🇺🇸 미국 · IT/기술 · "BILLIONS" · 총 6건
필터 보기현재 지수
50.0
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 10,368건을 분석한 결과, 뉴스 심리지수는 50.0(균형)입니다. 긍정 0건(0.0%)·중립 10,368건(100.0%)·부정 0건(0.0%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 19.4(중도 균형)입니다.
A new policy brief argues AI may already be adding hundreds of billions to the global economy—but official statistics aren’t built to see it.
Photonics is considered to be a more efficient alternative to the current process of transferring data using electricity, which could be crucial to the AI boom.
Google has filed an appeal against a 2024 antitrust ruling that determined the company violated competition laws by paying billions to become the default search engine on Apple's iPhone devices. The post Google Appeals Antitrust Ruling, Claims Billions in Payments to Apple Didn’t Influence Search Decision appeared first on Breitbart.
Over the next few decades, billions of autonomous, AI-powered robots will work alongside people in factories, perform tedious tasks in warehouses, care for the elderly, assist in unsafe disaster areas, deliver packages and food to our doorsteps, and eventually help out in our homes. Some will look like us, and many won’t. What is certain is that regardless of form factor, robots will all rely heavily on AI in order to deliver real-world value. In 2025, total investments in robotics companies reached a record US $40.7 billion, accounting for 9 percent of all venture funding. The multibillion dollar question therefore is this: What will it take for AI-powered robots to begin to have a serious economic impact? Many of today’s robotics and AI companies are making bold claims, such as that humanoid robots will soon be coming into our homes, but there’s still a big gap between promise and reality. The promise of robots that live and work alongside us has been the stuff of science fiction for a very long time. And while many programmers have tried to make that promise a reality, the physical world is just too complicated for traditional computer programs to handle the endless complexity it presents. Thanks to AI, robots are no longer being programmed—instead, they learn to operate in the real world. With enough practice, they can learn to perceive and understand the world around them, reason about that world, and use that reason and understanding to perform tasks that are useful, reliable, and safe. The two of us have worked at the forefront of AI and robotics for the last decade, as a Professor in Robotics at Oregon State University and Co-Founder of Agility Robotics, and as former CEO of the Everyday Robots moonshot at Google X. Our experience deploying AI-powered robots in real-world settings has given us a perspective on where AI can be used to great benefit in complex robotic systems in the near term and where we are still on the frontier of science fiction. We believe AI will enable an inflection point in robotics advances, but that it will be through the well-engineered application of coordinated systems of different AI tools rather than a single ChatGPT-style breakthrough. As the excitement around AI is matched only by the uncertainty of what will be possible, here are five hard truths that will define AI in robotics. 1. The YouTube-to-Reality Gap Is Real For years, we have been seeing videos on YouTube with humanoid robots performing amazing moves on everything from a dance floor to an obstacle course. The inside knowledge in robotics is to “never trust a YouTube robot video.” The gap between real robots that can perform real work in unstructured human environments and carefully scripted and edited robot performances remains significant. The latest performance to get a lot of attention was a martial arts show featuring Unitree humanoid robots performing with children at the Chinese 2026 Spring Festival Gala. While impressive, this falls into a long lineage of tightly scripted robotic performances, where everything has been carefully choreographed and planned in advance. The low-level controls, synchronization, and choreography were stunning, yet the Spring Gala robot performance showed a level of autonomy and intelligence much closer to industrial robots building cars in a factory than something that will show up in your living room any time soon. Seeing these kinds of demos nevertheless raises questions about where robotics really is. If robots can perform kung fu moves and do backflips and dance, why aren’t they also showing up on factory floors yet? And why can’t they do the dishes in my home after dinner? The simple answer is this: Making AI-powered robots capable of performing general tasks in varied human environments is still really hard. While impressive technological feats like those at the Spring Festival may make it look like we could be very close, the use of AI in these demos is only for low-level motor control (to keep the robots from falling over) and therefore is only a small part of the solution for robots to be general purpose in the real, unstructured spaces where we humans live