Perception as prediction using general value functions in autonomous driving applications Daniel Graves, Kasra Rezaee †, and Sean Scheideman ‡ Abstract -- We propose and demonstrate a framework called perception as prediction for autonomous driving that uses general value functions (GVFs) to learn predictions. Perception as prediction learns data-driven predictions relating to the impact of actions on the agent's perception of the world. It also provides a data-driven approach to predict the impact of the anticipated behavior of other agents on the world without explicitly learning their policy or intentions. We demonstrate perception as prediction by learning to predict an agent's front safety and rear safety with GVFs, which encapsulate anticipation of the behavior of the vehicle in front and in the rear, respectively. The safety predictions are learned through random interactions in a simulated environment containing other agents. We show that these predictions can be used to produce similar control behavior to an LQR-based controller in an adaptive cruise control problem as well as provide advanced warning when the vehicle behind is approaching dangerously. The predictions are compact policy-based predictions that support prediction of the long term impact on safety when following a given policy. We analyze two controllers that use the learned predictions in a racing simulator to understand the value of the predictions and demonstrate their use in the real-world on a Clearpath Jackal robot and an autonomous vehicle platform. I NTRODUCTION Understanding the world by learning predictions and using those predictions to act intelligently in the world is becoming an important topic of research, cf [1][2][3][4][5][6]. Modern theory of the brain shows that we are predictive machines that constantly try to match incoming sensory inputs with predictions [7].

Recent findings in neuroscience suggest that the human brain represents information in a geometric structure (for instance, through conceptual spaces). In order to communicate, we flatten the complex representation of entities and their attributes into a single word or a sentence. In this paper we use graph convolutional networks to support the evolution of language and cooperation in multi-agent systems. Motivated by an image-based referential game, we propose a graph referential game with varying degrees of complexity, and we provide strong baseline models that exhibit desirable properties in terms of language emergence and cooperation. We show that the emerged communication protocol is robust, that the agents uncover the true factors of variation in the game, and that they learn to generalize beyond the samples encountered during training.

We propose a new Graph Neural Network that combines recent advancements in the field. We give theoretical contributions by proving that the model is strictly more general than the Graph Isomorphism Network and the Gated Graph Neural Network, as it can approximate the same functions and deal with arbitrary edge values. Then, we show how a single node information can flow through the graph unchanged.

Ea ch process description is shaped like a formalized business policy consisting of the following set of features: - the file(s) to be processed; - the software that carries out the processing; - the purpose of the processing; - the entities that can access the results of the processing; - the details of where the results are stored and for how long; - the obligations that are fulfilled while (or before) carrying out the processing; - the legal basis of the processing. It is not hard to see that the first five elements in the above list match SPECIAL's usage policy language (UPL) introduced in Section 3. As far as the above elements are concerned, the only difference between UPL expressions and a business policy is the granularity of attribute values. Fo r example, the involved data (specified in the first element of the above list) are not expressed as a general, content-oriented category, but rather as a concrete set of data sourc es or data items. Such objects can be modeled as instances or subclasses of the general data categories illustrated in Section 3, thereby creating a link between digital artifacts and usage policies. Similar considerations hold for the other a t-tributes: - processing is not necessarily described in the abstract terms adopted by the processing vocabulary introduced in Section 3; in a business policy, this can be specified by naming concrete software procedures; - the purpose of data processing may be directly related to the data controller's mission and products; - recipients may consist of a concrete list of legal and/or physical persons, as opposed to general categories such as Ours or ThirdParty; - storage may be specified by a list of specific data repositories, at the level of files and hosts. With this level of granularity, specific authorizations can be derived from the business policy, for example: The indicated software procedure can read the indicated data sources. The results can be written in the specified repositories. The specified recipients can read the repositories...

