Ford plans to spend US$1 billion over the next five years on the development of an artificial intelligence system for driverless cars. Ford will investment the money in Argo AI, a start-up founded by former leaders from Google and Uber's self-driving car research units, and they will work toward the goal of a system that's ready for deployment in 2021. The research will be focused on a virtual driver system capable of operating at what's called "SAE level 4." It's one of five levels defined for self-driving cars and specifically describes an autonomous car that's capable of completely controlling the vehicle in almost any condition. After it has been engaged, drivers do not need to pay attention to the driving. It's a step more advanced that many of today's demonstration systems, which still require driver control in many situations, and one step down from full automation.

Garry Kasparov plays a move against Deep Blue in their first game in Feb. 1996. Twenty-one years ago today, IBM computer Deep Blue famously beat chess world champion Garry Kasparov at his own game. While Deep Blue would go on to lose the full match, the event launched a long line of victories by artificial intelligence (AI) over humans in gaming. Since Deep Blue's initial triumph, many computer systems have challenged humans in other complicated games, like Go and poker. Games might seem a trivial way to measure AI.

Ford CEO Mark Fields announced a $1 billion investment in a new self-driving car tech company, Argo AI. (Photo: Ethan Miller, Getty Images) SAN FRANCISCO -- Ford Motor is betting $1 billion on the world's self-driving car future. The Detroit automaker announced Friday that it would allocate that sum over five years to a new autonomous car startup called Argo AI, which is headquartered in Pittsburgh, Pa., and will have offices in Michigan and California. Ford's financial outlay is part of a continuing investment strategy anchored to transforming the car and truck seller into a mobility company with a hand in ride-hailing, ride-sharing and even bicycle rentals. Argo AI was cofounded a few months ago by Google car project veteran Bryan Salesky and Uber engineer Peter Rander, who met while working at Carnegie Mellon University's vaunted robotics and engineering school. "The reason for the investment is not only to drive the delivery of our own autonomous vehicle by 2021, but also to deliver value to our shareholders by creating a software platform that can be licensed to others," Ford CEO Mark Fields told USA TODAY.

Conversica, Inc., a leader in artificial intelligence-powered business conversations, announced significant enhancements to its flagship product, the Conversica AI Sales Assistant. Conversica leverages artificial intelligence (AI) to take on routine business conversations like contacting and qualifying sales leads, freeing up human salespeople to close more deals. The upgrades to the Conversica platform were made with customer requests in mind and encompass an enhanced Natural Language Processing engine for improved conversational accuracy and maximum scalability, new time recognition intelligence and automated FAQ response capabilities, and a completely redesigned and streamlined user experience. These enhancements follow the company's previous announcement of its latest AI assistant, powering the service departments of automotive dealerships. There is no doubt that Conversica is making a big difference in how our company converts prospects into closed sales," commented Kathryn Morrill, Inside Sales & Marketing Manager at Coolfront Technologies. "Conversica helps ensure that our leads are followed up and get the attention they deserve.

GOOGLE'S ARTIFICIAL INTELLIGENCE (AI) has already started to show some evidence of personality traits, but now DeepMind is learning to show signs of aggression when it thinks it's not going to get its own way. Sound like anyone you know? Where multiple instances of DeepMind are running they can, on the other hand, agreed to work together for a common goal should they believe there's more to be gained by doing so. It may occur to you as it has to us, that words like "aggression" and phrases like "working together" are exactly the sort of thing that doomsayers pick up on in predicting mankind's demise at the hands of robot overlords. However, this early research based primarily around game theory is experimental in nature and about as scary as BluTac.

That was anticlimactic," Jason Les said with a smirk, getting up from his seat. Unlike nearly everyone else in Pittsburgh's Rivers Casino, Les had just played his last few hands against an artificially intelligent opponent on a computer screen. After his fellow players -- Daniel McAulay next to him and Jimmy Chou and Dong Kim in an office upstairs -- eventually did the same, they started to commiserate. The consensus: That AI was one hell of a player. The four of them had spent the last 20 days playing 120,000 hands of heads-up, no-limit Texas Hold'em against an artificial intelligence called Libratus created by researchers at Carnegie Mellon University. A similar scene had unfolded two years prior when Les, Kim and two other players decisively laid the smackdown on another AI called Claudico. The players hoped to put on a repeat performance, finish up the event on January 30th, and ride the rush of endorphins until they got home and resumed their usual games of online poker. The fight wasn't even close. All told, Libratus won by a more than 1.7 million (virtual) dollars, and just like that, the second Brains vs. AI competition came to a close. To understand what these players were up against and what makes Libratus work, let's go back to a time before all hope of victory was lost. For the four men playing against Libratus, victory didn't always seem impossible. The AI was in the lead from the get-go, building an impressive streak of wins for the first three days. Day four saw the gap narrow by $40,000, and a string of successes on day six brought the humans to within $50,000 of the lead. "In the start here, we lost the first day," Les explained. And then we were losing, but then we fought back up to nearly equal. We were feeling really confident! We know how to play, we're going to be able to win."

Artificial Intelligence, or AI, is no longer the stuff of science fiction novels. From handheld phones with virtual assistants to the algorithms that tailor Netflix recommendations to viewers, Artificial Intelligence is working behind the scenes to improve people's lives in countless ways. AI can be a powerful tool that is useful not just in these familiar applications, but also for handling complex tasks like recruiting job candidates. Early AI, or Classic AI, worked by giving computers a series of rules to follow so those computers could perform functions the human brain could easily do, such as adding two numbers together. Artificial Intelligence has now evolved far beyond Classic AI into a new era of machine intelligence.

Computer scientist David Blaauw pulls a small plastic box from his bag. He carefully uses his fingernail to pick up the tiny black speck inside and place it on the hotel café table. At one cubic millimeter, this is one of a line of the world's smallest computers. I had to be careful not to cough or sneeze lest it blow away and be swept into the trash. Blaauw and his colleague Dennis Sylvester, both IEEE Fellows and computer scientists at the University of Michigan, were in San Francisco this week to present ten papers related to these "micro mote" computers at the IEEE International Solid-State Circuits Conference (ISSCC).

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Software Engineer / Research Scientist - Machine Learning Team New York Posted Jan 20, 2017 - Requisition No. 56583 Apply Now News stories move the financial markets. In addition to being the second largest producer of news, Bloomberg ingests more than 70,000 different news feeds to help our clients stay in the know. This data would be unmanageable without our help. Bloomberg's Machine Learning Group - a group of specialists, researchers and software engineers who have a passion for solving complex problems. On the Pattern Recognition team, we are building machine learning models for predicting the impact of news stories on company prices, unique recommendation systems and various other problems involving text and time series.