The history of Artificial Intelligence," said my computer science professor on the first day of class, "is a history of failure." This harsh judgment summed up 50 years of trying to get computers to think. Sure, they could crunch numbers a billion times faster in 2000 than they could in 1950, but computer science pioneer and genius Alan Turing had predicted in 1950 that machines would be thinking by 2000: Capable of human levels of creativity, problem solving, personality, and adaptive behavior. Maybe they wouldn't be conscious (that question is for the philosophers), but they would have personalities and motivations, like Robbie the Robot or HAL 9000. Not only did we miss the deadline, but we don't even seem to be close.

Walter Pitts was used to being bullied. He'd been born into a tough family in Prohibition-era Detroit, where his father, a boiler-maker, had no trouble raising his fists to get his way. One afternoon in 1935, they chased him through the streets until he ducked into the local library to hide. The library was familiar ground, where he had taught himself Greek, Latin, logic, and mathematics--better than home, where his father insisted he drop out of school and go to work. Outside, the world was messy. Inside, it all made sense. Not wanting to risk another run-in that night, Pitts stayed hidden until the library closed for the evening. Alone, he wandered through the stacks of books until he came across Principia Mathematica, a three-volume tome written by Bertrand Russell and Alfred Whitehead between 1910 and 1913, which attempted to reduce all of mathematics to pure logic. Pitts sat down and began to read. For three days he remained in the library until he had read each volume cover to cover--nearly 2,000 pages in all--and had identified several mistakes. Deciding that Bertrand Russell himself needed to know about these, the boy drafted a letter to Russell detailing the errors.

Here's the upshot: IBM is looking to speed up training for Watson, neural networks and machine learning. It's going to use its research and hardware knowhow as well as the OpenPower ecosystem to do it.

We are now looking for a Solutions Architect. The Solutions Architect will have a primary role servicing our Dell business, focusing on the sales-out technical support of GPU-enabled Dell servers. What you'll be doing: · Provide technical support of our datacenter products that are included in Dell servers, including software development, training, benchmarking, and consultation during customer sales meetings · Support key company initiatives including development of Deep Learning assets, and providing support for penetration of our platform into Deep Learning research, development, and deployment. Responsibilities: · First and primary point of technical support for all NVIDIA products provided to partners · Identify and analyze all reported customer issues, and will personally solve technical issues to the extent possible. Location: Austin, TX What we need to see: · 4 years experience in a relevant field, such as the computer industry or technical computing · BS, MS, or Ph.D in relevant discipline e.g.

I remember sitting in a conference audience in the late 1990s during the fat part of the first dot-com expansion curve, when everyone was complaining that the Internet was irrationally overhyped. Then, pre-Google Eric Schmidt took the stage and told us that, "I actually think the Internet is underhyped." As a tech journalist in those days, I'd had the privilege of long talks with Schmidt and hadn't wasted the opportunity to learn. Other people laughed, but I knew he was serious -- and he was right. The point is, I've begun to get the sense that most marketers aren't yet taking AI seriously enough.

Machine ethics offers an alternative solution for artificial intelligence (AI) safety governance. In order to mitigate risks in human-robot interactions, robots will have to comply with humanity's ethical and legal norms, once they've merged into our daily life with highly autonomous capability. In terms of technical challenges, there are still many open questions in machine ethics. For example, what is deontic logic and how can it be used for improving AI safety? How do we fashion the knowledge representation for ethical robots? These are all significant questions for us to investigate. In this interview, we invite Prof. Ronald C. Arkin to share his insights on robot ethics, with a focus on its technical aspects.

In many ways, we're close to a tipping point in the development of artificial intelligence technologies, with fleets of self-driving cars, hive drones, automated retail experiences, and more. Companies of every size now must grapple with how AI will affect and possibly eliminate their sector, and how they can adapt and leverage AI and machine learning (ML) to disrupt themselves before they get disrupted. While it's crucial for companies to start participating in the AI movement or risk getting left behind, it's important to do so strategically to ensure AI initiatives are truly helping to achieve ultimate business goals. AI-ML will make many of our day-to-day lives better and more productive by augmenting what we do already in much more accurate and efficient ways. Tech innovators like Uber, Google, and Facebook are making big bets on AI through acquisitions and internal organizational makeovers that make AI a strategic priority.

Apache Spark is a lightning-fast framework for distributed computing that combines speed, scalability, in-memory processing, and fault tolerance with sophisticated analytics – perfect for dealing with massive datasets. This eBook takes you on a tour of Spark's powerful API; helps you create your first Spark program in Scala, Java, and Python; and gives detailed examples of real-world machine learning models from recommender systems to dimensionality reduction. You'll also learn about advanced topics like working with online machine learning and model evaluation methods using Spark Streaming. This eBook is free for today only so don't miss out!

If you ask 100 people for the definition of "artificial intelligence," you'll get at least 100 answers, if not more. At AWS, we define it as a service or system which can perform tasks that usually require human-level intelligence such as visual perception, speech recognition, decision making, or translation. On this new AWS blog, we'll be covering these areas and more, with in-depth technical content, customer stories, and new feature announcements. The challenges related to building sophisticated AI systems center mostly around scale: the datasets are large, training is computationally hungry, and inferring predictions can be challenging to do at scale or on lower-power and mobile devices. Customers have been using AWS to solve these general problems for years, and the ability to be able to access storage, GPUs, CPUs, and IoT services on demand has emerged as a perfect fit for intelligent systems in production.

Ford is investing $1bn (£801m) over the next five years in tech startup Argo AI to help the Detroit company achieve its goal to deliver a fully autonomous vehicle by 2021, the companies announced on Friday. Pittsburgh-based Argo AI was founded last year by Bryan Salesky and Peter Rander who previously worked at Google and Uber respectively on the companies' self-driving vehicle initiatives. "We've made significant progress and are on track to achieve our goal," Ford president and CEO Mark Fields wrote in a Medium post. "At the same time, we know rapidly evolving technologies and fierce competition require us to remain flexible and open to new ways of strengthening our team." In August last year, Ford announced plans to sell fully autonomous self-driving cars, without steering wheels, for urban ride-sharing fleets by 2021.