AlphaGo Zero: The Most Significant Research Advance in AI

#AlphaGoZero: The Most Significant Research Advance in #AI  #AlphaGo

  • The previous version of AlphaGo beat the human world champion in 2016.
  • The new AlphaGo Zero beat the previous version by 100 games to 0, and learned Go completely on its own.
  • Here is the Nature paper explaining technical details (also PDF version: Mastering the Game of Go without Human Knowledge One of the main reasons for success was the use of a novel form of Reinforcement learning in which AlphaGo learned by playing itself.The system starts with a neural net that…
  • It plays millions of games against itself and tuned the neural network to predict next move and the eventual winner of the games.The updated neural network was merged with the Monte Carlo Tree Search algorithm to create a new and stronger version of AlphaGo Zero, and the process resumed.
  • In each iteration, the performance improved by a small amount, but because it can play millions of games a day, AlphaGo Zero surpassed thousands of years of human knowledge of Go in just 3 days., from DeepMind post This is a hugely significant advance for AI and Machine Learning research.Here…


The previous version of AlphaGo beat the human world champion in 2016. The new AlphaGo Zero beat the previous version by 100 games to 0, and learned Go completely on its own. We examine what this means for AI.
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Five years ago, artificial intelligence was struggling to identify cats. Now it’s trying to tackle 5000 species — Quartz

Five years ago, #AI was struggling to identify cats. Now it’s trying to tackle 5000 species

  • Google’s neural network, software which uses statistics to approximate how the brain learns, taught itself to detect the shapes of cats and humans with more than 70% accuracy.
  • “Over the last five years it’s been pretty incredible, the progress of deep [neural] nets,” says Grant Van Horn, lead competition organizer and graduate student at California Institute of Technology.
  • Van Horn says this latest Google competition differs from ImageNet, which forces algorithms to identify a wide variety of objects like cars and houses and boats, because iNat requires AI to examine the “nitty-gritty details” that separate one species from another.
  • On a scale from general image recognition (ImageNet) to specific (facial recognition,where most faces generally look the same and only slight variations matter), iNat lies somewhere in the middle, Van Horn says.
  • Van Horn, who has specialized in building AI that distinguishes differences between birds, said that the iNat competition illustrates how AI is beginning to help people learn about the world around them, rather than just help them organize their photos, for instance.

In 2012, Google made a breakthrough: It trained its AI to recognize cats in YouTube videos. Google’s neural network, software which uses statistics to approximate how the brain learns, taught itself to detect the shapes of cats and humans with more than 70% accuracy.  It was a 70% improvement over any other machine learning at the time. Five years later,…
Continue reading “Five years ago, artificial intelligence was struggling to identify cats. Now it’s trying to tackle 5000 species — Quartz”

Automotive Technology Solutions Overview

  • DRIVER’S ED FOR SELF-DRIVING CARS: HOW OUR DEEP LEARNING TECH TAUGHT A CAR TO DRIVE
  • Now we can successfully train a CNN to operate on NVIDIA DRIVE PX and understand the contextual rules-of-engagement between a vehicle and the road.
  • Subscribe and get the latest news on NVIDIA automotive solutions.
  • Giving Cars the Power to See, Think, and Learn
  • Driverless cars in the first global autonomous motorsports competition, Roborace Championship, will be powered by the NVIDIA DRIVE PX 2 AI supercomputers.

Paving the way for autonomous cars, NVIDIA DRIVE car computers helps cars see, think, and learn.
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AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

#AI, #Deep Learning, and #MachineLearning: A Primer – Andreessen Horowitz

  • Things are clearly progressing rapidly when it comes to machine intelligence.
  • From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.
  • Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.
  • Now to put the fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge.
  • “One person, in a literal garage, building a self-driving car.”

Read the full article, click here.


@MikeTamir: “#AI, #Deep Learning, and #MachineLearning: A Primer – Andreessen Horowitz”


“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.


AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

The Evolution Of Computer Science [INFOGRAPHIC]

Quite cool 😉

The Evolution Of Computer Science [INFOGRAPHIC] 

 #fintech #AI @valuewalk

  • Learn more about the history of computer science from this infographic.
  • It was the first large-scale electronic computer, and it was operated using the same type of technology as that automatic fabric loom over 140 years prior- punch cards.
  • It’s hard to imagine that just a few decades ago computers filled entire rooms, but advancements in technology and research have allowed for the development of the modern marvels we enjoy today.
  • Then in 1801 an automatic fabric loom was invented that used punch cards to control the pattern of the fabric.
  • There were a lot of developments that had to happen to get us to this point, and it all started with the invention of the binary number system in 1703 by Gottfried Leibniz, who was considered by many to be the first computer scientist.

Read the full article, click here.


@SpirosMargaris: “Quite cool 😉

The Evolution Of Computer Science [INFOGRAPHIC]

#fintech #AI @valuewalk”


Computer Science: By 1945 ENIAC had come on to scene, filling a 20 by 40 foot room with 18,000 vacuum tubes. It was the first large-scale electronic computer


The Evolution Of Computer Science [INFOGRAPHIC]

AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

#AI, #DeepLearning, and #MachineLearning: a 45-minute video primer at  #BigData #DataScience

  • Things are clearly progressing rapidly when it comes to machine intelligence.
  • From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.
  • Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.
  • Now to put the fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge.
  • “One person, in a literal garage, building a self-driving car.”

Read the full article, click here.


@KirkDBorne: “#AI, #DeepLearning, and #MachineLearning: a 45-minute video primer at #BigData #DataScience”


“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.


AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

AI, Deep Learning, and Machine Learning: A Primer

  • Things are clearly progressing rapidly when it comes to machine intelligence.
  • From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.
  • Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.
  • Now to put the fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge.
  • “One person, in a literal garage, building a self-driving car.”

Read the full article, click here.


@republicofmath: “AI, Deep Learning, and Machine Learning: A Primer”


“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.


AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz