NVIDIA's (NASDAQ:NVDA) quarterly revenues have been surging at double-digit rates in the last three quarters and, looking at the guidance for the next quarter, it's clear that the company is expecting the growth rate to continue unabated in the near future. One of the key factors that has added fuel to their engines is NVIDIA's new growth drivers: the data center segment and auto segment. In the most recent quarter, NVIDIA's data center unit reported 151 million in sales, a growth of 109.72% The growth in data center revenues is much higher than other segments, and there are several reasons why this growth can, in fact, continue its breakneck pace for several more quarters. NVIDIA's expertise in the GPU segment has given it a range of must-have products for hyperscale data centers.

AI is uniquely able to provide a text-free shopping journey, one that transforms mobile shopping into a wordless visual conversation. As much time as we spend on our cellphones, we actually don't use them to buy all that much. According to eMarketer, while mobile phones boast about 30% of all retail traffic, they only account for 11% of revenue. We browse on our phones, sure, but when it comes to buying, we actually use our desktops or head to a brick-and-mortar location that carries products we're interested in. So why is that exactly?

Self-driving cars are a rapidly evolving technology which just a couple of years back was considered science fiction. Envision getting in your car, typing or speaking a location into your vehicle's interface, then giving it a chance to drive you to your destination while you read a book, surf the web or rest. Self-driving vehicles – the stuff of science fiction since the principal roads were paved – are coming, and they're going to radically change what it's like to get from point A to point B. An automobile technology firm based out of US has made a self-directed mini bus that will be driven around Washington DC exclusively by artificial intelligence. Arizona-based Local Motors has made Olli, a self-driving mini bus made using 3D printed parts that will be autonomously controlled by the IBM Watson supercomputer. It can carry up to 12 individuals and the mini bus will likewise interact specifically with passengers.

California transportation authorities made two major changes in their policy on autonomous vehicles. The state, which is already relatively progressive with its laws for self-driving automobiles, continues to be a leader in adopting policies that give companies testing driverless cars more latitude. The first change, a new bill signed into law on Sept. 29, gives the Contra Costa Transportation Authority permission to test a pilot project on public roads without having a driver behind the wheel. Prior to this, the state only allowed public road testing if a human driver was in the driver's seat and "capable of taking immediate manual control of the vehicle in the event of an autonomous technology failure or other emergency." The bill requires the autonomous vehicles to be insured for 5 million, for the self-driving automobiles to not exceed 35 miles per hour on the road, and for testing data to be shared with the government and while placing geographic restrictions.

Up until very recently, computers needed a complicated and extremely precise set of instructions in order to accomplish even the simplest of tasks. Who among us remembers programming via punch cards? Computer programming languages have evolved over the years, but the biggest step has been moving towards the elimination of complicated programming. In other words, teaching computers to learn for themselves, dubbed machine learning. Because machine learning is such a promising leap forward in technological ability, it has the very real potential to affect every person in every field of business in the near future.

We protect children and save lives.Our technology analyzes textual data created by students (chat, email, documents, etc) for the purpose of identifying child abuse, bullying, suicide, and other serious concerns that affect today's youth.You will work on a small team to design and implement innovative ways to better solve this problem.We have a number of challenges in both the quantity of data we analyze, and the timeframe in which we must make accurate predictions to better allow our 24hr monitoring staff to make decisions on how best to intervene in serious cases.Put your Machine Learning knowledge to use in a way that can make a real difference in the lives of children.

Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft, offers a surprising story about how machine learning, population data, and the cloud are coming together to reimagine eye care in India.

For the past decade, electric cars have been the next big thing in the auto world. Now, self-driving cars are starting to steal that thunder.

In recent years Artificial Intelligence (AI) has rapidly gone from an obscure academic research field, to an ever more useful and ubiquitous applied discipline. We increasingly rely on AI for more and more of our everyday tasks, and whole lines of work are being thoroughly transformed by its advances. AI's increasing ubiquity is not making it any easier to understand. AI concepts and techniques are still domain of advanced undergraduate or graduate school level courses. There are a few popular AI books out there, but most of them don't get "under the hood" of how AI actually works. "Artificial Intelligence for Humans, Volume 2: Nature-Inspired Algorithms" is the second volume in the series of short introductions to the general field of modern Artificial Intelligence.

Maciej Ceg?owski's (previously) speech at the Library of Congress, "Deep-Fried Data," describes the way that data begs to be analyzed and how machine learning is like a deep-fat fryer -- a fryer makes anything you put in it "kind of" delicious, and machine learning "kind of" finds insights in your data-set. But unless you know what your food is being fried in, you have no idea what's actually happening to it. And unless you know what data is used to train your machine-learning system, you can't know if it's finding real insight or just serving as a "money-laundry for bias." Ceg?owski does a great job on the structural limits and seductive appeal of machine learning, but then moves on to how archivists and librarian can use large data-sets, and the weird problems of providing data to strangers who use it in ways you may find disturbing or frivolous, or just inexplicable. From here, Ceg?owski talks about where data-sets to analyze can come from -- whether you can work with companies addicted to the surveillance business-model and keep your ethics intact -- and what good archiving practice should be in an era of dynamic documents served to rapidly obsoleted technologies.