can reason across multiple modes of information: text, image, sound, action. The line between model and agent is already blurring, as LLMs gain the ability to browse the web, run code, and call external tools. The next generation won’t just answer questions; it will perform tasks.

For startups, the opportunity — and the risk — lies in that transition. The most valuable companies in this new era may not be those that build the biggest models, but those that build useful ones: specialized systems tuned for medicine, law, logistics, or finance, where reliability matters more than raw capability. The winners will understand that scale is a means, not an end.

And for society, the challenge is to decide what kind of intelligence we want to live with. If we treat these models as collaborators — imperfect, explainable, constrained — they could amplify human potential on a scale unseen since the printing press. If we chase the illusion of autonomy, they could just as easily entrench bias, confusion, and dependency.

The debate over large language models will not end in a lab. It will play out in courts, classrooms, boardrooms, and living rooms — anywhere humans and machines learn to share the same cognitive space. Whether we call that cooperation or competition will depend on how we design, deploy, and, ultimately, define these tools.

Perhaps Hinton’s offhand remark about being psychoanalyzed by his own creation wasn’t just a joke. It was an omen. AI is no longer something we use; it’s something we’re reflected in. Every model trained on our words becomes a record of who we are — our reasoning, our prejudices, our brilliance, our contradictions. The schism among scientists mirrors the one within ourselves: fascination colliding with fear, ambition tempered by doubt.

In the end, the question isn’t whether LLMs are the future. It’s whether we are ready for a future built in their image.

A new collaboration between ChainGPT, an AI company specialising in blockchain development tools and Secret Network, a privacy-focused blockchain platform, is redefining how developers can safely build smart contracts with artificial intelligence. Together, they’ve achieved a major industry first: an AI model trained exclusively to write and audit Solidity code is now running inside a Trusted Execution Environment (TEE). For the blockchain ecosystem, this marks a turning point in how AI, privacy and on-chain development can work together.

For years, smart-contract developers have faced a trade-off. AI assistants could speed up coding and security reviews, but only if developers uploaded their most sensitive source code to external servers. That meant exposing intellectual property, confidential logic and even potential vulnerabilities. In an industry where trust is everything, this risk held many teams back from using AI at all.

ChainGPT’s Solidity-LLM aims to solve that problem. It is a specialised large language model trained on over 650,000 curated Solidity contracts, giving it a deep understanding of how real smart contracts are structured, optimised and secured. And now, by running inside SecretVM, the Confidential Virtual Machine that powers Secret Network’s encrypted compute layer, the model can assist developers without ever revealing their code to outside parties.

“Confidential computing is no longer an abstract concept,” said Luke Bowman, COO of the Secret Network Foundation. “We've shown that you can run a complex AI model, purpose-built for Solidity, inside a fully encrypted environment and that every inference can be verified on-chain. This is a real milestone for both privacy and decentralised infrastructure”.

SecretVM makes this workflow possible by using hardware-backed encryption to protect all data while computations take place. Developers don’t interact with the underlying hardware or cryptography. Instead, they simply work inside a private, sealed environment where their code stays invisible to everyone except them—even node operators. For the first time, developers can generate, test and analyse smart contracts with AI while keeping every detail confidential.

This shift opens new possibilities for the broader blockchain community. Developers gain a private coding partner that can streamline contract logic or catch vulnerabilities without risking leaks. Auditors can rely on AI-assisted analysis while keeping sensitive audit material protected. Enterprises working in finance, healthcare or governance finally have a path to adopt AI-driven blockchain automation without raising compliance concerns. Even decentralised organisations can run smart-contract agents that make decisions privately, without exposing internal logic on a public chain.

The system also supports secure model training and fine-tuning on encrypted datasets. This enables collaborative AI development without forcing anyone to share raw data—a meaningful step toward decentralised and privacy-preserving AI at scale.

By combining specialised AI with confidential computing, ChainGPT and Secret Network are shifting the trust model of on-chain development. Instead of relying on centralised cloud AI services, developers now have a verifiable, encrypted environment where they keep full control of their code, their data and their workflow. It’s a practical solution to one of blockchain’s biggest challenges: using powerful AI tools without sacrificing privacy.

As the technology evolves, the roadmap includes confidential model fine-tuning, multi-agent AI systems and cross-chain use cases. But the core advancement is already clear: developers now have a way to use AI for smart contract development that is fast, private and verifiable—without compromising the security standards that decentralised systems rely on.

