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Time of India
16-07-2025
- Science
- Time of India
Stanford professor calls out the narrative of AI 'replacing' humans. Says, 'if AI takes away our dignity, something is wrong'
Not Just Tasks, But Human Worth You Might Also Like: Will the AI takeover spare politicians? Expert predicts 3 unexpected careers that could survive by 2045 From Lab to Life: Leading by Example A Shift in the Narrative In a world increasingly captivated by the power and promise of artificial intelligence , Dr. Fei-Fei Li—one of the most influential voices in global AI research—has made a poignant and powerful appeal: 'AI should never take away our dignity.' Speaking on a recent podcast hosted by the Berggruen Institute, the Stanford professor and former Google AI chief expressed concern over how society talks about technology, especially the use of the word 'replace.''It really, truly bothers me when people use the word 'replace' when it's connected to AI,' Li said during the conversation. 'I think we should really replace that word, and think about AI as augmenting or enhancing humans rather than replacing them.'Her words, both calm and firm, come at a time when industries around the globe are debating the future of jobs, creativity, and even decision-making amid the rise of machine intelligence. While she's no stranger to cutting-edge AI systems—being the creator of ImageNet, the dataset that helped launch the deep learning revolution—Li insists that AI must serve humanity, not displace it. Fei-Fei Li 's reflections on AI go beyond functionality and productivity. She warns of reducing human beings to a series of mechanical tasks that machines can outperform. 'Biologically, we run slower, walk slower, can't fly, can't lift as much, can't calculate as fast. But we are so much more than those narrow tasks,' she this broader sense of what it means to be human that drives her call for a more responsible and emotionally aware use of technology. 'Dignity is at the core of our being,' she said. 'Everybody needs to find their dignity and their value, and that should not be taken away by AI.'Her statement echoes the values at the heart of the Stanford Human-Centered AI Institute , which she co-directs, and AI4ALL, a nonprofit she co-founded to promote diversity and inclusion in the AI academia, Fei-Fei Li is walking the talk. In 2024, she co-founded World Labs , an AI startup developing spatial intelligence systems that seek to understand the three-dimensional physical world in a way that augments human capability. She recently raised $230 million for the venture, showing that technological innovation and ethical leadership can go was recently appointed to the United Nations Scientific Advisory Board and named one of TIME's 100 Most Influential People in AI. Her credentials include roles at Google Cloud, board directorship at Twitter, and a long list of accolades from top institutions such as Princeton and podcast message has struck a chord with many who feel uneasy about the unchecked acceleration of automation. Rather than succumb to fear or techno-optimism, Li offers a path grounded in empathy, shared values, and intentional design. 'If AI applications take away that sense of dignity,' she warns, 'there's something wrong.'As AI continues to reshape our world—from how we work to how we relate to each other—voices like Fei-Fei Li's remind us that progress must not come at the cost of our humanity. The challenge ahead, she suggests, is not only technical but moral: to build AI systems that enhance our potential without eclipsing our essence.
Economic Times
16-07-2025
- Science
- Economic Times
Stanford professor calls out the narrative of AI 'replacing' humans. Says, 'if AI takes away our dignity, something is wrong'
Synopsis Stanford AI expert Fei-Fei Li has voiced deep concerns over the narrative of AI 'replacing' humans. In a podcast with the Berggruen Institute, she emphasized that AI should augment human abilities, not undermine dignity. Drawing on her vast experience, she urged technologists to design systems that preserve human value rather than reduce people to mere tasks. Fei-Fei Li, renowned AI pioneer and Stanford professor, warns that AI must never strip humans of their dignity. Speaking on a podcast, she argued against the word 'replace' in AI discourse, advocating for tools that empower rather than displace. (Image: LinkedIn/ Fei Fei Li) In a world increasingly captivated by the power and promise of artificial intelligence, Dr. Fei-Fei Li—one of the most influential voices in global AI research—has made a poignant and powerful appeal: 'AI should never take away our dignity.' Speaking on a recent podcast hosted by the Berggruen Institute, the Stanford professor and former Google AI chief expressed concern over how society talks about technology, especially the use of the word 'replace.' 'It really, truly bothers me when people use the word 'replace' when it's connected to AI,' Li said during the conversation. 'I think we should really replace that word, and think about AI as augmenting or enhancing humans rather than replacing them.' Her words, both calm and firm, come at a time when industries around the globe are debating the future of jobs, creativity, and even decision-making amid the rise of machine intelligence. While she's no stranger to cutting-edge AI systems—being the creator of ImageNet, the dataset that helped launch the deep learning revolution—Li insists that AI must serve humanity, not displace it. Fei-Fei Li's reflections on AI go beyond functionality and productivity. She warns of reducing human beings to a series of mechanical tasks that machines can outperform. 'Biologically, we run slower, walk slower, can't fly, can't lift as much, can't calculate as fast. But we are so much more than those narrow tasks,' she explained. It's this broader sense of what it means to be human that drives her call for a more responsible and emotionally aware use of technology. 'Dignity is at the core of our being,' she said. 