In Reasoning about Action and Planning, one synthesizes the agent plan by taking advantage of the assumption on how the environment works (that is, one exploits the environment's effects, its fairness, its trajectory constraints). In this paper we study this form of synthesis in detail. We consider assumptions as constraints on the possible strategies that the environment can have in order to respond to the agent's actions. Such constraints may be given in the form of a planning domain (or action theory), as linear-time formulas over infinite or finite runs, or as a combination of the two (e.g., FOND under fairness). We argue though that not all assumption specifications are meaningful: they need to be consistent, which means that there must exist an environment strategy fulfilling the assumption in spite of the agent actions. For such assumptions, we study how to do synthesis/planning for agent goals, ranging from a classical reachability to goal on traces specified in LTL and LTLf/LDLf, characterizing the problem both mathematically and algorithmically.

An important task for a recommender system to provide interpretable explanations for the user. This is important for the credibility of the system. Current interpretable recommender systems tend to focus on certain features known to be important to the user and offer their explanations in a structured form. It is well known that user generated reviews and feedback from reviewers have strong leverage over the users' decisions. On the other hand, recent text generation works have been shown to generate text of similar quality to human written text, and we aim to show that generated text can be successfully used to explain recommendations. In this paper, we propose a framework consisting of popular review-oriented generation models aiming to create personalised explanations for recommendations. The interpretations are generated at both character and word levels. We build a dataset containing reviewers' feedback from the Amazon books review dataset. Our cross-domain experiments are designed to bridge from natural language processing to the recommender system domain. Besides language model evaluation methods, we employ DeepCoNN, a novel review-oriented recommender system using a deep neural network, to evaluate the recommendation performance of generated reviews by root mean square error (RMSE). We demonstrate that the synthetic personalised reviews have better recommendation performance than human written reviews. To our knowledge, this presents the first machine-generated natural language explanations for rating prediction.

For starters, computers simply lacked the processing power to make things happen. Floppy disk-drive machines paled in sophistication compared with modern smartphones, and computer chips wouldn't hold a million components until 1989. Yet another obstacle dogged any dreams of AI from taking form. In 1984, the American Association of Artificial Intelligence held a fateful meeting where field pioneer Marvin Minsky, of all people, warned the business community that investor enthusiasm for artificial intelligence would eventually lead to disappointment. Sure enough, AI investment began to collapse.

That patent, awarded April 25, 1961, recognizes Robert Noyce as the inventor of the silicon integrated circuit (IC). Integrated circuits forever changed how computers were made while adding power to a process of another kind: the growth of a then-nascent field called artificial intelligence (AI). And the potential of Noyce's invention truly took flight when he and Gordon Moore founded Intel on July 18, 1968. Fifty years later, the "eternal spring" of artificial intelligence is in full swing. To understand how we arrived, here's the truth in a nutshell: The rise of artificial intelligence is intertwined with the history of faster, more robust microprocessors.

Over the past decade, machine learning systems have begun to play a key role in many high-stakes decisions: Who is interviewed for a job? Who is approved for a bank loan? Who is admitted to a school? Human decision makers are susceptible to many forms of prejudice and bias, such as those rooted in gender and racial stereotypes. One might hope that machines would be able to make decisions more fairly than humans.

Artificial intelligence (AI) will be a good thing for the digital workplace. Despite concerns about job redundancies and shifting enterprise priorities, recent research from global auditing company PwC suggests that, far from hindering employees or even making some jobs obsolete, AI will help workers achieve business objectives quicker and more effectively. To do that, however, organizations will need to invest in different types of AI. The research, part of PwC's Economic Outlook for 2018, predicted the main contributor to the UK's economic gains between 2017 and 2030 will come from consumer product enhancements stimulating consumer demand (8.4 percent). The research identified AI as a key factor in this growth, by driving a greater choice of products, increasing personalization and making those products more affordable over time.

HealthTech presents a huge opportunity, but how can businesses leverage these advantages, and how can Data Science help? The healthcare industry is huge and in turn, the size of the Health Tech industry is significantly larger than in other fields. In the USA alone, the healthcare market is more than 17% of GDP. A recent article published by TechCrunch highlights the vulnerabilities in the industry based on its consistent and rapid expansion: "the healthcare market represents $3 trillion, almost 20 percent, of the U.S. economy. This market also is plagued by a level of gross inefficiency and under-performance largely unseen in any other industries in our post-internet world".

There is a pending boom of artificial intelligence (AI) capabilities, and organizations will capitalize on them as AI continues to scale, evolve and mature. Over time, AI adoption at the enterprise level will cause an incremental upending of traditionally known roles, skills and talent composition within every single business function. According to the 2018 Human Capital Trends Report, "The transition to digital business tools is at the top of the strategic agenda for many businesses today." If mobile, social and cloud computing were some of the biggest platform shifts and technological investments that dominated the beginning of the 21st century, AI investments will be considered the next wave of major technological shifts. Forrester predicts that most (if not all) technology functions will be augmented or replaced by AI.

A new study by CapGemini finds that consumers are ready to embrace AI support, and that AI interactions, if properly designed, can enhance the personal connection they feel to brands. The study is comprehensive – it is based on surveys of 10,000 consumers from 10 different markets and covers all ages, income groups and employee status. It is current, as the surveys were conducted this May. These surveys were supplemented with three virtual focus group discussions with 8-10 consumers per focus group in the U.S., France and Germany. Research for the report included interviews with several key industry stakeholders and academics.

Clearly, Roborace doesn't believe in bad luck. Last week, on Friday the 13th, the company chose to run its self-driving Robocar in front of a feverish crowd at England's Goodwood Festival of Speed. It was only the second time the team had demonstrated its futuristic vehicle publicly, following an unassisted lap in Paris roughly 13 months ago. There was no room for error. The absence of a human cockpit gives the car an unusually low profile.