Business Insider
중도 성향
IT/기술
5 people who pivoted into AI jobs — and how they did it
Employees shared how they made the leap to AI-related roles, including what they discussed in their job interviews and résumés.
🇺🇸 미국 · IT/기술 · "ROLES" · 총 12건
필터 보기현재 지수
50.0
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 12,058건을 분석한 결과, 뉴스 심리지수는 50.0(균형)입니다. 긍정 1건(0.0%)·중립 12,056건(100.0%)·부정 1건(0.0%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 19.0(중도 균형)입니다.
Employees shared how they made the leap to AI-related roles, including what they discussed in their job interviews and résumés.
Actors who flocked to the booming micro-drama industry are losing roles as producers swap them with AI-generated performers. Is Hollywood next?
India’s largest streaming platform, JioHotstar, is recruiting for more than 75 AI roles as the platform moves to build out a dedicated artificial intelligence division, the company revealed Wednesday. The push spans engineering, production automation and creative technology, with the streamer positioning India’s multilingual scale and engineering base as the foundation for a broader global […]
The expansion of AI data centers is spurring demand for security roles, as these facilities face increased threats and public opposition.
See how much Nvidia pays in 2026, with engineers and research scientists earning over $300,000. Salary data shows pay ranges across about 1,200 roles.
A LinkedIn report supports some of the billionaire’s predictions, showing job postings mentioning “storytellers” doubled over the past year.
This article is adapted by the author with permission from Tech Policy Press. Read the original article. South Africa is not just another developing country struggling to govern artificial intelligence; it is the exception with leverage, and the window to act on it is closing. It holds approximately 88 percent of global platinum-group metal reserves, critical inputs to parts of the semiconductor and data-center supply chains that make AI infrastructure possible. It hosts the largest data-center market on the continent. Its existing hyperscaler relationships give it procurement leverage that most African states will never have. And a major geopolitical contest over AI infrastructure is being fought on its soil right now, between Chinese and American technology companies competing for control of the systems that will underpin an entire continent’s public sector. In physics, leverage requires three things: a fulcrum, a lever arm, and the ability to apply force. The Bushveld Complex, the world’s largest platinum-group metal deposit, is the fulcrum: a mineral endowment that gives South Africa a position in the semiconductor supply chain that no other African state holds. The since-withdrawn draft policy is the lever arm. The unresolved “OPTION” provisions in the policy are where force would be applied. Without a policy that specifies what South Africa wants in return for market access, the lever arm sits unused, and the weight of two of the world’s largest technology ecosystems settles exactly where those ecosystems want it to settle. This makes South Africa a global test case. Not because its proposed means of governance is exemplary, but because it is the one developing country with enough structural leverage to negotiate genuinely different terms, and the one that is choosing, through inaction, not to. The recent announcement of a new panel to update the draft policy is an important opportunity. But the deeper failure is not that an AI policy contained bad references. It is that no verification process caught them before the document entered the public domain. That is a systems problem, not merely a political one. It points to a missing layer in how governments are adopting AI. The contest already underway Last year, Huawei pitched an emerging-product bundle to tech executives across the continent. Huawei was now bundling access to DeepSeek’s large language model with its own cloud and storage infrastructure. The price differential was stark—in some cases by more than 90 percent. At the same time, Microsoft announced plans to spend ZAR 5.4 billion ($300 million) by the end of 2027 on cloud and AI infrastructure in South Africa, building on a prior ZAR 20.4 billion investment. Google, Amazon Web Services, and Oracle already have cloud regions in the country. According to one analysis, the country’s data-center market was valued at US $2.16 billion in 2024, the largest in Africa. These are not commercially neutral investments. Huawei’s infrastructure reach has been explicitly linked to Chinese strategic objectives, including a documented track record of providing governments with surveillance infrastructure through its Safe Cities network. U.S. hyperscaler investment comes with its own dependency structure: closed models, pricing set unilaterally, and terms of access that no African government has meaningfully shaped. South Africa is being asked to choose between these dependency models without a policy that specifies what it wants in return. The leverage it has There is a particular irony in South Africa’s position. The country whose mines supply platinum-group metals essential to semiconductor manufacturing, and through them to AI compute, has drafted a policy that treats it as a consumer of AI systems rather than a stakeholder in their governance. South Africa digs up the minerals that make AI possible. It has no say over the AI built from them. The AI triad framework covers algorithms, compute, and data. South Africa has no frontier model development capacity. South Africa holds significant data assets in financial services, health care, and agriculture, with no clear framework for their sovereign management. South Africa possesses PGM (Platinum Group Metals) leverage of global significance on the compute axis, currently being transferred without meaningful condition. It also has exceptionally high solar irradiance and significant renewable-energy potential. A country that can offer both critical mineral