First--try some of the introductory tutorial/competitions. Those get your feet wet. Then just jump head first into a competition. Try and be active on the forums. I have found that the best way to learn is just struggle with it (in most anything--I faked my way into a DB engineer once, 2 years later I was teaching the course on SQL at a Fortune 100 company--I had my share of run-ins with the Admin though--we were on a first name basis)).

A startup technology company, AppZen, has introduced new technology that is leveraging artificial intelligence to examine expense reports for signs of fraud. "We are an AI company and we've built a solution for back-office automation," AppZen CEO Anant Kale told me. "As part of that, our first focus area is back office expense processing, which is essentially how to find compliance issues within expenses. We are focusing on automating the research and reasoning that human auditors do today and putting it into machines." Kale noted that most companies don't really audit anywhere near all of their expense reports.

Despite the ongoing efforts of banks to attract millennials, they are still failing to make an impression. According to Gallup, only 23% of millennials are actively engaged with their bank, making millennials the least engaged generation. Considering that fully engaged customers bring considerable benefits and higher revenues – this is a big problem. If banks expect millennials to "simply grow up," they may be facing a tough road ahead. Financial institutions – banks and others -- that find a way to communicate with millennials on their terms and give them the financial tools they want are more likely to end up the winners.

Government officials say autonomous vehicles will make transportation safer, more accessible, more efficient and cleaner and last week, the Department of Transportation released guidelines for the testing and deployment of automated vehicles, which detail how the vehicles should perform, and include a model for state policies. Self-driving vehicles are just the tip of the autonomous revolution. In 2016, autonomous robot doctors perform surgery; algorithms invest your money; robocops patrol shopping malls; and if you end up in hospital, a computer system can determine how quickly you get treated. Many decisions made by autonomous machines have moral implications -- yet little is determined about what ethics machines follow, or who decides what those ethical assumptions should be. In Florida in May, Joshua Brown died when an autopilot system did not recognize a tractor-trailer turning in front of his Tesla Model S and his car plowed into it -- the first fatality involving an autonomous vehicle.

Developing a computer that can be as decisive and intelligent as humans is on IBM's mind, and it's making progress toward achieving that goal. IBM's computer chip called TrueNorth is designed to emulate the functions of a human brain. The company is now running tests and benchmarking TrueNorth to demonstrate how fast and power efficient the chips can be compared to today's computers. The results of the head-to-head contest are impressive. IBM says TrueNorth can engage in deep learning and make decisions based on associations and probabilities, much like human brains.

Summary The olfactory system, like other sensory systems, can detect specific stimuli of interest amidst complex, varying backgrounds. To gain insight into the neural mechanisms underlying this ability, we imaged responses of mouse olfactory bulb glomeruli to mixtures. We used this data to build a model of mixture responses that incorporated nonlinear interactions and trial-to-trial variability and explored potential decoding mechanisms that can mimic mouse performance when given glomerular responses as input. We find that a linear decoder with sparse weights could match mouse performance using just a small subset of the glomeruli ( 15). However, when such a decoder is trained only with single odors, it generalizes poorly to mixture stimuli due to nonlinear mixture responses.

Specialized tools for seeing through blur and pixelation have been popping up throughout this year, like the Max Planck Institute's work on identifying people in blurred Facebook photos. The attack uses Torch (an open-source deep learning library), Torch templates for neural networks, and standard open-source data. "Just take a bunch of training data, throw some neural networks on it, throw standard image recognition algorithms on it, and even with this approach…we can obtain pretty good results." To build the attacks that identified faces in YouTube videos, researchers took publicly-available pictures and blurred the faces with YouTube's video tool.

It's becoming much easier to crack internet privacy measures, especially blurred or pixelated images. Those methods make it tough for people to see sensitive information such as obscured license plate numbers or censored faces, but researchers from University of Texas at Austin and Cornell University say that the practice is wildly insecure in the age of machine learning. Using simple deep learning tools, the three-person team was able identify obfuscated faces and numbers with alarming accuracy. On an industry standard dataset where humans had 0.19% chance of identifying a face, the algorithm had 71% accuracy (or 83% if allowed to guess five times). The algorithm doesn't produce a deblurred image--it simply identifies what it sees in the obscured photo, based on information it already knows.

As many noted during the AI Now Experts' Workshop, the means to create and train AI systems are expensive and limited to a handful of large actors. Or, put simply, it's not possible to DIY AI without significant resources. Training AI models requires a huge amount of data – the more the better. It also requires significant computing power, which is expensive. This limits fundamental research to those who can afford such access, and thus limits the possibility of democratically creating AI systems that serve the goals of diverse populations.

It's difficult to predict how artificial intelligence technology will change over the next 10 to 20 years, but there are plenty of gains to be made.