Attendees inspect a Google self-driving car that's now part of Waymo's test fleet, at a conference in Paris in June. Google's self-driving car group, which helped ignite a race among automakers and tech firms to develop cars that drive themselves, is spinning off as a stand-alone unit dubbed Waymo. While Google was an early leader in popularizing the vision of vehicles driven by artificial intelligence rather than a human, it now faces intense competition to commercialize the technology. Uber, the ride-hailing giant, this year acquired robot-trucking tech developer Otto, a company begun by Google alumni, and has set a goal of getting autonomous vehicles into its fleet as soon as possible. Likewise, Elon Musk is pushing Tesla Motors to be a leader in driverless car technology and has begun equipping all of its electric vehicles with sensors and computing power intended to automate control of driving functions.

Datacenters, especially the really big guys known as the Super 7 (Alibaba,, Baidu, Facebook, Google, Microsoft and Tencent), are experiencing significant growth in key workloads that require more performance than can squeezed out of even the fastest CPUs. Applications such as Deep Neural Networks (DNN) for Artificial Intelligences (AIs), complex data analytics, 4K live streaming video and advanced networking and security features are increasingly being offloaded to super-fast accelerators that can provide 10X or more the performance of a CPU. NVIDIA GPUs in particular have benefited enormously from the training portion of machine learning, reporting a 193% Y/Y last quarter in their datacenter segment, which is now approaching a $1B run-rate business. Microsoft has recently announced that Field Programmable Gate Array (FPGA) accelerators have become pervasive in their datacenters. Soon after, announced that Baidu is using their devices for acceleration of machine learning applied to speech processing and autonomous vehicles.

Jean took NYC Data Science Academy 12 week full time Data Science Bootcamp pr... between Sept 23 to Dec 18, 2015. The post was based on his first class project(due at 2nd week of the program). Check out the full report here! You will find all the details of the code behind the analysis and the visualisations. For this project, we wish to present and explore the data provided by Lending Club.

If it wasn't clear enough, AI, and more specifically the machine learning sub-branch, is a big deal -- and not just for Google. It's not much of a "next big thing" aimed at supplanting everything that has come before it from above, but rather a more silent revolution branching out from underneath. In this new and fascinating video shared by Google, the company gives us a more comprehensive look at the importance of machine learning and the many ways it's being used to reshape the very way certain things are done. As one of the Googlers interviewed puts it, "The promise of AI and machine learning is that we can actually produce solutions to previously unsolved problems that will really help people." We see machine learning used in tasks from massive things like image and voice recognition and even blindness prevention, but it's also used for minor, very specific tasks.

Mobey Day Toronto speakers and participants discussed the converging factors driving the evolution of the financial services industry. From the indelible mark left by the pervasiveness of smartphones to recent developments in augmented and machine learning, attendees were treated to insights on how collaboration between financial services, fintech and government can contribute to redefining banking and payments. "The major winners will be financial services companies that embrace technology." Atakan Cetinsoy (@atakante) December 7, 2016 "People need banking, not banks." Wolfond is at #MobeyDay explaining #fintech solutions offered by @SecureKey-- Ryan Weaver (@ryandrjweaver) December 7, 2016 "Insurance companies are falling behind," was a repeated notion.

Udacity is positioned perfectly to benefit from the rush on talent in a number of growing areas of interest among tech companies and startups. The online education platform has added 14 new hiring partners across its Artificial Intelligence Engineer, Self-Driving Car Engineer and Virtual Reality Developer Nanodegree programs, as well as in its Predictive Analytics Nanodegree, including standouts like Bosch, Harma, Slack, Intel, Amazon Alexa and Samsung. That brings the total number of hiring partners for Udacity to over 30, which means a lot of potential soft landings for graduates of its nanodegree programs. The nanodegree offered by Udacity is its own original form of accreditation, which is based on a truncated field of study that spans months, rather than years, and allows students to direct the pace of their own learning. It also all takes place online, so students can potentially learn from anywhere. For Udacity, hiring partners help prove the value of their program to potential students, as they're effectively votes of confidence made by exactly the kinds of companies where students are looking to get jobs.

On Monday, Microsoft announced a new Microsoft Ventures fund dedicated to artificial intelligence (AI) investments, according to TechCrunch. The fund, part of the company's investment arm that launched in May, will back startups developing AI technology and includes Element AI, a Montreal-based incubator that helps other companies embrace AI. The fund further supports Microsoft's focus on AI. The company has been steadily announcing major initiatives in support of the technology. For example, in September, it announced a major restructuring and formed a new group dedicated to AI products.

In the last few years, commercial organizations have relied heavily on immersive technology, effectively transforming operations, and even market competition. The internet makes cross-device integration increasingly common, particularly as more devices become connected. And as it becomes easier to connect with one another, the lines between reality and the digital world are increasingly blurred. Although artificial intelligence (AI) and machine learning were huge buzzwords in 2016, in the coming year we expect to see even more cloud users become accustomed to dealing with bots and synchronize their lives and operations to include digital assistants. Last year, Google and Microsoft added more powerful AI services to their cloud platforms.

That's what I find myself asking my Amazon Echo voice-activated device more often than any other -- not aloud but on the inside, as the comedian Bobby Collins might say, because you don't really want a robotic presence in your house doubting your commitment to your mutual relationship. Alexa, you might know, is the female persona inhabiting the Echo, a Wi-Fi-enabled black cylinder about the size of a Pringles can, which you prime to answer your questions or perform services by invoking her name. I was given a $179 Echo last year as a gift, and a $49 Echo Dot -- a squashed down version endowed with a lousier speaker but equipped with Bluetooth capability -- as another gift for Father's Day. According to Amazon PR, these devices have ranked among the firm's most popular items. With Christmas approaching, Amazon has been pushing the Dot mercilessly as a gift item, even bundling it in six-packs.

When it comes to search, there's Google and there's everyone else -- the company is basically synonymous with searching the internet. But Omnity, a relatively new company from San Francisco, thinks own search that's based on "semantic mapping" offers something that Google can't do. Omnity's trick is that it looks for the connections between documents on the internet based on rare words -- the theory that research that has several of the same rare words will likely be about related topics, even if that research doesn't directly link to or cite each other. Thus far, Omnity has operated primarily by selling enterprise plans to companies and educational institutions. Omnity can search not only all of the public datasets it scans (like patents, scientific, engineering and medical documents, clinical trials, case law, SEC filings and so forth) but also a company's internal documents -- for some companies, Omnity indexes 150 petabytes of data.