Prominent voices of the tech world assume: not only AI might pose a threat to humanity, it also is racist and sexist. Since the AI machines learn from real people, they are very likely to adopt the same behavioral patterns, which may further be spreading inequality in the workplace, at home and in our legal and judicial systems. Take a small example from last year: Users discovered that Google's photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologized; it was unintentional. It is mainly a data problem.

Increasingly becoming a necessity in Ecology and Earth Science research, handling complex data can be a tough nut when traditional statistical methods are applied. As its first publication, the new technologically-advanced Open Access journal One Ecosystem features a review paper describing the benefits of using machine learning technologies when working with highly-dimensional and non-linear data. Natural sciences, such as Ecology and Earth science, focus on the complex interactions between biotic and abiotic systems in order to infer understand these systems and make predictions. Traditional statistical methods can impose unrealistic assumptions that result in unsound conclusions as the era of'big data' meets ecology and earth science. Machine-learning-based methods, capable of inferring missing data and handling complex interactions, are more apt for handling complex scientific data.

At a recent presentation in San Francisco, CB Insights CEO Anand Sanwal said half-jokingly that if startups want attention from investors, they should put phrases like "artificial intelligence" and "machine learning" in their pitch deck. While there's an argument to be made that AI is over-hyped as a technology, there's data to back up Sanwal's tongue-in-cheek advice: Mergers and acquisitions of AI startups increased by a factor of seven between 2011 and 2015, from five to more than 35 deals, according to the research firm. The increase runs against the grain of what the Silicon Valley Business Journal reported as an overall decline in the number of exits and deals of startups across fields starting in the second quarter of 2014. The poster child for the recent surge in interest in AI: Twitter's acquisition of machine learning startup Magic Pony Technology, announced earlier this month. If you look at the deal primarily as an "acquihire," Twitter is reportedly paying 13 million per machine-learning PhD.

Mumbai: There is tremendous buzz around machine learning, broadly described as a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. However, despite an exponential increase in power, computers have typically proved incompetent at things that are really simple to human beings--like recognizing the dog in a picture containing a dog, or understanding speech. The trend, however, is changing. Consider'Deep Learning', which describes a collection of techniques that allow computational tasks that were previously thought impossible. Facebook Inc, for instance, uses it to identify faces, and when Google Inc recently announced that their algorithms could not only'see' a dog but also identify it as a Pomeranian, they heralded the maturity of Deep Learning techniques.

When Tesla Motors CEO Elon Musk proclaims that artificial intelligence is "our biggest existential threat," it makes headlines worldwide. But what goes unreported is that the very search engines people used to find Musk's comments are themselves an example of how AI has subtly but forcefully become a part of everyday, real-world life. When it comes to a discussion of AI, it helps to have a sense of history--as well as a sense of humor. Thanks to premonitory proclamations by Musk, Microsoft's Bill Gates, Cambridge's Stephen Hawking and other prominent technologists, AI has become a popular topic again, after a 20-year cooling-off period. It's tempting to assume that the "dire warnings" about AI being a threat to mankind were mostly tongue-in-cheek, but the end result is that just as it happened in the 1980s and '90s, the hype over AI is again outpacing the reality (virtual and otherwise). The first question that needs to be answered though is: Whatever happened to AI and why did it go underground for so many years?

I consider Ray Kurzweil a very close friend and a very smart person. Ray is a brilliant technologist, futurist, and a director of engineering at Google focused on AI and language processing. As reported, "of the 147 predictions that Kurzweil has made since the 1990s, fully 115 of them have turned out to be correct, and another 12 have turned out to be "essentially correct" (off by a year or two), giving his predictions a stunning 86% accuracy rate." Two weeks ago, Ray and I held an hour-long webinar with my Abundance 360 CEOs about predicting the future. During our session, there was one of Ray's specific predictions that really blew my mind. "In the 2030s," said Ray, "we are going to send nano-robots into the brain (via capillaries) that will provide full immersion virtual reality from within the nervous system and will connect our neocortex to the cloud. Just like how we can wirelessly expand the power of our smartphones 10,000-fold in the cloud today, we'll be able to expand our ...

Using deep learning techniques, a group of researchers has trained a computer to recognise events in videos on YouTube -- even the ones the software has never seen before like riding a horse, baking cookies or eating at a restaurant. Researchers from Disney Research and Shanghai's Fudan University used both scene and object features from the video and enabled link between these visual elements and each type of event to be automatically determined by a machine-learning architecture known as neural network. "Notably, this approach not only works better than other methods in recognising events in videos, but is significantly better at identifying events that the computer programme has never or rarely encountered previously," said Leonid Sigal, senior research scientist at Disney Research. Automated techniques are essential for indexing, searching and analysing the incredible amount of video being created and uploaded daily to the Internet. "With multiple hours of video being uploaded to YouTube every second, there is no way to describe all of that content manually. If we don't know what's in all those videos, we can't find things we need and much of the videos' potential value is lost," noted Jessica Hodgins, vice president at Disney Research.

This is the fourth in a continuing series of stories previewing sessions of importance to cloud application developers at the Cloud Expo conference, which takes place June 7 to 9 at the Jacob Javits Center in New York. IT analyst Denis Pombriant explains why platforms are on the rise and how to choose the right one. He also highlights the importance of having an ecosystem and puts a spotlight on issues in cloud development. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.

Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to improve safety, performance, and reliability in today's modern wind turbines. Speaker Bio Stuart Gillen is the Director of Business Development at SparkCognition. In this role, he is responsible for driving business engagements, partner development, marketing activities, and go-to market strategy.

Last year Hrayr used convolutional networks to identify spoken language from short audio recordings for a TopCoder contest and got 95% accuracy. After the end of the contest we decided to try recurrent neural networks and their combinations with CNNs on the same task. The best combination allowed to reach 99.24% and an ensemble of 33 models reached 99.67%. As before, the inputs of the networks are spectrograms of speech recordings. It seems spectrograms are the standard way to represent audio for deep learning systems (see "Listen, Attend and Spell" and "Deep Speech 2: End-to-End Speech Recognition in English and Mandarin").