Here's a task for you: Type "I want to get started with machine learning" into your favourite search engine. I get back a whole list of options from Coursera, Azure, Amazon, kdnuggets, reddit,… and I could continue on and on. So where should one get started? In this post, I want share the experiences of UK Technical Evangelist and ML Expert Amy Nicholson. Amy will share her experience of using the Cortana Intelligence Gallery in conjunction with the Azure ML Studio, and how that combination helped her break down the otherwise high barriers into popular ML techniques and start building her own ML models as well as her knowledge about this space. Amy is a graduate from The University of Sheffield in the UK, where she studied Computer Science.

Whether you're trying to lose weight, eat healthier, or track your diet for other reasons, there's no truly easy and automatic way to log your food intake. Today's best options are apps that require you to pick the food items you're consuming or manually enter that information. But what if the app knew, without your help, that you'd just spent a half hour crunching on cookies? That's the idea behind the "Diet Eyeglasses" revealed earlier this month at the IEEE Body Sensor Networks conference in San Francisco. The smart glasses have built-in sensors that detect muscle activity related to chewing, which could enable the continuous and unobtrusive monitoring of every morsel users chow down on.

The world's smallest lens has been created using 3D printing, and it's just twice the width of a human hair. Its makers claim the lens could lead to cameras the size of a grain of salt, revolutionising surgery, surveillance, robotics and drone technology. Researchers in Germany created a triplet lens device by combining three of the lenses into a'pinhead' device. It is capable of razor sharp images and can attach to the end of an optical fibre, and is thin enough to fit in the hollow of a syringe. Researchers in Germany have created the world'd s smallest lens using 3D printing.

The world, and financial markets in particular, were stunned by Britain's vote to exit the EU. The markets were quite confident that Britain would stay in the EU. The S&P 500 rose sharply towards the end of the trading day before the Brexit vote, which some analysts claimed as a vote by investors against the Brexit. In the meanwhile, a unique model that we developed using media sentiment data made the opposite prediction and expected a sharp decline in markets that we saw overnight. During my graduate work at Stanford University, I learned how modern machine learning algorithms can be used to identify complex and deep patterns in noisy datasets – patterns that may be very difficult to detect with less sophisticated algorithms or human intuition.

Europe's robots to become'electronic persons' under draft plan. Not convinced this is a fantastic plan http://reut.rs/28PTvdh Got a free pass to @hopeconf for a woman interested in information security who has never attended the conference before. RT @tdhopper I suspect that the final years of my life will be spent wandering the streets mumbling "nonlinearities" repeatedly. Did Texas follow up with: we don't understand the question.

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?

This structural design can support calculations being made upon thousands of layers, and it was this aspect of the architecture that gave rise to the name'deep learning'. Marchand-Maillet explains: "Each artificial neurone is assigned an input value, which it computes using a mathematical function, only firing if the output exceeds a pre-defined threshold." In this way, it reproduces the behaviour of real neurones, which only fire and transmit information when the input signal (the potential difference across the entire neural circuit) reaches a certain level. In the artificial model, the results of a single layer are weighted, added up and then sent as the input signal to the following layer, which processes that input using different functions, and so on and so forth. For example, if a system is trained with great quantities of photos of apples and watermelons, it will progressively learn to distinguish them on the basis of diameter, says Marchand-Maillet. If it cannot decide (e.g., when processing a picture of a tiny watermelon), the subsequent layers take over by analysing the colours or textures of the fruit in the photo, and so on.

Cybercriminals on the lookout for a vehicle to hack may soon face stiff opposition, as experts claim to have made the first hack-proof car. Would-be hackers have turned to targeting cars as on-board computers become increasingly common to control locking and safety systems – such as auto braking and power steering. Experts say that bolstering security and making vehicles impervious to cyber criminals is a crucial step as autonomous vehicles roll out in coming years. The system relies on a series of firewalls and unique data keys which can't be cloned to keep vehicles from being hacked. It will be fitted in electric Metrocabs, set to hit London's streets (pictured) The system, developed by Camberley-based firm Frazer-Nash Research, will be unveiled tomorrow at Connected Cars '16 at Olympia in London and will be fitted in electric Metrocabs, which began trials on London's streets last year.

Cloud accounting software startup Smacc has raised 3.5 million in Series A funding from Cherry Ventures, Rocket Internet, Dieter von Holtzbrinck Ventures, Grazia Equity, and a number of angel investors. The Potsdam-based company uses AI to automate the accounting and financial processes for small and medium sized enterprises. The company's proprietary technology uses machine learning to continuously improve the automation process and provide real time financial transparency. Customers' financial data is secured with "state-of-the-art" encryption, according to the startup, and stored in data centres in Germany. "The capital from the financing round will be used to expand our proprietary technology platform and further accelerate growth," said cofounder Janosch Novak.

The factory floor is a marvel of automation. With a press of a button, the whole place can seem to run itself. But although today's factories use automated workflows, process change is still mostly manual. When demands arise in an industrial environment, managers and engineers must interrupt the automation to update the processes that make the machines go. Now, thanks to machine learning algorithms, it's becoming possible for smart software to scrutinize data from a variety of sources -- sensors on machines or changes in supply chains, for instance -- and redesign processes in real time.