By now, it's almost old news: big data will transform medicine. It's essential to remember, however, that data by themselves are useless. To be useful, data must be analyzed, interpreted, and acted on. Thus, it is algorithms -- not data sets -- that will prove transformative. We believe, therefore, that attention has to shift to new statistical tools from the field of machine learning that will be critical for anyone practicing medicine in the 21st century.

Artificial Intelligence is continually pushing the boundaries of what machines are capable of. But could machines ever turn out to be superior beings to us? The answer is obviously'yes', at least in numerous things where our brains used to be the unchallenged champion of creativity and intelligence. First, Google acquired a startup called DNNresearch, snapping a portion of the world's principal experts in a prospering field of artificial intelligence known as deep learning. Much like Facebook, Microsoft, and others, Google sees deep learning as the future of AI on the web, a superior method for taking care of everything from voice and image recognition to language translation.

New Gartner research explores how retailers gain competitive advantage through the application of algorithms that reduce costs and grow top-line revenue. CIOs can use this research to identify use cases that will improve business performance in the unified commerce retail marketplace. Gartner describes algorithmic business as the "enablement of business value through the action of algorithms on data" and regards algorithms themselves as a way to encapsulate and produce intellectual property, knowledge and insight in a reusable form. Algorithms are a set of rules for solving a problem in a finite number of steps, as for finding the greatest common divisor. New technologies create opportunities to advance algorithms, incorporating many more data inputs and steps and even decision-making capability.

Artificial intelligence is learning to take on an increasing number of sophisticated tasks. Google Deepmind's AI is now able to imitate human speech, and just this past August IBM's Watson successfully diagnosed a rare case of leukemia. Rather than viewing these advances as threats to job security, we can look at them as opportunities for AI to fill in critical gaps in existing service providers, such as mental healthcare professionals. In the US alone, nearly eight percent of the population suffers from depression (that's about one in every 13 American adults), and yet about 45 percent of this population does not seek professional care due to the costs. There are many barriers to getting quality mental healthcare, from searching for a provider who's within your insurance network to screening multiple potential therapists in order to find someone you feel comfortable speaking with.

Uber's Pittsburgh Experiment, featuring semi-autonomous vehicles, is up and running. If only its fleet could distinguish the proper path down a one-way street. And Google is reporting smashing results for its autonomous vehicle program. This is a public service alert for all you Yinzers out there: Get off the road; you're in danger. While we're at it, to unemployed tech bros desperate to get a foot in the Silicon Valley door: Don't take a gig as a Google autonomous vehicle test driver.

The biggest new product to come out of Salesforce this year is an artificial intelligence feature called "Einstein." Einstein basically collects and analyzes a bunch of data to push out "smarter" and more predictive analytics for Salesforce users. But early reviews of Einstein seem to be mixed so far. Most users agree it's still at a very early stage and is a couple years away from becoming a mainstream product for large business users. "Our sense is that the recent launch of Einstein is very early in terms of technology readiness, as well as customer awareness and market adoption...it will likely take another year or two before these features/products gain meaningful adoption," Cowen & Co. analyst Derrick Wood wrote in a note published Monday.

That was the experience of Tech Elevator coding bootcamp graduate Kyle Pierson, who recently began his career as a software developer for LMI. He calls video games a "stepping stone" toward his coding career. As a child, he remembers asking his dad to look up cheat codes for Twisted Metal 2 online. "When he found a webpage that contained those codes, I realized that the computer was like a library where I could get anything I wanted," he says. This innate curiosity led to him wondering how computer programs worked.

In just a few short weeks, we'll be electing our next American President. Among myriad other duties, whoever gets the job will be tasked with overseeing one of the most significant technological expansions the world has ever seen. He or she will need to understand these new technologies to help the country reap the most benefits from them. Of all the innovations just on the horizon, the one with the most game-changing potential is 5G wireless technology. For the last 30 years, the technology industry mostly focused on connecting people to other people.

An international team of researchers has trained a robot to imitate natural human actions, in the hope that humans and robots can coordinate their actions during critical events such as surgeries. Researchers from Italy's Polytechnic University of Milan led an international team that trained a robot to imitate natural human actions. The work demonstrates humans and robots can effectively coordinate their actions during high-stakes events such as surgeries. Over time, the research could lead to improvements in safety during medical procedures because robots do not tire and can complete an endless series of precise movements. Robotic co-workers "will just allow us to decrease workload and achieve better performances in several tasks, from medicine to industrial applications," says Polytechnic University of Milan's Elena De Momi.

As the interest in data science, predictive analytics and machine learning has grown in direct correlation to the amount of data that is now being captured by everyone from start ups to enterprise organisations, endjin are spending increasing amounts of time working with businesses who are looking for deeper and more valuable insights into their data. As such, we've evolved a pragmatic approach to the machine learning process, based on a series of iterative experiments and relying on evidence-based decision making to answer the most important business questions. In this series of posts, we're going to look at what machine learning really is (and isn't), the endjin process and some examples of how and where we've put it to use. So what do machine learning and data science actually mean? My previous post argued that there's no mad science or dark art at play, just a pragmatic process based around trial and error with statistics.