Speech and language delays are common, yet access to therapy remains limited. In Taiwan, only about 2,200 licensed speech-language pathologists serve hundreds of thousands of children who need support—especially those with autism spectrum disorders or significant communication challenges. As a result, many children miss crucial periods of language development simply because help isn’t available soon enough.

MIRDC’s new AI-powered interactive speech therapy system aims to close that gap. Instead of focusing solely on articulation, it targets a wider range of language skills that many children struggle with: oral expression, comprehension, sentence building and conversational ability. This makes it a more complete tool for childhood speech and language development.

The system combines game-based learning, AI-driven guidance and automated language assessment into one platform that can be used both in clinics and at home. This integrated design helps children practice more consistently, providing therapists and parents with clearer insight into their progress.

The interactive game modules are built around clinically validated therapy methods. Imitation exercises, picture cards, storybooks and conversational prompts are turned into structured game levels, each aligned with a specific developmental goal. This step-by-step approach helps children move from simple naming tasks to more complex comprehension and response skills, all within a sequenced curriculum.

A key differentiator is the system’s real-time AI speech interpretation. As the child talks, the AI analyzes the response and generates tailored therapeutic cues—such as imitation, modeling, expansion or extension—based on the conversation. These are the same strategies used by speech-language pathologists, but now children can access them continuously, supporting more effective at-home practice and reducing long gaps between sessions.

After each session, the system automatically conducts a data-driven language assessment using 20 objective indicators across semantics, syntax and pragmatics. This provides clinicians and families with measurable, easy-to-understand reports that show how the child is progressing and which skills need more attention—something many traditional tools do not offer.

By offering a personalized, scalable and clinically grounded solution, MIRDC’s AI therapy system helps address the ongoing shortage of speech-language services. It doesn’t replace therapists; instead, it extends their reach, allows for more consistent practice and helps families support their child’s communication at home.

As an added recognition of its impact, the system recently earned two R&D 100 Awards, including the Silver Award for Corporate Social Responsibility. But at its core, the project remains focused on a simple mission: making high-quality speech therapy accessible to every child who needs a voice.

Atomathic, the company once known as Neural Propulsion Systems, is stepping into the spotlight with a bold claim: its new AI platforms can help machines “see the invisible”. With the commercial launch of AIDAR™ and AISIR™, the company says it is opening a new chapter for physical AI, AI sensing and advanced sensor technology across automotive, aviation, defense, robotics and semiconductor manufacturing.

The idea behind these platforms is simple yet ambitious. Machines gather enormous amounts of signal data, yet they still struggle to understand the faint, fast or hidden details that matter most when making decisions. Atomathic says its software closes that gap. By applying AI signal processing directly to raw physical signals, the company aims to help sensors pick up subtle patterns that traditional systems miss, enabling faster reactions and more confident autonomous system performance.

"To realize the promise of physical AI, machines must achieve greater autonomy, precision and real-time decision-making—and Atomathic is defining that future," said Dr. Behrooz Rezvani, Founder and CEO of Atomathic. "We make the invisible visible. Our technology fuses the rigor of mathematics with the power of AI to transform how sensors and machines interact with the world—unlocking capabilities once thought to be theoretical. What can be imagined mathematically can now be realized physically."

This technical shift is powered by Atomathic’s deeper mathematical framework. The core of its approach is a method called hyperdefinition technology, which uses the Atomic Norm and fast computational techniques to map sparse physical signals. In simple terms, it pulls clarity out of chaos. This enables ultra-high-resolution signal visualization in real time—something the company claims has never been achieved at this scale in real-time sensing.

AIDAR and AISIR are already being trialled and integrated across multiple sectors and they’re designed to work with a broad range of hardware. That hardware-agnostic design is poised to matter even more as industries shift toward richer, more detailed sensing. Analysts expect the automotive sensor market to surge in the coming years, with radar imaging, next-gen ADAS systems and high-precision machine perception playing increasingly central roles.

Atomathic’s technology comes from a tight-knit team with deep roots in mathematics, machine intelligence and AI research, drawing talent from institutions such as Caltech, UCLA, Stanford and the Technical University of Munich. After seven years of development, the company is ready to show its progress publicly, starting with demonstrations at CES 2026 in Las Vegas.

