Lex Fridman shared this lecture by Vivienne Sze in January 2020 as part of the MIT Deep Learning Lecture Series.

Website: https://deeplearning.mit.edu
Slides: http://bit.ly/2Rm7Gi1
Playlist: http://bit.ly/deep-learning-playlist

LECTURE LINKS:
Twitter: https://twitter.com/eems_mit
YouTube: https://www.youtube.com/channel/UC8cviSAQrtD8IpzXdE6dyug
MIT professional course: http://bit.ly/36ncGam
NeurIPS 2019 tutorial: http://bit.ly/2RhVleO
Tutorial and survey paper: https://arxiv.org/abs/1703.09039
Book coming out in Spring 2020!

OUTLINE:
0:00 – Introduction
0:43 – Talk overview
1:18 – Compute for deep learning
5:48 – Power consumption for deep learning, robotics, and AI
9:23 – Deep learning in the context of resource use
12:29 – Deep learning basics
20:28 – Hardware acceleration for deep learning
57:54 – Looking beyond the DNN accelerator for acceleration
1:03:45 – Beyond deep neural networks

Eric Reiss (FatDUX Group) started working with user experience (UX) long before the term was even known and speaks about Ethics in AI.

Whenever we say, “That’s not my problem,” or, “My company won’t let me do that,” we are handing over our ethical responsibility to someone else – for better or for worse. Do innocent decisions evolve so that they promote racism or gender discrimination through inadvertent cognitive bias or unwitting apathy? Far too often they do.

We, as technologists, hold incredible power to shape the things to come. I would like to share my thoughts with you so you can use this power to truly build a better world for those who come after us!

Lex Fridman interview Daniel Kahneman in this thought provoking interview.

Daniel Kahneman is winner of the Nobel Prize in economics for his integration of economic science with the psychology of human behavior, judgment and decision-making. He is the author of the popular book “Thinking, Fast and Slow” that summarizes in an accessible way his research of several decades, often in collaboration with Amos Tversky, on cognitive biases, prospect theory, and happiness. The central thesis of this work is a dichotomy between two modes of thought: “System 1” is fast, instinctive and emotional; “System 2” is slower, more deliberative, and more logical. The book delineates cognitive biases associated with each type of thinking. This conversation is part of the Artificial Intelligence podcast.

OUTLINE:
0:00 – Introduction
2:36 – Lessons about human behavior from WWII
8:19 – System 1 and system 2: thinking fast and slow
15:17 – Deep learning
30:01 – How hard is autonomous driving?
35:59 – Explainability in AI and humans
40:08 – Experiencing self and the remembering self
51:58 – Man’s Search for Meaning by Viktor Frankl
54:46 – How much of human behavior can we study in the lab?
57:57 – Collaboration
1:01:09 – Replication crisis in psychology
1:09:28 – Disagreements and controversies in psychology
1:13:01 – Test for AGI
1:16:17 – Meaning of lifeOUTLINE:
0:00 – Introduction
2:36 – Lessons about human behavior from WWII
8:19 – System 1 and system 2: thinking fast and slow
15:17 – Deep learning
30:01 – How hard is autonomous driving?
35:59 – Explainability in AI and humans
40:08 – Experiencing self and the remembering self
51:58 – Man’s Search for Meaning by Viktor Frankl
54:46 – How much of human behavior can we study in the lab?
57:57 – Collaboration
1:01:09 – Replication crisis in psychology
1:09:28 – Disagreements and controversies in psychology
1:13:01 – Test for AGI
1:16:17 – Meaning of life

Siraj Raval has a video exploring a paper about genomics and creating reliable machine learning systems.

Deep learning classifiers make the ladies (and gentlemen) swoon, but they often classify novel data that’s not in the training set incorrectly with high confidence. This has serious real world consequences! In Medicine, this could mean misdiagnosing a patient. In autonomous vehicles, this could mean ignoring a stop sign. Machines are increasingly tasked with making life or death decisions like that, so it’s important that we figure out how to correct this problem! I found a new, relatively obscure yet extremely fascinating paper out of Google Research that tackles this problem head on. In this episode, I’ll explain the work of these researchers, we’ll write some code, do some math, do some visualizations, and by the end I’ll freestyle rap about AI and genomics. I had a lot of fun making this, so I hope you enjoy it!

Likelihood Ratios for Out-of-Distribution Detection paper: https://arxiv.org/pdf/1906.02845.pdf 

The researcher’s code: https://github.com/google-research/google-research/tree/master/genomics_ood

Siraj Raval gets back to inspiring people to get into AI and pokes fun at himself.

Almost exactly 4 years ago I decided to dedicate my life to helping educate the world on Artificial Intelligence. There were hardly any resources designed for absolute beginners and the field was dominated by PhDs. In 2020, thanks to the extraordinary contributions of everyone in this community, all that has changed. It’s easier than ever before to enter into this field, even without an IT background. We’ve seen brave entrepreneurs figure out how to deploy this technology to save lives (medical imaging, automated diagnosis) and accelerate Science (AlphaFold). We’ve seen algorithmic advances (deepfakes) and ethical controversies (automated surveillance) that shocked the world. The AI field is now a global, cross-cultural movement that’s not limited to academics alone. And that’s something all of us should be proud of, we’re all apart of this. I’ve packed a lot into this episode! I’ll give my annual lists of the best ML language and libraries to learn this year, how to learn ML in 2020, as well as 8 predictions about where this field is headed. I had a lot of fun making this, so I hope you enjoy it!

Great Learning has provided this free 7 hour course on statistics for Data Science.

This course will be taught by Dr.Abhinanda Sarkar who has his Ph.D. in Statistics from Stanford University. He has taught applied mathematics at the Massachusetts Institute of Technology (MIT); been on the research staff at IBM; led Quality, Engineering Development, and Analytics functions at General Electric (GE); and has co-founded OmiX Labs.

These are the topics covered in this full course:

  1. Statistics vs Machine Learning – 2:22
  2. Types of Statistics [Descriptive, Prescriptive and Predictive] – 9:05
  3. Types of Data – 1:50:45
  4. Correlation – 2:46:02
  5. Covariance – 2:52:33
  6. Introduction to Probability – 4:26:55
  7. Conditional Probability with Baye’s Theorem – 5:24:00
  8. Binomial Distribution – 6:17:01
  9. Poisson Distribution – 6:36:02

Katherine Bindley of the Wall Street Journal is at CES to take a look at the latest AI-infused cameras on the market.

Two new smart systems use cameras, artificial intelligence and an assortment of sensors to keep watch over you—Patscan looks for threats in public spaces, while Eyeris monitors the driver and passengers in a car. WSJ’s Katherine Bindley visits CES to explores their advantages, as well as their privacy costs.