Zillow's visual recognition effort can recognize the difference

Investors believe it is "inevitable" that artificial intelligence will destroy millions of jobs and that governments are unprepared for such an impact, according to a survey published on Thursday. Artificial intelligence (AI), or the process by which computers or robots take on tasks that need human intelligence, is one of the key themes of this week's Web Summit in Lisbon. The poll among 224 venture capitalists attending the conference showed 53 percent believed AI would destroy millions of jobs and 93 percent saw governments as unprepared for this. The survey also found that 83 percent of the investors canvassed expect Britain's exit from the European Union to damage Europe's economy and 77 percent believe it will damage British startups. London is widely seen as the main tech startup hub in Europe, thanks to its large pool of talent and a much bigger pool of funding than in rival centres.

Andrew Heikkila is a tech enthusiast and writer from Boise, Idaho. More posts by this contributor: Let's start getting excited about robots taking our jobs Let's start getting excited about robots taking our jobs Let's start getting excited about robots taking our jobs Artificial intelligence has fascinated mankind for more than half a century, with the first public mention of computer intelligence recorded during a London lecture by Alan Turing in 1947. More recently, the public has been exposed to headlines that have increasingly contained references to the growing power of AI, whether that's been AlphaGo's defeat of legendary Go player Lee Se-dol, Microsoft's racist AI bot named Tay or any other number of new developments in the machine learning field. Once a plot device for science-fiction tales, AI is becoming real -- and human beings are going to have to define their relationship with it sooner rather than later. Peter Diamandis, co-founder and vice-chairman at Human Longevity, Inc., touches on that relationship in a post he authored on LinkedIn, titled "The next sexual revolution will be digitized."

Someone across the room throws you a ball and you catch it. Actually, this is one of the most complex processes we've ever attempted to comprehend – let alone recreate. Inventing a machine that sees like we do is a deceptively difficult task, not just because it's hard to make computers do it, but because we're not entirely sure how we do it in the first place. What actually happens is roughly this: the image of the ball passes through your eye and strikes your retina, which does some elementary analysis and sends it along to the brain, where the visual cortex more thoroughly analyzes the image. It then sends it out to the rest of the cortex, which compares it to everything it already knows, classifies the objects and dimensions, and finally decides on something to do: raise your hand and catch the ball (having predicted its path).

Let's start with the basics: Accenture defines artificial intelligence (AI) as a collection of multiple technologies that together enable machines to sense, comprehend, act and learn, either on their own or to augment human activities. Indeed, we are seeing AI at a tipping point – quickly coming of age and beginning to mature at a much faster rate than ever before. This is because it is now possible, due to the availability of massive, inexpensive cloud-accessible computing power and low-cost storage, combined with algorithms, to sift rapidly through enormous volumes of data. Companies need to know how to harness AI effectively. Corporate executives seem convinced of its potential – according to Accenture's 2016 Technology Vision survey, 70 per cent of corporate executives said they are making significantly more investments in AI-related technologies than two years ago, with 55 per cent stating that they plan on using machine learning and embedded AI solutions like Amelia extensively.

This article was posted by Xiu-Shen Wei. Xiu-Shen Wei is a 2nd-year Ph.D. candidate of Department of Computer Science and Technology in Nanjing University and a member of LAMDA Group. Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-arts in visual object recognition, object detection, text recognition and many other domains such as drug discovery and genomics. In addition, many solid papers have been published in this topic, and some high quality open source CNN software packages have been made available. There are also well-written CNN tutorials or CNN software manuals.

Machine Learning is an important concept in computer science and for higher education in general that is developing rapidly. Greg Corrado, a senior research scientist at Google, described the ML basics that educators and IT managers in higher education all need to be aware of. Although machine learning is not entirely new, it has gotten much more attention since last March, when it was used to defeat Lee Sedol, the Go world champion. But even before that, ML has been powering apps like Google photos, speech recognition, text-to-speech converters, and face recognition. The reason it is coming to the forefront now is that the computational resources that it requires have become readily available.

Recently I decided to try my hand at the Extraction of product attribute values competition hosted on CrowdAnalytix, a website that allows companies to outsource data science problems to people with the skills to solve them. I usually work with image or video data, so this was a refreshing exercise working with text data. The challenge was to extract the Manufacturer Part Number (MPN) from provided product titles and descriptions that were of varying length – a standard RegEx problem. After a cursory look at the data, I saw that there were 54,000 training examples so I decided to give Deep Learning a chance. Here I describe my solution that landed me a 4th place position on the public leaderboard.

For IT organizations, machine learning is looking like an essential capability in the decade ahead. For the past few months, Google CEO Sundar Pichai has been extolling the value of AI and machine learning to his company. Gartner has added machine learning to its 2016 Hype Cycle, putting it at the peak of inflated expectations. The Hype Cycle, said Gartner research director Mike J. Walker in a statement, lists technologies that show "promise in delivering a high degree of competitive advantage over the next five to 10 years." Now, researchers at the Stanford University School of Medicine have demonstrated trained computers can outperform doctors when evaluating the slides of lung cancer patients, a finding which underscores the value of machine learning for data analysis tasks involving image recognition.

In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output. Linear regression with one variable is also known as "univariate linear regression." Univariate linear regression is used when you want to predict a single output value from a single input value . We're doing supervised learning here, so that means we already have an idea about what the input/output cause and effect should be. The "error", at each point, between the line fit and the data is the difference between the right- and left-hand sides of the equations above.