Our research explored a new approach to an old problem: we reformulated principal component analysis (PCA), a type of eigenvalue problem, as a competitive multi-agent game we call EigenGame.

In comparison to some other sports, football has been rather late with starting to systematically collect large sets of data for scientific analytics purposes aiming to progress teams' gameplay. This is for several reasons, with the most prominent being that there are far less controllable settings of the game compared to other sports (large outdoor pitch, dynamic game, etc.), and also the dominant credo to rely mainly on human specialists with track records and experience in professional football. On these lines, Arrigo Sacchi, a successful Italian football coach and manager who never played professional football in his career, responded to criticism over his lack of experience with his famous quote when becoming a coach at Milan in 1987: "I never realised that to be a jockey you had to be a horse first." Football Analytics poses challenges that are well suited for a wide variety of AI techniques, coming from the intersection of 3 fields: computer vision, statistical learning and game theory (visualised in Figure 2). While these fields are individually useful for football analytics, their benefits become especially tangible when combined: players need to take sequential decision-making in the presence of other players (cooperative and adversarial) and as such game theory, a theory of interactive decision making, becomes highly relevant.

Whether it's talking AI or smarter chatbots, Google has spent the last several years teaching AI how to communicate better with humans. Now, the company is showing off its latest research that could take these efforts to the next level. The company previewed LaMDA ("Language Model for Dialogue Applications"), research it says represents a "breakthrough conversation technology" that will one day enable people to have natural, open-ended conversations about any topic with Google's AI. The technology is still in a research phase, but it could have huge implications for existing Google products like search and Assistant. While existing chatbots are often trained on a specific topic or programmed to give canned responses, LaMDA "can engage in a free-flowing way about a seemingly endless number of topics," according to Google.

We asked GPT-3 questions typically reserved for Google. The results will shock you. One of my highlights from last week was getting access to the GPT-3 beta. Of course, our first instinct was to chat with it and to explore ways to possibly deploy it on Chai, but during a meeting we had a question and it occurred to us that GPT-3 may know the answer. We asked it "Who are the top 10 VCs?".

Understanding pedestrian crossing behavior is an essential goal in intelligent vehicle development, leading to an improvement in their security and traffic flow. In this paper, we developed a method called IntFormer. It is based on transformer architecture and a novel convolutional video classification model called RubiksNet. Following the evaluation procedure in a recent benchmark, we show that our model reaches state-of-the-art results with good performance ($\approx 40$ seq. per second) and size ($8\times $smaller than the best performing model), making it suitable for real-time usage. We also explore each of the input features, finding that ego-vehicle speed is the most important variable, possibly due to the similarity in crossing cases in PIE dataset.

Learn to write programs using the foundational AI algorithms powering everything from NASA's Mars Rover to DeepMind's AlphaGo Zero. Learn to write AI programs using the algorithms powering everything from NASA's Mars Rover to DeepMind's AlphaGo Zero.

GPT-3 from OpenAI has captured public attention unlike any other AI model in the 21st century. The sheer flexibility of the model in performing a series of generalized tasks with near-human efficiency and accuracy is what makes it so exciting. It has created a paradigm shift in the world of Natural Language Processing(NLP), where till now the models were trained based on the ungeneralized approach to excel at one or two tasks. GPT-3 is the first step towards democratizing access to technology. It enables audiences from all walks of life to solve complex technical problems from the comfort of a user-friendly interface, which allows you to design training prompts for specific AI problems using natural language.

The advent of Transformers in 2017 completely changed the world of neural networks. Ever since, the core concept of Transformers has been remixed, repackaged, and rebundled in several models. The results have surpassed the state of the art in several machine learning benchmarks. In fact, currently all top benchmarks in the field of natural language processing are dominated by Transformer-based models. Some of the Transformer-family models are BERT, ALBERT, and the GPT series of models.

Analogies play a crucial role in commonsense reasoning. The ability to recognize analogies like "eye is to seeing what ear is to hearing," sometimes referred to as analogical proportions, shape how humans structure knowledge and understand language. In a new study that looks at whether AI models can understand analogies, researchers at Cardiff University used benchmarks from education as well as more common datasets. They found that while off-the-shelf models can identify some analogies, they sometimes struggle with complex relationships, raising questions about to what extent models capture knowledge. Large language models learn to write humanlike text by internalizing billions of examples from the public web.

Now a team of Google researchers has published a proposal for a radical redesign that throws out the ranking approach and replaces it with a single large AI language model, such as BERT or GPT-3--or a future version of them. The idea is that instead of searching for information in a vast list of web pages, users would ask questions and have a language model trained on those pages answer them directly. Search engines have become faster and more accurate, even as the web has exploded in size. AI is now used to rank results, and Google uses BERT to understand search queries better. Yet beneath these tweaks, all mainstream search engines still work the same way they did 20 years ago: web pages are indexed by crawlers (software that reads the web nonstop and maintains a list of everything it finds), results that match a user's query are gathered from this index, and the results are ranked.