and work. 2. Data Is An Unsolved Challenge Large Language Models (LLMs) like OpenAI’s ChatGPT and Anthropic’s Claude were initially trained on an internet-scale database of text. The world woke up one day in late 2022 to ChatGPT demonstrating that AI computers could suddenly “speak” to us in prose or verse and about seemingly any topic. LLMs have turned out to generalize well and are now able to take multimodal input (text, images, video) and produce multimodal output. Importantly, the corpus of training data was both enormous and human-generated, which are characteristics that form the gold standard for AI training. The fastest path to robots as part of everyday life may emerge through a range of robot forms performing increasingly sophisticated applications and employing a range of AI tools.Agility Robotics Giving AI a body (in the form of a robot), so that it can engage with people in the physical world, continues to be a very difficult and broadly unsolved problem. AI models for general-purpose robotics must simultaneously satisfy multiple, often conflicting, physical, geometric, and temporal limitations while operating in unstructured, dynamic environments. In order to generalize, robot models need to be trained on data gathered in a high-dimensional configuration space, where “dimensions” represent text, lighting conditions, degrees of freedom, joint limits, velocities, force, and safety boundaries, just to mention a few. Importantly, this must be good data—it must contain many examples from what amounts to an infinite number of possible configurations in the physical world. Since there are very few existing sources of data like this, approaches like teleoperation, video analysis, motion capture of humans, and self-exploration in simulation and in the real world are all seen as important ways to collect data. It’s a herculean task. For example, at Everyday Robots at Google X, we ran 240 million robot instances in our simulator over the course of 2022 to collect training data, mostly to train a trash-sorting model. Similar amounts of data will be needed for every skill to get to a similar level of capability, which is not yet human level. 3. There Will Be No Single Robot AI We are far away from a moment where a single AI model might allow general-purpose robots to live and work alongside us. General-purpose robots can have wheels or legs. They can have one, two, three, or more arms. Some have propellers and can fly, while others may be designed to operate under water. Some will drive on busy roads. The physical world is infinitely varied and complex. And then there are all the people and other animals that will be surrounding the robots. How do you train a model to operate a robot safely and reliably in all of these settings? The simple answer is: You don’t. At least not for quite some time. We believe the winning AI architecture leading to the next big breakthroughs in general-purpose robotics will be “agentic AI” for robots, which are high-level coordinating models that can reason, plan, use tools, and learn from outcomes to execute complex tasks with limited supervision. Agentic, high-level models running on robots will invoke a system of specialized ones for different types of tasks. We will likely soon see multiple robots collaborating and coordinating with each other through their onboard agentic AI models. AI tools are unlocking new and powerful capabilities in robotics, which in turn will enable new solutions and new markets. It’s encouraging to see these new models being made broadly available, some even as open-source solutions. This availability is akin to what happened with the internet: Real progress occurred when it became ubiquitous. We anticipate an inevitable democratization of complex behaviors in robotics with wide access to these AI tools and technologies. 4. Hardware Is Still Very Hard Robots are complex systems with many parts that all need to work together with great precision. For a robot to be useful and safe, every part of it must be coordinated, from its perception systems to the computer controlling it, all the way down to its individual actuators. Actuators—that is, the motors and gears—are a good example of an important part of the robot where what got us here won’t get us there. The actuators used at scale by most industrial robots will not work for robots that will operate in human environments. If these robots accidentally collide with an obstacle, the resulting impacts are harsh, forces are high, and things break. Humans don’t move in this way. We are far more compliant in how we interact with the world, and we’re constantly making contact with our environment and using that contact to help us accomplish things. Consider the challenge of inserting a key in a lock: Humans typically don’t do this by aligning the key perfectly with the keyhole. Instead, we just feel for the edge of the keyhole and jiggle the key in. Robots need to be able to operate in novel ways to achieve comparable capabilities by using a new class of actuators that are sensitive to force and able to have a compliant interaction with the environment. While these kinds of actuators do exist, they are not yet generally available at scale for robot systems designed to operate around people. 