In the community question answering (CQA) system, the answer selection task aims to identify the best answer for a specific question, and thus is playing a key role in enhancing the service quality through recommending appropriate answers for new questions. Recent advances in CQA answer selection focus on enhancing the performance by incorporating the community information, particularly the expertise (previous answers) and authority (position in the social network) of an answerer. However, existing approaches for incorporating such information are limited in (a) only considering either the expertise or the authority, but not both; (b) ignoring the domain knowledge to differentiate topics of previous answers; and (c) simply using the authority information to adjust the similarity score, instead of fully utilizing it in the process of measuring the similarity between segments of the question and the answer. We propose the Knowledge-enhanced Attentive Answer Selection (KAAS) model, which enhances the performance through (a) considering both the expertise and the authority of the answerer; (b) utilizing the human-labeled tags, the taxonomy of the tags, and the votes as the domain knowledge to infer the expertise of the answer; (c) using matrix decomposition of the social network (formed by following-relationship) to infer the authority of the answerer and incorporating such information in the process of evaluating the similarity between segments. Besides, for vertical community, we incorporate an external knowledge graph to capture more professional information for vertical CQA systems. Then we adopt the attention mechanism to integrate the analysis of the text of questions and answers and the aforementioned community information. Experiments with both vertical and general CQA sites demonstrate the superior performance of the proposed KAAS model.

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.

Artificial intelligence is becoming the fastest disruptor and generator of wealth in history. It will have a major impact on everything. Over the next decade, more than half of the jobs today will disappear and be replaced by AI and the next generation of robotics. AI has the potential to cure diseases, enable smarter cities, tackle many of our environmental challenges, and potentially redefine poverty. There are still many questions to ask about AI and what can go wrong.

In a clear signal of its ambitions in the AI chip market, Intel this morning announced that it has acquired Habana Labs, an Israel-based developer of programmable AI and machine learning accelerators for cloud datacenters. The deal is worth approximately $2 billion, and Intel says it will strengthen its AI strategy as Habana begins sampling its proprietary silicon to customers. Habana -- which raised $75 million in venture capital last November -- will remain an independent business unit and continue to be led by its current management team. It will report to Intel's data platforms group. Board chair Avigdor Willenz will serve as senior adviser to the business unit, as well as to Intel. "This acquisition advances our AI strategy, which is to provide customers with solutions to fit every performance need -- from the intelligent edge to the datacenter," said Navin Shenoy, executive vice president and general manager of the data platforms group at Intel.

SANTA CLARA Calif., Dec. 16, 2019 – Intel Corporation today announced that it has acquired Habana Labs, an Israel-based developer of programmable deep learning accelerators for the data center for approximately $2 billion. The combination strengthens Intel's artificial intelligence (AI) portfolio and accelerates its efforts in the nascent, fast-growing AI silicon market, which Intel expects to be greater than $25 billion by 20241. "This acquisition advances our AI strategy, which is to provide customers with solutions to fit every performance need – from the intelligent edge to the data center," said Navin Shenoy, executive vice president and general manager of the Data Platforms Group at Intel. "More specifically, Habana turbo-charges our AI offerings for the data center with a high-performance training processor family and a standards-based programming environment to address evolving AI workloads." Intel's AI strategy is grounded in the belief that harnessing the power of AI to improve business outcomes requires a broad mix of technology – hardware and software – and full ecosystem support.

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.

Facial recognition may not be as secure as previously thought. Researchers found that the technology can be fooled by using a 3D-printed mask depicting a different person's face. The mask was able to trick payment a system at a border checkpoint in China a passport-control gate in Amsterdam. The security flaw was discovered by researchers with the artificial intelligence firm Kneron, which determined criminals only need is a lifelike mask of a person to bypass security checkpoints. Kneron CEO Albert Liu said in a statement: 'Technology providers should be held accountable if they do not safeguard users to the highest standards.'