Here's How Google Will Use A.I. to Help Fight Cancer Could killer AI robots bring down America? How UC Berkeley's New Center Could Prevent a Military A.I. Apocalypse Beauty.AI App the 1st international beauty contest judged by AI A treasure hunter went missing in the Rocky Mountains, and a computer algorithm found him ... Drive.ai wants to give self-driving cars more brainpower, personality

The world's oldest board game still has a few moves to play. Go, a game of strategy and instinct considered more difficult to master than chess, was created roughly in the same era as the written word. The game is uniquely human - or, it was. Last year, a computer program called AlphaGo defeated an internationally ranked professional player. The computer's win signaled a significant evolution of information technology (IT) and artificial intelligence (AI), according to Fei-Yue Wang, a professor at the Chinese Academy of Sciences.

When I first became interested in using deep learning for computer vision I found it hard to get started. There were only a couple of open source projects available, they had little documentation, were very experimental, and relied on a lot of tricky-to-install dependencies. A lot of new projects have appeared since, but they're still aimed at vision researchers, so you'll still hit a lot of the same obstacles if you're approaching them from outside the field. In this article -- and the accompanying webcast -- I'm going to show you how to run a pre-built network, and then take you through the steps of training your own. I've listed the steps I followed to set up everything toward the end of the article, but because the process is so involved, I recommend you download a Vagrant virtual machine that I've pre-loaded with everything you need.

Consumer-facing predictive systems paint a seductive picture: espresso machines that start brewing just as you think it's a good time for coffee; office lights that dim when it's sunny and office workers don't need them; just in time diaper delivery. The value proposition is of a better user experience, but how will that experience actually be delivered when the systems involved regularly behave in unpredictable, often inscrutable, ways? Past machine learning systems in predictive maintenance and finance were designed by and for specialists, while recommender systems suggested, but rarely acted autonomously. Semi-autonomous machine learning-driven predictive systems are now in consumer-facing domains from smart homes to self-driving vehicles. Such systems aim to do everything from keeping plants healthy and homes safe to "nudging" people to change their behavior.

Hewlett Packard Enterprise (HPE) today announced a new tool that combines machine learning APIs and cognitive services with visual development to democratize those cutting-edge technologies for mainstream developers. The new offering is called HPE Haven OnDemand Combinations and, as the name suggests, it's built upon the existing HPE Haven OnDemand, a cloud platform used for Big Data development that provides machine learning APIs and services. HPE described HPE Haven OnDemand Combinations -- available now upon request -- as the quickest way to add intelligence to apps, as it reportedly smoothes the often tedious process developers and data scientists have to go through to leverage existing data-related APIs in their software development. "The new offering provides a pre-built catalogue of cognitive services and an intuitive drag-and-drop interface," HPE said in a statement today. "Developers can chain together multiple machine learning APIs into combinations and copy and paste the code directly into their development projects, to quickly and easily create breakthrough mobile and enterprise applications."

Alan Turing was a multi-talented British mathematician who helped to both win the Second World War and invent the earliest computers, both while leading the Allied code-breaking efforts at Blechley Park. However, this impact on history may have been even greater through his academic work; his seminal paper On Computable Numbers laid down the foundations for modern computer theory, and his thinking on artificial intelligence is still some of the most influential today. He devised the famous Turing Test for true AI: if an AI can endure a detailed, text-based interrogation by a human tester or testers, and those testers cannot accurately determine whether they are speaking to a human or a robot, then true artificial intelligence has been achieved. With all we now know about the ability of neural networks to find patterns in behavior, this does seem like a somewhat low bar to consciousness -- but it's easy to remember, historically important, and it has alliteration, which means it's famous.

The complexity in languages arises from ambiguities, structure, context and domain dependencies, and word ordering. As discussed in our previous blog, machines and humans learn language. Natural Language Processing (NLP) is the branch of AI that enables effective communication by obtaining useful information from computing system. The goal of NLP is to enhance communication by extracting semantic and pragmatic meaning from human interactions. The presence of NLP to assist our text composition has been around for a while.We are familiar with spell-checks and grammatical corrections as a useful feature of Word processing software like Microsoft Word.Such NLP technology to process and construct our text is now universally implemented in most web-platforms and mobile devices to assist our language inputs. Enormous amount of data is generated every day from our daily business activities and online-interactions.

Electronics giant Flextronics, through its solar gear subsidiary NEXTracker, has acquired a young startup called BrightBox Technologies, which builds predictive modeling and machine learning software, the companies announced late on Monday. NEXTracker makes hardware, called trackers, which automatically tilt solar panels throughout the day to face the sun to increase the amount of power that panels generate. These trackers are generally used on large solar panel farms in remote locations that sell their power to utilities or large companies. Flextronics (now called Flex) itself bought NEXTracker last year for 330 million. Now, NEXTracker is acquiring BrightBox Technologies, a three-year-old company based in Berkeley, Calif. that has developed software to optimize heating and cooling systems in buildings.

I'm working my way through the Coursera course on Machine learning by Andrew Ng, and I'm really confused about the bias unit? In the programming assignment, we always set the parameter for the bias unit to 0, so how does it affect the neural network in any way? Since the bias unit will be multiplied by it's parameter/weight (which seems to always be zero), wouldn't it be completely insignificant?

Artificial intelligence is going to have a massive impact on multiple business verticals over time. The displacement of both blue collar and white collar work by machine learning is going to cause major societal displacements in the next 10-20 years[0]. While there is a lot of discussion in the popular press about general purpose AI (aka AGI - which is defined as a machine that can perform any intellectual task a person can), much less emphasis has been placed on near-term specific vertical markets or areas that AI and machine learning (ML) are likely to transform in the coming 5 years. In short, I think AGI is still 10 years away, but vertical products driven by AI will be transformative in the coming years. The areas listed below are underinvested by entrepreneurs and VCs.