5. Real Value Comes From “Easy” Tasks There’s a big difference between tasks that look impressive and real-world tasks that provide value. Robotics is a perfect example of Moravec’s paradox, which states that tasks that are hard for humans are easy for computers (like multiplying two big numbers), and tasks easy for humans (like a toddler’s movements) are extremely difficult for computers and robots. Serving customers is an unforgiving reality check, because customers only care about solving the real problems they have. If we are to deploy AI-based robot solutions, they must outperform the way things are currently done while demonstrating reliable performance metrics and safety. Agility Robotics’ early work to deploy our humanoid robot Digit in customer locations led to the realization that our first obstacle was safety: Robots that balance and manipulate objects in human spaces bring new types of risk to the workplace. In the first humanoid deployments, physical barriers were necessary, and Agility kicked off a multi-year engineering effort to solve the safety challenge, touching nearly every aspect of robot design and relying heavily on new AI-based approaches to human detection and behavior control. Everyday Robots at Google deployed robots in 2019 that worked autonomously in office buildings doing chores like cleaning cafe tables and sorting trash. We quickly learned how “messy” and difficult the real world is for a robot. This experience informed the architecture and deployment of our AI systems while also gathering real-world data that could be combined with simulation data for training and improving models. This focus on creating a product to meet specific customer needs and deploying robots in real-world settings is the only way to inform the structure of the AI tools and infrastructure for near-term utility on a path towards long-term broader capability and generality. There will be no “aha” moment, no silver bullet algorithm, and no volume of data sufficient to produce a general-purpose robot without extensive real-world experience. AI Robots Are Coming, One Step at a Time As we look to the future, there is no doubt that the world is bringing AI into the physical world through robots. We are at the beginning of a “Cambrian explosion“ of useful, intelligent machines. We believe AI is not one tool, but a huge frontier of technical approaches that is unlocking new capabilities so powerful, they will define our economy moving forward. This will happen not in one single definitive moment, but as an ongoing set of small and large breakthroughs, where AI-driven robots begin to provide real value in a few tasks, and then a few more, with impacts unfolding across numerous $100 billion-plus markets that will dramatically improve the quality of our lives.
This sponsored article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia. Melbourne’s reputation as a global events city, from the Australian Open tennis and Formula 1 Australian Grand Prix to hosting NFL regular season games, now intersects with a different form of scale: large-scale compute, data-intensive research, and advanced engineering. Long recognized for delivering complex international events, the city is applying the same organisational capability to the infrastructure that underpins modern AI research, positioning Melbourne at the convergence of global convening and high-performance digital systems. Consistently ranked among the world’s most livable cities, Melbourne was named Time Out’s Best City in the World in 2026, the first Australian city to hold the title. Melbourne, Australia’s premier conference destination. Tourism Australia More materially for research and innovation, Melbourne is also the nation’s fastest‑growing capital, attracting increasing concentrations of engineering and technology talent, investment and international engagement. Australia’s artificial intelligence (AI) ecosystem is entering a new phase, defined less by isolated initiatives and more by the convergence of compute infrastructure, research intensity and international collaboration. Melbourne sits at this intersection. Melbourne’s trajectory highlights what enables research at scale: access to frontier-grade compute, proximity to industry-ready infrastructure, and repeated opportunities for global research communities to convene. Sovereign AI compute, expanding hyperscale data center campuses and a growing pipeline of international research-led conferences are reshaping the city’s research landscape. Together, these elements position Melbourne as a focal point for applied AI research, advanced engineering and data-intensive science. The growing global influence of AI engineering, underscored by NVIDIA CEO Jensen Huang receiving the 2026 IEEE Medal of Honor, reflects the scale of this shift. In Melbourne, these