inputs and the energy to power the infrastructure those minerals help build occupies a negotiating position of unusual strength. The Draft Policy proposes no minimum terms for hyperscaler investment, no data sovereignty requirements, no technology transfer conditions and no compute visibility mechanism. Multiple provisions are explicitly left unresolved, marked “OPTION,” including the most consequential choices about how governance will function. Infrastructure decisions made now determine what is renegotiable later, and the answer is: very little. Three futures, one default The three infrastructure futures on offer each create a structurally different form of dependency, and only one creates sovereign capability. The Huawei-hosted DeepSeek integration offers low cost and open-source weights, but with data stored on infrastructure potentially accessible under Chinese legal frameworks, creating surveillance dependency in a pattern already documented across Africa. The second is U.S. closed-model dependency: higher capability, more reliable data protection, but complete API dependency on developers abroad. The third is locally hosted open-weight infrastructure: models governed under South African data-sovereignty rules, on infrastructure subject to minimum terms, developed with South African data. As Nathan Lambert at Interconnects has observed, open-weight models are likely the only realistic way to get sovereign AI off the ground as a real effort, enabling local communities and economies to integrate meaningfully with the technology. But this requires procurement conditions, not goodwill. What binding governance looks like The GovAI “Governing Through the Cloud” framework identifies four roles compute providers should accept as conditions of operating at scale: securers (protecting model weights and training data), record keepers (maintaining infrastructure usage logs), verifiers (confirming customer compliance with safety standards) and enforcers (restricting access when violations occur). These are operational requirements, not theoretical categories—specific, enforceable, and well within the bargaining power of a market of South Africa’s size and mineral position. A detailed policy analysis submitted to the Department of Communications and Digital Technologies (DCDT) identifies the specific provisions the final policy must contain: mandatory minimum terms for foreign compute infrastructure investments above ZAR 500 million (~$30 million); a compute reporting threshold; a National AI Safety Institute mandate covering defensive monitoring of AI capability accumulation; and National AI Champion Sector designations to create data assets for domestic model development. Each provision converts a structural advantage into a governance instrument before that advantage is foreclosed by market reality. Just as modern software security increasingly depends on knowing what components are inside a system—model provider, training data, compute environment, evaluation methods, update cadence, human review points, and failure-reporting procedures—public-sector AI governance requires a clear account of the stack before deployment, not after a problem surfaces. A public institution that cannot verify the sources in its own AI policy is unlikely to be ready to verify the AI systems it procures, deploys, or regulates. Why this is the continental test case South Africa’s choices will establish a regional precedent for what is commercially negotiable in AI infrastructure. If South Africa negotiates data-sovereignty guarantees and technology-transfer conditions as requirements for hyperscaler investment, it creates a replicable model. If Microsoft’s $300 million investment and Huawei’s infrastructure expansion proceed on standard commercial terms, as they are currently, it normalizes extractive AI infrastructure across the continent. The lesson is not specific to Africa. Governments everywhere are producing AI strategies while lacking AI assurance infrastructure. South Africa is an early warning, not an isolated case. The public comment period closed when the policy was withdrawn. But a parallel process remains live: the National Treasury’s Draft General Public Procurement Regulations—the legal instrument that will govern every government AI contract—closes for comment on June 15. Those regulations contain no AI-specific provisions. South Africa has more AI leverage than any country on the continent. Some argue, with force, that governance requirements risk deterring the infrastructure investment South Africa urgently needs: compute capacity, reliable energy, venture capital, and talent retention. That concern deserves a direct answer. Minimum procurement terms, compute reporting thresholds, and technology transfer conditions are not barriers to investment. They are the conditions under which investment serves the host country rather than extracting from it. Infrastructure built without minimum terms produces dependency. Infrastructure built with them produces leverage. To serve the public interest, its AI policy must use it. When late last month News24 reported AI-hallucinated references in the draft AI policy, Minister of Communications and Digital Technologies Solly Malatsi withdrew the draft policy. That was a mistake that could cost South Africa and the rest of the continent the initiative on this urgent issue. His more recent constitution of an independent panel is a belated step in the right direction, if it can turn South Africa’s leverage into policy. The panel—chaired by Professor Benjamin Rosman of the Wits Machine Intelligence and Neural Discovery Institute, and including Professors Vukosi Marivate and Alison Gillwald of Research ICT Africa and Dr. Jabu Mtsweni of the Council for Scientific and Industrial Research—has the technical and governance credibility to produce a stronger document. What it has not yet produced is a timeline. No revised draft has been scheduled. South Africa remains without a formal AI governance framework in the interim.
