In its latest step towards general-purpose AI systems, DeepMind has proposed XLand, a virtual environment, to formulate new learning algorithms, which control how agent trains and the games on which it trains. XLand was introduced via a paper titled, "Open-Ended Learning Leads to Generally Capable Agents", in which DeepMind researchers demonstrated a technique to train an agent capable of playing many different games without requiring human interaction data The repetitive process of trial and error has proven effective in teaching computer systems to play many games, including chess, shogi, Go, and StarCraft II. However, one of the main challenges with reinforcement learning-trained systems is a lack of training data. Systems trained by reinforcement learning are unable to adapt their learned behaviours to new tasks because they are not trained on a broad enough set of tasks. For instance, AlphaZero performed well against some of the world's best chess, shogi, and Go programmes even though it was aware of only the game's basic rules.

GPT-3 is probably a foundational layer for the future because it was developed using huge quantities of training data and computer power to reach state-of-the-art general-purpose performance. This has been brought to the attention of the AI community by Percy Liang. Foundational models create "a single point of failure, so any defects, any biases which these models have, any security vulnerabilities . . .

They coined a new term, "Foundation Models" to characterize the new paradigm, joined forces in a "Center for Research on Foundation Models", and published the massive 212-page report "On the Opportunities and Risks of Foundation Models." Although the term is new, the general approach is not. You train a big neural network (like the well-known GPT-3) on an enormous amount of data, and then you adapt ("fine-tune") the model to a bunch of more specific tasks (in the words of the report, "a foundation model ...[thus] serves as [part of] the common basis from which many task-specific models are built via adaptation"). The basic model thus serves as the "foundation" (hence the term) of AIs that carry out more specific tasks. The approach started to gather momentum in 2018, when Google developed the natural language processing model called BERT, and it became even more popular with the introduction last year of OpenAI's GPT-3.

The Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence modeling. This includes time series analysis, forecasting and natural language processing (NLP). Learn about why RNNs beat old-school machine learning algorithms like Hidden Markov Models. The basics of machine learning and neurons (just a review to get you warmed up!) Neural networks for classification and regression (just a review to get you warmed up!) How to predict stock prices and stock returns with LSTMs in Tensorflow 2 (hint: it's not what you think!) All of the materials required for this course can be downloaded and installed for FREE.

In this work, we propose an algorithm performing short-term predictions of the flux of vehicles on a stretch of road, using past measurements of the flux. This algorithm is based on a physics-aware recurrent neural network. A discretization of a macroscopic traffic flow model (using the so-called Traffic Reaction Model) is embedded in the architecture of the network and yields flux predictions based on estimated and predicted space-time dependent traffic parameters. These parameters are themselves obtained using a succession of LSTM ans simple recurrent neural networks. Besides, on top of the predictions, the algorithm yields a smoothing of its inputs which is also physically-constrained by the macroscopic traffic flow model. The algorithm is tested on raw flux measurements obtained from loop detectors.

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.

Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert

It is expected that by 2050, the application of this technology can reduce vehicle emissions by 50%, and the road traffic casualty rate will be close to zero [1]. For industry players, the main testing method is the real vehicle road test. However, Kalra et al. [2] of RAND Corporation conclude that at the 95% confidence level, road testing of more than 14.2 billion km is required to prove that the fatality rate of autonomous vehicles is 20% lower than that of human drivers. Therefore, virtual testing will be the primary way of validation and verification of autonomous vehicles. Reinforcement Learning (RL) agents learn by interacting with the environment, adjust their policy by obtaining rewards, and maximize the reward function by balancing exploration and exploitation, expecting to find the optimal policy corresponding to the maximum cumulative reward [3]. Deep Reinforcement Learning (DRL), combining the perception capability of Deep Learning (DL) and the decision-making capability of RL [4], is suitable for solving the autonomous driving decision-making problem, which is a typical application of timeseries decisions in a complex environment. Many existing studies apply DRL to the intersection [5], lane changing [6], [7] scenarios, etc. Still, to the best of our knowledge, there is no standardized system for training and testing scenarios, evaluation metrics, and baseline methods performance comparisons.

With the development of social platforms, people are more and more inclined to combine into groups to participate in some activities, so group recommendation has gradually become a problem worthy of research. For group recommendation, an important issue is how to obtain the characteristic representation of the group and the item through personal interaction history, and obtain the group's preference for the item. For this problem, we proposed the model GIP4GR (Graph Neural Network with Interaction Pattern For Group Recommendation). Specifically, our model use the graph neural network framework with powerful representation capabilities to represent the interaction between group-user-items in the topological structure of the graph, and at the same time, analyze the interaction pattern of the graph to adjust the feature output of the graph neural network, the feature representations of groups, and items are obtained to calculate the group's preference for items. We conducted a lot of experiments on two real-world datasets to illustrate the superior performance of our model.

An essential part of the scientific research process is the publication of the obtained results at a suitable venue, i.e., a particular conference, workshop, or journal. The related selection problem for the best fitting scientific venue has many different aspects, such as the fit of the research topics, the prospects of acceptance, and the prestige of the venue. The complexity of the selection is further exacerbated by the growing number of publication venues, the increasing granularity of research topics, and the exponentially surging number of publications. To support researchers with this task, different methods have been proposed, e.g., based on Latent Dirichlet Allocation [9], hybrid approaches incorporating social networks [14, 13], or procedures that draw from background ontologies [20, 16]. Moreover, recent approaches based on deep learning methods achieved high accuracy in recommendations [6]. All these methods have in common that their recommendations are insufficiently explained. For example, Kobs et al. [6] solely highlight words from the input article that were essential for a recommendation. With the present work we show a new approach for recommending venues that improves on explainability. From the information a scientist provides, such as paper title, abstract and, possibly, a list of keywords, our method creates a ranking over k thematically fitting venues.