Suppose the future of autonomy depends on machines perceiving the world with far greater fidelity. In that case, Atomathic is betting that the next leap forward won’t come from more hardware, but from rethinking the math behind the signal—and redefining what physical AI can do.

It’s no surprise that artificial intelligence, while remarkably capable, can also go astray—spinning convincing but entirely fabricated narratives. From politics to academia, AI’s “hallucinations” have repeatedly shown how powerful technology can go off-script when left unchecked.

Take Grok-2, for instance. In July 2024, the chatbot misled users about ballot deadlines in several U.S. states, just days after President Joe Biden dropped his re-election bid against former President Donald Trump. A year earlier, a U.S. lawyer found himself in court for relying on ChatGPT to draft a legal brief—only to discover that the AI tool had invented entire cases, citations and judicial opinions. And now, the academic world has its own cautionary tale.

Recently, a journal paper from the Department of Social Work and Social Administration at the University of Hong Kong was found to contain fabricated citations—sources apparently created by AI. The paper, titled “Forty Years of Fertility Transition in Hong Kong,” analyzed the decline in Hong Kong’s fertility rate over the past four decades. Authored by doctoral student Yiming Bai, along with Yip Siu-fai, Vice Dean of the Faculty of Social Sciences and other university officials, the study identified falling marriage rates as a key driver behind the city’s shrinking birth rate. The authors recommended structural reforms to make Hong Kong’s social and work environment more family-friendly.

But the credibility of the paper came into question when inconsistencies surfaced among its references. Out of 61 cited works, some included DOI (Digital Object Identifier) links that led to dead ends, displaying “DOI Not Found.” Others claimed to originate from academic journals, yet searches yielded no such publications.

Speaking to HK01, Yip acknowledged that his student had used AI tools to organize the citations but failed to verify the accuracy of the generated references. “As the corresponding author, I bear responsibility”, Yip said, apologizing for the damage caused to the University of Hong Kong and the journal’s reputation. He clarified that the paper itself had undergone two rounds of verification and that its content was not fabricated—only the citations had been mishandled.

Yip has since contacted the journal’s editor, who accepted his explanation and agreed to re-upload a corrected version in the coming days. A formal notice addressing the issue will also be released. Yip said he would personally review each citation “piece by piece” to ensure no errors remain.

As for the student involved, Yip described her as a diligent and high-performing researcher who made an honest mistake in her first attempt at using AI for academic assistance. Rather than penalize her, Yip chose a more constructive approach, urging her to take a course on how to use AI tools responsibly in academic research.

Ultimately, in an age where generative AI can produce everything from essays to legal arguments, there are two lessons to take away from this episode. First, AI is a powerful assistant, but only that. The final judgment must always rest with us. No matter how seamless the output seems, cross-checking and verifying information remain essential. Second, as AI becomes integral to academic and professional life, institutions must equip students and employees with the skills to use it responsibly. Training and mentorship are no longer optional; they’re the foundation for using AI to enhance, not undermine, human work.

Because in this age of intelligent machines, staying relevant isn’t about replacing human judgment with AI, it’s about learning how to work alongside it.

Neuron7.ai, a company that builds AI systems to help service teams resolve technical issues faster, has launched Neuro. It is a new kind of AI agent built for environments where accuracy matters more than speed. From manufacturing floors to hospital equipment rooms, Neuro is designed for situations where a wrong answer can halt operations.

What sets Neuro apart is its focus on reliability. Instead of relying solely on large language models that often produce confident but inaccurate responses, Neuro combines deterministic AI — which draws on verified, trusted data — with autonomous reasoning for more complex cases. This hybrid design helps the system provide context-aware resolutions without inventing answers or “hallucinating”, a common issue that has made many enterprises cautious about adopting agentic AI.

“Enterprise adoption of agentic AI has stalled despite massive vendor investment. Gartner predicts 40% of projects will be canceled by 2027 due to reliability concerns”, said Niken Patel, CEO and Co-Founder of Neuron7. “The root cause is hallucinations. In service operations, outcomes are binary. An issue is either resolved or it is not. Probabilistic AI that is right only 70% of the time fails 30% of your customers and that failure rate is unacceptable for mission-critical service”.

That concern shaped how Neuro was built. “We use deterministic guided fixes for known issues. No guessing, no hallucinations — and reserve autonomous AI reasoning for complex scenarios. What sets Neuro apart is knowing which mode to use. While competitors race to make agents more autonomous, we're focused on making service resolution more accurate and trusted”, Patel explained.