The Huntsville Center for Technology gives high school students a foothold in the skilled trades amid a job market starved for that talent.
In some workplace training videos, actors are being replaced by AI. NPR's Scott Simon talks to actor Paul Clayton, who has appeared in more than 1,000 corporate acting roles.
When Ana Inês Inácio goes to work at the Netherlands Organization for Applied Scientific Research (TNO) in The Hague, she thinks about signals most people never notice: radio waves moving between satellites, sensors, and future wireless networks. The integrated circuits the research scientist designs lay the foundation for next-generation RF sensor systems critical to advancing radar technologies. Ana Inês Inácio EMPLOYER Netherlands Organization for Applied Scientific Research, TNO TITLE Scientist IEEE MEMBER GRADE Senior member ALMA MATER University of Aveiro, in Portugal Those invisible RF signals are only part of what earned the IEEE senior member her global recognition. Inácio recently received the IEEE–Eta Kappa Nu Outstanding Young Professional Award for “leadership in IEEE Young Professionals, fostering innovation and inclusivity, and pioneering advancements in RF sensor systems, bridging technical excellence with impactful community engagement.” The recognition from IEEE’s honor society reflects a career built along two parallel paths: advancing RF circuit design while helping engineers worldwide build professional communities. “I’ve always liked building things,” Inácio says. “Sometimes that means circuits; sometimes it means helping people connect and grow together.” That blend of technical innovation and global leadership gives her work impact far beyond the laboratory. EE lessons at the kitchen table Inácio grew up in Vales do Rio, a rural village near Covilhã in central Portugal. The region was known for farming and textiles, she says. Many residents worked in the textile industry, including her grandfather, who repaired machinery such as industrial looms. He became her first engineering teacher without ever holding the formal title. Through correspondence courses delivered by mail, he taught himself electrical systems. At home, he explained electricity to his granddaughter while he repaired the household’s appliances and wiring. “He would show me why something broke and how we could fix it,” she recalls. It sparked her curiosity. Her mother was a tailor who later managed other tailors. Her father left his factory job to attend culinary school and now cooks at an elder-care facility. Curiosity was a trait that ran through the family. By high school, Inácio was drawn equally to mathematics and physics and to biology and geology, she says. Encouragement from teachers and an uncle, an engineer, ultimately steered her toward electronics engineering. Conducting research on integrated circuits In 2008 she enrolled in an integrated master’s degree program in electrical and telecommunications engineering at the Universidade de Aveiro in Portugal, a five-year degree that combined undergraduate and graduate studies. An opportunity to study abroad changed her path. In 2012 she moved to the Netherlands to study at Eindhoven University of Technology (TU/e) through a six-month European exchange program with UAveiro. A professor encouraged her to stay on, so she completed her final year of masters in the Netherlands. She focused on techniques to improve the linearization of RF power amplifiers at Thales. The company, based in Hengelo, Netherlands, designs and produces electronics for defense and security. She earned her master’s degree from UAveiro in 2013. After graduating, she joined the integrated circuit design group at the University of Twente, in The Netherlands, conducting collaborative research as part of a nationally funded program on linearization techniques for RF front-end systems. The experience introduced her to international research culture and persuaded her to pursue a career abroad, she says. Engineering the future of wireless Inácio joined TNO in 2018 as a junior scientist and innovator: her first professional industry job. Today she designs integrated RF front-end systems—the circuits that allow devices to transmit and receive wireless signals. The components sit at the core of modern communications, enabling sensor networks, satellite links, and emerging 6G technologies. Her work aims to tackle a central challenge: getting greater performance from smaller chips. “As communication evolves, we need more bandwidth to transfer more data at higher speeds,” she says. “The question is how much complexity you can integrate into one system while keeping it efficient.” Unlike commercial lab environments, which reuse established designs, research projects often start from scratch. Each transmit-receive chain—the signal path that converts digital data to radio waves and back again—is tailored to specific requirements. Her work focuses on improving key circuit characteristics including linearity (ensuring that the signals that go out of the antenna are not distorted) as well as noise reduction (so design blocks can be optimized). Advanced design techniques help devices communicate more reliably while consuming less energy, a critical need for large sensor networks such as the Internet of Things, she says. Artificial intelligence is beginning to influence her field, she says: “AI is already helping us work faster. The real challenge is learning how to use it to make better designs, not just quicker ones.” A