'Everybody needs to find their dignity and their value, and that should not be taken away by AI.' Her statement echoes the values at the heart of the Stanford Human-Centered AI Institute, which she co-directs, and AI4ALL, a nonprofit she co-founded to promote diversity and inclusion in the AI sector. Beyond academia, Fei-Fei Li is walking the talk. In 2024, she co-founded World Labs, an AI startup developing spatial intelligence systems that seek to understand the three-dimensional physical world in a way that augments human capability. She recently raised $230 million for the venture, showing that technological innovation and ethical leadership can go hand-in-hand. Li was recently appointed to the United Nations Scientific Advisory Board and named one of TIME's 100 Most Influential People in AI. Her credentials include roles at Google Cloud, board directorship at Twitter, and a long list of accolades from top institutions such as Princeton and Caltech. Her podcast message has struck a chord with many who feel uneasy about the unchecked acceleration of automation. Rather than succumb to fear or techno-optimism, Li offers a path grounded in empathy, shared values, and intentional design. 'If AI applications take away that sense of dignity,' she warns, 'there's something wrong.' As AI continues to reshape our world—from how we work to how we relate to each other—voices like Fei-Fei Li's remind us that progress must not come at the cost of our humanity. The challenge ahead, she suggests, is not only technical but moral: to build AI systems that enhance our potential without eclipsing our essence.
Business Insider
13-06-2025
- Business
- Business Insider
Top AI researchers say language is limiting. Here's the new kind of model they are building instead.
As OpenAI, Anthropic, and Big Tech invest billions in developing state-of-the-art large-language models, a small group of AI researchers is working on the next big thing. Computer scientists like Fei-Fei Li, the Stanford professor famous for inventing ImageNet, and Yann LeCun, Meta's chief AI scientist, are building what they call "world models." Unlike large-language models, which determine outputs based on statistical relationships between the words and phrases in their training data, world models predict events based on the mental constructs that humans make of the world around them. "Language doesn't exist in nature," Li said on a recent episode of Andreessen Horowitz's a16z podcast. "Humans," she said, "not only do we survive, live, and work, but we build civilization beyond language." Computer scientist and MIT professor, Jay Wright Forrester, in his 1971 paper "Counterintuitive Behavior of Social Systems," explained why mental models are crucial to human behavior: Each of us uses models constantly. Every person in private life and in business instinctively uses models for decision making. The mental images in one's head about one's surroundings are models. One's head does not contain real families, businesses, cities, governments, or countries. One uses selected concepts and relationships to represent real systems. A mental image is a model. All decisions are taken on the basis of models. All laws are passed on the basis of models. All executive actions are taken on the basis of models. The question is not to use or ignore models. The question is only a choice among alternative models. If AI is to meet or surpass human intelligence, then the researchers behind it believe it should be able to make mental models, too. Li has been working on this through World Labs, which she cofounded in 2024 with an initial backing of $230 million from venture firms like Andreessen Horowitz, New Enterprise Associates, and Radical Ventures. "We aim to lift AI models from the 2D plane of pixels to full 3D worlds — both virtual and real — endowing them with spatial intelligence as rich as our own," World Labs says on its website. Li said on the No Priors podcast that spatial intelligence is "the ability to understand, reason, interact, and generate 3D worlds," given that the world is fundamentally three-dimensional. Li said she sees applications for world models in creative fields, robotics, or any area that warrants infinite universes. Like Meta, Anduril, and other Silicon Valley heavyweights, that could mean advances in military applications by helping those on the battlefield better perceive their surroundings and anticipate their enemies' next moves. The challenge of building world models is the paucity of sufficient data. In contrast to language, which humans have refined and documented over centuries, spatial intelligence is less developed. "If I ask you to close your eyes right now and draw out or build a 3D model of the environment around you, it's not that easy," she said on the No Priors podcast. "We don't have that much capability to generate extremely complicated models till we get trained." To gather the data necessary for these models, "we require more and more sophisticated data engineering, data acquisition, data processing, and data synthesis," she said. That makes the challenge of building a believable world even greater. At Meta, chief AI scientist Yann LeCun has a small team dedicated to a similar project. The team uses video data to train models and runs simulations that abstract the videos at different levels. "The basic idea is that you don't predict at the pixel level. You train a system to run an abstract representation of the video so that you can make predictions in that abstract representation, and hopefully this representation will eliminate all the details that cannot be predicted," he said at the AI Action Summit in Paris earlier this year. That creates a simpler set of building blocks for mapping out trajectories for how the world will change at a particular time. LeCun, like Li, believes these models are the only way to create truly intelligent AI. "We need AI systems that can learn new tasks really quickly," he said recently at the National University of Singapore. "They need to understand the physical world — not just text and language but the real world — have some level of common sense, and abilities to reason and plan, have persistent memory — all the stuff that we expect from intelligent entities."