At the heart of Neuro is the Smart Resolution Hub, Neuron7’s central intelligence layer that consolidates service data, knowledge bases and troubleshooting workflows into one conversational experience. This means a technician can describe a problem — say, a diagnostic error in an MRI scanner — and Neuro can instantly generate a verified, step-by-step solution. If the problem hasn’t been encountered before, it can autonomously scan through thousands of internal and external data points to identify the most likely fix, all while maintaining traceability and compliance.

Neuro’s architecture also makes it practical for real-world use. It integrates seamlessly with enterprise systems such as Salesforce, Microsoft, ServiceNow and SAP, allowing companies to embed it within their existing support operations. Early users of Neuron7’s platform have reported measurable improvements — faster resolutions, higher customer satisfaction and reduced downtime — thanks to guided intelligence that scales expert-level problem solving across teams.

The timing of Neuro’s debut feels deliberate. As organizations look to move past the hype of generative AI, trust and accountability have become the new benchmarks. AI systems that can explain their reasoning and stay within verifiable boundaries are emerging as the next phase of enterprise adoption.

“The market has figured out how to build autonomous agents”, Patel said. “The unsolved problem is building accurate agents for contexts where errors have consequences. Neuro fills that gap”.

Neuron7 is building a system that knows its limits — one that reasons carefully, acts responsibly and earns trust where it matters most. In a space dominated by speculation, that discipline may well redefine what “intelligent” really means in enterprise AI.

Encyclopaedias have always been mirrors of their time — from heavy leather-bound volumes in the 19th century to Wikipedia’s community-edited pages online. But as the world’s information multiplies faster than humans can catalogue it, even open platforms struggle to keep pace. Enter Botipedia, a new project from INSEAD, The Business School for the World, that reimagines how knowledge can be created, verified and shared using artificial intelligence.

At its core, Botipedia is powered by proprietary AI that automates the process of writing encyclopaedia entries. Instead of relying on volunteers or editors, it uses a system called Dynamic Multi-method Generation (DMG) — a method that combines hundreds of algorithms and curated datasets to produce high-quality, verifiable content. This AI doesn’t just summarise what already exists; it synthesises information from archives, satellite feeds and data libraries to generate original text grounded in facts.

What makes this innovation significant is the gap it fills in global access to knowledge. While Wikipedia hosts roughly 64 million English-language entries, languages like Swahili have fewer than 40,000 articles — leaving most of the world’s population outside the circle of easily available online information. Botipedia aims to close that gap by generating over 400 billion entries across 100 languages, ensuring that no subject, event or region is overlooked.

"We are creating Botipedia to provide everyone with equal access to information, with no language left behind", says Phil Parker, INSEAD Chaired Professor of Management Science, creator of Botipedia and holder of one of the pioneering patents in the field of generative AI. "We focus on content grounded in data and sources with full provenance, allowing the user to see as many perspectives as possible, as opposed to one potentially biased source".

Unlike many generative AI tools that depend on large language models (LLMs), Botipedia adapts its methods based on the type of content. For instance, weather data is generated using geo-spatial techniques to cover every possible coordinate on Earth. This targeted, multi-method approach helps boost both the accuracy and reliability of what it produces — key challenges in today’s AI-driven content landscape.

Additionally, the innovation is also energy-efficient. Its DMG system operates at a fraction of the processing power required by GPU-heavy models like ChatGPT, making it a sustainable alternative for large-scale content generation.

By combining AI precision, linguistic inclusivity and academic credibility, Botipedia positions itself as more than a digital library — it’s a step toward universal, unbiased access to verified knowledge.

"Botipedia is one of many initiatives of the Human and Machine Intelligence Institute (HUMII) that we are establishing at INSEAD", says Lily Fang, Dean of Research and Innovation at INSEAD. "It is a practical application that builds on INSEAD-linked IP to help people make better decisions with knowledge powered by technology. We want technologies that enhance the quality and meaning of our work and life, to retain human agency and value in the age of intelligence".

By harnessing AI to bridge gaps of language, geography and credibility, Botipedia points to a future where access to knowledge is no longer a privilege, but a shared global resource.

