An associative memory is a framework of content-addressable memory that stores a collection of message vectors (or a dataset) over a neural network while enabling a neurally feasible mechanism to recover any message in the dataset from its noisy version. Designing an associative memory requires addressing two main tasks: 1) learning phase: given a dataset, learn a concise representation of the dataset in the form of a graphical model (or a neural network), 2) recall phase: given a noisy version of a message vector from the dataset, output the correct message vector via a neurally feasible algorithm over the network learnt during the learning phase. This paper studies the problem of designing a class of neural associative memories which learns a network representation for a large dataset that ensures correction against a large number of adversarial errors during the recall phase. Specifically, the associative memories designed in this paper can store dataset containing exp( n ) n -length message vectors over a network with O ( n ) nodes and can tolerate Ω( n / polylog) adversarial errors. This paper carries out this memory design by mapping the learning phase and recall phase to the tasks of dictionary learning with a square dictionary and iterative error correction in an expander code, respectively.

Atrial fibrillation (AF), the most common arrhythmia with clinical significance, is a serious public health problem. Yet a number of studies show that current AF management is suboptimal due to a knowledge gap between primary care physicians and evidence-based treatment recommendations. This gap is caused by a number of barriers such as a lack of knowledge about new therapies, challenges associated with multi-morbidity, or a lack of patient engagement in therapy planning. The decision support tools proposed to address these barriers handle individual barriers but none of them tackle them comprehensively. Responding to this challenge, we propose AFGuide -- a clinical decision support system to educate and support primary care physicians in developing evidence-based and optimal AF therapies that take into account multi-morbid conditions and patient preferences. AFGuide relies on artificial intelligence techniques (logical reasoning) and preference modeling techniques, and combines them with mobile computing technologies. In this paper we present the design of the system and discuss its proposed implementation and evaluation.

Symmetry-breaking is a technique for efficiently solving SAT instances that contain high degrees of symmetry among the variables of the instance. When satisfiability problems are represented as a relational schema, symmetries between objects in the domain can be detected directly from evidence, that is, variables known to have a particular setting prior to solving. These symmetries between domain objects are called term symmetries. In this work, we present two novel extensions to the technique of term equivalent symmetry breaking which allow the detection and exploitation of conditional or hidden symmetries, those relationships between domain objects that are obscured until the instance is partially solved. We give promising preliminary experimental results for this technique, and discuss how the techniques could be extended for use in probabilistic domains.

Extensive-form games with imperfect recall are an important model of dynamic games where the players forget previously known information. Often, imperfect recall games are the result of an abstraction algorithm that simplifies a large game with perfect recall. Unfortunately, solving an imperfect recall game has fundamental problems since a Nash equilibrium does not have to exist. Alternatively, we can seek maxmin strategies that guarantee an expected outcome. The only existing algorithm computing maxmin strategies in imperfect recall games, however, requires approximating a bilinear program that is proportional to the size of the game and thus has a limited scalability. We propose a novel algorithm for computing maxmin strategies that combines this approximate algorithm with an incremental strategy-generation technique designed previously for extensive-form games with perfect recall. Experimental evaluation shows that the novel algorithm builds only a fraction of the game tree and improves the scalability by several orders of magnitude. Finally, we demonstrate that our algorithm can solve an abstracted variant of a large game faster compared to the algorithms operating on the unabstracted perfect-recall variant.

Title: What are the phenomena to be explained?

If you're looking to play around with robotics, Lego's Mindstorms EV3 is a great way to get started. So is the ultra-versatile Raspberry Pi. Combining the two to create a Rubik's Cube-solving robot? That sounds like a good time to us! The Lego bricks take care of the physical moves required to solve the puzzle.

Among the employees in highest demand are what Paul Kent calls "analytically creative problem-solvers." Almost every company has mounds of data, but it's these creative problem-solvers who use it to make profit-oriented decisions, like which product to make, what price to charge, or when to send someone a coupon. Paul Kent thinks about these star employees because, as vice president of big data initiatives in the R&D group at SAS Institute, his job is to look down the road and anticipate which kinds of creative problems to solve next with all that data. Kent sees big opportunities in two broad areas: speed and the cloud. Companies have spent the past few years gathering their data into one place.

Lecture 20, which focuses on the AI business, is not available in MIT Lecture Videos due to unknown reasons..

Artificial Intelligence is when a machine mimics the cognitive functions that humans associate with other human minds, such as learning and problem solving, reasoning, problem solving, knowledge representation, social intelligence and general intelligence. The central problems of AI include reasoning, knowledge, planning, learning, natural language processing perception and the ability to move and manipulate objects. Approaches include statistical methods, computational intelligence, soft computing and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. AI platform is defined as some sort of hardware architecture or software framework (including application frameworks), that allows software to run.

One of the visionaries in the field of AI shares his thoughts on AI, from the beginning of the last decade. From Ray Kurzweil's revolutionary book The Age of Intelligent Machines, published in 1990. It is only a word that people use to name those unknown processes with which our brains solve problems we call hard. But whenever you learn a skill yourself, you're less impressed or mystified when other people do the same. This is why the meaning of "intelligence" seems so elusive: it describes not some definite thing but only the momentary horizon of our ignorance about how minds might work. It is hard for scientists who try to understand intelligence to explain precisely what they do, since our working definitions change from year to year. But it is not at all unusual for sciences to aim at moving targets. Biology explores the moving frontier of what we understand of what happens inside our bodies. Only a few decades ago the ability of organisms to reproduce seemed to be a deep and complex mystery.