Facebook has acquired Ozlo, a Palo Alto-based artificial intelligence startup, to help Messenger build out a more elaborate virtual assistant for users. Ozlo specializes in understanding text-based conversations, and claims it can understand and provide answers to questions that don't necessarily have simple yes or no answers -- what Ozlo calls "probabilistic assertions of truth." On the company's website, for example, a short demo shows its AI assistant answering a question about whether or not a restaurant is "group friendly" based on pulling and understanding multiple reviews. Ozlo's website claims it has 30 employees, and a Facebook spokesperson says the "majority of the team" will be joining Messenger in Facebook's offices in either Menlo Park, Calif., or Seattle, Wash. Facebook is buying the company's technology and workforce, and it sounds as though Ozlo's technology will fold into Messenger's existing AI efforts, though it's unclear if that includes Messenger's existing virtual assistant, M. "They're just going to be working with [Messenger] to continue their work with artificial intelligence and machine learning," a spokesperson said.

LOS ANGELES, CA--(Marketwired - Jul 31, 2017) - SIGGRAPH 2017 - NVIDIA (NASDAQ: NVDA) today announced that it is bringing the power of artificial intelligence to rendering with the launch of NVIDIA OptiX 5.0 SDK with powerful new ray-tracing capabilities. Running OptiX 5.0 on the NVIDIA DGX Station -- the company's recently introduced deskside AI workstation -- will give designers, artists and other content-creation professionals the rendering capability of 150 standard CPU-based servers. This access to GPU-powered accelerated computing will provide extraordinary ability to iterate and innovate with speed and performance, at a fraction of the cost. "Developers using our platform can enable millions of artists and designers to access the capabilities of a render farm right at their desk," said Bob Pette, Vice President, Professional Visualization, NVIDIA. "By creating OptiX-based applications, they can bring the extraordinary power of AI to their customers, enhancing their creativity and dramatically improving productivity."

The current political discourse sounds like people are speaking two different languages, according to a new study. Penn State psychologists determined that even though Donald Trump and Hillary Clinton were both speaking English during the 2016 presidential election, semantically, they were so far apart that the difference was actually worse than if one of them were speaking Chinese. In a series of studies, the researchers found a split between the words democrats and republican presidential candidates used and how they meant them - they also found this'semantic divide' appears to be growing and may continue to make the possibility of a bipartisan dialogue difficult, if not impossible. The Penn State researchers compiled a list of 213 single words and 397 word phrases, including 136 politically charged words like'minority,' 'spending' and'justice.' They looked at all Presidential elections since 1999 and used an artificial intelligence neural network to statistically examine how often the words appeared together.

Special note: we're teaching a fully updated part 1, in person, for seven weeks from Oct 30, 2017, at the USF Data Institute. See the course page for details and application form. Part 1 of the course has now been viewed by tens of thousands of students, introducing them to nearly all of today's best practices in deep learning, and providing many hours of hands-on practical coding exercises. We have collected some stories from graduates of part 1 on our testimonials page. Today, we are launching Part 2: Cutting Edge Deep Learning for Coders.

Negotiating in a new language As Fast Co. Design reports, Facebook's researchers recently noticed its new AI had given up on English. "Agents will drift off from understandable language and invent code-words for themselves," Fast Co. Design reports Facebook AI researcher Dhruv Batra said. AI language translates human ones In a separate case, Google recently improved its Translate service by adding a neural network. While they appear nonsensical, the results observed by teams such as Google Translate indicate they actually represent the most efficient solution to major problems.

There is a great read by Ed Newton-Rex that outlines the differences between Artificial Intelligence and Machine Learning in simple terms. The recent article from Frank Chen on the expected use of artificial intelligence in startups highlights an upcoming shift in the industry. He compares, and rightfully so, the pervasiveness of AI to other game changers in the industry such as having a relational database in 1980 or rich Windows client in 1987 and so on. AI is nothing new in computer science -- modern AI research was born in 1956 at a Dartmouth workshop (coincidentally included two, now-famous, Carnegie Mellon professors with a building named after them -- Allen Newell and Herbert Simon). Even though we're still some ways away from building our one true AI overlord aka Skynet, over the past two decades we have seen significant advances in the field of machine learning (ML).

