Machine learning's expansive capacity to quickly turn big health data into evidence-based care will challenge all practitioners of medicine to either grow along with the technology or accept getting left behind by it. And radiologists will be among the first to feel its push (if they're not among the rads who are already working with it). So predict a pair of medical thought leaders in commentary published online Sept. 29 in the New England Journal of Medicine. Emergency physician Ziad Obermeyer, MD, MPhil, of Harvard and oncologist/bioethicist Ezekiel Emanuel, MD, PhD, of the University of Pennsylvania note that the AI subfield of machine learning draws out rules from data. This is distinct from AI "expert systems" algorithms, which apply human-created rules to draw conclusions about specific scenarios.

As machine learning in finance has been harshly questioned of late, Deutsche Bank, noting the repeated discussions in the media, issues their own take in a 110-page report out September 30. The is that while machine learning can be "very relevant" in finance, "dangerous pitfalls" exist. After defining machine learning – "machine learning is an empirical, algorithmic approach to the problems already tackled by Statistics" – the report explained the nuance. The more the topic is explained the more the strengths and weaknesses in adaptive trading and investment methods are clear. Understanding how an algorithmic approach in investing can, to various degrees, be dependent on category organization.

Neither artificial intelligence nor fintech – or even the union of the two – is really anything new, despite the recent buzz regarding all of the above. With that being said, we are indeed approaching that point where the underlying technology begins to make a noticeable difference in people's lives. From there, it won't be long before we begin to wonder how we ever lived without artificially intelligent financial advisors implementing our own personal monetary policy. Well, did you know that digital camera technology has been around since the 1970s but simply wasn't good enough to get mainstream traction until decades later? Don't worry, Kodak didn't see it coming, either.

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.

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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.