We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction. We call this framework Planning with Abstract Learned Models (PALM). By representing subtasks symbolically using a new formal structure, the lifted abstract Markov decision process (L-AMDP), PALM learns models that are independent and modular. Through our experiments, we show how PALM integrates planning and execution, facilitating a rapid and efficient learning of abstract, hierarchical models. We also demonstrate the increased potential for learned models to be transferred to new and related tasks.

In many scenarios, complete and incomplete information coexist. For this reason, the knowledge representation and database communities have long shown interest in simultaneously supporting the closed- and the open-world views when reasoning about logic theories. Here we consider the setting of querying possibly incomplete data using logic theories, formalized as the evaluation of an ontology-mediated query (OMQ) that pairs a query with a theory, sometimes called an ontology, expressing background knowledge. This can be further enriched by specifying a set of closed predicates from the theory that are to be interpreted under the closed-world assumption, while the rest are interpreted with the open-world view. In this way we can retrieve more precise answers to queries by leveraging the partial completeness of the data. The central goal of this paper is to understand the relative expressiveness of OMQ languages in which the ontology is written in the expressive Description Logic (DL) ALCHOI and includes a set of closed predicates. We consider a restricted class of conjunctive queries. Our main result is to show that every query in this non-monotonic query language can be translated in polynomial time into Datalog with negation under the stable model semantics. To overcome the challenge that Datalog has no direct means to express the existential quantification present in ALCHOI, we define a two-player game that characterizes the satisfaction of the ontology, and design a Datalog query that can decide the existence of a winning strategy for the game. If there are no closed predicates, that is in the case of querying a plain ALCHOI knowledge base, our translation yields a positive disjunctive Datalog program of polynomial size. To the best of our knowledge, unlike previous translations for related fragments with expressive (non-Horn) DLs, these are the first polynomial time translations.

Abstract--In medicine, a communicating virtual patient or doctor allows students to train in medical diagnosis and dev elop skills to conduct a medical consultation. In this paper, we describe a conversational virtual standardized patient sy stem to allow medical students to simulate a diagnosis strategy o f an abdominal surgical emergency. We exploited the semantic properties captured by distributed word representations t o search for similar questions in the virtual patient dialogue syste m. We created two dialogue systems that were evaluated on dataset s collected during tests with students. The first system based on handcrafted rules obtains 92.29% as F 1-score on the studied clinical case while the second system that combines rules an d semantic similarity achieves 94.88%. It represents an error reduction of 9.70% as compared to the rules-only-based system. The medical diagnosis practice is traditionally bedside taught. Theoretical courses are supplemented by internshi ps in hospital services. The medical student observes the practi ce of doctors and interns and practices himself under their contr ol. This type of learning has the disadvantage to confront immediately the medical student with complex situations withou t practical training (technical and human) beforehand.

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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.

It's been estimated that China's government has detained as many as a million members of the country's Muslim population in so-called "re-education camps," in part of a campaign that has alarmed human rights activists across the world. This week, drawing on 403 pages of leaked government documents, The New York Times published new details of how the ongoing crackdown took shape under Chinese President Xi Jinping and other leadership in the Communist Party of China, how government workers who resisted the plan were sidelined, and what officials were instructed to tell young people whose families had been detained. "They're in a training school set up by the government to undergo collective systematic training, study and instruction," the talking points read, adding, "You have nothing to worry about." The Chinese government's campaign against those it says have been exposed to extremism is centered on an autonomous region, Xinjiang, where nearly half of the 25 million residents are a Muslim people called the Uighurs. Earlier in November, a FRONTLINE documentary called In the Age of AI examined how, as part of its crackdown involving the Uighurs, China's government has made Xinjiang a test project for forms of extreme digital surveillance.

Discussions on the interplay of humans and Artificial Intelligence tend to pose the issue in the language of opposition. However, according to the thinking of evolutionary biologist Richard Dawkins, tools such as AI can be better thought of as part of our extended phenotype. A phenotype refers to the observable characteristic of an organism, and the idea of the extended phenotype is that this should not be limited to biological processes, but include all of the effects that the genes have upon their environment, both internally and externally. We are used to defining ourselves strictly by the space we occupy in the physical world. The numbers of non-human cells that occupy our own body outnumber the number of human cells and vast colonies of bacteria swarm within the interior of our digestive tract. Author Robert Svoboda compares the human to a minority government ruling a primarily non-human population.

A few short years ago, personal digital assistants like Amazon's Alexa, Apple's Siri and Google Assistant sounded futuristic. Now, the future is here and this future is embedded, augmented and ubiquitous. Digital assistants can be found in your office, home, car, hotel, phone and many other places. They have recently undergone massive transformation and run on operating systems that are fuelled by artificial intelligence (AI). They observe and collect data in real-time and have the capability to pull information from different sources such as smart devices and cloud services and put the information into context using AI to make sense of the situation.

Life sciences companies are likely to begin experimenting further with AI in their workflows in the coming years, but they face challenges in AI adoption due to strict regulations. Machine learning has a "black box" problem, meaning that it's in many cases impossible to know how a machine learning algorithm comes to its conclusions. An AI application that detects cancer, for example, may not be able to show an oncologist how it determined the presence of cancer in a patient's body. As a result, if the oncologist used the application to diagnose a patient, they wouldn't be able to explain to the patient what makes them sure they have cancer. This issue relegates AI applications in life sciences to experiments and pilots, and widespread adoption, although likely inevitable, may not come for a while as public opinion shifts toward accepting that its diagnoses are informed by decision-making artificial intelligence and regulations evolve to match.

JPMorgan Chase sees artificial intelligence (AI) as critical to its future success. And the mega-bank has a big advantage over many of its smaller rivals: the massive amount of data it gathers from sources such as the 50% of U.S. households with which it has some form of relationship and the $6 trillion worth of payment flows it handles daily. But until recently, identifying and pulling in relevant data to train AI models was taking up around 60% of the time of the bank's growing army of data scientists. That was an inefficient use of an expensive and relatively scarce resource. Now a new data platform the bank has developed, called OmniAI, is helping it to get relevant data into its models much faster.