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AI overview: 2. The Big Bang and the years that followed

AI overview: 2. The Big Bang and the years that followed

FromLondon Futurists


AI overview: 2. The Big Bang and the years that followed

FromLondon Futurists

ratings:
Length:
32 minutes
Released:
Sep 7, 2022
Format:
Podcast episode

Description

In this episode, co-hosts Calum Chace and David Wood continue their review of progress in AI, taking up the story at the 2012 "Big Bang".00.05: Introduction: exponential impact, big bangs, jolts, and jerks00.45: What enabled the Big Bang01.25: Moore's Law02.05: Moore's Law has always evolved since its inception in 196503.08: Intel's tick tock becomes tic tac toe03.49: GPUs - Graphic Processing Units04.29: TPUs - Tensor Processing Units04.46: Moore's Law is not dead or dying05.10: 3D chips05.32: Memristors05.54: Neuromorphic chips06.48: Quantum computing08.18: The astonishing effect of exponential growth09.08: We have seen this effect in computing already. The cost of an iPhone in the 1950s.09.42: Exponential growth can't continue forever, but Moore's Law hasn't reached any theoretical limits10.33: Reasons why Moore's Law might end: too small, too expensive, not worthwhile11.20: Counter-arguments12.01: "Plenty more room at the bottom"12.56: Software and algorithms can help keep Moore's Law going14.15: Using AI to improve chip design14.40: Data is critical15.00: ImageNet, Fei Fei Lee, Amazon Turk16.10: AIs labelling data16.35: The Big Bang17.00: Jürgen Schmidhuber challenges the narrative17.41: The Big Bang enabled AI to make money18.24: 2015 and the Great Robot Freak-Out18.43: Progress in many domains, especially natural language processing19.44: Machine Learning and Deep Learning20.25: Boiling the ocean vs the scientific method's hypothesis-driven approach21.15: Deep Learning: levels21.57: How Deep Learning systems recognise faces22.48: Supervised, Unsupervised, and Reinforcement Learning24.00: Variants, including Deep Reinforcement Learning and Self-Supervised Learning24.30: Yann LeCun's camera metaphor for Deep Learning26.05: Lack of transparency is a concern27.45: Explainable AI. Is it achievable?29.00: Other AI problems29.17: Has another Big Bang taken place? Large Language Models like GPT-330.08: Few-shot learning and transfer learning30.40: Escaping Uncanny Valley31.50: Gato and partially general AIMusic: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain DeclarationFor more about the podcast hosts, see https://calumchace.com/ and https://dw2blog.com/
Released:
Sep 7, 2022
Format:
Podcast episode

Titles in the series (80)

Anticipating and managing exponential impact - hosts David Wood and Calum Chace