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ARTIFICIAL INTELLIGENCE

| Trends and upcoming inventions in the world of robotics, Artificial intelligence (AI), and computer simulation
 
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Everybody Dance Now

 
 
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Peter Thiel on Trump, Gawker, and Leaving Silicon Valley (Full Interview)

Peter Thiel (entrepreneur) joins Dave Rubin to discuss creating PayPal, why he moved his company out of SIlicon Valley, political correctness, the changing media landscape, his company ‘Palantir,’ libertarianism, his views on Trump, his take down of Gawker, and much more.
 
 
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Details of Artificial Intelligence Threat Detection from Microsoft Windows Defender Research

Microsoft highlights their use of Artificial Intelligence(AI), and Machine Learning(ML), to detect a February 3, 2018 outbreak of the Emotet virus. Their use of gradient boosting, ensemble models, feature vectors, and client-side and cloud machine learning models are discussed.
 
 
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Joe Rogan Experience #1169 - Elon Musk

Elon Musk is a business magnet, investor and engineer.
 
 
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Joe Rogan Experience #938 - Lawrence Krauss

 
 
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The Cyborg Beetles Designed to Save Human Lives

In the future, we may have remote-controlled insects to reach places humans cannot. At least that's what Dr. Hirotaka Sato, an aerospace engineer from Singapore, is hoping. Motherboard went to Dr. Sato's lab in Singapore to take one of his cyborg beetles for a test flight.
 
 
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Ubisoft Using Artificial Intelligence to Catch Bugs

Ubisoft announced that its Commit Assistant AI tool catches bugs before programmers make the decision.

Ubisoft said that as much of 70% of development costs come from development phase bugs that can be caught by the tool.
 
 
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How did Pew Research Center identify Twitter bots?

In the context of ongoing debates over the role and nature of bots, Pew Research Center set out to better understand how many of the links being shared on Twitter are being promoted by bots rather than humans. Our analysis found that an estimated two-thirds of tweeted links to popular websites ar...
 
 
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Ian Goodfellow, Research Scientist OpenAI : Generative Adversarial Networks (GANs) #AIWTB 2016

Ian Goodfellow, Research Scientist at OpenAI takes us through Generative Adversarial Networks at AI With The Best online developer conference September 2016 #AIWTB. Followed by a super Q&A session Q&A 28:40 Can you explain the problem about GANs not knowing how to place body parts and t...
 
 
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Generative Adversarial Nets - Fresh Machine Learning #2

This episode of Fresh Machine Learning is all about a relatively new concept called a Generative Adversarial Network. A model continuously tries to fool another model, until it can do so with ease. At that point, it can generate novel, authentic looking data! Very exciting stuff. The demo code f...
 
 
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Canonical Evolution Strategies Beat Q*bert in an Unexpected Way

Machine learning researchers using canonical evolution strategies were able to rack up near limitless points in the timeless video game Q*bert.

The solution exploits a bug in the game play where Q*bert does not ever complete the second level, but somehow jumps around collecting points.
 
 
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AI "Stop Button" Problem - Computerphile

How do you implement an on/off switch on a General Artificial Intelligence? Rob Miles explains the perils.
 
 
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Augmented Intelligence and High Frequency Trading Algos

Speaker Bio: Dr. Sean Gourley is the founder and CTO of Quid. He is a Physicist by training and has studied the mathematical patterns of war and terrorism. He is building tools to augment human intelligence. Description of Talk: The speed of human strategic thinking is fundamentally limited ...
 
 
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Deep Neural Networks for Bot Detection

Researchers have used contextual long short-term memory (LSTM) and deep neural networks to detect bots at the tweet level using both content of the message and metadata about the user.
 
 
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Details of AI Threat Detection from Microsoft Windows Defender Research

Microsoft highlights their use of Artificial Intelligence(AI), and Machine Learning(ML), to detect a February 3, 2018 outbreak of the Emotet virus. Their use of gradient boosting, ensemble models, feature vectors, and client-side and cloud machine learning models are discussed.
 



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