Welcome to a new section in our Machine Learning Tutorial series: Deep Learning with Neural Networks and TensorFlow. The artificial neural network is a biologically-inspired methodology to conduct machine learning, intended to mimic your brain (a biological neural network). The Artificial Neural Network, which I will now just refer to as a neural network, is not a new concept. The idea has been around since the 1940's, and has had a few ups and downs, most notably when compared against the Support Vector Machine (SVM). For example, the Neural Network was popularized up until the mid 90s when it was shown that the SVM, using a new-to-the-public (the technique itself was thought up long before it was actually put to use) technique, the "Kernel Trick," was capable of working with non-linearly separable datasets.

Days after Tesla CEO Elon Musk said that artificial intelligence (AI) was the biggest risk, Facebook has shut down one of its AI systems after chatbots started speaking in their own language defying the codes provided. According to a report in Tech Times on Sunday, the social media giant had to pull the plug on the AI system that its researchers were working on "because things got out of hand". "The AI did not start shutting down computers worldwide or something of the sort, but it stopped using English and started using a language that it created," the report noted. Initially the AI agents used English to converse with each other but they later created a new language that only AI systems could understand, thus, defying their purpose. This led Facebook researchers to shut down the AI systems and then force them to speak to each other only in English.

Satellites in space and a robot under Lake Erie's surface are part of a network of scientific tools trying to keep algae toxins out of drinking water supplies in the shallowest of the Great Lakes. It's one of the most wide-ranging freshwater monitoring systems in the U.S., researchers say, and some of its pieces soon will be watching for harmful algae on hundreds of lakes nationwide. Researchers are creating an early warning system using real-time data from satellites that in recent years have tracked algae bloom hotpots such as Florida's Lake Okeechobee and the East Coast's Chesapeake Bay. Satellites in space and a robot under Lake Erie's surface are part of a network of scientific tools trying to keep algae toxins out of drinking water supplies in the Great Lakes. The system in development will cast a wider net at a time when many states can't afford to monitor every lake threatened by harmful algae.

Days after Tesla CEO Elon Musk said that artificial intelligence (AI) was the biggest risk, Facebook has shut down one of its AI systems after chatbots started speaking in their own language defying the codes provided. According to a report in Tech Times on Sunday, the social media giant had to pull the plug on the AI system that its researchers were working on "because things got out of hand". The AI did not start shutting down computers worldwide or something of the sort, but it stopped using English and started using a language that it created," the report noted. Initially, the AI agents used English to converse with each other but they later created a new language that only AI systems could understand, thus, defying their purpose. This led Facebook researchers to shut down the AI systems and then force them to speak to each other only in English. In June, researchers from the Facebook AI Research Lab (FAIR) found that while they were busy trying to improve chatbots, the "dialogue agents" were creating their own language. Soon, the bots began to deviate from the scripted norms and started communicating in an entirely new language which they created without human input, media reports said. Using machine learning algorithms, the "dialogue agents" were left to converse freely in an attempt to strengthen their conversational skills. The researchers also found these bots to be "incredibly crafty negotiators". "After learning to negotiate, the bots relied on machine learning and advanced strategies in an attempt to improve the outcome of these negotiations," the report said. "Over time, the bots became quite skilled at it and even began feigning interest in one item in order to'sacrifice' it at a later stage in the negotiation as a faux compromise," it added. Although this appears to be a huge leap for AI, several experts including Professor Stephen Hawking have raised fears that humans, who are limited by slow biological evolution, could be superseded by AI. Others like Tesla's Elon Musk, philanthropist Bill Gates and ex-Apple founder Steve Wozniak have also expressed their concerns about where the AI technology was heading. Interestingly, this incident took place just days after a verbal spat between Facebook CEO and Musk who exchanged harsh words over a debate on the future of AI. "I've talked to Mark about this (AI).