Lex Fridman interviews William MacAskill, a philosopher, ethicist, and one of the originators of the effective altruism movement.

His research focuses on the fundamentals of effective altruism – the use of evidence and reason to help others by as much as possible with our time and money, with a particular concentration on how to act given moral uncertainty. He is the author of Doing Good Better – Effective Altruism and a Radical New Way to Make a Difference. He is a co-founder and the President of the Centre for Effective Altruism (CEA) that encourages people to commit to donate at least 10% of their income to the most effective charities. He co-founded 80,000 Hours, a non-profit that provides research and advice on how you can best make a difference through your career. This conversation is part of the Artificial Intelligence podcast.

Time Index:

  • 0:00 – Introduction
  • 2:39 – Utopia – the Long Reflection
  • 10:25 – Advertisement model
  • 15:56 – Effective altruism
  • 38:28 – Criticism
  • 49:02 – Biggest problems in the world
  • 53:40 – Suffering
  • 1:01:40 – Animal welfare
  • 1:09:23 – Existential risks
  • 1:19:08 – Existential risk from AGI

Here’s an interesting project for those of you with young kids at home looking for ways to find a way to entertain and educate them while under a COVID-19 lockdown.

Using an object detection AI model, a game engine, an Amazon Polly and a Selenium automation framework running on an NVIDIA Jetson Nano to build Qrio, a bot which can speak, recognise a toy and play a relevant video on YouTube. My wife and I have a super curious […]

Deep learning has the potential to answer questions which have been unanswered or unable to be predicted until now.

There are a variety of other machine learning algorithms, which can be used to find insights from data.

The financial sector has been using machine learning techniques for a long time in order to gain business growth through higher profit. Credit card fraud detection, stock market prediction, among others, are some of the popular machine learning approaches in this sector, which the companies have actively adopted to streamline their business operations.

Here’s an interesting read about predicting banking crises with AI.

In this article, we will discuss a deep learning technique — deep neural network — that can be deployed for predicting banks’ crisis. This experiment is based on the African economic, banking and systemic crisis data where inflation, currency crisis and bank crisis of 13 African countries between 1860 to 2014 is given. By predicting through a deep learning model, we will see that this model gives a high accuracy in this task.

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

Lex Fridman interviews Nick Bostrom.

Nick Bostrom is a philosopher at University of Oxford and the director of the Future of Humanity Institute. He has worked on fascinating and important ideas in existential risks, simulation hypothesis, human enhancement ethics, and the risks of superintelligent AI systems, including in his book Superintelligence. I can see talking to Nick multiple times on this podcast, many hours each time, but we have to start somewhere. This conversation is part of the Artificial Intelligence podcast.

Time Index:

  • 0:00 – Introduction
  • 2:48 – Simulation hypothesis and simulation argument
  • 12:17 – Technologically mature civilizations
  • 15:30 – Case 1: if something kills all possible civilizations
  • 19:08 – Case 2: if we lose interest in creating simulations
  • 22:03 – Consciousness
  • 26:27 – Immersive worlds
  • 28:50 – Experience machine
  • 41:10 – Intelligence and consciousness
  • 48:58 – Weighing probabilities of the simulation argument
  • 1:01:43 – Elaborating on Joe Rogan conversation
  • 1:05:53 – Doomsday argument and anthropic reasoning
  • 1:23:02 – Elon Musk
  • 1:25:26 – What’s outside the simulation?
  • 1:29:52 – Superintelligence
  • 1:47:27 – AGI utopia
  • 1:52:41 – Meaning of life

In this video, you’ll learn how you can use Azure Event Grid, Azure Machine Learning and Github Actions to create a continuous integration and continuous deployment workflow. You’ll see how to automate the model training and model deployment process end to end.

Time Index:

  • [00:45] Intro
  • [01:09] Demo – Continuous integration steps
  • [04:43] Demo – Continuous deployment steps
  • [08:15] Demo- Test the endpoint

For More Info:

In this video, learn how you can use Azure Event Grid and Azure Machine Learning to trigger and consume machine learnings events. We talk about why eventing is important and how you can enable scenarios such as run failure alerts and retraining models.

Jump To:

  • [00:50] What is Event Grid?
  • [01:32] Why is this useful?
  • [02:32] Demo – How to set up an event subscription
  • [03:40] Demo – How to filter events
  • [05:30] Demo – Logic app example