parallel vocation with IEEE While her technical career flourished in research labs, an additional journey unfolded through IEEE. Inácio joined the organization in 2009 as a student after discovering UAveiro’s student branch. What began as curiosity evolved into a long-term leadership path. She advanced through roles within Region 8—covering Europe, Africa, and the Middle East—one of the organization’s most culturally diverse regions. She was the student branch’s vice chair, and the region’s student representative for more than 22,000 IEEE members. She also served as the Young Professionals Affinity Group chair for the IEEE Benelux Section, which encompasses Belgium, the Netherlands, and Luxembourg. Currently, she serves as the immediate past chair of the Region 8 Young Professionals Committee, and vice chair and IEEE Member and Geographical Activities representative on the IEEE Young Professionals Committee. In those roles, she represents close to 135,000 IEEE members. In addition, she is an active member of the IEEE Microwave Theory and Technology Society, currently serving as its Young Professionals liaison. Her involvement with IEEE has boosted her professional confidence, she says. “IEEE didn’t directly give me promotions at my day job, but it gave me leadership skills, networking opportunities, and the ability to work with people from everywhere,” she says. Those experiences now shape her collaborations at TNO, where international teamwork is essential. The IEEE-HKN Outstanding Young Professional Award recognizes that combination of technical excellence and community impact, she says. Looking back, Inácio sees a clear thread connecting her childhood curiosity, her international career, and her IEEE leadership: Engineering, she says, is ultimately about people as much as it is about technology.
Cybersecurity consultants have never been more in demand. Information security analyst roles are projected to grow nearly 30 percent between now and 2034, according to the U.S. Bureau of Labor Statistics. More than 15 million cybercrime incidents occurred worldwide in 2024, Statista reported. Data breaches are costly and pose direct safety risks. Statista reported that more than US $10 trillion is spent annually repairing the damage caused by cybercrime, most commonly phishing, spoofing, extortion, and data breaches. In one example in the United States, breathalyzer devices installed in vehicles became disabled, leaving hundreds of drivers stranded, as detailed in an IEEE Spectrum article. To help you acquire the skills you need to distinguish yourself from other cybersecurity job candidates, the IEEE Computer Society offers a “What Makes a Great Cybersecurity Consultant” guide. The 23-page PDF includes hard and soft skills you need, a list of certifications to pursue, and key IEEE cybersecurity conferences for staying updated on developments in the field. The guide includes advice from two cybersecurity experts. John D. Johnson, an IEEE senior member, is the founder and CEO of Aligned Security in Bettendorf, Iowa. Ricardo J. Rodriguez is an associate professor of computer science and systems engineering at the Universidad de Zaragoza, in Spain, who researches digital forensics and other cybersecurity topics. “Technology, remote work, and a shortage of skilled workers make this the ideal time to consider becoming a cybersecurity consultant,” Johnson says in the guide. “Consulting can give you the flexibility, variety, and control over where you want your career to go.” Hard and soft skills At a minimum, cybersecurity professionals should have a general understanding of IT including operating systems, communication protocols, network architecture, and programming languages such as C++, Java, and Python. They also should be well-versed in security auditing, firewall management, penetration testing, and encryption technologies. The principles of ethical hacking and coding would be handy as well. “To be able to defend a system well, you first have to know how to attack it,” Rodriguez says. The guide explains that there are now more technologies available to help cybersecurity consultants monitor threats and protect systems. They include security orchestration, automation, and response (SOAR) platforms, which automate workflows to collect security data, streamline incident response, and automate repetitive tasks. Rodriguez points to advances in domain name system security extensions (DNSSEC), which uses digital signatures based on public-key cryptography to strengthen the authentication of the domain name system. By validating data authenticity, DNSSEC safeguards against attacks such as DNS spoofing and guarantees that users connect to the correct IP address. Technologies such as artificial intelligence, blockchain, and quantum computing will increasingly be used to help thwart cyberattacks, the guide suggests. AI is expected to enhance the quality of data analysis, Rodriguez says. Although hard skills are important, soft skills are just as crucial, according to the guide. Critical thinking, project management, flexibility, teamwork, and organizational and presentation skills are essential. It’s not enough to be good at analyzing security vulnerabilities; you also need to clearly describe the situation and explain possible solutions. “Soft skills are important to achieve good team cohesion,” Rodriguez says, “because consultants often lead diverse teams from within their client’s organization.” “It’s essential,” Johnson adds, “that