factors form a reinforcing research flywheel linking infrastructure, discovery and collaboration. Rather than focusing on startup density or short-term commercial output, Melbourne’s trajectory highlights what enables research at scale: access to frontier-grade compute, proximity to industry-ready infrastructure, and repeated opportunities for global research communities to convene. NVIDIA CEO Jensen Huang received the 2026 IEEE Medal of Honor.IEEE Sovereign AI foundations The most recent cornerstone of Melbourne’s AI capability is MAVERIC (Monash AdVanced Environment for Research and Intelligent Computing), Australia’s largest university-based AI supercomputer. Built and deployed by Monash University in partnership with NVIDIA, Dell Technologies, and CDC Data Centres, MAVERIC has been engineered specifically for large scale AI and data intensive science, with medical research representing a key priority. Indeed, in these regards MAVERIC has been designed to function as a Next Generation Trusted Research Environment thus ensuring that it is state-of-the-art and provides a safe and secure framework for the analysis of large sensitive datasets. Designed to support research projects including cancer and neurodegenerative disease detection, clinical trial analysis and drug discovery through to materials science and engineering, MAVERIC enables Australian researchers to train and evaluate large models domestically while keeping highly sensitive datasets secure and under national jurisdiction. This sovereign design is particularly relevant in fields such as medical research where privacy, regulation or intellectual property constraints limit the use of offshore cloud resources. Monash University Vice-Chancellor and President Professor Sharon Pickering with researchers [left to right] Professor Anton Peleg, Professor Victoria Mar, Professor James Whisstock, Vice-President (Strategy and Major Projects) Teresa Finlayson, and Professor Patrick Kwan.Eamon Gallagher (Australian Financial Review) Technically, the system reflects the latest shifts in high performance AI architecture. Built on NVIDIA GB200 NVL72 platforms and integrated using Dell’s rack scale infrastructure, MAVERIC employs closed loop liquid cooling to reduce water consumption compared with conventional air-cooled systems, aligning large scale compute growth with sustainability objectives while supporting high density, high throughput workloads. Professor James Whisstock, Deputy Dean Research of Monash’s Faculty of Medicine, Nursing, and Health Sciences commented, “MAVERIC provides a huge leap forward in our compute capability that will revolutionize our researchers’ ability to address the most challenging and important research questions across the fields of medical research, information technology, and STEM disciplines. It will seed wonderful new cross-disciplinary collaborations, underpin the work of our best and brightest young researchers and will allow our scientists to continue to make major discoveries that positively impact the Australian and global population more broadly.” “MAVERIC provides a huge leap forward in our compute capability that will revolutionize our researchers’ ability to address the most challenging and important research questions across the fields of medical research, information technology, and STEM disciplines.” —Professor James Whisstock, Deputy Dean Research of Monash’s Faculty of Medicine, Nursing, and Health Sciences Monash University frames MAVERIC not as a standalone asset, but as part of the national research infrastructure, intended to strengthen collaboration across academia, healthcare, government and industry. This approach positions Melbourne at the forefront of sovereign AI enabled research in the region. Data center scale as research infrastructure The infrastructure demands of modern AI research extend well beyond individual systems. Melbourne’s expanding data center footprint now supports hyperscale compute, applied AI deployment and large-scale research workloads simultaneously. Total data center investment, US$ billions.Source: Data Centres Global Report 2025 In February 2026, CDC Data Centres opened its first Melbourne campus in Brooklyn, with two live facilities and a third in planning. Combined with CDC’s Laverton campus, Melbourne is projected to host more than 800 megawatts of sovereign digital capacity, critical for AI workloads requiring sustained access to high-density power, cooling and secure environments. Parallel investment is underway in Fishermans Bend, where NEXTDC is developing a AUD $2 billion AI and digital infrastructure hub adjacent to the Innovation Precinct. Planned facilities include an AI Factory, a Mission Critical Operations Center and a Technology Center of Excellence, enabling sovereign AI, high-performance computing and cross-sector collaboration across health, defence and finance. Melbourne hosts Australia’s largest cluster of AI firms, with 188 companies, and more than 40 data centers currently operate across Victoria. The Victorian Government