Mint
09-06-2025
- Business
- Mint
There is a vast hidden workforce behind AI
WHEN DEEPSEEK, a hotshot Chinese firm, released its cheap large language model late last year it overturned long-standing assumptions about what it will take to build the next generation of artificial intelligence (AI). This will matter to whoever comes out on top in the epic global battle for AI supremacy. Developers are now reconsidering how much hardware, energy and data are needed. Yet another, less discussed, input in machine intelligence is in flux too: the workforce. To the layman, AI is all robots, machines and models. It is a technology that kills jobs. In fact, there are millions of workers involved in producing AI models. Much of their work has involved tasks like tagging objects in images of roads in order to train self-driving cars and labelling words in the audio recordings used to train speech-recognition systems. Technically, annotators give data the contextual information computers need to work out the statistical associations between components of a dataset and their meaning to human beings. In fact, anyone who has completed a CAPTCHA test, selecting photos containing zebra crossings, may have inadvertently helped train an AI. This is the 'unsexy" part of the industry, as Alex Wang, the boss of Scale AI, a data firm, puts it. Although Scale AI says most of its contributor work happens in America and Europe, across the industry much of the labour is outsourced to poor parts of the world, where lots of educated people are looking for work. The Chinese government has teamed up with tech companies, such as Alibaba and to bring annotation jobs to far-flung parts of the country. In India the IT industry body, Nasscom, reckons annotation revenues could reach $7bn a year and employ 1m people there by 2030. That is significant, since India's entire IT industry is worth $254bn a year (including hardware) and employs 5.5m people. Annotators have long been compared to parents, teaching models and helping them make sense of the world. But the latest models don't need their guidance in the same way. As the technology grows up, are its teachers becoming redundant? Data annotation is not new. Fei Fei Li, an American computer scientist known as 'the godmother of AI", is credited with firing the industry's starting gun in the mid-2000s when she created ImageNet, the largest image dataset at the time. Ms Li realised that if she paid college students to categorise the images, which was then how most researchers did things, the task would take 90 years. Instead, she hired workers around the world using Mechanical Turk, an online gig-work platform run by Amazon. She got some 3.2m images organised into a dataset in two and a half years. Soon other AI labs were outsourcing annotation work this way, too. Over time developers got fed up with the low-quality annotation done by untrained workers on gig-work sites. AI-data firms, such as Sama and iMerit, emerged. They hired workers across the poor world. Informal annotation work continued but specialist platforms emerged for AI work, like those run by Scale AI, which tests and trains workers. The World Bank reckons that between 4.4% and 12.4% of the global workforce is involved in gig work, including annotation for AI. Krystal Kauffman, a Michigan resident who has been doing data work online for a decade, reckons that tech companies have an interest in keeping this workforce hidden. 'They are selling magic—this idea that all these things happen by themselves," Ms Kauffman, says. 'Without the magic part of it, AI is just another product." A debate in the industry has been about the treatment of the workers behind AI. Firms are reluctant to share information on wages. But American annotators generally consider $10-20 per hour to be decent pay on online platforms. Those in poor countries often get $4-8 per hour. Many must use monitoring tools that track their computer activity and are penalised for being slow. Scale AI has been hit with several lawsuits over its employment practices. The firm denies wrongdoing and says: 'We plan to defend ourselves vigorously." The bigger issue, though, is that basic annotation work is drying up. In part, this was inevitable. If AI was once a toddler who needed a parent to point things out and to help it make sense of the world around it, the technology has grown into an adolescent who needs occasional specialist guidance and advice. AI labs increasingly use pre-labelled data from other AI labs, which use algorithms to apply labels to datasets. Take the example of self-driving tractors developed by Blue River Technology, a subsidiary of John Deere, an agricultural-equipment giant. Three years ago the group's engineers in America would upload pictures of farmland into the cloud and provide iMerit staff in Hubli, India, with careful instructions on what to label: tractors, buildings, irrigation equipment. Now the developers use pre-labelled data. They still need iMerit staff to check that labelling and to deal with 'edge cases", for example where a dust cloud obscures part of the landscape or a tree throws shade over crops, confusing the model. A process that took months now takes weeks. From baby steps The most recent wave of AI models has changed data work more