you demonstrate to clients you’re a team player and a capable communicator, and that you meet your commitments.” Security certifications Possessing security-specific credentials is a valuable way to demonstrate your expertise to potential clients, according to the guide. Because hundreds of certifications are available, Johnson says, pinpointing the most relevant ones can be challenging. Some people focus on theoretical knowledge, while others want to cover practical applications of technology. “Survey the industry and compare it to your skills,” Johnson recommends. “Decide what you want to do, and identify where you have gaps in your skills and experience.” Here are four of the nine certifications listed in the guide that are frequently cited as being important. All the providers are cybersecurity organizations. Certified information security manager. This globally recognized certification from the ISACA is for professionals managing enterprise information security. Certified cloud security professional. Offered by ISC2, this credential validates advanced technical skills in designing, managing, and securing cloud infrastructure. Certified ethical hacker. This certification from the International Council of E-Commerce Consultants (C-Council) confirms proficiency in using methods commonly employed by malicious hackers to detect vulnerabilities. Offensive security certified professional. A hands-on, 24-hour certification exam offered by OffSec covers practical testing skills. Additional industry-specific certifications might be required for organizations in finance, government, health care, or manufacturing. Sound general knowledge—backed by experience, training, and certification—is an essential foundation for being a specialist, Johnson says. Conferences and networking opportunities Events sponsored by the IEEE Computer Society can help you learn about the latest research and advancements in cybersecurity: IEEE Symposium on Security and Privacy, from 18 to 21 May in San Francisco. IEEE European Symposium on Security and Privacy, from 6 to 10 July in Lisbon. IEEE International Conference on Cyber Security and Resilience, from 3 to 5 August in Lisbon. IEEE Secure Development Conference, from 14 to 16 October in Indianapolis. Conferences can give you insight into the field and let you do some networking, but it’s important to network elsewhere as well, experts say. Consider joining the IEEE Technical Community on Security and Privacy, which connects experts and professionals advancing research in areas such as encryption, operating system security, and data privacy. Learning and meeting people keeps your knowledge sharp and can lead to mentorship opportunities with established cybersecurity consultants, Johnson says. Other IEEE resources The IEEE Computer Society’s cybersecurity resources page offers a wealth of information including fundamentals, possible career paths, and standards development. To keep you updated on trends, the society publishes IEEE Transactions on Privacy and the IEEE Security and Privacy magazine. In addition to the guide, the IEEE Learning Network offers nearly 30 courses on cybersecurity. And you can find research papers in the IEEE Xplore Digital Library.
This article is brought to you by DAIMON Robotics. This April, Hong Kong-based DAIMON Robotics has released Daimon-Infinity, which it describes as the largest omni-modal robotic dataset for physical AI, featuring high resolution tactile sensing and spanning a wide range of tasks from folding laundry at home to manufacturing on factory assembly lines. The project is supported by collaborative efforts of partners across China and the globe, including Google DeepMind, Northwestern University, and the National University of Singapore. The move signals a key strategic initiative for DAIMON, a two-and-a-half-year-old company known for its advanced tactile sensor hardware, most notably a monochromatic, vision-based tactile sensor that packs over 110,000 effective sensing units into a fingertip-sized module. Drawing on its high-resolution tactile sensing technology and a distributed out-of-lab collection network capable of generating millions of hours of data annually, DAIMON is building large-scale robot manipulation datasets that include vast amounts of tactile sensing data. To accelerate the real-world deployment of embodied AI, the company has also open-sourced 10,000 hours of its data. Prof. Michael Yu Wang, co-founder and chief scientist at DAIMON Robotics, has pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision.DAIMON Robotics Behind the strategy is Prof. Michael Yu Wang, DAIMON’s co-founder and chief scientist. Prof. Wang earned his PhD at Carnegie Mellon — studying manipulation under Matt Mason — and went on to found the Robotics Institute at the Hong Kong University of Science and Technology. An IEEE Fellow and former Editor-in-Chief of IEEE Transactions on Automation Science and Engineering, he has spent roughly four decades in the field. His objective is to address the missing “insensitivity” of robot manipulation, which practically relies on the dominant Vision-Language-Action (VLA) model. He and his team have pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision. We spoke with Prof. Wang about how tactile feedback aims to change dexterous manipulation, how the dataset initiative is foreseen to improve our understanding of robotic hands in natural environments, and where — from hotels to convenience stores in China — he sees