has complemented this growth with an initial AUD $5.5 million investment in the Sustainable Data Center Action Plan. Together, these developments reinforce Melbourne’s role as a national and increasingly global hub for high-performance AI infrastructure as model complexity and infrastructure dependency continue to accelerate. Applied AI research at scale Monash University is home to MAVERIC, Australia’s largest university-based AI supercomputer, built and deployed by Monash in partnership with NVIDIA, Dell Technologies, and CDC Data Centres.Monash University Melbourne’s research strength is underpinned by a dense university network with deep capability across AI, data science and engineering. Institutions including Monash University, the University of Melbourne, Deakin University, La Trobe University, RMIT University and Swinburne University of Technology collectively support research across machine learning, robotics, human-computer interaction, extended reality and advanced manufacturing. This concentration fosters applied collaboration where AI intersects with medicine, sustainability, cognitive systems and immersive technologies. For visiting researchers, it provides access not only to academic expertise but also to live infrastructure environments where research can be tested and validated, reinforcing Melbourne’s position as one of the Asia-Pacific’s most integrated AI research ecosystems. Conferences as research accelerators Plenary session at Melbourne Convention and Exhibition Center.Melbourne Convention Bureau Melbourne’s selection as host city for a growing number of international technology conferences reflects the convergence of research capability and infrastructure maturity. In September 2026, Data Center World Australia and The AI Summit Australia will be co-located at the Melbourne Convention and Exhibition Center, bringing together global leaders across AI, digital infrastructure and enterprise technology. The pairing highlights a broader reality: advances in AI are inseparable from the infrastructure that enables them. Melbourne’s expanding data center footprint now supports hyperscale compute, applied AI deployment and large-scale research workloads simultaneously. Research-led conferences are also expanding Melbourne’s global footprint. ICONIP 2026, hosted by Deakin University, will bring up to 700 researchers in neural networks and machine learning, followed in 2027 by IEEE VR, the leading conference on virtual reality and 3D user interfaces, attracting up to 1,000 delegates. In this context, conferences function not simply as events, but as infrastructure for knowledge transfer, supporting standards exchange, collaboration and system-level learning at global scale. A global platform for advancing research Sovereign compute, data center scale and a strong conference pipeline create a reinforcing cycle, enabling researchers to engage directly with infrastructure and industry well beyond the event itself. By closing the gap between theory and deployment, Melbourne supports deeper technical exchange and more enduring global research networks. This role was recognized in 2025 when the IEEE awarded Melbourne Convention Bureau the 2025 Organisational Supporting Friend of IEEE Member and Geographic Activities (MGA) — the first convention bureau in the Asia Pacific region to receive the acknowledgement as a result of the longstanding partnership with the IEEE Victorian Section. Melbourne Convention Bureau (MCB) representative Fatima Aboudrar, Senior Business Development Manager, with Vijay S. Paul, Immediate Past Chair, IEEE Victorian Section, receiving Supporting Friend Member recognition in 2025. As AI research becomes increasingly dependent on infrastructure scale, sovereign capability, and global collaboration, Melbourne is moving beyond hosting conversations to actively enabling the systems that advance AI and data‑driven research at global scale. Conference support in Melbourne Your browser does not support the video tag. Why host a conference in Melbourne, Australia.Melbourne Convention Bureau This ecosystem is underpinned by Melbourne’s highly accessible city center, where world-class venues, research institutions and industry hubs are located in close proximity. Free public transport and a compact city footprint enable seamless movement from conference floor to real-world application. Melbourne Convention Bureau (MCB) is a not-for-profit state government agency with over 60 years’ experience, that provides IEEE and its members with free support to bring international conferences to Melbourne, Australia. MCB’s support spans early-stage exploration and international bidding through to securing government funding, connecting organizers with venues, accommodation and event suppliers, and providing destination support for conference planning and delivery. Organizations considering a conference in Australia are encouraged to connect with MCB’s dedicated team, which supports IEEE conferences in Melbourne. Enquiries can be directed to info@melbournecb.com.au.
Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese tech giant’s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that’s what he told IEEE Spectrum in an exclusive Q&A. Ng’s current efforts are focused on his company Landing AI, which built a platform called LandingLens to help manufacturers improve visual inspection with computer vision. He has also become something of an evangelist for what he calls the data-centric AI movement, which he says can yield “small data” solutions to big issues in AI, including model efficiency, accuracy, and bias. Andrew Ng on... What’s next for really big models The career advice he didn’t listen to Defining the data-centric AI movement Synthetic data Why Landing AI asks its customers to do the work The great advances in deep learning over the past decade or so have been powered by ever-bigger models crunching ever-bigger amounts of data. Some people argue that that’s an unsustainable trajectory. Do you agree that it can’t go on that way? Andrew Ng: This is a big question. We’ve seen foundation models in NLP [natural language processing]. I’m excited about NLP models getting even bigger, and also about the potential of building foundation models in computer vision. I think there’s lots of signal to still be exploited in video: We have not been able to build foundation models yet for video because of compute bandwidth and the cost of processing video, as opposed to tokenized text. So I think that this engine of scaling up deep learning algorithms, which has been running for something like 15 years now, still has steam in it. Having said that, it only applies to certain problems, and there’s a set of other problems that need small data solutions. When you say you want a foundation model for computer vision, what do you mean by that? Ng: This is a term coined by Percy Liang and some of my friends at Stanford to refer to very large models, trained on very large data sets, that can be tuned for specific applications. For example, GPT-3 is an example of a foundation model [for NLP]. Foundation models offer a lot of promise as a new paradigm in developing machine learning applications, but also challenges in terms of making sure that they’re reasonably fair and free from bias, especially if many of us will be building on top of them. What needs to happen for someone to build a foundation model for video? Ng: I think there is a scalability problem. The compute power needed to process the large volume of images for video is significant, and I think that’s why foundation models have arisen first in NLP. Many researchers are working on this, and I think we’re seeing early signs of such models being developed in computer vision. But I’m confident that if a semiconductor maker gave us 10 times more processor power, we could easily find 10 times more video to build such models for vision. Having said that, a lot of what’s happened over the past decade is that deep learning has happened in consumer-facing companies that have large user bases, sometimes billions of users, and therefore very large data sets. While that paradigm of machine learning has driven a lot of economic value in consumer software, I find that that recipe of scale doesn’t work for other industries. Back to top It’s funny to hear you say that, because your early work was at a consumer-facing company with millions of users. Ng: Over a decade ago, when I proposed starting the Google Brain project to use Google’s compute infrastructure to build very large neural networks, it was a controversial step. One very senior person pulled me aside and warned me that starting Google Brain would be bad for my career. I think he felt that the action couldn’t just be in scaling up, and that I should instead focus on architecture innovation. “In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.” —Andrew Ng, CEO & Founder, Landing AI I remember when my students and I published the first NeurIPS workshop paper advocating using CUDA, a platform for processing on GPUs, for deep learning—a different senior person in AI sat me down and said, “CUDA is really complicated to program. As a programming paradigm, this seems like too much work.” I did manage to convince him; the other person I did not convince. I expect they’re both convinced now. Ng: I think so, yes. Over the past year as I’ve been speaking to people about the data-centric AI movement, I’ve been getting flashbacks to when I was speaking to people about deep learning and scalability 10 or 15 years ago. In the past year, I’ve been getting the same mix of “there’s nothing new here” and “this seems like the wrong direction.” Back to top How do you define data-centric AI, and why do you consider it a movement? Ng: Data-centric AI is the discipline of systematically engineering the data needed to successfully build an AI system. For an AI system, you have to implement some algorithm, say a neural network, in code and then train it on your data set. The dominant paradigm over the last decade was to download the data set while you focus on improving the code. Thanks to that