The plan aims to use AI to boost competitiveness and productivity and address issues such as social inequality. The Brazilian government has taken another step towards the creation of public policies around artificial intelligence (AI). A national AI strategy will be created as a response to the worldwide race for leadership in the field and the need to discuss the future of work, education, tax, research and development as well as ethics as the application of related technologies becomes more pervasive. A public consultation has been launched to gather input around how AI can solve the country's main issues, identify priority areas of focus for the development and use of the technologies, as well as limits for it. According to the summary on the purpose of the consultation, which ends on January 31, 2020, the government understands that AI can bring improvements to the country's competitiveness and productivity, as well as the provision of public services, quality of life and to reduce social inequality in the southern hemisphere's biggest economy.

Patricia A. V argas Heriot-W att University Heriot-W att University Edinburgh, UK P .A.V argas@hw.ac.uk Abstract --This paper describes a competition proposal for evolving Intelligent Agents for the game-playing framework called EvoMan. The framework is based on the boss fights of the game called Mega Man II developed by Capcom. For this particular competition, the main goal is to beat all of the eight bosses using a generalist strategy. In other words, the competitors should train the agent to beat a set of the bosses and then the agent will be evaluated by its performance against all eight bosses. At the end of this paper, the competitors are provided with baseline results so that they can have an intuition on how good their results are.

Ontology-mediated querying and querying in the presence of constraints are two key database problems where tuple-generating dependencies (TGDs) play a central role. In ontology-mediated querying, TGDs can formalize the ontology and thus derive additional facts from the given data, while in querying in the presence of constraints, they restrict the set of admissible databases. In this work, we study the limits of efficient query evaluation in the context of the above two problems, focussing on guarded and frontier-guarded TGDs and on UCQs as the actual queries. We show that a class of ontology-mediated queries (OMQs) based on guarded TGDs can be evaluated in FPT iff the OMQs in the class are equivalent to OMQs in which the actual query has bounded treewidth, up to some reasonable assumptions. For querying in the presence of constraints, we consider classes of constraint-query specifications (CQSs) that bundle a set of constraints with an actual query. We show a dichotomy result for CQSs based on guarded TGDs that parallels the one for OMQs except that, additionally, FPT coincides with PTime combined complexity. The proof is based on a novel connection between OMQ and CQS evaluation. Using a direct proof, we also show a similar dichotomy result, again up to some reasonable assumptions, for CQSs based on frontier-guarded TGDs with a bounded number of atoms in TGD heads. Our results on CQSs can be viewed as extensions of Grohe's well-known characterization of the tractable classes of CQs (without constraints). Like Grohe's characterization, all the above results assume that the arity of relation symbols is bounded by a constant. We also study the associated meta problems, i.e., whether a given OMQ or CQS is equivalent to one in which the actual query has bounded treewidth.

Evaluating data separation in a geometrical space is fundamental for pattern recognition. A plethora of dimensionality reduction (DR) algorithms have been developed in order to reveal the emergence of geometrical patterns in a low dimensional visible representation space, in which high-dimensional samples similarities are approximated by geometrical distances. However, statistical measures to evaluate directly in the low dimensional geometrical space the sample group separability attaiend by these DR algorithms are missing. Certainly, these separability measures could be used both to compare algorithms performance and to tune algorithms parameters. Here, we propose three statistical measures (named as PSI-ROC, PSI-PR, and PSI-P) that have origin from the Projection Separability (PS) rationale introduced in this study, which is expressly designed to assess group separability of data samples in a geometrical space. Traditional cluster validity indices (CVIs) might be applied in this context but they show limitations because they are not specifically tailored for DR. Our PS measures are compared to six baseline cluster validity indices, using five non-linear datasets and six different DR algorithms. The results provide clear evidence that statistical-based measures based on PS rationale are more accurate than CVIs and can be adopted to control the tuning of parameter-dependent DR algorithms.

Two of the most widely quoted and shared poems in the closing years of this decade were William Butler Yeats's "The Second Coming" ("Things fall apart; the centre cannot hold"), and W.H. Auden's "September 1, 1939" ("Waves of anger and fear / Circulate over the bright / And darkened lands of the earth"). Yeats's poem, written just after World War I, spoke of a time when "The best lack all conviction, while the worst / Are full of passionate intensity." Auden's poem, written in the wake of Germany's invasion of Poland, described a world lying "in stupor," as democracy is threatened and "the enlightenment driven away." Apocalypse is not yet upon our world as the 2010s draw to an end, but there are portents of disorder. The hopes nourished during the opening years of the decade -- hopes that America was on a progressive path toward growing equality and freedom, hopes that technology held answers to some of our most pressing problems -- have given way, with what feels like head-swiveling speed, to a dark and divisive new era.