touch-enabled robots making their first real-world inroads. Daimon-Infinity is the world’s largest omni-modal dataset for Physical AI, featuring million-hour scale multimodal data, ultra-high-res tactile feedback, data from 80+ real scenarios and 2,000+ human skills, and more.DAIMON Robotics The Dataset Initiative This month, DAIMON Robotics released the largest and most comprehensive robotic manipulation dataset with multiple leading academic institutions and enterprises. Why releasing the dataset now, rather than continuing to focus on product development? What impact will this have on the embodied intelligence industry? DAIMON Robotics has been around for almost two and a half years. We have been committed to developing high-resolution, multimodal tactile sensing devices to perceive the interaction between a robot’s hand (particularly its fingertips) and objects. Our devices have become quite robust. They are now accepted and used by a large segment of users, including academic and research institutes as well as leading humanoid robotics companies. As embodied AI continues to advance, the critical role of data has been clearer. Data scarcity remains a primary bottleneck in robot learning, particularly the lack of physical interaction data, which is essential for robots to operate effectively in the real world. Consequently, data quality, reliability, and cost have become major concerns in both research and commercial development. This is exactly where DAIMON excels. Our vision-based tactile technology captures high-quality, multimodal tactile data. Beyond basic contact forces, it records deformation, slip and friction, material properties and surface textures — enabling a comprehensive reconstruction of physical interactions. Building on our expertise in multimodal fusion, we have developed a robust data processing pipeline that seamlessly integrates tactile feedback with vision, motion trajectories, and natural language, transforming raw inputs into training-ready dataset for machine learning models. Recognizing the industry-wide data gap, we view large-scale data collection not only as our unique competitive advantage, but as a responsibility to the broader community. By building and open-sourcing the dataset, we aim to provide the high-quality “fuel” needed to power embodied AI, ultimately accelerating the real-world deployment of general-purpose robotic foundation models. The robotics industry is highly competitive, and many teams have chosen to focus on data. DAIMON is releasing a large and highly comprehensive cross-embodiment, vision-based tactile multimodal robotic manipulation dataset. How were you able to achieve this? We have a dedicated in-house team focused on expanding our capabilities, including building hardware devices and developing our own large-scale model. Although we are a relatively small company, our core tactile sensing technology and innovative data collection paradigm enable us to build large-scale dataset. Our approach is to broaden our offering. We have built the world’s largest distributed out-of-lab data collection network. Rather than relying on centralized data factories, this lightweight and scalable system allows data to be gathered across diverse real-world environments, enabling us to generate millions of hours of data per year. “To drive the advancement of the entire embodied AI field, we have open-sourced 10,000 hours of the dataset for the broader community.” —Prof. Michael Yu Wang, DAIMON Robotics This dataset is being jointly developed with several institutions worldwide. What roles did they play in its development, and how will the dataset benefit their research and products? Besides China based teams, our partners include leading research groups from universities, such as Northwestern University and the National University of Singapore, as well as top global enterprises like Google DeepMind and China Mobile. Their decision to partner with DAIMON is a strong testament to the value of our tactile-rich dataset. Among the companies involved there are some that have already built their own models but are now incorporating tactile information. By deploying our data collection devices across research, manufacturing and other real-world scenarios, they help us to gather highly practical, application-driven data. In turn, our partners leverage the data to train models tailored to their specific use cases. Furthermore, to drive the advancement of the entire embodied AI field, we have open-sourced 10,000 hours of the dataset for the broader community. Equipped with Daimon’s visuotactile sensor, the gripper delicately senses contact and precisely controls force to pick up a fragile eggshell.Daimon Robotics From VLA to VTLA: Why Tactile Sensing Changes the Equation The mainstream paradigm in robotics is currently the Vision-Language-Action (VLA) model, but your team has proposed a Vision-Tactile-Language-Action (VTLA) model. Why is it necessary to incorporate tactile sensing? What does it enable robots to achieve, and which tasks are likely to fail without tactile feedback? Over these years of working to make generalist robots capable of performing manipulation tasks, especially dexterous manipulation — not just power grasping or holding an object, but manipulating objects and using tools to impart forces and motion onto parts — we see these robots being used in household as well as industrial assembly settings. It is well established that tactile information is essential for providing