A machine learning algorithm designed to teach computers how to recognize photos, speech patterns, and hand-written digits has now been applied to a vastly different set a data: identifying different phases of condensed matter. In a project half-jokingly called "Phasebook," two Perimeter researchers showed that a neural network system – a standard part of today's powerful artificial intelligence (AI) algorithms – can also identify phase transitions between states of matter. The research, published today in the journal Nature Physics, validates the idea that the relationship between theoretical physics and AI can be a fruitful, two-way exchange. The fields have long been linked. AI research has often tapped physicists to help develop machine learning for industry.

Artificial Intelligence (AI) is often used as a plot device for doom-and-gloom science-fiction stories set in the future. The reality, however, is that AI has been around for quite some time--and it's incredibly useful for businesses. In fact, this technology has been assisting us daily in ways we barely even consider. Whenever you say, "Hey Siri," shop on Amazon, play songs on Pandora, or search for a photo on Facebook, AI is quietly working behind the scenes to deliver what you need. Over the past decade, this technology has slowly integrated into our everyday lives, but progress is rapidly picking up speed.

Otto's California Truck Testing May Depend on Whether Drivers Keep a Foot on the Accelerator or Not When Otto first began testing its technology in California last year, a co-founder of the company sought to assure regulators it was developing driver-assistance systems on the state's public roads and not self-driving semi trucks. Otto, which hopes to develop fully self-driving trucks, would not be required to obtain an autonomous-testing permit from the state if it was only testing driver-assistance features. But it would be Read More ... Places: Americas North America United States West Pacific California Why is Ford investing $1 billion over five years in a Pittsburgh startup called Argo AI? It's looking to gain an edge in self-driving car technology in the four-year countdown to 2021. That's when Ford has said it will be shipping cars with autonomous driving. Several other automakers said in 2016 they'd have self-driving cars within five years, too.

When Sarah Matthews was left completely blind three years ago after suffering a head injury in an accident, she feared she would never lead a normal life again. The mum of one was struggling to do basic tasks, such as getting things out of a cupboard. But even more agonising was the fact that she could no longer recognise her family and friends. The injury damaged the optic nerve in both eyes, leaving her unable to know when her five-year-old son Evan was coming over for a hug. But thanks to advances in science, artificial intelligence has transformed her life.

Every company is now a data company, capable of using machine learning in the cloud to deploy intelligent apps at scale, thanks to three machine learning trends: data flywheels, the algorithm economy, and cloud-hosted intelligence. That was the takeaway from the inaugural Machine Learning / Artificial Intelligence Summit, hosted by Madrona Venture Group* last month in Seattle, where more than 100 experts, researchers, and journalists converged to discuss the future of artificial intelligence, trends in machine learning, and how to build smarter applications. With hosted machine learning models, companies can now quickly analyze large, complex data, and deliver faster, more accurate insights without the high cost of deploying and maintaining machine learning systems. "Every successful new application built today will be an intelligent application," Soma Somasegar said, venture partner at Madrona Venture Group. "Intelligent building blocks and learning services will be the brains behind apps."

"Microsoft Monday" takes a look back at the past week of news related to Microsoft. This week, "Microsoft Monday" includes details about the acquisition of "Wiki" by Agile Extensions, a leaked Project NEON screenshot hinting at Windows 10 design changes, a federal judge ruling in favor of Microsoft in regards to the Justice Department lawsuit, the new suggested reminders feature for Cortana, Visual Studio 2017 launching on March 7th, the Surface Hub "Try & Buy Program" and more! In the past, Microsoft has been notorious for making design changes that did not sit well with consumers like we have seen in Windows Vista and Windows 8. Tom Hounsell, the creator of a Windows build tracker website called BuildFeed, published this leaked screenshot of the Windows 10 Project NEON design: The most notable Project NEON change is the removal of the window borders. This new user interface change could cause confusion in terms of resizing and moving windows. For example, some users may be confused about where to place the mouse in order to perform those functions.

In recent years the areas of Data Science, Artificial Intelligence, Machine Learning & Big Data have become massively popular. This tremendous growth has been fueled by (i) the increased collection & availability of new data; (ii) continually increasing processing power and storage; & (iii) the open source movement making tools widely available. As a result, we have already witnessed profound changes to how we work, rest and play. And this trend will only increase ... Join the SQA on March 9th for Fuzzy Day 2017 to explore what "data science", "artificial intelligence", "machine learning", "big data" and " insert any other buzzword here! " really mean for investment managers.

The property industry has been stagnant for decades. How we rent or buy property has not changed significantly since the late 1900s, with deposits still often required to be paid in hard cash and consumers obligated to trawl through countless listings to find a property they might be interested in – which may or may not have already been sold or let. However, a wave of technological innovation is sweeping in to transform this sleeping giant of an industry. Recognising the cultural shift towards mobile, social media and the desire for 24/7 flexible communication, start-ups determined on using the very latest software and technology are driving the industry forward firmly into the modern era. A huge part of this technological revolution is focused on harnessing predictive data analytics and machine learning. Machine learning (ML) has become something of a buzzword over the past few years and it is already starting to be integrated within several property platforms.

When it comes to machine learning and data science, there are so many language options to choose from. Data scientist Jean-Francois Puget is looking at which one is best. What programming language should you learn to get a machine learning or data science job? I could provide my own answer to it and explain why, but I'd rather look at some data first. After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let's look at some data.