The Microsoft Azure channel explains how KPMG Japan uses Azure Arc to build out a seamless data solution.

KPMG Ignition Tokyo, the centerpiece of KPMG Japan’s digital strategy, delivers specialty software solutions to its global clients. With a multi-cloud and hybrid approach, the firm is rolling out its next-generation, AI-based audit software built on Azure, and implementing Azure Arc to deliver seamless solutions for clients across multiple hybrid data estates.

Lex Fridman interviews Jitendra Malik, a professor at Berkeley and one of the seminal figures in the field of computer vision, the kind before the deep learning revolution, and the kind after.

He has been cited over 180,000 times and has mentored many world-class researchers in computer science. This conversation is part of the Artificial Intelligence podcast.

Content index:

  • 0:00 – Introduction
  • 3:17 – Computer vision is hard
  • 10:05 – Tesla Autopilot
  • 21:20 – Human brain vs computers
  • 23:14 – The general problem of computer vision
  • 29:09 – Images vs video in computer vision
  • 37:47 – Benchmarks in computer vision
  • 40:06 – Active learning
  • 45:34 – From pixels to semantics
  • 52:47 – Semantic segmentation
  • 57:05 – The three R’s of computer vision
  • 1:02:52 – End-to-end learning in computer vision
  • 1:04:24 – 6 lessons we can learn from children
  • 1:08:36 – Vision and language
  • 1:12:30 – Turing test
  • 1:16:17 – Open problems in computer vision
  • 1:24:49 – AGI
  • 1:35:47 – Pick the right problem

Lex Fridman interviews Brian Kernighan in the latest episode of his podcast.

Brian Kernighan is a professor of computer science at Princeton University. He co-authored the C Programming Language with Dennis Ritchie (creator of C) and has written a lot of books on programming, computers, and life including the Practice of Programming, the Go Programming Language, his latest UNIX: A History and a Memoir. He co-created AWK, the text processing language used by Linux folks like myself. He co-designed AMPL, an algebraic modeling language for large-scale optimization. This conversation is part of the Artificial Intelligence podcast.

Outline:

  • 0:00 – Introduction
  • 4:24 – UNIX early days
  • 22:09 – Unix philosophy
  • 31:54 – Is programming art or science?
  • 35:18 – AWK
  • 42:03 – Programming setup
  • 46:39 – History of programming languages
  • 52:48 – C programming language
  • 58:44 – Go language
  • 1:01:57 – Learning new programming languages
  • 1:04:57 – Javascript
  • 1:08:16 – Variety of programming languages
  • 1:10:30 – AMPL
  • 1:18:01 – Graph theory
  • 1:22:20 – AI in 1964
  • 1:27:50 – Future of AI
  • 1:29:47 – Moore’s law
  • 1:32:54 – Computers in our world
  • 1:40:37 – Life

Lex Fridman interviews Sergey Levine in episode 108 of his podcast.

Sergey Levine is a professor at Berkeley and a world-class researcher in deep learning, reinforcement learning, robotics, and computer vision, including the development of algorithms for end-to-end training of neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, and deep RL algorithms. This conversation is part of the Artificial Intelligence podcast.

Episode outline:

  • 0:00 – Introduction
  • 3:05 – State-of-the-art robots vs humans
  • 16:13 – Robotics may help us understand intelligence
  • 22:49 – End-to-end learning in robotics
  • 27:01 – Canonical problem in robotics
  • 31:44 – Commonsense reasoning in robotics
  • 34:41 – Can we solve robotics through learning?
  • 44:55 – What is reinforcement learning?
  • 1:06:36 – Tesla Autopilot
  • 1:08:15 – Simulation in reinforcement learning
  • 1:13:46 – Can we learn gravity from data?
  • 1:16:03 – Self-play
  • 1:17:39 – Reward functions
  • 1:27:01 – Bitter lesson by Rich Sutton
  • 1:32:13 – Advice for students interesting in AI
  • 1:33:55 – Meaning of life

Lex Fridman interviews Peter Singer in this enlightening episode of his podcast.

Peter Singer is a professor of bioethics at Princeton, best known for his 1975 book Animal Liberation, that makes an ethical case against eating meat. He has written brilliantly from an ethical perspective on extreme poverty, euthanasia, human genetic selection, sports doping, the sale of kidneys, and happiness including in his books Ethics in the Real World and The Life You Can Save. He was a key popularizer of the effective altruism movement and is generally considered one of the most influential philosophers in the world. This conversation is part of the Artificial Intelligence podcast.

Content index:

  • 0:00 – Introduction
  • 5:25 – World War II
  • 9:53 – Suffering
  • 16:06 – Is everyone capable of evil?
  • 21:52 – Can robots suffer?
  • 37:22 – Animal liberation
  • 40:31 – Question for AI about suffering
  • 43:32 – Neuralink
  • 45:11 – Control problem of AI
  • 51:08 – Utilitarianism
  • 59:43 – Helping people in poverty
  • 1:05:15 – Mortality

deeplizard demonstrates how to create a confusion matrix, which will aid us in being able to visually observe how well a neural network is predicting during inference.

VIDEO SECTIONS

  • 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:34 Plotting a Confusion Matrix
  • 02:48 Reading a Confusion Matrix
  • 04:56 Collective Intelligence and the DEEPLIZARD HIVEMIND

Lex Fridman interviews Matt Botvinick in this latest episode of his podcast.

Matt Botvinick is the Director of Neuroscience Research at DeepMind. He is a brilliant cross-disciplinary mind navigating effortlessly between cognitive psychology, computational neuroscience, and artificial intelligence. This conversation is part of the Artificial Intelligence podcast.

Content outline:

  • 0:00 – Introduction
  • 3:29 – How much of the brain do we understand?
  • 14:26 – Psychology
  • 22:53 – The paradox of the human brain
  • 32:23 – Cognition is a function of the environment
  • 39:34 – Prefrontal cortex
  • 53:27 – Information processing in the brain
  • 1:00:11 – Meta-reinforcement learning
  • 1:15:18 – Dopamine
  • 1:19:01 – Neuroscience and AI research
  • 1:23:37 – Human side of AI
  • 1:39:56 – Dopamine and reinforcement learning
  • 1:53:07 – Can we create an AI that a human can love?