Bohemian Rhapsody is one of the most well-known rock songs in the western world, and has been watched more than a billion times on YouTube alone. It's likely that pretty much everyone has had at least one crack at aping singer Freddie Mercury's unique vocal delivery. Now, Google, Queen and UMG have teamed up to build the FreddieMeter, an system to determine how close you can get to Mercury's voice. FreddieMeter is a collaboration between the band, Google's Creative Lab and the various record labels involved. It's been designed to analyze a person's voice to see how their pitch, timbre and melody comes in comparison to Mercury's own.

AutoAI, a powerful, automated AI development capability in IBM Watson Studio, won the Best Innovation in Intelligent Automation Award yesterday at the AIconics AI Summit in San Francisco. Chosen by a panel of 13 independent judges, the AIconics awards recognize breakthroughs in AI for business. To share what went behind the development of AutoAI and how it accelerates time to value with data science projects, I interviewed one of our principal inventors: Jean-Francois Puget, PhD, a distinguished engineer for machine learning and optimization at IBM and a two-time Kaggle Grandmaster. What challenge led you to start developing AutoAI? Jean-Francois Puget: As data scientists, our work is a mix of applying general-purpose recipes and creating domain-specific insights.

Over July 4th weekend in 1981, several hundred game nerds gathered at a banquet hall in San Mateo, California. Personal computing was still in its infancy, and the tournament was decidedly low-tech. Each match played out on a rectangular table filled with paper game pieces, and a March Madness-style tournament bracket hung on the wall. The game was called Traveller Trillion Credit Squadron, a role-playing pastime of baroque complexity. Contestants did battle using vast fleets of imaginary warships, each player guided by an equally imaginary trillion-dollar budget and a set of rules that spanned several printed volumes. If they won, they advanced to the next round of war games--until only one fleet remained. Doug Lenat, then a 29-year-old computer science professor at nearby Stanford University, was among the players. But he didn't compete alone. He entered the tournament alongside Eurisko, the artificially intelligent system he built as part of his academic research. Eurisko ran on dozens of machines inside Xerox PARC--the computer research lab just down the road from Stanford that gave rise to the graphical user interface, the laser printer, and so many other technologies that would come to define the future of computing. That year, Lenat taught Eurisko to play Traveller. Doug Lenat says his common-sense engine is a new dawn for AI. The rest of the tech world doesn't really agree with him.

Mobile is now the first and primary channel for consumers to interact with various products and services. Gradual and consistent advancement in the technology industry has created a necessity for enterprises and businesses to incorporate various features that supports and enhances the usability for the next breed of mobile users. Next Generation applications may require a new, improved and innovative approach to development that also helps in the rapid and exponential growth of businesses. The latest technological trends like Artificial Intelligence, Internet of Things, AR / VR, and cloud-driven mobile app development have gained significant popularity in recent years. Because of that developers are more focused on leveraging these cutting-edge technologies by offering more robust and scalable next-generation mobile apps that exceed the standards of what businesses and customers expect.

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Hollywood producer Gale Anne Hurd ("Terminator," "Alien," "The Walking Dead") says Google, Tesla and Apple can watch "The Terminator" to learn something about their Future

At EPFL's School of Engineering, researchers in the Electromagnetic Compatibility Laboratory, led by Farhad Rachidi, have developed a simple and inexpensive system that can predict when lightning will strike to the nearest 10 to 30 minutes, within a 30-kilometer radius. The system uses a combination of standard meteorological data and artificial intelligence. The research paper has been published in Climate and Atmospheric Science, a Nature partner journal. The researchers are now planning to use their technology in the European Laser Lightning Rod project. "Current systems are slow and very complex, and they require expensive external data acquired by radar or satellite," explains Amirhossein Mostajabi, the PhD student who came up with the technique.

The AWS Certified Advanced Networking Official Study Guide – Specialty Exam helps to ensure your preparation for the AWS Certified Advanced Networking – Specialty Exam. Expert review of AWS fundamentals align with the exam objectives, and detailed explanations of key exam topics merge with real-world scenarios to help you build the robust knowledge base you need to succeed on the exam--and in the field as an AWS Certified Networking specialist. Coverage includes the design, implementation, and deployment of cloud-based solutions; core AWS services implementation and knowledge of architectural best practices; AWS service architecture design and maintenance; networking automation; and more. You also get one year of free access to Sybex's online interactive learning environment and study tools, which features flashcards, a glossary, chapter tests, practice exams, and a test bank to help you track your progress and gauge your readiness as exam day grows near. The exam assumes existing competency with advanced networking tasks, and assesses your ability to apply deep technical knowledge to the design and implementation of AWS services.

In this lesson, you'll learn how to: Here, I'll show you the logic behind each technique, and you are going to be able to apply machine learning in different situations. No more talking, let's get straight to it. Assuming that you have Anaconda and Jupyter Notebooks installed, create a new notebook. Let's import the pyplot module from the library matplotlib. Pyplot is useful for generating simple charts from data.

Firm leverages AI technology similar to facial recognition to figure out which small molecules can bind most effectively with targeted enzymes. Does the future of drug development lie in a kind of facial-recognition technology for enzymes? That is the hope of X-37 LLC, a drug-development startup that is using artificial intelligence and a deep neural network developed by San Francisco-based Atomwise. "We think that this is an approach and a technology that is really going to transform drug discovery across the board," said Dr. David Collier, CEO of X-37, which is partly owned by Atomwise and is based in South San Francisco, California. It was founded last year.