dramatically. Since 2022, when OpenAI first let the public play with its ChatGPT chatbot, there has been a rush of interest in large language models. Data from Pitchbook, a research firm, suggest that global venture-capital funding for AI startups jumped by more than 50% in 2024 to $131.5bn, even as funding for other startups fell. Much of it is going into newer techniques for developing AI, which do not need data annotated in the same way. Iva Gumnishka at Humans in the Loop, a social enterprise, says firms doing low-skilled annotation for older computer-vision and natural-language-processing clients are being 'left behind". There is still demand for annotators, but their work has changed. As businesses start to deploy AI, they are building smaller specialised models and looking for highly educated annotators to help. It has become fairly common for adverts for annotation jobs to require a PhD or skills in coding and science. Now that researchers are trying to make AI more multilingual, demand for annotators who speak languages other than English is growing, too. Sushovan Das, a dentist working on medical-AI projects at iMerit, reckons that annotation work will never disappear. 'This world is constantly evolving," he says. 'So the AI needs to be improved time and again." New roles for humans in training AI are emerging. Epoch AI, a research firm, reckons the stock of high-quality text available for training may be exhausted by 2026. Some AI labs are hiring people to write chunks of text and lines of code that models can be trained on. Others are buying synthetic data, created using computer algorithms, and hiring humans to verify it. 'Synthetic data still needs to be good data," says Wendy Gonzalez, the boss of Sama, which has operations east Africa. The other role for workers is in evaluating the output from models and helping to hammer it into shape. That is what got ChatGPT to perform better than previous chatbots. Xiaote Zhu at Scale AI provides an example of the sort of open-ended tasks being done on the firm's Outlier platform, which was launched in 2023 to facilitate the training of AI by experts. Workers are presented with two responses from a chatbot recommending an itinerary for a holiday to the Maldives. They need to select which response they prefer, rate it, explain why the answer is good or bad and then rewrite the response to improve it. Ms Zhu's example is a fairly anodyne one. Yet human feedback is also crucial to making sure AI is safe and ethical. In a document that was published after the launch of ChatGPT in 2022, OpenAI said it had hired experts to 'qualitatively probe, adversarially test and generally provide feedback" on its models. At the end of that process the model refused to respond to certain prompts, such as requests to write social-media content aimed at persuading people to join al-Qaeda, a terrorist group. Flying the nest If AI developers had their way they would not need this sort of human input at all. Studies suggest that as much as 80% of the time that goes into the development of AI is spent on data work. Naveen Rao at Databricks, an AI firm, says he would like models to teach themselves, just as he would like his own children to do. 'I want to build self-efficacious humans," he says. 'I want them to have their own curiosity and figure out how to solve problems. I don't want to spoon-feed them every step of the way." There is a lot of excitement about unsupervised learning, which involves feeding models unlabelled data, and reinforcement learning, which uses trial and error to improve decision-making. AI firms, including Google DeepMind, have trained machines to win at games like Go and chess by playing millions of contests against themselves and tracking which strategies work, without any human input at all. But that self-taught approach doesn't work outside the realms of maths and science, at least for the moment. Tech nerds everywhere have been blown away by how cheap and efficient DeepSeek's model is. But they are less impressed by DeepSeek's attempt to train AI using feedback generated by computers rather than humans. The model struggled to answer open-ended questions, producing gobbledygook in a mixture of languages. 'The difference is that with Go and chess the desired outcome is crystal clear: win the game," says Phelim Bradley, co-founder of Prolific, another AI-data firm. 'Large language models are more complex and far-reaching, so humans are going to remain in the loop for a long time." Mr Bradley, like many techies, reckons that more people will need to get involved in training AI, not fewer. Diversity in the workforce matters. When ChatGPT was released a few years ago, people noticed that it overused the word 'delve". The word became seen as 'AI-ese", a telltale sign that the text was written by a bot. In fact, annotators in Africa had been hired to train the model and the word 'delve" is more commonly used in African English than it is in American or British English. In the same way as workers' skills and knowledge are transferred to models, their vocabulary is, too. As it turns out, it takes more than just a village to raise a child. Clarification: This article has been amended to reflect Scale AI's claim that most of its labour is based in America and Europe.