Analog Devices (ADI), a global semiconductor company, launched CodeFusion Studio™ 2.0 on November 3, 2025. The new version of its open-source development platform is designed to make it easier and faster for developers to build AI-powered embedded systems that run on ADI’s processors and microcontrollers.

“The next era of embedded intelligence requires removing friction from AI development”, said Rob Oshana, Senior Vice President of the Software and Digital Platforms group at ADI. “CodeFusion Studio 2.0 transforms the developer experience by unifying fragmented AI workflows into a seamless process, empowering developers to leverage the full potential of ADI's cutting-edge products with ease so they can focus on innovating and accelerating time to market”.

The upgraded platform introduces new tools for hardware abstraction, AI integration and automation. These help developers move more easily from early design to deployment.

CodeFusion Studio 2.0 enables complete AI workflows, allowing teams to use their own models and deploy them on everything from low-power edge devices to advanced digital signal processors (DSPs).

Built on Microsoft Visual Studio Code, the new CodeFusion Studio offers built-in checks for model compatibility, along with performance testing and optimization tools that help reduce development time. Building on these capabilities, a new modular framework based on Zephyr OS lets developers test and monitor how AI and machine learning models perform in real time. This gives clearer insight into how each part of a model behaves during operation and helps fine-tune performance across different hardware setups.

Additionally, the CodeFusion Studio System Planner has also been redesigned to handle more device types and complex, multi-core applications. With new built-in diagnostic and debugging features — like integrated memory analysis and visual error tracking — developers can now troubleshoot problems faster and keep their systems running more efficiently.

This launch marks a deeper pivot for ADI. Long known for high-precision analog chips and converters, the company is expanding its edge-AI and software capabilities to enable what it calls Physical Intelligence — systems that can perceive, reason, and act locally.  

“Companies that deliver physically aware AI solutions are poised to transform industries and create new, industry-leading opportunities. That's why we're creating an ecosystem that enables developers to optimize, deploy and evaluate AI models seamlessly on ADI hardware, even without physical access to a board”, said Paul Golding, Vice President of Edge AI and Robotics at ADI. “CodeFusion Studio 2.0 is just one step we're taking to deliver Physical Intelligence to our customers, ultimately enabling them to create systems that perceive, reason and act locally, all within the constraints of real-world physics”.

Shanghai-based robotics firm AgiBot has taken a major step toward bringing artificial intelligence into real manufacturing. The company announced that its Real-World Reinforcement Learning (RW-RL) system has been successfully deployed on a pilot production line run in partnership with Longcheer Technology.  It marks one of the first real applications of reinforcement learning in industrial robotics.

The project represents a key shift in factory automation. For years, precision manufacturing has relied on rigid setups: robots that need custom fixtures, intricate programming and long calibration cycles. Even newer systems combining vision and force control often struggle with slow deployment and complex maintenance. AgiBot’s system aims to change that by letting robots learn and adapt on the job, reducing the need for extensive tuning or manual reconfiguration.

The RW-RL setup allows a robot to pick up new tasks within minutes rather than weeks. Once trained, the system can automatically adjust to variations, such as changes in part placement or size tolerance, maintaining steady performance throughout long operations. When production lines switch models or products, only minor hardware tweaks are needed. This flexibility could significantly cut downtime and setup costs in industries where rapid product turnover is common.

The system’s main strengths lie in faster deployment, high adaptability and easier reconfiguration. In practice, robots can be retrained quickly for new tasks without needing new fixtures or tools — a long-standing obstacle in consumer electronics production. The platform also works reliably across different factory layouts, showing potential for broader use in complex or varied manufacturing environments.

Beyond its technical claims, the milestone demonstrates a deeper convergence between algorithmic intelligence and mechanical motion.Instead of being tested only in the lab, AgiBot’s system was tried in real factory settings, showing it can perform reliably outside research conditions.

This progress builds on years of reinforcement learning research, which has gradually pushed AI toward greater stability and real-world usability. AgiBot’s Chief Scientist Dr. Jianlan Luo and his team have been at the forefront of that effort, refining algorithms capable of reliable performance on physical machines. Their work now underpins a production-ready platform that blends adaptive learning with precision motion control — turning what was once a research goal into a working industrial solution.

Looking forward, the two companies plan to extend the approach to other manufacturing areas, including consumer electronics and automotive components. They also aim to develop modular robot systems that can integrate smoothly with existing production setups.