Collaborating Authors


An Introduction to Reinforcement Learning - Lex Fridman, MIT


We were delighted to be joined by Lex Fridman at the San Francisco edition of the Deep Learning Summit, taking part in both a'Deep Dive' session, allowing for a great amount of attendee interaction and collaboration, alongside a fireside chat with OpenAI Co-Founder & Chief Scientist, Ilya Sutskever. The MIT Researcher shared his thoughts on recent developments in AI and its current standing, highlighting its growth in recent years. Lex then referenced, Lee Sedol, the South Korean 9th Dan GO player, whom at this time is the only human to ever beat AI at a video game, which has since become somewhat of an impossible task, describing this feat as a seminal moment and one which changed the course of not only deep learning but also reinforcement learning, increasing the social belief in the subsection of AI. Since then, of course, we have seen video games and tactically based games, including Starcraft become imperative in the development of AI. The comparison of Reinforcement Learning to Human Learning is something which we often come across, referenced by Lex as something which needed addressing, with humans seemingly learning through "very few examples" as opposed to the heavy data sets needed in AI, but why is that?

Artificial intelligence that mimics the brain needs sleep just like humans, study reveals

The Independent - Tech

Artificial intelligence designed to function like a human could require periods of rest similar to those needed by biological brains. Researchers at Los Alamos National Laboratory in the US discovered that neural networks experienced benefits that were "the equivalent of a good night's rest" when exposed to an artificial analogue of sleep. "We were fascinated by the prospect of training a neuromorphic processor in a manner analogous to how humans and other biological systems learn from their environment during childhood development," said Yijing Watkins, a computer scientist at Los Alamos. The discovery was made by the team of researchers while working on a form of artificial intelligence designed to mimic how humans learn to see. The AI became unstable during long periods of unsupervised learning, as it attempted to classify objects using their dictionary definitions without having any prior examples to compare them to.

Artificial Intelligence: Reinforcement Learning in Python


Online Courses Udemy Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications Created by Lazy Programmer Team, Lazy Programmer Inc. English [Auto-generated], French [Auto-generated], 4 more Students also bought Bayesian Machine Learning in Python: A/B Testing Ensemble Machine Learning in Python: Random Forest, AdaBoost Machine Learning A-Z: Hands-On Python & R In Data Science Complete Python Developer in 2020: Zero to Mastery Natural Language Processing with Deep Learning in Python Preview this course GET COUPON CODE Description When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go.

Advanced AI: Deep Reinforcement Learning in Python


Online Courses Udemy Advanced AI: Deep Reinforcement Learning in Python, The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks Created by Lazy Programmer Team, Lazy Programmer Inc. English [Auto-generated], Indonesian [Auto-generated], 5 more Students also bought Deep Learning: Convolutional Neural Networks in Python Deep Learning: Recurrent Neural Networks in Python Unsupervised Machine Learning Hidden Markov Models in Python Bayesian Machine Learning in Python: A/B Testing Data Science: Supervised Machine Learning in Python Preview this course GET COUPON CODE Description This course is all about the application of deep learning and neural networks to reinforcement learning. If you've taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Reinforcement learning has been around since the 70s but none of this has been possible until now. The world is changing at a very fast pace.

For Pac-Man's 40th birthday, Nvidia uses AI to make new levels


Pac-Man turns 40 today, and even though the days of quarter-munching arcade machines in hazy bars are long behind us, the legendary game's still helping to push the industry forward. On Friday, Nvidia announced that its researchers have trained an AI to create working Pac-Man games without teaching it about the game's rules or giving it access to an underlying game engine. Nvidia's "GameGAN" simply watched 50,000 Pac-Man games to learn the ropes. That's an impressive feat in its own right, but Nvidia hopes the "generative adversarial network" (GAN) technology underpinning the project can be used in the future to help developers create games faster and train autonomous robots. "This is the first research to emulate a game engine using GAN-based neural networks," Nvidia researcher Seung-Wook Kim said in a press release.

NVIDIA's AI built Pac-Man from scratch in four days


When Pac-Man hit arcades on May 22nd 1980, it held the record for time spent in development having taken a whopping 17 months to design, code and complete. Now, 40 years later to the day, NVIDIA needed just four days to train its new GameGAN AI to wholly recreate it based only on watching another AI play through. Dubbed GameGAN, it's a generative adversarial network (hence, GAN) similar to those used to generate (and detect) photo-realistic images of people that do not exist. The generator is trained on a large sample dataset and then instructed to generate an image based on what it saw. The discriminator then compares the generated image to the sample dataset to determine how close the two resemble one another.

Neural Game Engine: Accurate learning of generalizable forward models from pixels Artificial Intelligence

Access to a fast and easily copied forward model of a game is essential for model-based reinforcement learning and for algorithms such as Monte Carlo tree search, and is also beneficial as a source of unlimited experience data for model-free algorithms. Learning forward models is an interesting and important challenge in order to address problems where a model is not available. Building upon previous work on the Neural GPU, this paper introduces the Neural Game Engine, as a way to learn models directly from pixels. The learned models are able to generalise to different size game levels to the ones they were trained on without loss of accuracy. Results on 10 deterministic General Video Game AI games demonstrate competitive performance, with many of the games models being learned perfectly both in terms of pixel predictions and reward predictions. The pre-trained models are available through the OpenAI Gym interface and are available publicly for future research here: \url{}

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning Machine Learning

In many real-world settings, a team of agents must coordinate its behaviour while acting in a decentralised fashion. At the same time, it is often possible to train the agents in a centralised fashion where global state information is available and communication constraints are lifted. Learning joint action-values conditioned on extra state information is an attractive way to exploit centralised learning, but the best strategy for then extracting decentralised policies is unclear. Our solution is QMIX, a novel value-based method that can train decentralised policies in a centralised end-to-end fashion. QMIX employs a mixing network that estimates joint action-values as a monotonic combination of per-agent values. We structurally enforce that the joint-action value is monotonic in the per-agent values, through the use of non-negative weights in the mixing network, which guarantees consistency between the centralised and decentralised policies. To evaluate the performance of QMIX, we propose the StarCraft Multi-Agent Challenge (SMAC) as a new benchmark for deep multi-agent reinforcement learning. We evaluate QMIX on a challenging set of SMAC scenarios and show that it significantly outperforms existing multi-agent reinforcement learning methods.

Alphabet's Next Billion-Dollar Business: 10 Industries To Watch - CB Insights Research


Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).