In conjunction with the Web Summit conference in Lisbon today, Facebook is unveiling artificial intelligence (AI) software that it's using in order to let users apply and switch artistic styles for live video streams on Android and iOS. After demonstrating the technology at a conference last month, Facebook is now testing the video style transfer technology on mobile in a few countries, and it will be deployed more widely in the near future. The Caffe2go technology Facebook developed in the past three months is an implementation of a hot type of AI called deep learning, which typically involves training on lots of data, like images, and then making inferences about new data. In this case, Facebook has developed pre-trained neural networks that can then make inferences about new data on the fly on mobile. Google did something similar with a part of Google Translate last year, but Google also recently demonstrated neural style transfer technology of its own, although it's not yet been shown to run on mobile devices.

Enterprise success in the digital economy requires levels of speed and flexibility that legacy IT systems simply can't provide. Fortunately, the emergence of big data analytics, artificial intelligence and automation gives enterprises powerful new tools to improve efficiency and decision-making. Over at Information Management, contributor Mark Feldner makes the case that "big data and artificial intelligence (AI) stand to become the driving force behind innovation in the workplace." "By automating previously manual processes, we have more capabilities to identify patterns in real-time and make predictions that can streamline how we run our businesses," Feldner says. The key to making customers happy is to understand and meet their needs.

With the rise of automation in nearly every industry, there is still a considerable debate on the nature of jobs responsible for the automation. Jobs can vary from linguistics in natural language processing, predictive modeling in data mining to software engineers in self-driving cars. However, there has to be some underlying distinction between the jobs, at least as far as machine learning (ML) is concerned. In simple terms, it is a process of training a system to perform a task without describing how it should perform the task. A more technical definition would be: "… a machine learns with respect to a particular task T, performance metric P, and type of experience E, if the system reliably improves its performance P at task T, following experience E." 1 This involves taking a series of inputs, feeding them into a system, and allowing a system to learn what is a desired output.

The researchers, working with Google's artificial intelligence division DeepMind, trained the software on more than 30,000 videos of test subjects speaking sentences. Over time, it would match certain words with particular lip movements to learn what words were being spoken. The researchers then played it further videos of people speaking sentences and the LipNet software succeeded with 93.4 per cent accuracy. This compares to 52.3 per cent for hearing impaired students, and surpassed other lip-reading programs. Unlike previous software, LipNet digested the phrases as full sentences, and allowing it to put words in context rather than decipher them individually allowed much greater accuracy.

Historically, when new technologies become easier to use, they transform industries. That's what's happening with artificial intelligence and big data; as the barriers to implementation disappear (cost, computing power, etc.), more and more industries will put the technologies into use, and more and more startups will appear with new ideas of how to disrupt the status quo with these technologies. By my predictions, the AI revolution isn't coming, it's already here, and we'll see it first in a few key sectors. Most people agree that healthcare is broken, and many startups believe that the biggest answer is putting the power back in the hands of the patient. We're all carrying the equivalent of Star Trek's tricorder around in our pockets (or an early version, at any rate) and smartphones and other smart devices will continue to advance and integrate with AI and big data to allow individuals to self-diagnose.

Dublin's quarterly meetup event for product focused: Machine Learning – Natural Language Processing – Deep Learning – Machine Vision – Augmented Intelligence – Neural Networks – Cognitive – Machine Ethics, in short… Artificial Intelligence. Dublin AI is a community that brings startups, corporates and academics in the AI sphere together to understand the current state of these technologies, its value add for businesses, and what is in store for the near future. Talks aim to reveal trends, industry insights and practical approaches of applying AI technologies. While some of our talks will take a technical deep dive, the forum is open to all those with a strong interest in the field.

Bill Gates calls this market the "holy grail" of computer science. There are currently 1,031 AI start-ups listed on AngelList, with an average valuation of $5.2 million -- which equals nearly $5.4 billion in venture capital investments. The three most-followed companies on that list are robotics company Autonomous, team productivity software maker Crux, and AI social news aggregator Zero Slant. Leo is a Tech and Consumer Goods Specialist who has covered the crossroads of Wall Street and Silicon Valley since 2012. His wheelhouse includes cloud, IoT, analytics, telecom, and gaming related businesses.

Recently, I did a webinar on Machine Learning and R. I received a number of questions during the presentation. Due to time constraints, I was unable to answer all of them, so I have provided the Question and Answers here. Question: Can I Use R in SQL Server to plot non-linear regression curves? We use IC50 and others in Michaelis-Menten kinetics for bio-chemical work. R running on SQL Server provides the functionality of standard CRAN R packages with the additional capability to run the SCALER functions provided by SQL Server's implementation of R. Any other functionality performed in R can therefore also be performed on SQL Server.

Artificial intelligence is difficult to develop because real intelligence is mysterious. This mystery manifests in language, or "the dress of thought" as the writer Samuel Johnson put it, and language remains a major challenge to the development of artificial intelligence. "There's no way you can have an AI system that's humanlike that doesn't have language at the heart of it," Josh Tenenbaum, a professor of cognitive science and computation at MIT told Technology Review in August. In September, Google announced that its Neural Machine Translation (GNMT) system can now "in some cases" produce translations that are "nearly indistinguishable" from those of humans. "Machine translation is by no means solved. GNMT can still make significant errors that a human translator would never make, like dropping words and mistranslating proper names or rare terms, and translating sentences in isolation rather than considering the context of the paragraph or page."

Our brains can comprehend things so well that it makes vision seem very easy. It doesn't take any time for a human to detect an anomaly, or identify the difference between a bus and a car, or to detect and recognize a human face, but it is incredibly hard for a computer to learn how to detect and recognize an object as easy as a human brain. In the last couple of years researchers have made tremendous progress on addressing this problem. They have come up with a solution using deep convolutional neural networks, a model which can perform hard visual recognition tasks which are close to or sometimes even better than the human brain. Convolutional Neural Networks, is a black box that constructs features we would otherwise have to handcraft ourselves, hence to create one it takes very high computing power and a lot of time.