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.

SCOTTSDALE, AZ--(Marketwired - September 21, 2016) - ReplyBuy, a next-generation sales & commerce platform that combines mobile messaging, payments & instant gratification for buyers revealed new functionality earlier today as the first company to introduce a form of Artificial Intelligence to the sports and entertainment vertical. The new V.I.P. style concierge service called ReplyBuy.ai Already enjoyed by organizations within the NBA, NHL, MLS, NFL and major universities, ReplyBuy wants to help industry partners and their affiliates meet consumers where they are communicating. We're also firm believers in what the future holds for Artificial Intelligence and concierge based services," says Josh Manley, ReplyBuy Founder & CEO. "We're laser-focused on introducing products with a DNA of increased speed, efficiency and consumer engagement from a B2B perspective.

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. By 2018, robots will supervise more than 3 million human workers; by 2020, smart machines will be a top investment priority for more than 30 percent of CIOs. Everything from journalism to customer service is already being replaced by AI that's increasingly able to replicate the experience and ability of humans. What was once seen as the future of technology is already here, and the only question left is how it will be implemented in the mass market. Over time, the insights gleaned from the industries currently taking advantage of AI -- and improving the technology along the way -- will make it ever more robust and useful within a growing range of applications.

Now envision a million of these pixels-a megapixel's worth-in an array that covers a thumbnail. Take one more mental trip: dive down onto the surface of the semiconductor hosting all of these pixels and marvel at each pixel's associated tech-mesh of more than 1,000 integrated transistors, which provide each and every pixel with a tiny reprogrammable brain of its own. That is the vision for DARPA's new Reconfigurable Imaging (ReImagine) program. "What we are aiming for," said Jay Lewis, program manager for ReImagine, "is a single, multi-talented camera sensor that can detect visual scenes as familiar still and video imagers do, but that also can adapt and change their personality and effectively morph into the type of imager that provides the most useful information for a given situation." This could mean selecting between different thermal (infrared) emissions or different resolutions or frame rates, or even collecting 3-D LIDAR data for mapping and other jobs that increase situational awareness.