End-to-end task-oriented dialog models have achieved promising performance on collaborative tasks where users willingly coordinate with the system to complete a given task. While in non-collaborative settings, for example, negotiation and persuasion, users and systems do not share a common goal. As a result, compared to collaborate tasks, people use social content to build rapport and trust in these non-collaborative settings in order to advance their goals. To handle social content, we introduce a hierarchical intent annotation scheme, which can be generalized to different non-collaborative dialog tasks. Building upon Transfer-Transfo (Wolf et al. 2019), we propose an end-to-end neural network model to generate diverse coherent responses. Our model utilizes intent and semantic slots as the intermediate sentence representation to guide the generation process. In addition, we design a filter to select appropriate responses based on whether these intermediate representations fit the designed task and conversation constraints. Our non-collaborative dialog model guides users to complete the task while simultaneously keeps them engaged. We test our approach on our newly proposed A NTIS CAM dataset and an existing P ERSUASIONF ORG OOD dataset. Both automatic and human evaluations suggest that our model outperforms multiple baselines in these two non-collaborative tasks. Introduction Considerable progress has been made building end-to-end dialog systems for collaborative tasks in which users cooperate with the system to achieve a common goal. Examples of collaborative tasks include making restaurant reservations and retrieving bus timetable information. Since users typically have clear and explicit intentions in collaborative tasks, existing systems commonly classify user utterances into predefined intents. In contrast, non-collaborative tasks are those where the users and the system do not strive to achieve the same goal.

"If you buy a Tesla today, I believe you're buying an appreciating asset, not a depreciating asset." Think about that statement for a second…you're buying an appreciating asset, not a depreciating asset. And what is driving the appreciation of that asset? Tesla cars become "smarter" and consequently more valuable with every mile each of the 400,000 Autopilot-equipped cars are driven. Imagine a mindset of leveraging Deep Reinforcement Learning with new operational data to create products (vehicles, trains, cranes, compressors, chillers, turbines, drills) that appreciate with usage because the products are getting more reliable, more predictive, more efficient, more effective, safer and consequently more valuable.

It's time for employers to start preparing for legislation recently signed into law in Illinois, the Artificial Intelligence Video Interview Act. The new law, which takes effect on January 1, 2020, regulates Illinois employers' use of artificial intelligence (AI) in the interview and hiring process. Under the AI Video Interview Act, employers that record video interviews and use AI technology to analyze applicants' suitability for employment must: Employers that conduct such interviews may not distribute videos to other parties, except as necessary to obtain expert assistance in evaluating a candidate's fitness for a particular position. In addition, an employer has only 30 days to destroy all video copies of the interview if an applicant seeks such destruction. This law highlights a myriad of privacy concerns for employers evaluating the costs and benefits of incorporating AI technology into their hiring practices.

Why did you think to combine Qiskit, a quantum-computing framework, with PyTorch, a machine-learning framework? Karel Dumon: Classical machine learning is currently benefiting hugely from the open-source community, and this is something we want to leverage in quantum too. Our project focuses on the potential application of quantum computing for machine learning, but also on the use of machine learning to help progress quantum computing itself. Through our project, we hope to make it easier for machine learning developers to explore the quantum world. Patrick Huembeli: To that effect, it makes Qiskit very accessible for people with a classical machine learning background -- they can treat the quantum nodes just as another layer of their machine learning algorithm.

I want to start off with a quick question – can you recognize the two personalities in the below image? I'm certain you got one right. For most of us early age math enthusiasts, the movie "A Beautiful Mind" is inextricably embedded into our memory. Russell Crowe plays the role of John Nash in the movie, a Nobel prize winner for economics (and the person on the left-hand side above). Now, you would remember the iconic scene often regarded as: "Don't go after the blonde". "….the best outcome would come when everyone in the group is doing what's best for himself and the group."

This publication draws on a wide range of expertise to illuminate the year ahead. Even so, all our contributors have one thing in common: they are human. But advances in technology mean it is now possible to ask an artificial intelligence (AI) for its views on the coming year. We asked an AI called GPT-2, created by Openai, a research outfit. GPT-2 is an "unsupervised language model" trained using 40 gigabytes of text from the internet.

Even if artificial intelligence is only a part of a larger solution, we must arm the gatekeepers of patent rights with better tools so they can better carry out the goals of the patent system.

At the Common Good in the Digital Age tech conference recently held in Vatican City, Pope Francis urged Facebook executives, venture capitalists, and government regulators to be wary of the impact of AI and other technologies. "If mankind's so-called technological progress were to become an enemy of the common good, this would lead to an unfortunate regression to a form of barbarism dictated by the law of the strongest," he said. In a related but contextually different conversation, this summer Joy Buolamwini testified before Congress with Rep. Alexandria Ocasio-Cortez (D-NY) that multiple audits found facial recognition technology generally works best on white men and worst on women of color. What these two events have in common is their relationship to power dynamics in the AI ethics debate. Arguments about AI ethics can wage without mention of the word "power," but it's often there just under the surface. In fact, it's rarely the direct focus, but it needs to be. Power in AI is like gravity, an invisible force that influences every consideration of ethics in artificial intelligence. Power provides the means to influence which use cases are relevant; which problems are priorities; and who the tools, products, and services are made to serve. It underlies debates about how corporations and countries create policy governing use of the technology.

Researchers, start-ups and multinationals have been named among the winners of this year's AI Awards. Now in its second year, the AI Awards are run by AI Ireland, a non-profit business with backing from Microsoft Ireland and Alldus International. The organisation runs a number of community websites and monthly meetups supporting the area of data science, machine learning and AI in Ireland. Seven awards were handed out as part of the event today (20 November), with those shortlisted coming from academia, start-ups, SMEs and multinationals. The first of these awards was for the use of AI in a sector, with the winner named as speech recognition start-up SoapBox Labs.

From pharma to hospitals and beyond, the potential applications in healthcare are promising. Late last year, we spoke for The World Bank about our proprietary AI in healthcare research, and speaking with governments, it's clear that there are hurdles that healthcare companies have to overcome to access data for training AI systems. Broadly, most of the folks that we speak with who are innovating in AI and healthcare are frustrated with how hard it is to streamline the data to make use of it for applications such as diagnosing illnesses.