Group items tagged

Computer Science is a branch of science that focuses on the theoretical and methodological implementation of computational based information processes and computer technologies in both hardware and software. Theoretical fields include such areas as information theory, database and information retrieval and programming language theory. Applied computer science features areas of study such as artificial intelligence, computer architecture, computer security and software engineering.

The Artificial Intelligence Group at UCSD engages in a wide range of theoretical and experimental research. Areas of particular strength include machine learning, probabilistic inference, neural computation, and cognitive modeling. Within these areas, students and faculty also pursue real-world applications to problems in computer vision, speech and audio processing, information retrieval, bioinformatics, brain-computer interfaces, and computer systems and networking. The Artificial Intelligence Group is part of a larger campus-wide effort in Computational Statistics and Machine Learning (COSMAL). Interdisciplinary collaborations are strongly supported and encouraged.

MIT Press CISnet brings together many of the MIT Press?s recent and classic titles in computer and information science in a fully searchable online library. Subscribers have access to a growing collection of MIT Press books on topics including programming, artificial intelligence, machine learning, human computer interaction, databases, digital libraries, networking, and robotics. CISnet is accessible from any computer with an Internet connection and from Web-enabled handhelds including the iPhone.

The modern definition of artificial intelligence (or AI) is "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success.[1] John McCarthy, who coined the term in 1956,[2] defines it as "the science and engineering of making intelligent machines."[3] Other names for the field have been proposed, such as computational intelligence,[4] synthetic intelligence[4][5] or computational rationality.[6] The term artificial intelligence is also used to describe a property of machines or programs: the intelligence that the system demonstrates.

"Torch is a scientific computing framework with wide support for machine learning algorithms. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation."

The Artificial Intelligence (AI) program at the University of Michigan comprises a multidisciplinary group of researchers conducting   theoretical, experimental, and applied investigations of intelligent systems. Current projects include research in rational decision   making, computational game theory, distributed systems of multiple agents, reinforcement learning, machine learning, cognitive modeling,   natural language processing, information retrieval, and robotics.

The DARPA PAL program (the Personalized Assistant that Learns) focused on improving the way that computers support humans through the use of cognitive systems-that is, systems that reason, learn from experience, and accept guidance in order to provide effective, personalized assistance. SRI International has led the PAL Framework effort to make available many of the successful machine learning and reasoning technologies developed on the PAL program for use by the broader DARPA, research, and military communities. Technical capabilities within the framework have been hardened, modularized, packaged, and where appropriate, adapted to meet industry standards to facilitate their incorporation into potential target applications. The framework further includes various infrastructure components and APIs to simplify integration with the framework technologies.

"The Artificial Intelligence (AI) program at the University of Michigan comprises a multidisciplinary group of researchers conducting   theoretical, experimental, and applied investigations of intelligent systems. Current projects include research in rational decision   making, computational game theory, distributed systems of multiple agents, reinforcement learning, machine learning, cognitive modeling,   natural language processing, information retrieval, and robotics."

"A research effort from Google AI that aims to build quantum processors and develop novel quantum algorithms to dramatically accelerate computational tasks for machine learning."

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importan

The Automated Mathematician (AM) is one of the earliest successful discovery systems. It was created by Doug Lenat in Lisp, and in 1977 led to Lenat being awarded the IJCAI Computers and Thought Award. AM worked by generating and modifying short Lisp programs which were t

"Introducing A.I. Experiments. Explore machine learning by playing with pictures, language, music, code, and more."

As a subset, Machine Learning focuses on growing from observations and experiences. The popularity of machine learning certification courses on online training platforms indicates that this is the thing of the future. Coming to R, it is one of the major languages for data science because of its excellent visualization features, a growing ecosystem of third-party packages, and tools for machine learning interpretability. R has emerged as a fantastic tool for scientific computing and has powerful statistical libraries. Beginners commonly use R for exploring the data before applying any automated learning.

The Association for the Advancement of Artificial Intelligence (AAAI) (formerly the American Association for Artificial Intelligence) is a nonprofit scientific society devoted to advancing the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. AAAI also aims to increase public understanding of artificial intelligence, improve the teaching and training of AI practitioners, and provide guidance for research planners and funders concerning the importance and potential of current AI developments and future directions.

"Magenta is a Google Brain project to ask and answer the questions, "Can we use machine learning to create compelling art and music? If so, how? If not, why not?" Our work is done in TensorFlow, and we regularly release our models and tools in open source. These are accompanied by demos, tutorial blog postings and technical papers. To follow our progress, watch our GitHub and join our discussion group."

"Colaboratory is a research project created to help disseminate machine learning education and research. It's a Jupyter notebook environment that requires no setup to use. For more information, see our FAQ. Click the button below to be added to the waitlist"

"Making sense of artificial intelligence This A-Z guide offers a series of simple, bite-sized explainers to help anyone understand what AI is, how it works and how it's changing the world around us."

"Semantic Scholar is a free, nonprofit, academic search engine from AI2."