Forbes
07-04-2025
- Business
- Forbes
Why Data Curation Is The Key To Enterprise AI
Nick Burling, Senior Vice President of Product at Nasuni. All the enterprise customers and end users I'm talking to these days are dealing with the same challenge. The number of enterprise AI tools is growing rapidly as ChatGPT, Claude and other leading models are challenged by upstarts like DeepSeek. There's no single tool that fits all, and it's dizzying to try to analyze all the solutions and determine which ones are best suited to the particular needs of your company, department or team. What's been lost in the focus on the latest and greatest models is the paramount importance of getting your data ready for these tools in the first place. To get the most out of the AI tools of today and tomorrow, it's important to have a complete view of your file data across your entire organization: the current and historical digital output of every office, studio, factory, warehouse and remote site, involving every one of your employees. Curating and understanding this data will help you deploy AI successfully. The potential of effective data curation is clear in the development of self-driving cars. Robotic vehicles can rapidly identify and distinguish between trees and cars in large part because of a dataset called ImageNet. This collection contains more than 14 million images of common everyday objects that have been labeled by humans. Scientists were able to train object recognition algorithms on this data because it was curated. They knew exactly what they had. Another example is the use of machine learning to identify early signs of cancer in radiological scans. Scientists were able to develop these tools in part because they had high-quality data (radiological images) and a deep understanding of the particulars of each image file. They didn't attempt to develop a tool that analyzed all patient data or all hospital files. They worked with a curated segment of medical data that they understood deeply. Now, imagine you're managing AI adoption and strategy at a civil engineering firm. Your goal is to utilize generative AI (GenAI) to streamline the process of creating proposals. And you've heard everyone in the AI world boasting about how this is a perfect use case. A typical civil engineering firm is going to have an incredibly broad range of files and complex models. Project data is going to be multimodal—a mix of text, video, images and industry-specific files. If you were to ask a standard GenAI tool to scan this data and produce a proposal, the result would be garbage. But let's say all this data was consolidated, curated and understood at a deeper level. Across tens of millions of files, you'd have a sense of which groups own which files, who accesses them often, what file types are involved and more. Assuming you had the appropriate security guardrails in place to protect the data, you could choose a tool specifically tuned for proposals and securely give that tool access to only the relevant files within your organization. Then, you'd have something truly useful that helps your teams generate better, more relevant proposals faster. Even with curation, there can be challenges. Let's say a project manager (PM) overseeing multiple construction sites wants to use a large language model (LLM) to automatically analyze daily inspection reports. At first glance, this would seem to be a perfect use case, as the PM would be working with a very specific set of files. In reality, though, the reports would probably come in different formats, ranging from spreadsheets to PDFs and handwritten notes. The dataset might include checklists or different phrasings representing the same idea. A human would easily recognize this collected data as variations of a site inspection report, but a general-purpose LLM wouldn't have that kind of world or industry knowledge. A tool like this would likely generate inaccurate and confusing results. Yet, having curated and understood this data, the PM would still be in a much better position. They'd recognize early that the complexity and variation in the inspection reports would lead to challenges and save the organization the expense and trouble of investing in an AI tool for this application. The opportunities that could grow out of organization-wide data curation stretch far beyond specific departmental use cases. Because most of your organization's data resides within your security perimeter, no AI model has been trained on those files. You have a completely unique dataset that hasn't yet been mined for insights. You could take the capabilities of the general AI models developed in training on massive, general datasets and (with the right security framework in place) fine-tune them to your organization's unique gold mine of enterprise data. This is already happening at an industry scale. The virtual paralegal Harvey has been fine-tuned on curated legal data, including case law, statutes, contracts, legal briefs and the rest. BioBERT, a model optimized for medical research, was trained on a curated dataset of biomedical texts. The researchers who developed this tool did so because biomedical texts have such a particular or specific language. Whether you want to embark on an ambitious project to create a fine-tuned model or select the right existing tool for a department or project team's needs, it all starts with data curation. In this period of rapid change and model evolution, the one constant is that if you don't know what sort of data you have, you're not going to know how to use it. 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