Led by Fei-Fei Li, the director of the Stanford University artificial intelligence lab and a newly minted Google employee, a team of academics recently explored a new way of tracking socioeconomic trends across the US. Rather than knocking on doors and asking questions, they pulled more than 50 million photos from Google Street View and fed them into neural networks. Simply by identifying the make, model, and year of automobiles appearing in the photos, the researchers said, their tech could accurately estimate the income, race, education, and voting patterns of citizens in particular precincts. If the number of sedans on a short stretch of road exceeded the number pickup trucks, for instance, they found that a city was 88 percent likely to vote for a Democrat during the next presidential election. If pickups exceeded sedans, a city was 82 percent likely vote Republican.

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The need for speed to process neural networks is far less a matter of processor capabilities and much more a function of memory bandwidth. As the compute capability rises, so too does the need to keep the chips fed with data--something that often requires going off chip to memory. That not only comes with a performance penalty, but an efficiency hit as well, which explains why so many efforts are being made to either speed that connection to off-chip memory or, more efficiently, doing as much in memory as possible. The advent of 3D or stacked memory opens new doors, especially for those with deep learning workloads. We have already talked about how memory is the next platform for machine learning, and have explored a number of architectures that seek to maximize on-chip memory by making it handle at least some of the compute via accumulation engines.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. She also made it clear that she intends to make money at it, despite Google's emphasis on undercutting rivals on the price of its cloud services. That point was seconded by Urs Holzle, senior vice president of Google's technical infrastructure, who repeated a claim he made last year that cloud revenues eventually surpass those of Google's massive ad business.

As we all push forward with the development of artificial intelligence (AI) solutions, software developers and data scientists increasingly want to leverage deep learning frameworks. To back up a bit, deep learning is a type of machine learning that can enable more complex solutions based on evaluation of abstractions of data. Scaling through added layers and processing, deep learning can build in aggregate from user input and experiences, much the way people learn. Deep learning frameworks enable algorithms to continually improve their performance on complex tasks like speech and image recognition. To get on this path to a new generation of AI solutions, developers and data scientists need to find ways to reduce the steep learning curve that comes with the deployment and configuration of deep learning frameworks. Then, find ways to accelerate the development, training, and deployment of models.

This post highlights some of the possible economic implications of the so-called "Fourth Industrial Revolution" -- whereby the use of new technologies and artificial intelligence (AI) threatens to transform entire industries and sectors. Some economists have argued that, like past technical change, this will not create large-scale unemployment, as labour gets reallocated. However, many technologists are less optimistic about the employment implications of AI. In this blog post we argue that the potential for simultaneous and rapid disruption, coupled with the breadth of human functions that AI might replicate, may have profound implications for labour markets. We conclude that economists should seriously consider the possibility that millions of people may be at risk of unemployment, should these technologies be widely adopted.

Nano, a Nature Research solution, has just been given a boost by artificial intelligence (AI), increasing the information available to users by sourcing data from over 400,000 relevant research papers. Launched in June 2016, Nano offers highly-indexed and structured information on nanotechnology. It first provided over 200,000 summaries of nanomaterials, containing information on properties, synthesis and applications. These were and continue to be drawn from 30 high-impact journals from all publishers and curated by experts from across this multidisciplinary field. The scale-up, announced today, will significantly increase the breadth of data available.

Another legal tech startup coming out of the UK: Cognitiv is applying artificial intelligence to automate contract analysis and management, offering businesses a way to automate staying on top of legal risks, obligations and changing regulatory landscapes. Co-founder Vasilis Tsolis might therefore be forgiven for viewing Brexit as a sizable opportunity for his startup -- though he more tactfully describes it as a "legislative challenge that we can help out with". "There's going to be a lot of changes in legislation, there's going to be a lot of changes in regulation, and you really need to know what's going to happen to your contracts and if you need to do any changes on your legal documents or not. So it's going to be a huge challenge," he says of Brexit. "I think this is going to happen more and more often," he adds, pointing to another incoming EU regulation that will be upping businesses' compliance needs in the near future: aka the GDPR, coming into force (including in the UK) in May 2018.

NEW DELHI: Norway's new powerhouse for artificial intelligence (AI) opens in Trondheim today. The new centre, Telenor-NTNU AI-Lab, will strengthen national competitiveness and add valuable, future-proof competencies to the Norwegian society. "Artificial intelligence is perhaps the single most important technology of our century. In the future, AI will drive your car, revolutionize cancer treatment and make public services more efficient. With this opening we want to accelerate the education, research and competency building which will be crucial for Norway's ability to compete in the digital future," says Sigve Brekke, President & CEO of Telenor Group.

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