Advances in generative modeling and adversarial learning have given rise to renewed interest in differentiable two-players games, with much of the attention falling on generative adversarial networks (GANs). Solving these games introduces distinct challenges compared to the standard minimization tasks that the machine learning (ML) community is used to. A symptom of this issue is ML and deep learning (DL) practitioners using optimization tools on game-theoretic problems. Recent work seeks to rectify this situation by bringing game theoretic tools into ML. At NeurIPS 2018 we held "Smooth games optimization in ML", a workshop with this scope and goal in mind.

By Prof. Lyle Ungar, University of Pennsylvania Artificial Intelligence (AI) is poised to revolutionize healthcare. The greatest effects in terms of market size, the use of AI in areas such as administrative workflow and fraud detection, will be almost invisible to healthcare consumers, but customer-facing AI will gradually become more prevalent, allowing people to use their phones to share images, blood pressure readings, and other information and receive automated advice. Such changes will be particularly important in rural regions, where access to doctors is more limited. The most impressive successes to date in the use of AI for healthcare have come in the automatic interpretation of medical images such as cat scans and magnetic resonance imaging (MRI) images. The most common AI "deep learning" models simply take a set of inputs (e.g.

Transfer learning is one of the most impactful recent breakthroughs in Natural Language Processing. Less than a year after its release, Google's BERT and its offspring (RoBERTa, XLNet, etc.) dominate most of the NLP leaderboards. While it can be a headache to put these enormous models into production, various solutions exist to reduce their size considerably. At NLP Town we successfully applied model distillation to train spaCy's text classifier to perform almost as well as BERT on sentiment analysis of product reviews. Recently the standard approach to Natural Language Processing has changed drastically.

Biomedical engineers at Duke University have devised a method for increasing the resolution of optical coherence tomography (OCT) down to a single micrometer in all directions, even in a living patient. The new technique, called optical coherence refraction tomography (OCRT), could improve medical images obtained in the multibillion-dollar OCT industry for medical fields ranging from cardiology to oncology. The results appear in a paper published online on August 19 in the journal Nature Photonics. "An historic issue with OCT is that the depth resolution is typically several times better than the lateral resolution," said Joseph Izatt, the Michael J. Fitzpatrick Professor of Engineering at Duke. "If the layers of imaged tissues happen to be horizontal, then they're well defined in the scan. But to extend the full power of OCT for live imaging of tissues throughout the body, a method for overcoming the tradeoff between lateral resolution and depth of imaging was needed."

This exhibition and events programme by artists Anna Dumitriu and Alex May critically explores robotics, artificial intelligence, machine learning and bio-computation through a series of installations and robotic artworks. The title and subject of the exhibition takes inspiration from Alan Turing's unpublished 1948 essay of the same name, which was in effect the first manifesto of artificial intelligence. The wider programme aims to act as an antidote to contemporary hype around deep learning and warnings about future AI winters by exploring ongoing research in the fields in artificial life, computational neuroscience and consciousness research, and their foundations. This event will comprise a series of short talks laying out contemporary research in computer science, artificial life, computational neuroscience and consciousness research from both scientific and artistic perspectives, followed by a panel discussion and audience questions. We will discuss the contemporary hype around deep learning and prophesies about impending AI winters by exploring ongoing research in the fields in artificial life, computational neuroscience, consciousness studies and robotics.

Mani Sarkar is a passionate developer mainly in the Java/JVM space, currently strengthening teams and helping them accelerate when working with small teams and startups, as a freelance software engineer/data/ml engineer. A Java Champion, software crafter, JCP Member, OpenJDK contributor, thought leader in the LJC and other developer communities and involved with @adoptopenjdk, @graalvm and other F/OSS projects. Writes code, not just on the Java/JVM platform but in other programming languages, hence likes to call himself a polyglot developer. He sees himself working in the areas of core Java, JVM, JDK, Hotspot, Graal, GraalVM, Truffle, VMs, Performance Tuning, Data and Machine Learning technologies. An advocate of a number of agile and software craftsmanship practices and a regular at many talks, conferences (Devoxx, VoxxedDays) and hands-on-workshops – speaks, participates, organises and helps out at many of them.

I expect to suffer some degree of writer's block pretty much every day for the rest of my life. I'm a journalist and a novelist; it comes with the territory. But I have a feeling I'm going to suffer less from now on, thanks to my new best friend, GPT-2. Let me back up a bit: Six months ago, the research lab OpenAI created an AI system that generates text -- from fake news to poetry -- that in some cases actually sounds like it's written by a human being. The OpenAI team has been rolling it out in stages, each time giving us a more powerful version of the language model they dubbed GPT-2, and carefully watching to see how we use it.

What we usually think of as Artificial Intelligence (AI) today (when we see human-like robots and holograms in our fiction, talking and acting like real people and having human-level or even superhuman intelligence and capabilities) is actually called Strong Artificial General Intelligence (AGI), and it does NOT exist anywhere on earth yet. What we actually have for AI today is much simpler and much more narrow Deep Learning (DL) that can only do some very specific tasks better than people. It has fundamental limitations that will not allow it to become Artificial General Intelligence, so if that is our goal, we need to innovate and come up with better networks and better methods for shaping them into an artificial brain. DL uses deep'neural' networks (DNNs) that really have very little in common with biological neurons. They are just summation units with an activation function feeding a static number to connections that instantly'communicate' that number to all the'neurons' in the next layer, each modulated by the weight of that connection.

Mourafiq: This talk is going to be about how to automate machine learning and deep learning workflows and processes. Before I start, I will talk a bit about myself. My name is Mourad [Mourafiq], I have a background in computer science and applied mathematics. I've been involved and working in the tech industry and the banking industry for the last eight years, and I've been involved in different roles involving mathematical modeling, software engineering, data analytics, data science. For the last two years, I've been working on a platform to automate and I manage the whole life cycle of machine learning and the model management, called Polyaxon. Since I will be talking about a lot of processes and best practices and ideas to basically streamline your model managements at work, I'll be referring a lot to Polyaxon as an example of a tool for doing these data science workflows. Several approaches and solutions are based on my own experience developing this tool, and talking with customers and the community users since the platform is open source. Polyaxon is a platform that tries to solve the machine learning life cycle. Basically, it tries to automate as much as possible so that you can iterate as fast as possible on your model production and model deployments. It has a no lock-in feature.

Generating sequences of musical notes from lyrics might sound like the stuff of science fiction, but thanks to AI, it might someday become as commonplace as internet radio. In a paper published on the preprint server Arxiv.org "Melody generation from lyrics has been a challenging research issue in the field of artificial intelligence and music, which enables to learn and discover latent relationship between interesting lyrics and accompanying melody," wrote the paper's coauthors. "With the development of available lyrics and melody dataset and [AI], musical knowledge mining between lyrics and melody has gradually become possible." As the researchers explain, notes have two musical attributes: pitch and duration.