feedback about contact states so that robots can guide their hands and fingers to perform reliable manipulation. Without tactile sensing, robots are severely limited. They struggle to locate objects in dark environments, and without slip detection, they can easily drop fragile items like glass. Furthermore, the inability to precisely control force often leads to failed manipulation tasks or, in severe cases, physical damage. Naturally, the VLA approach needs to be enhanced to incorporate tactile information. We expanded the VLA framework to incorporate tactile data, creating the VTLA model. An additional benefit of our tactile sensor is that it is vision-based: We capture visual images of the deformation on the fingertip surface. We capture multiple images in a time sequence that encodes contact information, from which we can infer forces and other contact states. This aligns well with the visual framework that VLA is based upon. Having tactile information in a visual image format makes it naturally suitable for integration into the VLA framework, transforming it into a VTLA system. That is the key advantage: Vision-based tactile sensors provide very high resolution at the pixel level, and this data can be incorporated into the framework, whether it is an end-to-end model or another type of architecture. DAIMON has been known for its vision-based tactile sensors that can pack over 110,000 effective sensing units.DAIMON Robotics The Technology: Monochromatic Vision-based Tactile Sensing You and your team have spent many years deeply engaged in vision-based tactile sensing and have developed the world’s first monochromatic vision-based tactile sensing technology. Why did you choose this technical path? Once we started investigating tactile sensors, we understood our needs. We wanted sensors that closely mimic what we have under our fingertip skin. Physiological studies have well documented the capabilities humans have at their fingertips — knowing what we touch, what kind of material it is, how forces are distributed, and whether it is moving into the right position as our brain controls our hands. We knew that replicating these capabilities on a robot hand’s fingertips would help considerably. When we surveyed existing technologies, we found many types, including vision-based tactile sensors with tri-color optics and other simpler designs. We decided to integrate the best of these into an engineering-robust solution that works well without being overly complicated, keeping cost, reliability, and sensitivity within a satisfactory range, thus ultimately developing a monochromatic vision-based tactile sensing technique. This is fundamentally an engineering approach rather than a purely scientific one, since a great deal of foundational research already existed. With the growing realization of the necessity of tactile data, all of this will advance hand in hand. DAIMON vision-based tactile sensor captures high-quality, multimodal tactile data.DAIMON Robotics Last year, DAIMON launched a multi-dimensional, high-resolution, high-frequency vision-based tactile sensor. Compared with traditional tactile sensors, where does its core advantage lie? Which industries could it potentially transform? The key features of our sensors are the density of distributed force measurement and the deformation we can capture over the area of a fingertip. I believe we have the highest density in terms of sensing units. That is one very important metric. The other is dynamics: the frequency and bandwidth — how quickly we can detect force changes, transmit signals, and process them in real time. Other important aspects are largely engineering-related, such as reliability, drift, durability of the soft surface, and resistance to interference from magnetic, optical, or environmental factors. A growing number of researchers and companies are recognizing the importance of tactile sensing and adopting our technology. I believe the advances in tactile sensing will elevate the entire community and industry to a higher level. One of our potential customers is deploying humanoid robots in a small convenience store, with densely packed shelves where shelf space is at a premium. The robot needs to reach into very tight spaces — tighter than books on a shelf — to pick out an object. Current two-jaw parallel grippers cannot fit into most of these spaces. Observing how humans pick up objects, you clearly need at least three slim fingers to touch and roll the object toward you and secure it. Thus, we are starting to see very specific needs where tactile sensing capabilities are essential. From Academia to Startup After 40 years in academia — founding the HKUST Robotics Institute, earning prestigious honors including IEEE Fellow, and serving as Editor-in-Chief of IEEE TASE — what motivated you to found DAIMON Robotics? I have come a long way. I started learning robotics during my PhD at Carnegie Mellon, where there were truly remarkable groups working on locomotion under Marc Raibert, who founded Boston Dynamics, and on manipulation under my advisor, Matt Mason, a leader in the field. We have been working on dexterous manipulation, not only at Carnegie Mellon, but globally for many years. However, progress has been limited for a long time, especially in building dexterous hands and making them work. Only recently have locomotion robots truly taken off, and only in the last few years have we begun to see major advancements in robot hands. There is clearly room for