NVIDIA debuted its Drive PX2 in-car supercomputer at CES in January, and now the company is showing off the Parker system on a chip powering it. The 256-core processor boasts up to 1.5 teraflops of juice for "deep learning-based self-driving AI cockpit systems," according to a post on NVIDIA's blog. That's in addition to 24 trillion deep learning operations per second it can churn out, too. For a perhaps more familiar touchpoint, NVIDIA says that Parker can also decode and encode 4K video streams running at 60FPS -- no easy feat on its own. However, Parker is significantly less beefy than NVIDIA's other deep learning initiative, the DGX-1 for Elon Musk's OpenAI, which can hit 170 teraflops of performance.

In the first post of our non-technical ML intro series we discussed some general characteristics of ML tasks. In this post we take a first baby step towards understanding how learning algorithms work. We'll continue the dialog between an ML expert and an ML-curious person. Ok I see that an ML program can improve its performance at some task after being trained on a sufficiently large amount of data, without explicit instructions given by a human. Let's start with an extremely simple example.

From broad population health management to highly individualized clinical decision support, advanced approaches to data analytics are changing the way providers interact with data and patients--even what it means to be a doctor. Cognitive computing technologies mimic the way the human brain draws connections between seemingly unrelated data. Powered with these technologies, intelligent machines can understand information in context and even have the ability to reason and learn. Cognitive computing encompasses various forms of artificial intelligence (AI), including machine learning, reasoning, natural language processing, speech and vision, human-computer interaction, dialog and narrative generation, and more. With cognitive computing, providers can uncover patterns in health data that previously were hidden, enabling them to do more than was possible before.

Could a computer be better at identifying depression than a primary care physician? That's the suggestion of a new study that focused on using machine learning to analyze Instagram photos. The study, conducted by a researcher from the department of psychology at Harvard University and another from the University of Vermont, analyzed nearly 44,000 photographs posted to Instagram, exploring factors like what filter was used and how makes "likes" a photo received. The study included photographs from 166 people, some of whom were depressed, and some of whom were not. Instagram offers a variety of filters to change how a photo appears, and the researchers discovered that healthy participants were more likely to use a filter than depressed people.

The people who are selling these machines want them to augment human intelligence, not replace it #ROSS #LegalTech https://t.co/TfcQ4Fm9mf With news that ROSS, the world's first AI lawyer, built upon IBM's Watson was'hired' by law firm Baker & Hostetler, there was much weeping and gnashing of teeth by organic, flesh-and-bone lawyers. They were then summarily rounded up by their new machine overlords and sealed in pods where their bioelectric energy was harnessed and used to power the very computers that now subjugated them. But for all the alarm-raising over the rise of AI and what it means for tomorrow's legal professionals, does machine intelligence pose a legitimate threat to the practice of law by human beings? Will clients in the near future be represented in court by Lawbot 3000?

Since it uses C 11 features, it requires a compiler with good C 11 support. Compilation is carried out using a Makefile, so you will need to have a working make. This will produce object files for all the classes as well as the main binary fasttext. If you do not plan on using the default system-wide compiler, update the two macros defined at the beginning of the Makefile (CC and INCLUDES). If you inted to build with Docker, a Docker file is available here fastText-Docker.

Geordie Rose, Founder of D-Wave (recent clients are Google and NASA) believes that the power of quantum computing is that we can exploit parallel universes' to solve problems that we have no other means of confirming. Simply put, quantum computers can think exponentially faster and simultaneously such that as they mature they will out pace us. Listen to his talk now!

In a post on its corporate blog on Monday, eBay explained how it uses machine learning technology to drive innovative new approaches to search experiences. But while that may sound good to the rest of the world, sellers may be wary of marketplaces that tinker with search, which is key to getting exposure to shoppers. Even before eBay started touting its skills in artificial intelligence and machine learning, sellers were concerned over eBay's use of an algorithm to influence which listings would appear higher on the search results page, something known as Best Match - the default sort order of listings. And that's exactly where eBay is concentrating its testing. In Monday's post, eBay wrote, "The largest scale application of machine learning technology at eBay is currently Best Match, the algorithm used to optimize relevance for buyers during their shopping experiences. It is a powerful tool for surfacing deals."

China's Cheetah Mobile, considered the world's leading mobile security and utility app developer, is preparing to accelerate its transition into a viable content-focused business, following a US 57 million purchase of media app operator News Republic in the second quarter. "This is a huge step for us," chief technology officer Charles Fan Chenggong told the South China Morning Post. "We can potentially reshape the news industry by introducing a strong, artificial intelligence-driven, personal news delivery service." The acquisition marks the most-ambitious bet to date by the New York-traded Cheetah Mobile, amid its recent struggle to boost advertising income. Analysts at the Blue Lotus Research Institute in Hong Kong said Cheetah had good "synergy" with News Republic, which works with more than 1,650 media partners.

This was the most often asked question at this year's Blackhat Conference 2016, especially for anyone with even a scent of Machine Learning algorithms in their product. With the biggest issue facing the SOC being the inability to sift through 1,000's of alerts per day due to a shortage in employees. It doesn't take a genius to get to the question of what it's going to cost me in man hours to sift through a new mouse-traps false positives. How many more Junior Analyst do I need to add to my team to look over my box? In the last five years I've watched more and more SOCs being built on the backs of Junior Security Analyst.