paradigm, over the last decade deep learning networks have improved significantly, to the point where for a lot of applications the code—the neural network architecture—is basically a solved problem. So for many practical applications, it’s now more productive to hold the neural network architecture fixed, and instead find ways to improve the data. When I started speaking about this, there were many practitioners who, completely appropriately, raised their hands and said, “Yes, we’ve been doing this for 20 years.” This is the time to take the things that some individuals have been doing intuitively and make it a systematic engineering discipline. The data-centric AI movement is much bigger than one company or group of researchers. My collaborators and I organized a data-centric AI workshop at NeurIPS, and I was really delighted at the number of authors and presenters that showed up. You often talk about companies or institutions that have only a small amount of data to work with. How can data-centric AI help them? Ng: You hear a lot about vision systems built with millions of images—I once built a face recognition system using 350 million images. Architectures built for hundreds of millions of images don’t work with only 50 images. But it turns out, if you have 50 really good examples, you can build something valuable, like a defect-inspection system. In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn. When you talk about training a model with just 50 images, does that really mean you’re taking an existing model that was trained on a very large data set and fine-tuning it? Or do you mean a brand new model that’s designed to learn only from that small data set? Ng: Let me describe what Landing AI does. When doing visual inspection for manufacturers, we often use our own flavor of RetinaNet. It is a pretrained model. Having said that, the pretraining is a small piece of the puzzle. What’s a bigger piece of the puzzle is providing tools that enable the manufacturer to pick the right set of images [to use for fine-tuning] and label them in a consistent way. There’s a very practical problem we’ve seen spanning vision, NLP, and speech, where even human annotators don’t agree on the appropriate label. For big data applications, the common response has been: If the data is noisy, let’s just get a lot of data and the algorithm will average over it. But if you can develop tools that flag where the data’s inconsistent and give you a very targeted way to improve the consistency of the data, that turns out to be a more efficient way to get a high-performing system. “Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.” —Andrew Ng For example, if you have 10,000 images where 30 images are of one class, and those 30 images are labeled inconsistently, one of the things we do is build tools to draw your attention to the subset of data that’s inconsistent. So you can very quickly relabel those images to be more consistent, and this leads to improvement in performance. Could this focus on high-quality data help with bias in data sets? If you’re able to curate the data more before training? Ng: Very much so. Many researchers have pointed out that biased data is one factor among many leading to biased systems. There have been many thoughtful efforts to engineer the data. At the NeurIPS workshop, Olga Russakovsky gave a really nice talk on this. At the main NeurIPS conference, I also really enjoyed Mary Gray’s presentation, which touched on how data-centric AI is one piece of the solution, but not the entire solution. New tools like Datasheets for Datasets also seem like an important piece of the puzzle. One of the powerful tools that data-centric AI gives us is the ability to engineer a subset of the data. Imagine training a machine-learning system and finding that its performance is okay for most of the data set, but its performance is biased for just a subset of the data. If you try to change the whole neural network architecture to improve the performance on just that subset, it’s quite difficult. But if you can engineer a subset of the data you can address the problem in a much more targeted way. When you talk about engineering the data, what do you mean exactly? Ng: In AI, data cleaning is important, but the way the data has been cleaned has often been in very manual ways. In computer vision, someone may visualize images through a Jupyter notebook and maybe spot the problem, and maybe fix it. But I’m excited about tools that allow you to have a very large data set, tools that draw your attention quickly and efficiently to the subset of data where, say, the labels are noisy. Or to quickly bring your attention to the one class among 100 classes where it would benefit you to collect more data. Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity. For example, I once figured out that a speech-recognition system was performing poorly when there was car noise in the background. Knowing that allowed me to collect more data with car noise in the background, rather than trying to collect more data for everything, which would have been expensive and slow. Back to top What about using synthetic data, is that often a good solution? Ng: I think synthetic data is an important tool in the tool chest of data-centric AI. At the NeurIPS workshop, Anima Anandkumar gave a great talk that touched on synthetic data. I think there are important uses of synthetic data that go beyond just being a preprocessing step for increasing the data set for a learning algorithm. I’d love to see more tools to let developers use synthetic data generation as part of the closed loop of iterative machine learning development. Do you mean that synthetic data would allow you to try the model on more data sets? Ng: Not really. Here’s an example. Let’s say you’re trying to detect defects in a smartphone casing. There are many different types of defects on smartphones. It could be a scratch, a dent, pit marks, discoloration of the material, other types of blemishes. If you train the model and then find through error analysis that it’s doing well overall but it’s performing poorly on pit marks, then synthetic data generation allows you to address the problem in a more targeted way. You could generate more data just for the pit-mark category. “In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models.” —Andrew Ng Synthetic data generation is a very powerful tool, but there are many simpler tools that I will often try first. Such as data augmentation, improving labeling consistency, or just asking a factory to collect more data. Back to top To make these issues more concrete, can you walk me through an example? When a company approaches Landing AI and says it has a problem with visual inspection, how do you onboard them and work toward deployment? Ng: When a customer approaches us we usually have a conversation about their inspection problem and look at a few images to verify that the problem is feasible with computer vision. Assuming it is, we ask them to upload the data to the LandingLens platform. We often advise them on the methodology of data-centric AI and help them label the data. One of the foci of Landing AI is to empower manufacturing companies to do the machine learning work themselves. A lot of our work is making sure the software is fast and easy to use. Through the iterative process of machine learning development, we advise customers on things like how to train models on the platform, when and how to improve the labeling of data so the performance of the model improves. Our training and software supports them all the way through deploying the trained model to an edge device in the factory. How do you deal with changing needs? If products change or lighting conditions change in the factory, can the model keep up? Ng: It varies by manufacturer. There is data drift in many contexts. But there are some manufacturers that have been running the same manufacturing line for 20 years now with few changes, so they don’t expect changes in the next five years. Those stable environments make things easier. For other manufacturers, we provide tools to flag when there’s a significant data-drift issue. I find it really important to empower manufacturing customers to correct data, retrain, and update the model. Because if something changes and it’s 3 a.m. in the United States, I want them to be able to adapt their learning algorithm right away to maintain operations. In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models. The challenge is, how do you do that without Landing AI having to hire 10,000 machine learning specialists? So you’re saying that to make it scale, you have to empower customers to do a lot of the training and other work. Ng: Yes, exactly! This is an industry-wide problem in AI, not just in manufacturing. Look at health care. Every hospital has its own slightly different format for electronic health records. How can every hospital train its own custom AI model? Expecting every hospital’s IT personnel to invent new neural-network architectures is unrealistic. The only way out of this dilemma is to build tools that empower the customers to build their own models by giving them tools to engineer the data and express their domain knowledge. That’s what Landing AI is executing in computer vision, and the field of AI needs other teams to execute this in other domains. Is there anything else you think it’s important for people to understand about the work you’re doing or the data-centric AI movement? Ng: In the last decade, the biggest shift in AI was a shift to deep learning. I think it’s quite possible that in this decade the biggest shift will be to data-centric AI. With the maturity of today’s neural network architectures, I think for a lot of the practical applications the bottleneck will be whether we can efficiently get the data we need to develop systems that work well. The data-centric AI movement has tremendous energy and momentum across the whole community. I hope more researchers and developers will jump in and work on it. Back to top This article appears in the April 2022 print issue as “Andrew Ng, AI Minimalist.”