Scientists can now monitor and record the activity of hundreds of neurons concurrently in the brain, and ongoing technology developments promise to increase this number manyfold. However, simply recording the neural activity does not automatically lead to a clearer understanding of how the brain works. In a new review paper published in Nature Neuroscience, Carnegie Mellon University's Byron M. Yu and Columbia University's John P. Cunningham describe the scientific motivations for studying the activity of many neurons together, along with a class of machine learning algorithms--dimensionality reduction--for interpreting the activity. In recent years, dimensionality reduction has provided insight into how the brain distinguishes between different odors, makes decisions in the face of uncertainty and is able to think about moving a limb without actually moving. Yu and Cunningham contend that using dimensionality reduction as a standard analytical method will make it easier to compare activity patterns in healthy and abnormal brains, ultimately leading to improved treatments and interventions for brain injuries and disorders.

Or why I don't want to listen to the Red Hot Chili Peppers. No offense to the Peppers, but just because I listen to alternative music and "people like me who listen to alt music" enjoy hearing the Peppers it does not then mean that I want to hear them. I am not "people like me", I am me. And I want the smart computers behind my favorite services to know me enough to deliver the content I want. It doesn't matter which service we talk about.

The capabilities of artificial intelligence and machine learning are accelerating, and many cybersecurity tasks currently performed by humans will be automated. There will still be plenty of work to go around so job prospects should remain good, especially for those who keep up with technology, broaden their skill sets, and get a better understanding of their company's business needs. Cybersecurity jobs won't go the way of telephone operators. Take, for example, Spain-based antivirus company Panda Security. When the company first started, there were a number of people reverse-engineering malicious code and writing signatures.

The development of Internet of Things (IoT) and artificial intelligence (AI) technologies raises the issue on whether they should also act ethically. On 26 October 2016, I attended the IoT Solutions World Congress, one of the largest events in the world on the Internet of Things, and I had the pleasure of being part of a panel on "Ethical Uses of Data", together with Edy Liongosari from Accenture, Prith Banerjee from Schneider Electric, Derek O'Halloran from the World Economic Forum, Sven Schrecker from Intel and David Blaszkowsky from the Financial Semantics Collaborative. In a few years, we will not own almost anything. Our car, our house and whatever we use during the course of the day will become "as a service". In this context, the sole asset that will belong to individuals is their "digital identity".

In September 2014, Apple made encryption default with the introduction of the iPhone 6. Then, in February 2016, a Los Angeles judge issued an order to Apple to help break into the encrypted iPhone belonging to a terrorist involved in a mass shooting. Apple had used some of the strongest encryption technologies and practices to protect its users and their data. The encryption technology did not discriminate between lawful and unlawful users. While there were many sides to this issue, it surfaced many important debates on security, privacy, and civil rights.

In the 2013 movie Her, Theodore Twombly, a lonely writer, falls in love with a digital assistant designed to meet his every need. She sorts emails, helps get a book published, provides personal advice, and ultimately becomes his girlfriend. The assistant, Samantha, is AI software capable of learning at an astonishing pace. Samantha will remain in the realm of science fiction for at least another decade, but less-functional digital assistants, called bots, are already here. These will be the most amazing technology advances we see in our homes in 2017.

Artificial Intelligence: Upping the Ante" poker competition nears its halfway point, Carnegie Mellon University's AI program, called Libratus, is opening a lead over its human opponents -- four of the world's best professional poker players. Libratus had amassed a lead of $459,154 in chips in the 49,240 hands played by the end of Day Nine. One of the pros, Jimmy Chou, said he and his colleagues initially underestimated Libratus, but have come to regard it as one tough player. "The bot gets better and better every day," Chou said. AI, which began Jan. 11 at Rivers Casino in Pittsburgh, pits Chou and three other leading players -- Dong Kim, Jason Les and Daniel McAulay -- against Libratus in a 20-day contest in which they will play 120,000 hands of Heads-Up, No-Limit Texas Hold'em poker. All four pros specialize in this two-player, unlimited bid form of Texas Hold'em and are considered among the world's top players of the game. While the pros are fighting for humanity's pride -- and shares of a $200,000 prize purse -- Carnegie Mellon researchers are hoping their computer program will establish a new benchmark for artificial intelligence by besting some of the world's most talented players. Libratus was developed by Tuomas Sandholm, professor of computer science, and his Ph.D. student, Noam Brown. Libratus is being used in this contest to play poker, an imperfect information game that requires the AI to bluff and correctly interpret misleading information to win. Ultimately programs like Libratus also could be used to negotiate business deals, set military strategy or plan a course of medical treatment -- all cases that involve complicated decisions based on imperfect information. AI contest in 2015, four leading pros amassed more chips than the AI, called Claudico. But Sandholm said he's feeling good about Libratus' chances as the competition proceeds. "The algorithms are performing great.

In October 2015 we launched the Google Brain Residency, a 12-month program focused on jumpstarting a career for those interested in machine learning and deep learning research. This program is an opportunity to get hands on experience using the state-of-the-art infrastructure available at Google, and offers the chance to work alongside top researchers within the Google Brain team. Our first group of residents arrived in June 2016, working with researchers on problems at the forefront of machine learning. The wide array of topics studied by residents reflects the diversity of the residents themselves -- some come to the program as new graduates with degrees ranging from BAs to Ph.Ds in computer science to physics and mathematics to biology and neuroscience, while other residents come with years of industry experience under their belts. They all have come with a passion for learning how to conduct machine learning research.

"The Automatic Emergency Braking (AEB) or Autopilot systems may not function as designed, increasing the risk of a crash." It's a simple sentence, delivered with the calm finality of bureaucratic certainty. It is a literal post-mortem, the bottom-line-up-front from the National Highway Traffic Safety Administration's investigation into the first fatal crash of an autonomous car--one made by Tesla Motors. The investigation into the crash closed today, and it will likely cast a long shadow over the future of self-driving cars, which have long been heralded as potentially life-saving devices. There was no particularly unique flaw to the Tesla that Joshua Brown, of Canton Ohio, was driving on May 7th, 2016.

The paper outlines the use of neural style transfer in Stewart's directorial debut, Come Swim, which is about to premiere at Sundance Film Festival. Neural style transfer turns normal images into impressionist art, and is used by popular photo app Prisma. The paper, first spotted by Quartz, is co-bylined with Adobe research engineer Bhautik J. Joshi and producer David Shapiro. It was published yesterday on ArXiv, a repository run by Cornell University for scientific papers that are not yet peer-reviewed. Come Swim describes itself as "half realist, half impressionist portraits" of one man's day.