Journal of Artificial Intelligence Research

Journal of Artificial Intelligence Research

JAIR is published by AI Access Foundation, a nonprofit public charity whose purpose is to facilitate the dissemination of scientific results in artificial intelligence. JAIR, established in 1993, was one of the first open-access scientific journals on the Web, and has been a leading publication venue since its inception. We invite you to check out our other initiatives.


Deep Learning, Knowledge Representation and Reasoning

Journal of Artificial Intelligence Research

The recent success of deep neural networks at tasks such as language modelling, computer vision, and speech recognition has attracted considerable interest from industry and academia. Achieving a better understanding and widespread use of such models involves the use of Knowledge Representation and Reasoning together with sound Machine Learning methodologies and systems. The goal of this special track, which closed in 2017, was to serve as a home for the publication of leading research in deep learning towards cognitive tasks, focusing on applications of neural computation to advanced AI tasks requiring knowledge representation and reasoning.


Columbia University DSI Alumni Use Machine Learning to Discover Coronavirus Treatments - insideBIGDATA

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Two graduates of the Data Science Institute (DSI) at Columbia University are using computational design to quickly discover treatments for the coronavirus. Andrew Satz and Brett Averso are chief executive officer and chief technology officer, respectively, of EVQLV, a startup creating algorithms capable of computationally generating, screening, and optimizing hundreds of millions of therapeutic antibodies. They apply their technology to discover treatments most likely to help those infected by the virus responsible for COVID-19.


Machine Learning in Python: Principal Component Analysis (PCA) for Handling High-Dimensional Data

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Machine Learning in Python: Principal Component Analysis (PCA) for Handling High-Dimensional Data In this video, I will be showing you how to perform principal component analysis (PCA) in Python using the scikit-learn package. PCA represents a powerful learning approach that enables the analysis of high-dimensional data as well as reveal the contribution of descriptors in governing the distribution of data clusters. Particularly, we will be creating PCA scree plot, scores plot and loadings plot. This video is part of the [Python Data Science Project] series. If you're new here, it would mean the world to me if you would consider subscribing to this channel.


AI at the Edge Still Mostly Consumer, not Enterprise, Market

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Data-driven experiences are rich, immersive and immediate. Think pizza delivery by drone, video cameras that can record traffic accidents at an intersection, freight trucks that can identify a potential system failure. These kinds of fast-acting activities need lots of data -- quickly. So they can't sustain latency as data travels to and from the cloud. That to-and-fro takes too long.


Deep reinforcement learning for supply chain and price optimization

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Supply chain and price management were among the first areas of enterprise operations that adopted data science and combinatorial optimization methods and have a long history of using these techniques with great success. Although a wide range of traditional optimization methods are available for inventory and price management applications, deep reinforcement learning has the potential to substantially improve the optimization capabilities for these and other types of enterprise operations due to impressive recent advances in the development of generic self-learning algorithms for optimal control. In this article, we explore how deep reinforcement learning methods can be applied in several basic supply chain and price management scenarios. The traditional price optimization process in retail or manufacturing environments is typically framed as a what-if analysis of different pricing scenarios using some sort of demand model. In many cases, the development of a demand model is challenging because it has to properly capture a wide range of factors and variables that influence demand, including regular prices, discounts, marketing activities, seasonality, competitor prices, cross-product cannibalization, and halo effects. Once the demand model is developed, however, the optimization process for pricing decisions is relatively straightforward, and standard techniques such as linear or integer programming typically suffice. For instance, consider an apparel retailer that purchases a seasonal product at the beginning of the season and has to sell it out by the end of the period. Assuming that a retailer chooses pricing levels from a discrete set (e.g., \$59.90, \$69.90, etc.) and can make price changes frequently (e.g., weekly), we can pose the following optimization problem: The first constraint ensures that each time interval has only one price, and the second constraint ensures that all demands sum up to the available stock level.


Show me your ID: Tunisia deploys 'robocop' to enforce COVID-19 lockdown

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Tunisia deployed a police robot to patrol streets of the capital and enforce a lockdown imposed to contain coronavirus spread. Known as PGuard, the "robocop" which is remotely operated and is equipped with thermal imaging cameras is seen calling out to suspected violators in a video, "What are you doing? You don't know there's a lockdown?"


Q&A: Solving connected car challenges with edge AI (Includes interview)

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Over 72.5 million connected car units are estimated to be sold by 2023, enabling nearly 70% of all passenger vehicles to actively exchange data with external sources. The amount of data resulting from these smart vehicles will be overwhelming for traditional data processing solutions to gather and analyze, as well as the associated latency of processing this data-- leading to potential life-or-death scenarios, according to Ramya Ravichandar, from Foghorn. We speak with Ravichandar, about how connected car manufacturers are implementing edge AI solutions for real-time video recognition, multi-factor authentication, and other innovative capabilities to decrease network latency and optimize data gathering, analyzing and security. Digital Journal: What are the current trends with autonomous and connected cars? Ramya Ravichandar: Automotive companies are looking to improve real-time functionalities and accelerate autonomous operations of passenger vehicles.


Intelligent Heuristics for the Game Isolation using AI and Minimax

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How do you create an intelligent player for a game? Artificial intelligence offers a variety of ways to program intelligence into computer opponents. In this article, we'll show how it works, using intelligent heuristics and a web-based game that you can try yourself. Artificial intelligence is becoming an increasingly important topic in the field of computer science. While advancements in machine learning continue to break records in areas including image recognition, voice recognition, translation, and natural language processing, many additional branches of AI continue to advance as well. One of the earliest applications of AI is in the area of game development. Specifically, artificial intelligence is often used to create opponent players in games. Early forms of AI players in games often consisted of traditional board games, such as chess, checkers, backgammon, and tic-tac-toe. Games of this type provide a fully observable and deterministic view at any point in the state of the game. This allows an AI player the ability to analyze all possible moves from both the human player and the AI player itself, thus determining the best likely move to take at any given time. AI players in video games have since expanded to a much broader range of gaming categories, where the best move or course of action is not always crystal clear. These include games that often utilize random events or actions, in addition to hidden views of the game or of the opponent's actions.