Bribery in elections is an important problem in computational social choice theory. However, bribery with money is often illegal in elections. Motivated by this, we introduce the notion of frugal bribery and formulate two new pertinent computational problems which we call Frugal-bribery and Frugal- $bribery to capture bribery without money in elections. In the proposed model, the briber is frugal in nature and this is captured by her inability to bribe votes of a certain kind, namely, non-vulnerable votes. In the Frugal-bribery problem, the goal is to make a certain candidate win the election by changing only vulnerable votes. In the Frugal-{dollar}bribery problem, the vulnerable votes have prices and the goal is to make a certain candidate win the election by changing only vulnerable votes, subject to a budget constraint of the briber. We further formulate two natural variants of the Frugal-{dollar}bribery problem namely Uniform-frugal-{dollar}bribery and Nonuniform-frugal-{dollar}bribery where the prices of the vulnerable votes are, respectively, all the same or different. We study the computational complexity of the above problems for unweighted and weighted elections for several commonly used voting rules. We observe that, even if we have only a small number of candidates, the problems are intractable for all voting rules studied here for weighted elections, with the sole exception of the Frugal-bribery problem for the plurality voting rule. In contrast, we have polynomial time algorithms for the Frugal-bribery problem for plurality, veto, k-approval, k-veto, and plurality with runoff voting rules for unweighted elections. However, the Frugal-{dollar}bribery problem is intractable for all the voting rules studied here barring the plurality and the veto voting rules for unweighted elections.

Task-oriented dialog systems have been applied in various tasks, such as automated personal assistants, customer service providers and tutors. These systems work well when users have clear and explicit intentions that are well-aligned to the systems' capabilities. However, they fail if users intentions are not explicit. To address this shortcoming, we propose a framework to interleave non-task content (i.e. everyday social conversation) into task conversations. When the task content fails, the system can still keep the user engaged with the non-task content. We trained a policy using reinforcement learning algorithms to promote long-turn conversation coherence and consistency, so that the system can have smooth transitions between task and non-task content. To test the effectiveness of the proposed framework, we developed a movie promotion dialog system. Experiments with human users indicate that a system that interleaves social and task content achieves a better task success rate and is also rated as more engaging compared to a pure task-oriented system.

Each year in the United States, more than 40,000 people die by suicide, and from 1999 to 2014, the suicide rate increased 24 percent. You might think that after generations of theories and data, we would be close to understanding how to prevent self-harm, or at least predict it. But a new study concludes that the science of suicide prediction is dismal, and the established warning signs about as accurate as tea leaves. There is, however, some hope. New research shows that machine-learning algorithms can dramatically improve our predictive abilities on suicides.

Health Catalyst and the Regenstrief Institute are working together to commercialize nDepth, Regenstrief's natural language processing technology. Indianapolis-based Regenstrief developed the technology to harness unstructured data. Salt-Lake City-based Health Catalyst, a data warehousing and analytics company, has been in the business of extracting data to boost care quality since it launched in 2008. It was developed within the Indiana Health Information Exchange, the largest and oldest HIE in the country. Regenstrief fine-tuned nDepth through extensive and repeated use, searching more than 230 million text records from more than 17 million patients.

The 2017 Benzinga Global Fintech Awards is a competition to showcase the companies with the most impressive technology that are paving the future in financial services and capital markets. I Know First: I Know First's self-learning forecasting algorithm utilizes artificial intelligence and machine learning techniques to find relationships and patterns in large sets of historical stock market data in order to analyze and predict behavior and identify the best daily market opportunities. I Know First: On the institutional side, our typical clients are family offices, hedge funds and other asset management firms, as well as banks. He succeeded by applying a wide range of artificial intelligence (AI) techniques, including neural networks and genetic algorithms. We have structured a tiered product offering, which consists of algorithmically generated forecast reports (standardized or tailored to client's needs) and development of systematic trading and allocation strategies for hedge and mutual funds as well as structuring of smart-beta or actively managed ETFs and other investment vehicles.

BARCELONA, SPAIN and REDWOOD CITY, CA--(Marketwired - Feb 27, 2017) - MOBILE WORLD CONGRESS - Aricent, a global design and engineering company, today announced the launch of Cognitive Services to enhance customer engagement and brand loyalty in a digital era. Artificial Intelligence (AI) is top of mind today for most business leaders who seek to provide unique value around their products and services for a truly compelling customer experience. AI is a wide ranging category containing many capabilities including Natural Language Processing, Natural Language Understanding, Machine Learning, Deep Learning and Computer Vision. However, launching successful products and services powered by AI is challenging. As per Gartner Research report titled Top 10 Strategic Technology Trends for 2017*, "Significant investment in skills, process and tools is needed to successfully exploit these techniques in terms of setup, integration, algorithm/approach selection, data preparation and model creation."

Neural networks are a rapidly developing paradigm for information processing based loosely on how neurons in the brain processes information. A neural network consists of multiple layers of node, where each node performs a unit of computation, and passes the result onto the next node. Multiple nodes can pass inputs to a single node, and vice-versa. The neural network also contains a set of weights, which can be refined over time as the network learns from sample data. The weights are used to describe and refine the connection strengths between nodes.

Most of us can guess the name of a stranger with up to 40 per cent accuracy, a study has found. This is because people grow to look more like their name, subconsciously changing their hairstyles, putting on weight, smiling or frowning. For example, society generally expects men called Bob to have rounder faces than those called Tim, because the word looks round. Can you beat the researchers' computer algorithm top score of 64 per cent accuracy in matching the names of strangers to their faces? Each correct match is worth 16.6 per cent.

The algorithm - The general workflow or logic that dictates the path the machine travels, based on chosen features and parameter values.

The UK government is planning to announce new measures to help artificial intelligence (AI) and robotics researchers to commercialise their breakthroughs. The Department for Culture, Media, and Sport (DCMS) announced on Monday that it will include a number of AI-related proposals in its upcoming Digital Strategy document, which will be unveiled in Parliament on Wednesday. As part of the Digital Strategy, DCMS said it expects to announce an AI review that will be led Southampton University professor Wendy Hall and ex-IBM scientist Jérôme Pesenti, who is now the CEO of London healthcare startup Benevolent.AI. The government is also expected to announce a £17.3 million investment into robotics and AI that will be given to UK universities via the Engineering and Physical Sciences Research Council (EPSRC). "There has been a lot of unwarranted negative hype around AI but it has the ability to drive enormous growth for the UK economy, create jobs, foster new skills, positively transform every industry and retain Britain's status as a world leader in innovative technology," said Hall in a statement.