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, discusses the human-centered approach to AI, addressing challenges like disinformation, bias, and job disruption. She emphasizes the importance of transparency, public education, and ethical values. The future of generative AI, multi-modality, and exploring AI's sentience are also explored.
Fei-Fei Li advocates for a human-centered approach in AI development, treating it as a tool to enhance humanity and address challenges like disinformation, bias, and job disruption.
Fei-Fei Li emphasizes the significance of large data sets and deep learning in training AI models, particularly in the field of computer vision.
Deep dives
Fei-Fei Li's Vision for AI
Fei-Fei Li, a computer scientist at Stanford University, discusses the development of computer vision technology and its impact on various industries. She highlights the role of computer vision in areas such as border checkpoints, driverless cars, and video calls. Li also emphasizes the significance of large data sets in training AI models and the revolution in deep learning. She addresses the ethical concerns related to bias, disinformation, and job disruption, urging responsible use and regulation of AI. Li expresses the need for public sector investment and education to address these challenges and ensure a human-centered approach in AI development.
The Fundamentals of Computer Vision
Fei-Fei Li delves into the concept of computer vision and its evolutionary origins. She explains that vision is a cornerstone of intelligence and how the ability to see has driven animal speciation and the development of the nervous system. Li discusses the process of making sense of sensory data and the challenges of computational analysis. She shares her journey in computer vision, starting with recognizing simple geometric shapes and later advancing to object recognition. Li highlights the importance of data sets like ImageNet in training AI models and the breakthrough moment of using deep learning neural networks.
The Power and Concerns of Generative AI
Fei-Fei Li reflects on the public release of chat GPT and the public's awe and concern about generative AI technology. She mentions the growing capabilities of language models and the anticipation of such breakthroughs within the AI community. Li discusses the potential risks and challenges associated with bias, disinformation, and job displacement. She emphasizes responsible use of AI, the need for education and transparency, and the importance of public sector investment in the development and assessment of AI.
Looking Ahead: The Future of AI
Fei-Fei Li envisions a future where AI, particularly computer vision, will enable machines to perform a wide range of tasks. She highlights the potential applications in various fields, such as autonomous driving, healthcare, and scientific research. Li acknowledges the concerns surrounding the misuse of AI, including bias, disinformation, and hallucinations in generative AI. She emphasizes the importance of addressing immediate social issues, investing in public sector development, and creating regulatory frameworks to ensure the responsible and beneficial use of AI technology.
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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