Quantum computers are advancing at a breathtaking pace, but this progress may soon stall due to out of this world force — literally.

Cosmic rays streaming down to Earth could interfere with the integrity of the information in these quantum computers.

An MIT study has measured how much cosmic rays could interfere with quantum computers Quantum computers are advancing at an exciting pace, but unfortunately this progress may soon stall. Cosmic rays streaming down to Earth could interfere with the integrity of the information in these quantum computers, and now […]

MIT OpenCourseWare provides a this course for free.

Given its prominence to neural networks and quantum computing, now is a good time to learn Linear Algebra.

MIT A 2020 Vision of Linear Algebra, Spring 2020Instructor: Gilbert StrangView the complete course: https://ocw.mit.edu/2020-visionYouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61iQEFiWLE21EJCxwmWvvek

Professor Strang describes independent vectors and the column space of a matrix as a good starting point for learning linear algebra. His outline develops the five shorthand descriptions of key chapters of linear algebra. 

Lex Fridman interviews Kate Darling in this episode of the AI Show.

Kate Darling is a researcher at MIT, interested in social robotics, robot ethics, and generally how technology intersects with society. She explores the emotional connection between human beings and life-like machines, which for me, is one of the most exciting topics in all of artificial intelligence. This conversation is part of the Artificial Intelligence podcast.

Time index:

  • 0:00 – Introduction
  • 3:31 – Robot ethics
  • 4:36 – Universal Basic Income
  • 6:31 – Mistreating robots
  • 17:17 – Robots teaching us about ourselves
  • 20:27 – Intimate connection with robots
  • 24:29 – Trolley problem and making difficult moral decisions
  • 31:59 – Anthropomorphism
  • 38:09 – Favorite robot
  • 41:19 – Sophia
  • 42:46 – Designing robots for human connection
    47:01 – Why is it so hard to build a personal robotics company?
    50:03 – Is it possible to fall in love with a robot?
    56:39 – Robots displaying consciousness and mortality
    58:33 – Manipulation of emotion by companies
    1:04:40 – Intellectual property
    1:09:23 – Lessons for robotics from parenthood
    1:10:41 – Hope for future of robotics

Lex Fridman interviews Eric Weinstein in the latest episode of his podcast.

Eric Weinstein is a mathematician with a bold and piercing intelligence, unafraid to explore the biggest questions in the universe and shine a light on the darkest corners of our society. He is the host of The Portal podcast, a part of which, he recently released his 2013 Oxford lecture on his theory of Geometric Unity that is at the center of his lifelong efforts in arriving at a theory of everything that unifies the fundamental laws of physics. This conversation is part of the Artificial Intelligence podcast.

Time Index:

  • 0:00 – Introduction
  • 2:08 – World War II and the Coronavirus Pandemic
  • 14:03 – New leaders
  • 31:18 – Hope for our time
  • 34:23 – WHO
  • 44:19 – Geometric unity
  • 1:38:55 – We need to get off this planet
  • 1:40:47 – Elon Musk
  • 1:46:58 – Take Back MIT
  • 2:15:31 – The time at Harvard
  • 2:37:01 – The Portal
  • 2:42:58 – Legacy

Lex Fridman delivers a talk with some advice about life and my own journey and passion in artificial intelligence.

The audience is a group of Drexel engineering students, friends and family in Philadelphia, delivered before the outbreak of the coronavirus pandemic.

Time Index:

  • 0:00 – Overview – The Voice poem
  • 6:46 – Artificial intelligence
  • 13:44 – Open problems in AI
  • 14:10 – Problem 1: Learning to understand
  • 17:15 – Problem 2: Learning to act
  • 19:28 – Problem 3: Reasoning
  • 20:44 – Problem 4: Connection between humans & AI systems
  • 23:57 – Advice about life as an optimization problem
  • 24:10 – Advice 1: Listen to your inner voice – ignore the gradient
  • 25:12 – Advice 2: carve your own path
  • 26:28 – Advice 2: Measure passion not progress
  • 28:10 – Advice 4: work hard
  • 29:05 – Advice 5: forever oscillate between gratitude and dissatisfaction
  • 31:10 – Q&A: Meaning of life
  • 33:11 – Q&A: Simulation hypothesis
  • 36:15 – Q&A: How do you define greatness?

MIT Introduction to Deep Learning 6.S191: Lecture 6 with Ava Soleimany.

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

Lecture Outline

  • 0:00 – Introduction
  • 0:58 – Course logistics
  • 3:59 – Upcoming guest lectures
  • 5:35 – Deep learning and expressivity of NNs
  • 10:02 – Generalization of deep models
  • 14:14 – Adversarial attacks
  • 17:00 – Limitations summary
  • 18:18 – Structure in deep learning
  • 22:53 – Uncertainty & bayesian deep learning
  • 28:09 – Deep evidential regression
  • 33:08 – AutoML
  • 36:43 – Conclusion

I always knew that reinforcement learning would teach us more about ourselves than any other kind of AI approach. This feeling was backed up in a paper published recently in Nature.

DeepMind, Alphabet’s AI subsidiary, has once again used lessons from reinforcement learning to propose a new theory about the reward mechanisms within our brains.

The hypothesis, supported by initial experimental findings, could not only improve our understanding of mental health and motivation. It could also validate the current direction of AI research toward building more human-like general intelligence.

It turns out the brain’s reward system works in much the same way—a discovery made in the 1990s, inspired by reinforcement-learning algorithms. When a human or animal is about to perform an action, its dopamine neurons make a prediction about the expected reward.