Data collected at very frequent intervals is usually extremely sparse and has no structure that is exploitable by modern tensor decomposition algorithms. Thus the utility of such tensors is low, in terms of the amount of interpretable and exploitable structure that one can extract from them. In this paper, we introduce the problem of finding a tensor of adaptive aggregated granularity that can be decomposed to reveal meaningful latent concepts (structures) from datasets that, in their original form, are not amenable to tensor analysis. Such datasets fall under the broad category of sparse point processes that evolve over space and/or time. To the best of our knowledge, this is the first work that explores adaptive granularity aggregation in tensors. Furthermore, we formally define the problem and discuss what different definitions of "good structure" can be in practice, and show that optimal solution is of prohibitive combinatorial complexity. Subsequently, we propose an efficient and effective greedy algorithm which follows a number of intuitive decision criteria that locally maximize the "goodness of structure", resulting in high-quality tensors. We evaluate our method on both semi-synthetic data where ground truth is known and real datasets for which we do not have any ground truth. In both cases, our proposed method constructs tensors that have very high structure quality. Finally, our proposed method is able to discover different natural resolutions of a multi-aspect dataset, which can lead to multi-resolution analysis.

SAN FRANCISCO, Dec. 17, 2019 (GLOBE NEWSWIRE) -- Holberton School, the two-year tuition-deferred college alternative educating the next generation of digital workers, announced the launch of their brand new Machine Learning curriculum which will be available at all eight world-wide Holberton campuses. The announcement was made at the flagship San Francisco campus featuring Grammy award-winner NE-YO, Black Girls Code founder and CEO Kimberly Bryant and representatives from Google (Tensorflow) and IBM. "Machine Learning, and by extension Artificial Intelligence, are increasingly dominating how we interact with technology at all levels, and the need for diversity has never been so urgent," said Gabriela de Queiroz, founder, AI Inclusive and R-Ladies. "Having programming skills isn't enough -- we need people who are aware of the ethical implications of AI, who can bring their diverse backgrounds, experiences, and perspectives to the workplace and incorporate them into the algorithms that will increasingly play a major role in healthcare, safety, and every other element of our lives." Machine Learning, which gives computers the capability to learn without being explicitly programmed, is already in use across the globe and is rapidly supplementing, and even replacing, traditional software development.

Climate change activist Greta Thunberg has been named one of the ten most influential people in science in 2019 by the journal Nature. The 16 year old has been named alongside a neurologist who brought pig brains back to life and a palaeontologist who shook up humanity's family tree. The prestigious British science journal, which celebrated its 150th anniversary this year, says the Swedish campaigner'channelled the rage of a generation'. She had outshone scientists who couldn't'galvanise global attention' the way she did and many are cheering her along, according to Nature. The ten most influential list also includes a physicist building quantum computers, a biologist editing genes in adult humans and a microbiologist fighting Ebola.

At the Tackling Climate Change workshop at this year's NeurIPS conference, some of the top minds in machine learning came together to discuss the effects of climate change on life on Earth, how AI can tackle the urgent problem, and why and how the machine learning community should join the fight. The panel included Yoshua Bengio, MILA director and University of Montreal professor; Jeff Dean, Google's AI chief; Andrew Ng, cofounder of Google Brain and founder of Landing.ai; and Cornell University professor and Institute for Computational Sustainability director Carla Gomes. The Tackling Climate Change workshop explored a wide range of topics, from the use of deep reinforcement learning to improve performance for ride-hailing services like Uber and Lyft to the application of deep learning to predict wildfire risk, detect avalanche deposits, improve plane efficiency with better wind forecasts, and conduct a global census of solar farms. The workshop is put together by Climate Change AI, a group that hosts workshops at AI research conferences and a forum for collaboration between machine learning practitioners and people from other fields. One essential step in better addressing the world's pressing challenges, says Bengio, is changing the way AI research is valued.

The US government must spend $25 billion on artificial intelligence research by 2025, stem the loss of foreign AI talent, and find new ways to prevent critical AI technology from being stolen and exported, according to a policy report issued Tuesday. Otherwise it risks falling behind China and losing its standing on the world stage. The report, from the Center for New American Security (CNAS), is the latest to highlight the importance of AI to the future of the US. It argues that the technology will define economic, military, and geopolitical power in coming decades. Advanced technologies, including AI, 5G wireless services, and quantum computing, are already at the center of an emerging technological cold war between the US and China. The Trump administration has declared AI a national priority, and it has enacted policies, such as technology export controls, designed to limit China's progress in AI and related areas.

