Babbage: Fei-Fei Li on how to really think about the future of AI
Nov 22, 2023
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Fei-Fei Li, pioneer in generative AI models, advocates for a human-centered approach to dealing with AI. She discusses the evolution of computer vision, the impact of generative AIs on society, concerns about bias and disinformation, and the potential sentience of AIs.
Fei-Fei Li emphasizes the need for a human-centered approach in AI, focusing on responsible development, transparency, and communication with the public while considering the challenges of disinformation, bias, and job disruption.
Fei-Fei Li highlights the importance of computer vision in AI development, discussing its evolution, the challenge of building machines that can truly 'see,' and the significance of the ImageNet database in revolutionizing AI training.
Deep dives
The Power and Risks of Chat GPT and Generative AI
Fei-Fei Li, a pioneer in AI, discusses the viral sensation of chat GPT and its impact on AI development. She emphasizes the need for a human-centered approach in AI, highlighting the importance of responsible development, transparency, and communication with the public. Li also addresses the concerns of bias, disinformation, job disruption, and misuse of generative AI. She acknowledges the progress made in combatting issues like hallucinations in AI models but emphasizes that responsible use and communication are equally important. Additionally, she envisions a future of multi-modal AI, combining images and videos with language models, but acknowledges the potential risks and misinformation associated with this advancement.
The Significance of Computer Vision and Seeing
Fei-Fei Li delves into the importance of computer vision and its role in AI development. She explains how seeing is a cornerstone of intelligence and highlights the evolution of vision in animals and humans. Li discusses the challenge of building machines that can truly 'see' and make sense of sensory data, going beyond the physical process of capturing images. She shares her journey in computer vision research, from early attempts at recognizing simple shapes to the development of the ImageNet database, which revolutionized AI training by using large-scale data sets. Li emphasizes that cracking the questions of computer vision is crucial in understanding intelligence and advancing AI technology.
The History and Impact of Deep Learning in Computer Vision
Fei-Fei Li provides an overview of the history of computer vision, specifically focusing on the breakthroughs in deep learning. She describes her early work in recognizing objects and scenes, and how she began training algorithms to see using large-scale data sets. Li explains the concept of deep learning, where neural networks with multiple layers learn to recognize objects by analyzing vast amounts of labeled data. She highlights the significance of the ImageNet competition in 2012, where the algorithm AlexNet demonstrated the power of deep learning and marked a watershed moment in AI's evolution. Li emphasizes that deep learning has become the foundation for the biggest AI models today.
The Responsibilities and Concerns in AI Development
Fei-Fei Li addresses the ethical concerns and responsibilities in the development and deployment of AI. She emphasizes the importance of recognizing and addressing issues such as bias, disinformation, and job disruption in AI systems. Li expresses her worry about the misuse of technology and advocates for responsible governance and regulation. She stresses the need for public education and better communication about the risks and benefits of AI. Li also highlights the importance of public investment and the role of the public sector in evaluating and assessing AI technology. She encourages a balanced approach that focuses on immediate pressing social issues while also considering the long-term potential of AI.
A year ago, the public launch of ChatGPT took the world by storm and it was followed by many more generative artificial intelligence tools, all with remarkable, human-like abilities. Fears over the existential risks posed by AI have dominated the global conversation around the technology ever since.
Fei-Fei Li, a pioneer that helped lay the groundwork that underpins modern generative AI models, takes a more nuanced approach. She’s pushing for a human-centred way of dealing with AI—treating it as a tool to help enhance—and not replace—humanity, while focussing on the pressing challenges of disinformation, bias and job disruption.
Fei-Fei Li is the founding co-director of Stanford University’s Institute for Human-Centred Artificial Intelligence. Fei-Fei and her research group created ImageNet, a huge database of images that enabled computers scientists to build algorithms that were able to see and recognise objects in the real world. That endeavour also introduced the world to deep learning, a type of machine learning that is fundamental part of how large-language and image-creation models work.
Host: Alok Jha, The Economist’s science and technology editor.
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