We introduce learning programs by learning from failures. In this approach, an inductive logic programming (ILP) system (the learner) decomposes the learning problem into three separate stages: generate, test, and constrain. In the generate stage, the learner generates a hypothesis (a logic program) that satisfies a set of hypothesis constraints (constraints on the syntactic form of hypotheses). In the test stage, the learner tests the hypothesis against training examples. A hypothesis fails when it does not entail all the positive examples or entails a negative example. If a hypothesis fails, then, in the constrain stage, the learner learns constraints from the failed hypothesis to prune the hypothesis space, i.e. to constrain subsequent hypothesis generation. For instance, if a hypothesis is too general (entails a negative example), the constraints prune generalisations of the hypothesis. If a hypothesis is too specific (does not entail all the positive examples), the constraints prune specialisations of the hypothesis. This loop repeats until (1) the learner finds a hypothesis that entails all the positive and none of the negative examples, or (2) there are no more hypotheses to test. We implement our idea in Popper, an ILP system which combines answer set programming and Prolog. Popper supports infinite domains, reasoning about lists and numbers, learning optimal (textually minimal) programs, and learning recursive programs. Our experimental results on three diverse domains (number theory problems, robot strategies, and list transformations) show that (1) constraints drastically improve learning performance, and (2) Popper can substantially outperform state-of-the-art ILP systems, both in terms of predictive accuracies and learning times.

Trinamix, a Platinum-level member of Oracle PartnerNetwork (OPN), announced the launch of Industry 4.0 Lab to help enterprises enable smart manufacturing using Oracle Internet of Things (IoT), Oracle Adaptive Intelligence, and Oracle Blockchain applications. Hosted at Trinamix's San Jose office, the lab will offer visiting customers a miniature experience that highlights how Oracle solutions will work in an industrial scenario, connected with machines through devices and gateways. Trinamix has been working on Industry 4.0 (I4.0) solutions for the last two years and has undertaken various initiatives to provide an end-to-end offering to its customers. Trinamix offers design thinking, creating quick proof of concept, road map and implementation services on I4.0. This lab drives Trinamix's focus on the newest and the next wave of disruptive technologies including Artificial Intelligence, blockchain, data science and factory automation.

However, according to a study by Appian last year, 91 percent of respondents believe they need to fix problems rather quickly than thoroughly, as they need to focus on updating their business operations. The speed of updates and customizations using DevOps measure is measured in days rather than months or years. Frequently, IT operations cannot keep up with this pace, and digitization will fail in the long term. The solution to many of the associated DevOps challenges lies in a different kind of digital transformation - based on machine learning and artificial intelligence (AI). Although it is often portrayed as a threat to the public, it offers companies an excellent opportunity to improve their productivity and security.

The global healthcare industry is booming. As per recent research, it is expected to cross the $2 trillion mark this year, despite the sluggish economic outlook and global trade tensions. Human beings, in general, are living longer and healthier lives. There is increased awareness about living organ donation. Robots are being used for gallbladder removals, hip replacements, and kidney transplants.

In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets). The ASNet is a neural network architecture that exploits the relational structure of (P)PDDL planning problems to learn a common set of weights that can be applied to any problem in a domain. By mimicking the actions chosen by a traditional, non-learning planner on a handful of small problems in a domain, ASNets are able to learn a generalised reactive policy that can quickly solve much larger instances from the domain. This work extends the ASNet architecture to make it more expressive, while still remaining invariant to a range of symmetries that exist in PPDDL problems. We also present a thorough experimental evaluation of ASNets, including a comparison with heuristic search planners on seven probabilistic and deterministic domains, an extended evaluation on over 18,000 Blocksworld instances, and an ablation study. Finally, we show that sparsity-inducing regularisation can produce ASNets that are compact enough for humans to understand, yielding insights into how the structure of ASNets allows them to generalise across a domain.

The problem of achieving common understanding between agents that use different vocabularies has been mainly addressed by techniques that assume the existence of shared external elements, such as a meta-language or a physical environment. In this article, we consider agents that use different vocabularies and only share knowledge of how to perform a task, given by the specification of an interaction protocol. We present a framework that lets agents learn a vocabulary alignment from the experience of interacting. Unlike previous work in this direction, we use open protocols that constrain possible actions instead of defining procedures, making our approach more general. We present two techniques that can be used either to learn an alignment from scratch or to repair an existent one, and we evaluate their performance experimentally.

The report published on Artificial Intelligence (AI) is a invaluable foundation of insightful data helpful for the decision-makers to form the business strategies related R&D investment, sales and growth, key trends, technological advancement, emerging market and more. The global Artificial Intelligence (AI) market report includes key facts and figures data which helps its users to understand current scenario of the global market along with anticipated growth. The Artificial Intelligence (AI) market report contains quantitative data such as global sales and revenue (USD Million) market size of different categories and sub categories such as regions, CAGR, market shares, revenue insights of market players, and others. The report also gives qualitative insights on the global Artificial Intelligence (AI) market, that gives the exact outlook of the global as well as country level Artificial Intelligence (AI) market. Major Companies Profiled in the Global Artificial Intelligence (AI) Market are: Intel Corporation, MicroStrategy, Amazon, NVIDIA, Baidu, Inc., Atomwise, Inc., Google, Alibaba, H2O ai, Microsoft Corporation, Samsung, IBM, Zebra Medical Vision, Inc., Facebook The focus of the global Artificial Intelligence (AI) market report is to define, categorized, identify the Artificial Intelligence (AI) market in terms of its parameter and specifications/ segments for example by product, by types, by applications, and by end-users.

" Naruto, an anime that teaches about many profound things like no matter how bad life puts you down, you got to get back up and move forward" as said by one of my stoned friends. He was high, but I couldn't refute him not because I was stoned too but because I agreed with him completely. A little context about how I got the idea, I am currently a visiting researcher in Australia at UTS. I am working on research in ML, XR and quantum domains. So, being the geek I am, I marveled at the possible implications of making a naruto game in AR where you make the signs and a Jutsu follows up. Think: When you do the Dog, hare, Dragon, boar, tiger signs, a fire dragon comes into existence and crashes onto the enemy..killing him if he doesn't counter-attack with a water or earth Jutsu.

The contribution of this paper is two-fold. First, we present ProbCast - a novel probabilistic model for multivariate time-series forecasting. We employ a conditional GAN framework to train our model with adversarial training. Second, we propose a framework that lets us transform a deterministic model into a probabilistic one with improved performance. The motivation of the framework is to either transform existing highly accurate point forecast models to their probabilistic counterparts or to train GANs stably by selecting the architecture of GAN's component carefully and efficiently. We conduct experiments over two publicly available datasets namely electricity consumption dataset and exchange-rate dataset. The results of the experiments demonstrate the remarkable performance of our model as well as the successful application of our proposed framework.

An educated and informed consumption of media content has become a challenge in modern times. With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in "echo chambers" and may fall prey to fake news and disinformation, lacking easy access to dissenting views. We suggest a novel task aiming to alleviate some of these concerns -- that of detecting articles that most effectively counter the arguments -- and not just the stance -- made in a given text. We study this problem in the context of debate speeches. Given such a speech, we aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it. We provide a large dataset of 3,685 such speeches (in English), annotated for this relation, which hopefully would be of general interest to the NLP community. We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance, suggesting room for further research. All data collected during this work is freely available for research.