O’Reilly and TensorFlow teamed up to present the first TensorFlow World last week.

It brought together the growing TensorFlow community to learn from each other and explore new ideas, techniques, and approaches in deep and machine learning.

Presenters in the keynote:

  • Jeff Dean, Google
  • Megan Kacholia, Google
  • Frederick Reiss, IBM
  • Theodore Summe, Twitter
  • Craig Wiley, Google
  • Kemal El Moujahid, Google

Lex Fridman recently interviewed Garry Kasparov, considered by many to be the greatest chess player of all time.

From 1986 until his retirement in 2005, he dominated the chess world, ranking world number 1 for most of those 19 years. While he has many historic matches against human chess players, in the long arc of history he may be remembered for his match again a machine, IBM’s Deep Blue. His initial victories and eventual loss to Deep Blue captivated the imagination of the world of what role Artificial Intelligence systems may play in our civilization’s future. That excitement inspired an entire generation of AI researchers, including myself, to get into the field. Garry is also a pro-democracy political thinker and leader, a fearless human-rights activist, and author of several books including How Life Imitates Chess which is a book on strategy and decision-making, Winter Is Coming which is a book articulating his opposition to the Putin regime, and Deep Thinking which is a book the role of both artificial intelligence and human intelligence in defining our future.  

EPISODE LINKS:

OUTLINE:

  • 0:00 – Introduction
  • 1:33 – Love of winning and hatred of losing
  • 4:54 – Psychological elements
  • 9:03 – Favorite games
  • 16:48 – Magnus Carlsen
  • 23:06 – IBM Deep Blue
  • 37:39 – Morality
  • 38:59 – Autonomous vehicles
  • 42:03 – Fall of the Soviet Union
  • 45:50 – Putin
  • 52:25 – Life

In this nature video explains Google’s recent announcement on reaching a major quantum computing milestone.

Google says they have reached ‘quantum supremacy’ with their quantum computer ‘Sycamore’. Nature reporter Elizabeth Gibney explains what this means, why IBM disagree, and the significance for quantum computing.

Read the full news article here: https://www.nature.com/articles/d41586-019-03213-z

Read the research paper here: https://www.nature.com/articles/s41586-019-1666-5

ExplainingComputers explores Edge computing definitions and concepts.

This non-technical video focuses on edge computing and cloud computing, as well as edge computing and the deployment of vision recognition and other AI applications.

Also introduced are mesh networks, SBC (single board computer) edge hardware, and fog computing.

Researchers at IBM have drafted some new algorithms designed specifically to take advantage of quantum computers’ unique properties. The only catch is that we still need to build the computer.

While designing algorithms before the computers themselves may sound backwards, this has happened before. Computational models for conventional computers date back to the 1800s when Charles Babbage and Ada Lovelace were pondering mechanical computing devices

From the article:

“We’ve developed a blueprint with new quantum data classification algorithms and feature maps. That’s important for AI because, the larger and more diverse a data set is, the more difficult it is to separate that data out into meaningful classes for training a machine learning algorithm. Bad classification results from the machine learning process could introduce undesirable results; for example, impairing a medical device’s ability to identify cancer cells based on mammography data.”

IBM has come up with a way to use quantum computers to improve machine learning algorithms, even though we don’t have anything approaching a quantum computer yet. The tech giant developed and tested a quantum algorithm for machine learning with scientists from Oxford University and MIT, showing how quantum […]