We consider the problem of the estimation of a high-dimensional probability distribution using model classes of functions in tree-based tensor formats, a particular case of tensor networks associated with a dimension partition tree. The distribution is assumed to admit a density with respect to a product measure, possibly discrete for handling the case of discrete random variables. After discussing the representation of classical model classes in tree-based tensor formats, we present learning algorithms based on empirical risk minimization using a $L^2$ contrast. These algorithms exploit the multilinear parametrization of the formats to recast the nonlinear minimization problem into a sequence of empirical risk minimization problems with linear models. A suitable parametrization of the tensor in tree-based tensor format allows to obtain a linear model with orthogonal bases, so that each problem admits an explicit expression of the solution and cross-validation risk estimates. These estimations of the risk enable the model selection, for instance when exploiting sparsity in the coefficients of the representation. A strategy for the adaptation of the tensor format (dimension tree and tree-based ranks) is provided, which allows to discover and exploit some specific structures of high-dimensional probability distributions such as independence or conditional independence. We illustrate the performances of the proposed algorithms for the approximation of classical probabilistic models (such as Gaussian distribution, graphical models, Markov chain).

In this paper new general modewise Johnson-Lindenstrauss (JL) subspace embeddings are proposed that are both considerably faster to generate and easier to store than traditional JL embeddings when working with extremely large vectors and/or tensors. Corresponding embedding results are then proven for two different types of low-dimensional (tensor) subspaces. The first of these new subspace embedding results produces improved space complexity bounds for embeddings of rank-$r$ tensors whose CP decompositions are contained in the span of a fixed (but unknown) set of $r$ rank-one basis tensors. In the traditional vector setting this first result yields new and very general near-optimal oblivious subspace embedding constructions that require fewer random bits to generate than standard JL embeddings when embedding subspaces of $\mathbb{C}^N$ spanned by basis vectors with special Kronecker structure. The second result proven herein provides new fast JL embeddings of arbitrary $r$-dimensional subspaces $\mathcal{S} \subset \mathbb{C}^N$ which also require fewer random bits (and so are easier to store - i.e., require less space) than standard fast JL embedding methods in order to achieve small $\epsilon$-distortions. These new oblivious subspace embedding results work by $(i)$ effectively folding any given vector in $\mathcal{S}$ into a (not necessarily low-rank) tensor, and then $(ii)$ embedding the resulting tensor into $\mathbb{C}^m$ for $m \leq C r \log^c(N) / \epsilon^2$. Applications related to compression and fast compressed least squares solution methods are also considered, including those used for fitting low-rank CP decompositions, and the proposed JL embedding results are shown to work well numerically in both settings.

Abstract--In this paper we investigate the problem of automatically naming pieces of assembly code. Where by naming we mean assigning to portion of code the string of words that wou ld be likely assigned by an human reverse engineer . We formally and precisely define the framework in which our investigatio n takes place. That is we define problem, we provide reasonable justifications for the choice that we made during our designi ng of the training and test steps and we performed a statistical an alysis of function names in a large real-world corpora of over 4 mill ions of functions. In such framework we test several baselines co ming from the field of NLP (e.g., Seq2Seq networks and transformer s). Moreover, we provide a set of tailored solutions that beat th e aforementioned baselines. Last few years have witnessed the growth of a trend consisting in the application of machine learning (ML) and natural language processing (NLP) techniques to the code, as illustrated in [14].

New York City, NY: December, 2019 – Published via (WiredRelease) – The report titled Artificial Intelligence (AI) in Agriculture Market is the latest additions to MarketResearch.biz's It offers detail information on restraints, challenges, leading growth drivers, driving forces, profit projection, size, CAGR, consumption, risk analysis, trends, and opportunities, competitive analysis of the Artificial Intelligence (AI) in Agriculture market up to the year 2029. Market participants can use this research on market dynamics to plan effective growth strategies and prepare for future challenges beforehand. Each trend of the Artificial Intelligence (AI) in Agriculture market is precisely analyzed and researched about by the market analysts. Firstly, the Artificial Intelligence (AI) in Agriculture Market Report provides a basic overview of the industry including definitions, classifications, applications and chain structure.

Hamish Fraser first encountered Babylon Health in 2017 when he and a colleague helped test the accuracy of several artificial intelligence-powered symptom checkers, meant to offer medical advice for anyone with a smartphone, for Wired U.K. Among the competitors, Babylon's symptom checker performed worst in identifying common illnesses, including asthma and shingles. Fraser, then a health informatics expert at the University of Leeds in England, figured that the company would need to vastly improve to stick around. "At that point I had no prejudice or knowledge of any of them, so I had no axe to grind, and I thought'Oh that's not really good,'" says Fraser, now at Brown University. "I thought they would disappear, right? Much has changed since the Wired U.K. article came out. Since early 2018, the London-based Babylon Health has grown from just 300 employees to approximately 1,500. The company has a valuation of more than $2 billion and says it wants to "put an affordable and accessible health service in the hands of every person on earth." In England, Babylon operates the fifth-largest practice under the country's mostly government-funded National Health Service, allowing patients near London and Birmingham to video chat with doctors or be seen in a clinic if necessary. The company claims to have processed 700,000 digital consultations between patients and physicians, with plans to offer services in other U.K. cities in the future. "I thought they would disappear, right?