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Top 10 TED Talks on Artificial Intelligence
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#1
Sam Harris — Can We Build AI Without Losing Control?
Sam Harris 2016 TED Talk remains the clearest articulation of why AI alignment is a serious philosophical and technical problem — not science fiction. Harris central argument: if we build a superintelligent system and its goals are not precisely aligned with human values, the result is catastrophic regardless of whether the system is malevolent. The comparison to a nuclear reactor without a shutdown mechanism and to a corporation optimizing for profit without ethical constraints are frameworks that have proven more useful than most subsequent AI safety communication. The remarkable thing about this talk in 2026 is how accurate its predictions were: Harris anticipated that AI progress would be rapid, that society would be unprepared for it, and that the difficulty of specifying human values formally would be the core technical problem. The talk is 17 minutes and has been viewed 8 million times. It is the best starting point for understanding why AI risk is a serious field of inquiry.
#2
Demis Hassabis — The Mind in the Machine
DeepMind CEO Demis Hassabis 2024 TED Talk is the most technically accurate description of how modern AI systems actually work from someone with direct knowledge of the systems he is describing. Hassabis explains the architecture of large language models, the training process, why emergent capabilities arise, and what the genuine limitations are — including hallucination, reasoning failures, and the difference between pattern matching and understanding. The most valuable section: his description of what AlphaFold and AlphaCode reveal about the potential of AI in scientific discovery, grounded in the specific mechanisms rather than generic claims about AI being transformative. Hassabis has a PhD in cognitive neuroscience and co-founded DeepMind from a conviction that understanding intelligence in brains would help build intelligence in machines. The talk is dense and rewards a second viewing.
#3
Fei-Fei Li — How We Teach Computers to Understand Pictures
Fei-Fei Li 2015 TED Talk on computer vision is the origin story of modern deep learning — she led the ImageNet project that catalyzed the 2012 deep learning revolution. The talk explains with unusual clarity how convolutional neural networks work, why the ImageNet dataset was necessary, and what children visual learning reveals about what computer vision must eventually achieve. The historical importance: this is not just a good explanation, it is the explanation given by the scientist who created the conditions for the breakthrough. Li is now Director of the Stanford Human-Centered AI Institute and has shaped AI policy at every level. The talk has aged well — the principles she explains about how neural networks learn visual features are still the foundation of modern computer vision. For understanding how AI went from academic curiosity to transformative technology, this is the primary source.