Austin-based SparkCognition, a startup applying artificial intelligence and machine learning to the cybersecurity market, has raised 6M in a Series B funding round. The funding came from CME Ventures, Verizon Ventures, The Entrepreneurs' Fund (TEF), and Alameda Ventures. The startup--led by Amir Husain--said the Series B was oversusbcribed. SparkCognition says it has developed machine learning technology which it is applying to both Internet-of-Things (IoT) and cyber security, to automatically detect and identify attacks in IoT infrastructure.

Austin-based SparkCognition, a startup applying artificial intelligence and machine learning to the cybersecurity market, has raised 6M in a Series B funding round. The funding came from CME Ventures, Verizon Ventures, The Entrepreneurs' Fund (TEF), and Alameda Ventures. The startup--led by Amir Husain--said the Series B was oversusbcribed. SparkCognition says it has developed machine learning technology which it is applying to both Internet-of-Things (IoT) and cyber security, to automatically detect and identify attacks in IoT infrastructure.

Artificial Intelligence has been the topic de jour lately with every corner of intellectual thought sounding in on the perils, and the potential rewards, of synthesizing a machine intelligence that could successfully perform any intellectual task that a human can. Elon Musk, Bill Gates and even Stephen Hawking have all suggested that an AI with this sort of general intelligence (also known as Strong AI or Full AI) could bring about an apocalypse that sees an end to human civilization, or even an end to the human race. There's no doubt that Strong AI is the subject of intense research by DARPA, MIT, Berkeley, IBM, Google and many others. But it's hard not to notice that despite all the anxiety, Strong AI today lives only in the imagination of science fiction writers and in the hopes and dreams of research scientist. At the prestigious "Future of AI" conference in San Juan this January, the estimates for when an AI might emerge vacillated wildly from 5 years to a hundred years in our future--its variables are that unknown.

A revolution in artificial intelligence is currently sweeping through computer science. The technique is called deep learning and it's affecting everything from facial and voice to fashion and economics. But one area that has not yet benefitted is natural language processing--the ability to read a document and then answer questions about it. That's partly because deep learning machines must first learn their trade from vast databases that are carefully annotated for the purpose. However, these simply do not exist in sufficient size to be useful.

Drive.ai is the 13th company to be granted a license to test autonomous vehicles on public roads in California. This is exciting news, especially because we had no idea that Drive.ai even existed until just last week. The company has been in stealth mode for the past year, working on applying deep learning techniques to self-driving cars. We spoke with two of Drive.ai's Its core team is made up of experts with a wealth of experience developing deep learning systems in all kinds of different domains, including natural language processing, computer vision, and (most recently) autonomous driving. "This team helped pioneer how to scale deep learning, which is one of the reasons why deep learning has been successful as of late," says Tandon, the company's CEO.

Always wanted to compete in a Kaggle competition, but not sure where to get started? Together with the team at Kaggle, we have developed a free interactive Machine Learning tutorial in Python that can be used in your Kaggle competitions! Step by step, through fun coding challenges, the tutorial will teach you how to predict survival rate for Kaggle's Titanic competition using Python and Machine Learning. DataCamp's interactive UI makes it easy to follow along from start to random forest! In this Machine Learning tutorial, you will gradually learn how basic machine learning techniques can help you to make better predictions.

Artificial Intelligence (AI) is invading our lives everyday. The basics of "if then else" logic among programming constructs allows a computer to make a decision based on inputs. When it becomes "magical" is when the complexity of that decision making begins to go beyond the simple algorithm or software application and begins to "think" in certain ways. Gamers will be very familiar with this concept as they play against the "AI" in their games. AI is now invading the world of investing.

The relationship between the human mind and body is something that has occupied philosophers at least since the father of modern philosophy, René Descartes, bequeathed his notorious "dualism" to his successors. Low-power machine vision company Movidius has teamed up with thermal imaging company FLIR Systems, bringing Artificial Intelligence capabilities to Boson, FLIR's latest thermal-imaging camera core. FLIR will now integrate the Myriad 2 Vision Processing Unit into its thermal core to create the most intelligent thermal imaging solution to date. When artificial intelligence technology intersects with abundant oil and gas seismic data, the outcome could yield a more accurate depiction of what lies beneath the surface, enabling cash-strapped drillers to better target sweet spots and maximize returns.It's all based on algorithms that essentially teach computers how to solve complex problems-in this instance, how to quickly and accurately find subsurface faults that lead to lucrative hydrocarbon discoveries. In previous articles, we've talked about the merits of artificial intelligence and big data and how these technologies can enable a multitude of industries to begin learning how to do things more effectively.

When British mathematician Sir Andrew J. Wiles was awarded the Abel Prize Laureate in math on 15 March for cracking a centuries-old hypothesis, a friend asked me, "Why did he get the prize, and will this solve any real-world problem?" Quoting from the statement that the Norwegian Academy of Science and Letters gave to the press, I told him that 63-year-old Wiles had been given the annual award "for his stunning proof of (French mathematician Pierre de) Fermat's last theorem by way of the modularity conjecture for semi-stable elliptic curves, opening a new era in number theory". So let me try to simplify it a bit. Number theory--also sometimes referred to as the "queen of mathematics" or "higher arithmetic"--is a branch of pure math, devoted primarily to the study of the properties of whole numbers. Fermat--a prominent mathematician of the 17th century--contributed significantly to number theory, probability theory, analytic geometry and the early development of infinitesimal calculus. Fermat's last theorem states that no three positive integers a, b, and c satisfy the equation an bn cn for any integer value of n that is greater than two.

The secret to big data is of course the ability to do work in parallel. Modern Big Data engines like Hadoop don't rely on the invention of clever new algorithms or artificial intelligence to produce impressive results; instead, they are based on the idea of taking lots of inputs, working on little pieces of it in lots of places at the same time, then bringing the results together. Usually, the results are much smaller than the inputs, small enough that human beings can look at them directly. In order to work on lots of small pieces at the same time, whatever task we're performing has to be structured to be run in parallel. Many algorithms we're used to seeing work well sequentially have to be tweaked at least a little to work in parallel, and some have to be discarded as unusable in parallel.