Computer systems predict objects' responses to physical forces

MIT News

Josh Tenenbaum, a professor of brain and cognitive sciences at MIT, directs research on the development of intelligence at the Center for Brains, Minds, and Machines, a multiuniversity, multidisciplinary project based at MIT that seeks to explain and replicate human intelligence. Presenting their work at this year's Conference on Neural Information Processing Systems, Tenenbaum and one of his students, Jiajun Wu, are co-authors on four papers that examine the fundamental cognitive abilities that an intelligent agent requires to navigate the world: discerning distinct objects and inferring how they respond to physical forces. By building computer systems that begin to approximate these capacities, the researchers believe they can help answer questions about what information-processing resources human beings use at what stages of development. Along the way, the researchers might also generate some insights useful for robotic vision systems. "The common theme here is really learning to perceive physics," Tenenbaum says.

Intelligent Agents: An A.I. View of Optimization


As a digital analyst or marketer, you know the importance of analytical decision making. Go to any industry conference, blog, meet up, or even just read the popular press, and you will hear and see topics like machine learning, artificial intelligence, and predictive analytics everywhere. Because many of us don't come from a technical/statistical background, this can be both a little confusing and intimidating. But don't sweat it, in this post, I will try to clear up a some of this confusion by introducing a simple, yet powerful framework – the intelligent agent – which will help link these new ideas with familiar tools and concepts like A/B Testing and Optimization. Note: the intelligent agent framework is used as the guiding principle in Russell and Norvig's excellent text Artificial Intelligence: A Modern Approach – it's an awesome book, and I recommend anyone who wants to learn more to go get a copy or check out their online AI course.



Should Humanoid robots have rights like people of the world? What would be the the soul purpose of allowing robots to have rights? Since the inception of the discipline of artificial intelligence (AI) in the 1950s, progress in the field has advanced at a rapid pace. We have seen the development of early neural networks, game AI, the Turing Test, and theories involving expert systems and intelligent agents, but according to Ben Goertzel, there is a specific element of AI today that is significantly missing. Goertzel is chairman of the board of the OpenCog Foundation, and a renowned researcher and author in contemporary AI.

Using Machine Learning Agents in a real game: a beginner's guide – Unity Blog


My name is Alessia Nigretti and I am a Technical Evangelist for Unity. My job is to introduce Unity's new features to developers. My fellow evangelist Ciro Continisio and I developed the first demo game that uses the new Unity Machine Learning Agents system and showed it at DevGamm Minsk 2017. This post is based on our talk and explains what we learned making the demo. At the same time, we invite you to join the ML-Agents Challenge and show off your creative use-cases of the toolkit.

Watson Virtual Agent Go viral for the right reasons


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Can tech make your videos more interesting?


Video is the fastest growing consumer of storage worldwide, and billions of people don't yet have video cameras. But it can also be a huge time sink as we watch videos whose information rich parts are buried in minutes of repetitive or irrelevant content. If we're going to domesticate the video explosion, we need to automate editing so videos show us what what is interesting to humans. In the paper Making a long story short: A Multi-Importance Semantic for Fast-Forwarding Egocentric Videos researchers Michel M. Silvaa,, propose a five step process: The semantic analysis is the key piece. Some of the analysis uses well-understood tools, such as facial recognition.

A global collaboration to create "artificial organisms" just went live


Mindfire, a new foundation with the goal of "decoding the mind" to help develop true artificial intelligence (AI) is launching November 17th in Zurich, Switzerland. Futurism spoke with the founder of Starmind and president of the foundation, Pascal Kaufmann to learn more about its goals and the path to reach them. "We cannot achieve True AI until we understand actual intelligence. Intelligence has evolved as a means of nature to successfully guide us through an ever-changing environment. This gave rise to behavior, emotions, and consciousness.

Playing the Beer Game Using Reinforcement Learning


The beer game is a widely used in-class game that is played in supply chain management classes to demonstrate a phenomenon known as the bullwhip effect. The game consists of a serial supply chain network with four players--a retailer, a wholesaler, a distributor, and a manufacturer. In each period of the game, the retailer experiences a random demand from customers. Then the four players each decide how much inventory of "beer" to order. The retailer orders from the wholesaler, the wholesaler orders from the distributor, the distributor from the manufacturer, and the manufacturer orders from an external supplier that is not a player in the game.

Model Training with Yufeng Guo


Play in new window Download Multiagent systems involve the interaction of autonomous agents that may be acting independently or in collaboration with each other. Examples of these systems include financial markets, robot soccer matches, and automated warehouses. Today's guest Peter Stone is a professor of computer science who specializies in multiagent systems and robotics.

Mapping's Intelligent Agents


We now take it for granted that our machines can sense almost any space in the world, from deep sea trenches to the chambers of the human heart. Building on thousands of years of research in physics, war, and natural history, doctors in the 1940s began using ultrasound to scan human and animal bodies. Taking cues from dolphins and bats and Leonardo da Vinci's early echolocation experiments, naval scientists in the early 20th century learned how to detect mines and submarines with sonar. Early cathode ray studies by Wilhelm Röntgen, Nikola Tesla, and Thomas Edison led to the development of x-ray photography, which enabled radiologists to see broken bones, art historians to read the layers of an oil painting, and physicists to study crystalline structures. Revolutions in machine sensing have transformed fields like medicine and engineering and creative production, several times over.