Independent component analysis (ICA) is a statistical method for transforming an observable multi-dimensional random vector into components that are as statistically independent as possible from each other. Usually the ICA framework assumes a model according to which the observations are generated (such as a linear transformation with additive noise). ICA over finite fields is a special case of ICA in which both the observations and the independent components are over a finite alphabet. In this thesis we consider a formulation of the finite-field case in which an observation vector is decomposed to its independent components (as much as possible) with no prior assumption on the way it was generated. This generalization is also known as Barlow's minimal redundancy representation and is considered an open problem. We propose several theorems and show that this hard problem can be accurately solved with a branch and bound search tree algorithm, or tightly approximated with a series of linear problems. Moreover, we show that there exists a simple transformation (namely, order permutation) which provides a greedy yet very effective approximation of the optimal solution. We further show that while not every random vector can be efficiently decomposed into independent components, the vast majority of vectors do decompose very well (that is, within a small constant cost), as the dimension increases. In addition, we show that we may practically achieve this favorable constant cost with a complexity that is asymptotically linear in the alphabet size. Our contribution provides the first efficient set of solutions to Barlow's problem with theoretical and computational guarantees. Finally, we demonstrate our suggested framework in multiple source coding applications.

A generative recurrent neural network is quickly trained in an unsupervised manner to model popular reinforcement learning environments through compressed spatio-temporal representations. The world model's extracted features are fed into compact and simple policies trained by evolution, achieving state of the art results in various environments. We also train our agent entirely inside of an environment generated by its own internal world model, and transfer this policy back into the actual environment. Interactive version of paper at https://worldmodels.github.io

In this paper, we investigate the problem of synthesizing strategies for linear temporal logic (LTL) specifications that are interpreted over finite traces -- a problem that is central to the automated construction of controllers, robot programs, and business processes. We study a natural variant of the finite LTL synthesis problem in which strategy guarantees are predicated on specified environment behavior. We further explore a quantitative extension of LTL that supports specification of quality measures, utilizing it to synthesize high-quality strategies. We propose new notions of optimality and associated algorithms that yield strategies that best satisfy specified quality measures. Our algorithms utilize an automata-game approach, positioning them well for future implementation via existing state-of-the-art techniques.

From facial recognition for unlocking our smartphones to speech recognition and intent analysis for voice assistance, artificial intelligence is all around us today. In the business world, AI is helping us uncover new insight from data and enhance decision-making. For example, online retailers use AI to recommend new products to consumers based on past purchases. And, banks use conversational AI to interact with clients and enhance their customer experiences. However, most of the AI in use now is "narrow AI," meaning it is only capable of performing individual tasks. In contrast, general AI – which is not available yet – can replicate human thought and function, taking emotions and judgment into account.

Donald Trump has a lot more to worry about than just Robert Mueller. That much has been clear since April, when details began to emerge from public court filings regarding the FBI raid on Trump's personal attorney, Michael Cohen, who pleaded guilty on Tuesday to a number of criminal charges, including some stemming from his work for Trump. Instead, it was carried out by FBI agents acting in coordination with Robert S. Khuzami, a deputy U.S. attorney in the Southern District of New York. Mueller had referred the Cohen case to Khuzami's office, but that was as far as his involvement apparently went. As I wrote at the time, the distribution of the investigation to a second office served to "potentially inoculate [it] from Trump's attacks against Mueller and potential meddling in the broader Russia investigation." Samuel W. Buell, the former lead Enron prosecutor, told me that would make it much more difficult to kill the investigation with a Saturday Night Massacre–style firing spree.

It is not uncommon to find a tool needed for a certain task unavailable. However, humans tend to circumvent such hurdle by improvising the usability of a suitable existing object in the environment. For a robot who is expected to work alongside humans in the real word is bound to face such obstacles and an effective way to carry on with the task for it would be to find a substitute. Robots that, for instance, have to hammer a nail into a wall should look for a conventional tool, a hammer, or resort to an appropriate substitute in case a hammer is unavailable. A selection of an appropriate substitute requires a knowledge driven deliberation to determine its suitability. Baber in Baber (2003a) suggested that humans are aided by conceptual knowledge about objects during the deliberation process. In other terms, humans generally have an intuitive understanding of objects and as such use qualitative form of knowledge about properties of objects - thus, conceptual knowledge - obtained from a combination of visual sensations, experiences and the outcomes of manual investigation to evaluate the applicability of a substitute.

Data streams occur widely in various real world applications. The research on streaming data mainly focuses on the data management, query evaluation and optimization on these data, however the work on reasoning procedures for streaming knowledge bases on both the assertional and terminological levels is very limited. Typically reasoning services on large knowledge bases are very expensive, and need to be applied continuously when the data is received as a stream. Hence new techniques for optimizing this continuous process is needed for developing efficient reasoners on streaming data. In this paper, we survey the related research on reasoning on expressive logics that can be applied to this setting, and point to further research directions in this area.

As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years. In this paper, we aim to present a survey to comprehensively introduce the current techniques proposed on this topic. Specifically, current SSL techniques can be mainly divided into two categories. The first category of SSL approaches can be called "concept learning", which emphasizes learning new concepts from only few related observations. The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis. The second category is called "experience learning", which usually co-exists with the large sample learning manner of conventional machine learning. This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures. More extensive surveys on both categories of SSL techniques are introduced and some neuroscience evidences are provided to clarify the rationality of the entire SSL regime, and the relationship with human learning process. Some discussions on the main challenges and possible future research directions along this line are also presented.

One of the world's oldest and most popular toys is getting a face-lift. Israel-based startup Particula has unveiled its spin on the Rubik's cube, dubbed the GoCube, that can connect to your phone and enables users to play against other people. It marks a major step up from when the original Rubik's cube, developed by Hungarian sculptor Erno Rubik, was first released in 1974. Since then, over 350 million Rubik's cubes have been sold worldwide. Israel-based startup Particula has unveiled its spin on the Rubik's cube, dubbed the GoCube (pictured), that can connect to your phone and enables users to play against other people The GoCube syncs up with smartphones and tablets using a Bluetooth connection, giving users access to the Battle feature, which lets them'play friends (or enemies) across the world, according to Particula.