Recent work has exhibited the surprising cross-lingual abi lities of multilingual BERT ( M-BERT) - surprising since it is trained without any cross-lingual objective and with no aligned data. In this work, we provide a compr ehensive study of the contribution of different components in M-BERT to its cross-lingual ability. The experimental study is done in the context of three typologically different languages - Spani sh, Hindi, and Russian - and using two conceptually different NLP tasks, textual en tailment and named entity recognition. Among our key conclusions is the fact th at the lexical overlap between languages plays a negligible role in the cross-ling ual success, while the depth of the network is an integral part of it. Embeddings of natural language text via unsupervised learn ing, coupled with sufficient supervised training data, have been ubiquitous in NLP in recent years an d have shown success in a wide range of monolingual NLP tasks, mostly in English. Training models f or other languages have been shown more difficult, and recent approaches relied on bilingual em beddings that allowed the transfer of supervision in high resource languages like English to mode ls in lower resource languages; however, inducing these bilingual embeddings required some level of supervision (Upadhyay et al., 2016). Not only the model is contextual, but its training also requires no supervisio n - no alignment between the languages is done. Nevertheless, and despite being trained with no exp licit cross-lingual objective, M-BERT produces a representation that seems to generalize well acr oss languages for a variety of downstream tasks (Wu & Dredze, 2019). In this work, we attempt to develop an understanding of the su ccess of M-BERT.

Planning is a critical component of any artificial intelligence system that concerns the realization of strategies or action sequences typically for intelligent agents and autonomous robots. Given predefined parameterized actions, a planning service should accept a query with the goal and initial state to give a solution with a sequence of actions applied to environmental objects. This paper addresses the problem by providing a repository of actions generically applicable to various environmental objects based on Semantic Web technologies. Ontologies are used for asserting constraints in common sense as well as for resolving compatibilities between actions and states. Constraints are defined using Web standards such as SPARQL and SHACL to allow conditional predicates. We demonstrate the usefulness of the proposed planning domain description language with our robotics applications.

Several examples for the explanation of Betti numbers, demonstrating their ability to capture structural information even in the presence of local deformations. Betti numbers are a way to describe the connectivity within a topological space. In simplest terms, the k -th Betti number β k counts the the number of k -dimensional holes in a topological space, for example, - β 0 is the number of connected components; - β 1 is the number of planar holes (1-dimensional holes); - β 2 is the number of two-dimensional voids (2-dimensional holes); - ... Betti numbers are topological invariants. If two Betti numbers are the same for two different spaces then the spaces are homotopy equivalent [1]. To demonstrate Betti numbers more 6 vividly, some examples are shown in Figure 1.

People need to be held accountable for the mistakes AI and algorithms make on their behalf, such as that seen in the government's robodebt scandal, according to Australian human rights commissioner Ed Santow. The proposal comes in a new discussion paper on the impact of new technologies on human rights in Australia, released by the commission on Tuesday. After the Australian government backed down on the use of automatic debt notices based on income averaging, and had legislation for its facial recognition system rejected by a government-dominated parliamentary committee, Santow said it was time to set some rules to govern how these new technologies are used. "Robodebt is just a prominent example of data science and government AI being used in decision-making," he said. All government use of AI should be enshrined in legislation, he said.

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?

An international machine learning conference is set to land in Australia in 2021 following a successful bid by CSIRO's Data61 and tendering partner …

This paper investigates the intriguing question of whether we can create learning algorithms that automatically generate training data, learning environments, and curricula in order to help AI agents rapidly learn. We show that such algorithms are possible via Generative Teaching Networks (GTNs), a general approach that is, in theory, applicable to supervised, unsupervised, and reinforcement learning, although our experiments only focus on the supervised case. GTNs are deep neural networks that generate data and/or training environments that a learner (e.g. a freshly initialized neural network) trains on for a few SGD steps before being tested on a target task. We then differentiate through the entire learning process via meta-gradients to update the GTN parameters to improve performance on the target task. GTNs have the beneficial property that they can theoretically generate any type of data or training environment, making their potential impact large. This paper introduces GTNs, discusses their potential, and showcases that they can substantially accelerate learning. We also demonstrate a practical and exciting application of GTNs: accelerating the evaluation of candidate architectures for neural architecture search (NAS), which is rate-limited by such evaluations, enabling massive speed-ups in NAS. GTN-NAS improves the NAS state of the art, finding higher performing architectures when controlling for the search proposal mechanism. GTN-NAS also is competitive with the overall state of the art approaches, which achieve top performance while using orders of magnitude less computation than typical NAS methods. Speculating forward, GTNs may represent a first step toward the ambitious goal of algorithms that generate their own training data and, in doing so, open a variety of interesting new research questions and directions.

--We introduce L EARN2 FIX, the first human-in-the-loop, semiautomatic repair technique when no bug oracle-except for the user who is reporting the bug-is available. Our approach negotiates with the user the condition under which the bug is observed. Only when a budget of queries to the user is exhausted, it attempts to repair the bug. A query can be thought of as the following question: "When executing this alternative test input, the program produces the following output; is the bug observed"? Through systematic queries, L EARN2 FIX trains an automatic bug oracle that becomes increasingly more accurate in predicting the user's response. Our key challenge is to maximize the oracle's accuracy in predicting which tests are bug-revealing given a small budget of queries. From the alternative tests that were labeled by the user, test-driven automatic repair produces the patch. Our experiments demonstrate that L EARN2 FIX learns a sufficiently accurate automatic oracle with a reasonably low labeling effort (lt. Given L EARN2 FIX's test suite, the GenProg test-driven repair tool produces a higher-quality patch (i.e., passing a larger proportion of validation tests) than using manual test suites provided with the repair benchmark. I NTRODUCTION Automatic program repair (APR) [1], [2] holds the promise of automating the tedious, manual task of patching bugs. In their seminal paper, Le Goues and colleagues [3] demonstrated that APR is both feasible and cost-effective even at the scale of several million lines of code. Given a failing test suite, APR changes the buggy program such that all test cases pass. However, what if no such test suite is available? Suppose, a user reports a bug and provides a test input to reproduce the bug. We envision a semiautomatic approach that keeps the human-in-the-loop and negotiates the condition under which the bug is observed before repairing the bug. Strategically, the user is asked: " F or this other input, the program produces that output; is the bug observed "? While the user might not have the expertise to understand the source code or to produce a patch, it seems reasonable to ask to distinguish expected from unexpected program behavior. Iteratively, an automatic bug oracle is trained to predict the user's responses with increasing accuracy. Using the trained oracle, the user can be asked more strategically.

Recent research from SAP and Oxford Economics demonstrated CFOs' strategic initiatives are taking a more active role in the direction of their businesses, rather than operating within a siloed financial function. The report showed that 88% respondents said CFO's are increasingly involved in the strategic decisions of their organisations.