The facial-recognition cameras installed near the bounce houses at the Warehouse, an after-school recreation center in Bloomington, Indiana, are aimed low enough to scan the face of every parent, teenager and toddler who walks in. The center's director, David Weil, learned earlier this year of the surveillance system from a church newsletter, and within six weeks he had bought his own, believing it promised a security breakthrough that was both affordable and cutting-edge. Since last month, the system has logged thousands of visitors' faces – alongside their names, phone numbers and other personal details – and checked them against a regularly updated blacklist of sex offenders and unwanted guests. The system's Israeli developer, Face-Six, also promotes it for use in prisons and drones. "Some parents still think it's kind of '1984,' " said Weil, whose 21-month-old granddaughter is among the scanned.

If companies fail to make artificial intelligence (AI) a core competency within the next five years, they will face either stagnation or elimination. Recent GlobalData research reveals that incumbents in virtually every industry are facing some kind of game-changing disruption from AI technologies, with some being better prepared than others for the challenges ahead. AI adoption is highest in the banking and financial services, automotive, technology and telecoms verticals while the construction, energy and education industries lag behind. The report also identifies the market leaders and notable disruptive start-ups across seven AI technology areas, namely; machine learning, data science, conversational platforms, computer vision, AI chips, smart robots and context-aware computing. "AI is finally beginning to have an impact on the global economy. This technology has the potential to transform how we live and work at an extremely rapid rate and it is already beginning to have an effect across industries, with well-established incumbents increasingly coming up against major disruption from AI platforms developed either by the tech giants (including Amazon, Google and Microsoft) or by AI-focused start-ups," said Ed Thomas, Senior Analyst, Thematic Research Technology at GlobalData.

This course helps to understand what AI is, how it works, and how to use it to build smart apps. You can learn how to build simple machine learning models and implement conversational bots. To learn about machine learning, enter this course to get both theoretical and practical knowledge. You will understand various concepts such as inductive bias, the PAC and Mistake-bound learning frameworks, minimum description length principle, and Ockham's Razor.

Google has chosen India as a major battleground to take on rivals Amazon and Microsoft in its bid to dominate cloud computing services, said Rick Harshman, MD-Asia Pacific for Google Cloud, in an interview. He said the company had made big strides in the country in terms of enterprises adopting its technologies such as cloud services, security, artificial intelligence and machine learning. How are Indian enterprises adopting your technologies, especially cloud and artificial intelligence? How large is the opportunity? Globally... only about 5%-10% of all workloads in IT run on the cloud. I think the estimates are quite conservative.

If companies fail to make artificial intelligence (AI) a core competency within the next five years, they will face either stagnation or elimination, according to GlobalData, a leading data and analytics company. The company's latest Thematic Research report: 'Artificial Intelligence – Thematic Research', reveals that incumbents in virtually every industry are facing some kind of game-changing disruption from AI technologies, with some being better prepared than others for the challenges ahead. AI adoption is highest in the banking and financial services, automotive, technology and telecoms verticals while the construction, energy and education industries lag behind. The report also identifies the market leaders and notable disruptive start-ups across seven AI technology areas, namely; machine learning, data science, conversational platforms, computer vision, AI chips, smart robots and context-aware computing. Ed Thomas, Senior Analyst, Thematic Research Technology at GlobalData, commented, ''Nearly 70 years since Alan Turing posed the question'Can machines think?', AI is finally beginning to have an impact on the global economy.

Mr. Smith Goes to Washington 1939 TCM Tue. 7 p.m. Mean Streets 1973 Cinemax Sun. 6 a.m. Batman Begins 2005 AMC Sun. Throw Momma From the Train 1987 EPIX Sun. Die Hard 1988 IFC Sun. I Know What You Did Last Summer 1997 Starz Tue. Gone in 60 Seconds 2000 CMT Wed. 8 p.m., Thur. Total Recall 1990 Encore Thur. 2 a.m. A Fish Called Wanda 1988 Encore Thur. 2 p.m., 9 p.m. The World Is Not Enough 1999 EPIX Sat. 4 p.m. Look Who's Talking 1989 OVA Sun. Die Hard With a Vengeance 1995 IFC Thur. Oil-platform workers, including an estranged couple, and a Navy SEAL make a startling deep-sea discovery. A clueless politician falls in love with a waitress whose erratic behavior is caused by a nail stuck in her head. After glimpsing his future, an ambitious politician battles the agents of Fate itself to be with the woman he loves. To help a friend, a suburban baby sitter drives into downtown Chicago with her two charges and a neighbor. Two teenage baby sitters and a group of children spend a wild night ...

The field of computer science is exceptionally vast and ever-expanding. It will take a lifetime to just fathom its depth, forget mastering all the diversified fields. However, it's'programming' which is ubiquitous in the various branches of computer science. Programming offers a plethora of opportunities to kick-start your professional career. Now if you dabble in the art of coding (the other term for'programming'), yet the multitude of options confuses you, pore over the following article to find your niche in computer science.

Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution for explainable recommendation. Two companion learning tasks of user preference modeling for recommendation} and \textit{opinionated content modeling for explanation are integrated via a joint tensor factorization. As a result, the algorithm predicts not only a user's preference over a list of items, i.e., recommendation, but also how the user would appreciate a particular item at the feature level, i.e., opinionated textual explanation. Extensive experiments on two large collections of Amazon and Yelp reviews confirmed the effectiveness of our solution in both recommendation and explanation tasks, compared with several existing recommendation algorithms. And our extensive user study clearly demonstrates the practical value of the explainable recommendations generated by our algorithm.

In 1971, Terry Winograd wrote the SHRDLU program while completing his PhD at MIT. SHRDLU features a world of toy blocks where the computer translates human commands into physical actions, such as "move the red pyramid next to the blue cube." To succeed in such tasks, the computer must build up semantic knowledge iteratively, a process Winograd discovered was brittle and limited. The rise of chatbots and voice activated technologies has renewed fervor in natural language processing (NLP) and natural language understanding (NLU) techniques that can produce satisfying human-computer dialogs. Unfortunately, academic breakthroughs have not yet translated to improved user experiences, with Gizmodo writer Darren Orf declaring Messenger chatbots "frustrating and useless" and Facebook admitting a 70% failure rate for their highly anticipated conversational assistant M. Nevertheless, researchers forge ahead with new plans of attack, occasionally revisiting the same tactics and principles Winograd tried in the 70s. OpenAI recently leveraged reinforcement learning to teach to agents to design their own language by "dropping them into a set of simple worlds, giving them the ability to communicate, and then giving them goals that can be best achieved by communicating with other agents."

Welcome to this course: The Complete Natural Language Processing (NLP) Course. Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Natural Language Processing (NLP) is used in many applications to provide capabilities that were previously not possible. It involves analyzing text to obtain intent and meaning, which can then be used to support an application. This comprehensive course will get you up-and-running with advanced tasks using Natural Language Processing Techniques with Python.