The last few years have seen a rise in novel differentiable graphics layers which can be inserted in neural network architectures. From spatial transformers to differentiable graphics renderers, these new layers leverage the knowledge acquired over years of computer vision and graphics research to build new and more efficient network architectures. Explicitly modeling geometric priors and constraints into neural networks opens up the door to architectures that can be trained robustly, efficiently, and more importantly, in a self-supervised fashion. At a high level, a computer graphics pipeline requires 3D objects and their absolute positioning in the scene, a description of the material they are made of, lights and a camera. This scene description is then interpreted by a renderer to generate a synthetic rendering.

DeepMind's artificial intelligence platforms have become legendary for their ability to master complex games like chess, shogi and Go, crushing our puny human brains with advanced machine learning techniques. Earlier this year a new version of the AI built for real-time strategy game StarCraft II, dubbed AlphaStar, was unveiled and carried on DeepMind's tradition of putting humans to shame, trampling some of the top human StarCraft II players in the world. On Wednesday, the DeepMind team published a new study of AlphaStar in the journal Nature, detailing just how far AlphaStar has come. And folks, it's bad news for any up-and-coming StarCraft II stars: The AI is now classed as a Grandmaster, which means it can beat 99.8% of all human players. Why would researchers build an AI for a niche video game title and what can it teach us about artificial intelligence and machine learning?

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Rudin et al., arXiv 2019 It's pretty clear from the title alone what Cynthia Rudin would like us to do! The paper is a mix of technical and philosophical arguments and comes with two main takeaways for me: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models. A model can be a black box for one of two reasons: (a) the function that the model computes is far too complicated for any human to comprehend, or (b) the model may in actual fact be simple, but its details are proprietary and not available for inspection. In explainable ML we make predictions using a complicated black box model (e.g., a DNN), and use a second (posthoc) model created to explain what the first model is doing. A classic example here is LIME, which explores a local area of a complex model to uncover decision boundaries.

Embedded vision solutions will be a key enabler for making automobiles fully autonomous. Giving an automobile a set of eyes – in the form of multiple cameras and image sensors – is a first step, but it also will be critical for the automobile to interpret content from those images and react accordingly. To accomplish this, embedded vision processors must be hardware optimized for performance while achieving low power and small area, have tools to program the hardware efficiently, and have algorithms to run on these processors. The significant automotive safety improvements in the past (e.g., shatter-resistant glass, three-point seatbelts, airbags), were passive safety measures designed to minimize damage during an accident. We now have technology that can actively help the driver avoid crashing in the first place.

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience.

DeepMind says it has created the first artificial intelligence to reach the top league of one of the most popular esport video games. It says Starcraft 2 had posed a tougher AI challenge than chess and other board games, in part because opponents' pieces were often hidden from view. Publication in the peer-reviewed journal Nature allows the London-based lab to claim a new milestone. But some pro-gamers have mixed feelings about it claiming Grandmaster status. DeepMind - which is owned by Google's parent company Alphabet - said the development of AlphaStar would help it develop other AI tools which should ultimately benefit humanity.

An artificial intelligence can now play the real-time strategy video game StarCraft II so well that it is better than 99.8 per cent of human players. The AI, called AlphaStar, was developed by tech firm DeepMind, which is owned by the same parent company as Google. AlphaStar played anonymously against human players in a series of online games on the official StarCraft II game server, Battle.net, and ended up ranked in the top 200 players for each of the leagues it competed in. StarCraft II is a popular science-fiction game that involves controlling armies and building infrastructure. Players must compromise between short-term payoffs and long-term gain.

An artificial intelligence (AI) system has reached the highest rank of StarCraft II, the fiendishly complex and wildly popular computer game, in a landmark achievement for the field. DeepMind's AlphaStar outperformed 99.8% of registered human players to attain grandmaster level at the game, which sees opponents build civilisations and battle their inventive, warmongering alien neighbours. The AI system mastered the game after 44 days of training, which involved learning from recordings of the best human players and then going up against itself and versions of the programme that intentionally tested its weaknesses. "AlphaStar has become the first AI system to reach the top tier of human performance in any professionally played e-sport on the full unrestricted game under professionally approved conditions," said David Silver, a researcher at DeepMind. More than $31m in prize money has been handed out from thousands of StarCraft II e-sport tournaments since the game was released in 2010. Players start with a small number of worker units that can gather resources, construct buildings, develop new units and technologies, and embark on scouting missions to gain intelligence on opponents.

The sheer amount of data that Google collects from its estimated 1.17 billion users is quite staggering. From your online browsing and search habits, places you go, what groceries you buy, and even your psychological and political leanings. This massive repository of data is mostly used for marketing, consumer research and more importantly, for predicting our behavior. From auto-completing your search terms to accurately guessing what consumer goods you're most likely to buy, Google's predictive algorithms are getting more powerful over time. With the ever-increasing role of artificial intelligence and deep machines in our lives, Google is increasingly applying its predictive expertise to the medical field.

The SKLearn lab will have a tutorial for sentiment analysis and mnist (via a Google Colab Notebook) with emphasis on how to improve performance, then time for students to try their own classifiers on a separate sentiment analysis task. The PyTorch lab willhave a tutorial on PyTorch and how to build feed-forward nets for the same tasks as in the Sklearn lab (with emphasis on how to improve performance), and time for students to try to build their own network for the separate sentiment analysis task.