The American Physical Society (APS) has recognized a summer intern at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) for producing an outstanding research poster at the world-wide APS Division of Plasma Physics (DPP) gathering last October. The student, Marco Miller, a senior at Columbia University majoring in applied physics, used machine learning to accelerate a leading PPPL computer code known as XGC as a participant in the DOE's Summer Undergraduate Laboratory Internship (SULI) program in 2019. The modifications, which will enable the XGC code to calculate more quickly, could help expand the physics included in detailed simulations of the plasma that fuels fusion reactions. The poster, prepared under the mentorship of PPPL physicist Michael Churchill, showed how Miller used machine learning techniques in his research and was presented at the APS-DPP conference in Fort Lauderdale, Florida. "It felt great to get the award," Miller said.

We are in the midst of an unprecedented surge of interest in machine learning (ML) and artificial intelligence (AI) technologies. These tools, which allow computers to make data-derived predictions and automate decisions, have become part of daily life for billions of people. Ubiquitous digital services such as interactive maps, tailored advertisements, and voice-activated personal assistants are likely only the beginning. Some AI advocates even claim that AI's impact will be as profound as "electricity or fire" that it will revolutionize nearly every field of human activity. This enthusiasm has reached international development as well.

HMD Global has announced that its new Nokia 2.3 smartphone is now available for pre-order in the United States. The Android 10 device offers a 6.2″ HD screen and a 13MP/2MP dual camera with a "Recommended Shot" feature that will automatically take a few additional photos before and after the camera shudder is pressed. The feature is supposed to help users select the optimal version of their photo, which is to say that it will recommend the shot in which everyone is smiling and generally looks their best. Other Nokia 2.3 highlights include a dedicated Google Assistant button and a battery that can last for up to two days thanks to its Adaptive Battery technology. The tech uses AI to gain a better understanding of a user's app behavior and optimize the performance of the phone.

As part of an annual look at global AI investment trends, CB Insights today reported that AI startups raised a record $26.6 billion in 2019, spanning more than 2,200 deals worldwide. That's compared to roughly 1,900 deals totaling $22.1 billion in 2018 and about 1,700 deals totaling $16.8 billion in 2017. The reported high recorded by CB Insights in the AI in Numbers report is in line with analysis by other organizations keeping an eye on investment in the AI ecosystem. The National Venture Capital Association earlier this month said that although overall venture capital spending took a dip last year, investors spent a record $18.4 billion on AI startups in the United States in 2019. With investment highest in fields like autonomous driving, drug research, finance, and facial recognition, the AI Index 2019 report released last month found more than $70 billion in global private investment in AI.

You've spent months studying data science, now it's time to find a job in the industry. Fortunately, companies all over the world are looking to hire data scientists -- and fast. According to LinkedIn's 2020 U.S. Emerging Jobs Report, skills related to Machine Learning, Deep Learning, TensorFlow, Python, Natural Language Processing, etc. seen more than 70% annual growth. According to an IBM survey, the openings for data and analytics talent in the US will continue to increase, reaching 133% growth in 2020, and creating more than 700,000 openings. Qualified candidates will have a multitude of vacancies to choose from when ready to seek out a new position in the field.

Casino executives, industry analysts and lawyers attended a conference at the UNLV Boyd School of Law to consult on how biometrics, AI and machine learning could shape the future of Las Vegas casinos, writes the Nevada Independent. While there are many opportunities for the gaming industry, most machine learning and facial recognition-enabled product ideas addressed customer service and customer recognition. These include slot machines that leverage facial biometrics to recognize important or banned players, and reduce fraud attempts, or facial recognition-equipped tables to help pit managers identify and track known players. "What we're seeing is this introduction of technology into the gaming industry in ways we've never seen before, and because of it, it started to raise issues -- or questions -- as to how this works and what the ramifications could be for things like patron privacy, anonymity and data protection," said Anthony Cabot, Distinguished Fellow in Gaming Law at the UNLV Boyd School of Law and event organizer. While speakers focused on presentations about competing laws and technology problems, there was not enough discussion on how to solve these problems, according to the report, yet Cabot hopes the gaming industry and regulators will join forces to deliver solutions.