The young man at the center of all the commotion smiled calmly on Wednesday, the day before the first meaningful game of his NBA career. Lonzo Ball is rarely any other way. "It is going to be a lot of fun," Ball said. On Thursday night the Lakers will host the Clippers in the season opener for both teams. The organization has goals that go beyond this season, but when it comes to the team itself, its coaches and players, their goal is simple.
Leo Tolstoy wrote in "Anna Karenina" that "All happy families are alike; each unhappy family is unhappy in its own way." Terror attacks are like unhappy families; each is different in its own awful way. To continue reading Michael Goodwin on the New York Post click here. Michael Goodwin is a Fox News contributor and New York Post columnist.
HER STORY: Irma was the first hurricane that Montgomery, who is originally from Missouri, experienced. She and her husband drove out of Miami Beach with their two dogs as the storm approached Florida and headed to Tampa, where she expected to run her business from a hotel. Several staffers were staying in Tampa as well. When the storm changed course and it appeared Tampa would take a direct hit, Montgomery and her husband fled to Atlanta, a drive that took 14 hours instead of the normal six to seven because the roads were packed.
Most chatbots use multiple technologies: natural language processing, knowledge management and sentiment analysis. Typically, the natural language processing will identify the intent of a question with some level of confidence and then, based on the confidence level, the chatbot will either ask a follow-up or disambiguate the question for the user. In addition to natural language processing technology, chatbots typically also rely on knowledge management systems. AI chatbots have been used with varying levels of success in healthcare to date, addressing use-cases including helping consumers select a benefit plan, providing customer service responses, helping triage symptoms, and guiding consumers to resources.
The effort points to ways in which Amazon and other companies could try to improve the tracking of trends in other areas of retail--making recommendations based on products popping up in social-media posts, for instance. For instance, one group of Amazon researchers based in Israel developed machine learning that, by analyzing just a few labels attached to images, can deduce whether a particular look can be considered stylish. An Amazon team at Lab126, a research center based in San Francisco, has developed an algorithm that learns about a particular style of fashion from images, and can then generate new items in similar styles from scratch--essentially, a simple AI fashion designer. The event included mostly academic researchers who are exploring ways for machines to understand fashion trends.
Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Kosinski, an assistant professor of organizational behavior, said he was studying links between facial features and political preferences, with preliminary results showing that AI is effective at guessing people's ideologies based on their faces. That means political leanings are possibly linked to genetics or developmental factors, which could result in detectable facial differences. Facial recognition may also be used to make inferences about IQ, said Kosinski, suggesting a future in which schools could use the results of facial scans when considering prospective students.
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A new $240 million center at MIT may help advance the field of artificial intelligence by developing novel devices and materials to power the latest machine-learning algorithms. The project, announced by IBM and MIT today, will research new approaches in deep learning, a technique in AI that has led to big advances in areas such as machine vision and voice recognition. But it will also explore completely new computing devices, materials, and physical phenomena, including efforts to harness quantum computers--exotic but potentially very powerful new machines--to make AI even more capable. And it will study the economic impact of artificial intelligence and automation, a hugely significant issue for society.
Yago was one of the first knowledge bases, developed by scientists at the Max Planck Institute for Informatics in Saarbrücken and the Télécom ParisTech in Paris. "If you, for example, do a internet search for the German term'Allianz', this is merely a collection of letters for the search engine," explains Professor Gerhard Weikum, Scientific Director at the Max Planck Institute for Computer Science in Saarbrücken. Today, Yago is a collaboration of the Max Planck Institute, the Télécom ParisTech University, where Suchanek now holds a professorship, and the Max Planck spin-off, Ambiverse. Last week, the researchers behind Yago were awarded the Prominent Paper Award which recognizes outstanding papers published in the Artificial Intelligence Journal (AIJ) that are exceptional in their significance and impact over the past 5 years.
Most of the machine learning algorithms were developed to solve a well-known problem in AI, which is called the'Knowledge Acquisition Bottleneck'. It deals with the question how subject matter experts (SMEs) can be enabled to work together with data scientists on knowledge models in an efficient and sustainable way (See also: Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling). Machine learning algorithms learn from data, and by that, successful implementations are obviously strongly related to data quality and the approaches taken to encode the semantics (meaning) of data. Facing the'Knowledge Acquisition Bottleneck' also means that experts' knowledge is recognized as an essential asset of any organization.