"The corridor connecting Cambridge, Milton Keynes, and Oxford could be the UK's Silicon Valley," the Infrastructure Commission said in a report published this week. The report recommended bringing forward £100 million in funding to create a western section of the East West Rail project by 2024, and that the government should commit up to a further £10 million in development funding to continue work on the central section, the part that would link Oxford with Cambridge. There used to be a rail link between Oxford and Cambridge but it was closed in 1967. It currently takes two and a half hours and two changes via London to travel by train between the university cities. The report describes the journey as "difficult, slow and unreliable," contrasting it to the strong north-south links to and from London.

Machine learning, artificial intelligence--whatever the label, it's fast becoming a way to reinvent enterprise IT mainstays and for the companies on top to stay on top. Consider four of the most familiar names in technology: Intel, Google, IBM, and Microsoft are investing heavily in ML/AI with hardware designs intended to greatly accelerate the next generation of applications. What it's doing: The world's best-known chipmaker recently introduced a new line of CPUs specifically aimed at ML applications: Knights Mill. It also mentioned plans to meld its CPUs with reprogrammable FPGA processors, a powerful but relatively underexploited technology for Intel. Why it's doing so: As the PC market continues to melt away like an Arctic glacier, Intel has been hunting for methods to make up the difference.

Onalytica analyzed tweets over the last 6 months mentioning the keywords M2M OR "Machine to Machine." They then identified the top 100 most influential brands and individuals leading the discussion on Twitter. Machine to Machine (M2M) refers to direct communication between devices using any communications channel, including wired and wireless. The M2M market is undergoing a fast transformation as enterprises are increasingly realizing the value of connecting geographically dispersed people, devices, sensors and machines to corporate networks. It is for precisely this reason that the Global M2M market is expected to grow to 27 billion devices, generating $1.6 trillion in revenue in 2024.

Google has released a handful of AI experiments that tap into advances in machine learning in creative ways. They include Quick, Draw!, a game in which an algorithm tries to guess what you're sketching, A.I. Duet, which lets you compose pieces of music with a creative computer, and ways to visualize how neural networks represent information and see the world. The projects show off some new AI features Google has built into an overhauled cloud computing platform. But they also help make AI less mysterious, and hint at ways in which the technology may become more accessible to all of us. Take Quick, Draw!, for example.

If you've already created an account, use your email address and password to sign in using the form below. Enjoy the benefits of The World's Leading New & Used Medical Equipment Marketplace.

Algorithms are now used throughout the public and private sectors, informing decisions on everything from education and employment to criminal justice. But despite the potential for efficiency gains, algorithms fed by big data can also amplify structural discrimination, produce errors that deny services to individuals, or even seduce an electorate into a false sense of security. Indeed, there is growing awareness that the public should be wary of the societalrisks posed by over-reliance on these systems and work to hold themaccountable. Various industry efforts, including a consortium of Silicon Valley behemoths, are beginning to grapple with the ethics of deploying algorithms that can have unanticipated effects on society. Algorithm developers and product managers need new ways to think about, design, and implement algorithmic systems in publicly accountable ways. Over the past several months, we and some colleagues have been trying to address these goals by crafting a set of principles for accountable algorithms.

The common, and recurring, view of the latest breakthroughs in artificial intelligence research is that sentient and intelligent machines are just on the horizon. Machines understand verbal commands, distinguish pictures, drive cars and play games better than we do. How much longer can it be before they walk among us? The new White House report on artificial intelligence takes an appropriately skeptical view of that dream. It says the next 20 years likely won't see machines "exhibit broadly-applicable intelligence comparable to or exceeding that of humans," though it does go on to say that in the coming years, "machines will reach and exceed human performance on more and more tasks."

Artificial intelligence can be an important tool for collecting as well as interpreting business related data. Currently around 45 companies specialize in AI for functions such as recommendations, search, multichannel marketing, merchandising, and conversational commerce. Nothing works better for accumulating and making sense of all the data than machine learning, or AI. When automating aspects of a retail business, AI is able to provide an intelligent as well as a speedy solution, including self-adapting algorithms which can show a company's patterns of behavior which would otherwise be hidden from humans reading the data on their own. From there you have a starting point for predicting patterns of behavior and interaction which drive the most customer interaction, or purchasing.

NVIDIA Tesla P100 GPUs and Tesla K80 GPUs will be available on Google Cloud Platform, starting early next year. Delivering the power of our Pascal GPU architecture from the cloud gives businesses another great option for helping to put their data to work and build AI services. On Google Cloud Platform, Tesla P100 GPUs will be available to Google Compute Engine and Google Cloud Machine Learning users around the world. The Tesla P100 delivers high performance and efficiency to power the most computationally demanding applications -- including a 12x increase in neural network training performance compared with a previous-generation offering. The Tesla K80 GPU accelerator delivers exceptional performance, with increased throughput that allows researchers to advance their scientific discoveries and developers to boost their web services.

U of T Engineering researchers Wenzhi Guo (ECE MASc 1T5) and Parham Aarabi (ECE) have designed a new machine learning algorithm that may soon enable your smartphone to give you an honest answer based on logic. The algorithm does not learn from an existing set of examples, but rather takes its data directly from human instructions. This methodology resulted in it outperforming conventional methods of training neural networks by a whopping 160 per cent. What is even more surprising is that the algorithm also outperformed its own training by nine per cent. It learned, for example, to recognize hair in pictures with more reliability than that enabled by the training.