advancing manipulation capabilities, which would enable robots to do work like humans. While at Hong Kong University of Science and Technology, I saw increasingly greater people entering this area in the form of students and postdoctoral researchers. We wanted to jumpstart our effort by leveraging the available capital and talent resources. Fortunately, one of my postdocs, Dr. Duan Jianghua, has a strong sense for commercial opportunities. Recognizing the rapid growth of robotics market and the unique value that our vision-based tactile sensing technology could bring, together we started DAIMON Robotics, and it has progressed well. The community has grown tremendously in China, Japan, Korea, the U.S., and Europe. Robots equipped with DAIMON technology have been deployed in factory settings. The company aims to enable robots to achieve “embodied intelligence” and close the gap between what they can see and what they can feel.DAIMON Robotics Business Model and Commercial Strategy What is DAIMON’s current business model and strategic focus? What role does the dataset release play in your commercial strategy? We started as a device company focused on making highly capable tactile sensors, especially for robot hands. But as technology and business developed, everyone realized it is not just about one component, rather the entire technology chain: devices, data of adequate quality and quantity, and finally the right framework to build, train, and deploy models on robots in real application environments. Our business strategy is best described as “3D”: Devices, Data, and Deployment. We build devices for data collection, our own ecosystem, and for deploying them in our partners’ potential application domains. This enables the collection of real-world tactile-rich data and complete closed-loop validation. This will become an integral part of the 3D business model. Most startups in this space are following a similar path until eventually some may become more specialized or more tightly integrated with other companies. For now, it is mostly vertical integration. Embodied Skills and the Convergence Moment You’ve introduced the concept of “embodied skills” as essential for humanoid robots to move beyond having just an advanced AI “brain.” What prompted this insight? What new capabilities could embodied skills enable? After the rapid evolution of models and hardware over the past two years, has your definition or roadmap for embodied skills evolved? We have come a long way now see a convergence point where electrical, electronic, and mechatronic hardware technologies have advanced tremendously in last two decades. Robots are now fully electric, do not require hydraulics, because hardware has evolved rapidly. Modern electronics provide tremendous bandwidth with high torques. If we can build intelligence into these systems, we can create truly humanoid robots with the ability to operate in unstructured environments, make decisions, and take actions autonomously. “Our vision is for robots to achieve robust manipulation capabilities and evolve into reliable partners for humans.” —Prof. Michael Yu Wang, DAIMON Robotics AI has arrived at exactly the right time. Enormous resources have been invested in AI development, especially large language models, which are now being generalized into world models that enable physical AI capabilities. We would like to see these manifested in real-world systems. While both AI and core hardware technologies continue to evolve, the focus is much clearer now. For example, human-sized robots are preferred in a home environment. This is an exciting domain with a promise of great societal benefit if we can eventually achieve safe, reliable, and cost-effective robots. The Road to Real-World Deployment Today, many robots can deliver impressive demos, yet there remains a gap before they truly enter real-world applications. What could be a potential trigger for real-world deployment? Which scenarios are most likely to achieve large-scale deployment first? I think the road toward large-scale deployment of generalist robots is still long, but we are starting to see signs of feasibility within specific domains. It is very similar to autonomous vehicles, where we are yet to see full deployment of robo-taxis, while we have already started to find mobile robots and smaller vehicles widely deployed in the hospitality industry. Virtually every major hotel in China now has a delivery robot — no arms, just a vehicle that picks up items from the hotel lobby (e.g., food deliveries). The delivery person just loads the food and selects the room number. It is up to the robot thereafter to navigate and reach the guest’s room, which includes using the elevator, to deliver the food. This is already nearly 100 percent deployed in major Chinese hotels. Hotel and restaurant robots are viewed as a model for deploying humanoid robots in specific domains like overnight drugstores and convenience stores. I expect complete deployment in such settings within a short timeframe, followed by other applications. Overall, we can expect autonomous robots, including humanoids, to progressively penetrate specific sectors, delivering value in each and expanding into others. Ultimately, our vision is for robots to achieve robust manipulation capabilities and evolve into reliable partners for humans. By seamlessly integrating into our homes and daily lives, they will genuinely benefit and serve humanity. This interview has been edited for length and clarity.