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.

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.

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.

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.

In an ever-changing world of increasingly digital businesses, marketers need to be able to respond instantly to a multitude of factors. They need to be as flexible as their industry is fluid. They need to be agile in their environments. A report from CMG Partners found that 63 percent of marketers indicated that agility is a high priority for them, but only 40 percent considered themselves as such. The same report found that marketing teams that do consider themselves agile are three times more likely to significantly grow market share. Considering this, it goes without saying that marketing departments need to examine what they can do to update their approach.

A free AI-based scholarly search engine that aims to outdo Google Scholar is expanding its corpus of papers to cover some 10 million research articles in computer science and neuroscience, its creators announced on 11 November. Since its launch last year, it has been joined by several other AI-based academic search engines, most notably a relaunched effort from computing giant Microsoft. Semantic Scholar, from the non-profit Allen Institute for Artificial Intelligence (AI2) in Seattle, Washington, unveiled its new format at the Society for Neuroscience annual meeting in San Diego. Some scientists who were given an early view of the site are impressed. "This is a game changer," says Andrew Huberman, a neurobiologist at Stanford University, California.

The CEO and founder of cloud-based accountancy software giant Xero says artificial intelligence (AI) and machine learning technologies will be "transformational" for finance over the next few years. Rod Drury told Business Insider during a recent interview in London: "We'll see more innovation in the next 2 years than we have in the 10 years, all driven by AI." The Xero founder says: "We're getting a massive hit on the R&D we've done around machine learning and AI. We think it's going to be transformational for the industry. "If you capture information from the bank statement and the invoice and the bills that are flying through, you can actually programme things to do a whole lot of work for you and you're just checking and making fixes, which trains the machine." Xero's accountancy software helps small and medium-sized businesses manage their accountants in the cloud but Drury believes much of the management -- things like categorizing expenditure and sending accounts to be checked -- could be automated by "smart" AI and machine learning programmes, which learn the habits of your business. "You can build unique system for each business," says Drury. "The first innovation in cloud accounting was actually getting these transactions into the cloud.