Was investigated in this paper the use of a search strategy in the problem of finding the best path among multiple origins and multiple destinations. In this kind of problem, it must be decided within a lot of combinations which is the best origin and the best destination, and also the best path between these two regions. One remarkable difficulty to answer this sort of problem is to perform the search in a reduced time. This monography is a extension of previous research in which the problem described here was studied only in a bus network in the city of Fortaleza. This extension consisted of an exploration of the search strategy in graphs that represent public ways in cities like Fortaleza, Mumbai and Tokyo.

This Artificial Intelligence Market research report is framed by using integrated advancements and latest technology to give the most excellent results. A method of standard market research analysis is put forth while elaborating the studies and estimations that are involved in this Artificial Intelligence Market report. Such plentiful information accompanied with deep market insights supports the decision of increasing or decreasing the production of goods depending on the general conditions of market and demand. The Artificial Intelligence Market report has a lot to offer to both established and new players in the industry with which they can completely understand the Artificial Intelligence Market. Global Artificial Intelligence Market is accounted for $15.70 billion in 2017 and is expected to reach $300.26 billion by 2026 growing at a CAGR of 38.8% during the forecast period. Artificial intelligence is an intelligence established by machines, in contrast to the natural intelligence displayed by humans and other animals.

The art of making perfumes and colognes hasn't changed much since the 1880s, when synthetic ingredients began to be used. Expert fragrance creators tinker with combinations of chemicals in hopes of producing compelling new scents. So Achim Daub, an executive at one of the world's biggest makers of fragrances, Symrise, wondered what would happen if he injected artificial intelligence into the process. Would a machine suggest appealing formulas that a human might not think to try? Daub hired IBM to design a computer system that would pore over massive amounts of information--the formulas of existing fragrances, consumer data, regulatory information, on and on--and then suggest new formulations for particular markets. The system is called Philyra, after the Greek goddess of fragrance.

Conflicting information in an agent's knowledge base may lead to a semantical defect, that is, a situation where it is impossible to draw any plausible conclusion. Finding out the reasons for the observed inconsistency (so-called diagnoses) and/or restoring consistency in a certain minimal way (so-called repairs) are frequently occurring issues in knowledge representation and reasoning. In this article we provide a series of first results for these problems in the context of abstract argumentation theory regarding the two most important reasoning modes, namely credulous as well as sceptical acceptance. Our analysis includes the following problems regarding minimal repairs/diagnoses: existence, verification, computation of one and enumeration of all solutions. The latter problem is tackled with a version of the so-called hitting set duality first introduced by Raymond Reiter in 1987. It turns out that grounded semantics plays an outstanding role not only in terms of complexity, but also as a useful tool to reduce the search space for diagnoses regarding other semantics.

IMPROVED MUL TI-ST AGE TRAINING OF ONLINE A TTENTION-BASED ENCODER-DECODER MODELS Abhinav Garg, Dhananjaya Gowda, Ankur Kumar, Kwangyoun Kim, Mehul Kumar, Chanwoo Kim Speech Processing Lab, AI Center, Samsung Research, Korea ABSTRACT In this paper, we propose a refined multistage multi-task training strategy to improve the performance of online attention-based encoder-decoder (AED) models. A three-stage training based on three levels of architectural granularity namely, character encoder, byte pair encoding (BPE) based encoder, and attention decoder, is proposed. Also, multi-task learning based on two-levels of linguistic granularity namely, character and BPE, is used. We explore different pre-training strategies for the encoders including transfer learning from a bidirectional encoder. Our models achieve a word error rate (WER) of 5.04% and 4.48% on the Librispeech test-clean data for the smaller and bigger models respectively after fusion with long short-term memory (LSTM) based external language model (LM). Index T erms-- Attention based encoder-decoder models, online attention, multistage training, multi-task learning 1. INTRODUCTION Recently, attention-based encoder-decoder (AED) models have gained popularity for developing end-to-end neural network based automatic speech recognition (ASR) systems [1, 2, 3]. One of the primary advantages of AED models is that the language information is tightly coupled into the decoder, obviating the need for an external language model (LM). AED models have been shown to perform better than other end-to-end models, namely, connectionist temporal classification (CTC) and recurrent neural network transducer (RNN-T) models [4].