CBR looks at the'Artificial intelligence: an overview for policy-makers' report from the Government Office for Science. The UK government is driving the artificial intelligence agenda, pinpointing it as a future technology driving the fourth revolution and billing its importance on par with the steam engine. The report on Artificial Intelligence by the Government Office for Science follows the recent House of Commons Committee report on Robotics and AI, setting out the opportunities and implications for the future of decision making. In a report which spans government deployment, ethics and the labour market, Digital Minister Matt Hancock provided a foreword which pushed AI as a technology which would benefit the economy and UK citizens. "As one the world's leading digital nations, artificial intelligence presents a huge opportunity for the UK. Get this right, and we can create a more prosperous economy with better and more fulfilling jobs," Mr Hancock wrote in the report.

The new Tobii Pro Spectrum is eye-tracking hardware that sits below a screen or real-world stimuli. Innovations in eye tracking technology have had clear commercial applications, unlocking possibilities in virtual reality, advancing sophisticated market research and even empowering people with disabilities to control their devices with their eyes. The technology's applications in more basic scientific research have been just as impactful: It helps, for instance, psychologists working on detecting signs of autism and neuroscientists studying the effects of treatments on Parkinson's disease. Swedish eye-tracking company Tobii Group has had a hand in developing just about all of these applications through its different divisions. On Thursday, Tobii Pro, the research-focused arm of the company, is rolling out new hardware and software products that have the potential to put eye-tracking technology in the hands of more scientific researchers.

IBM's Project Intu is an experimental framework to help developers equip devices with Watson's AI features. IBM has released tools for developers to plug its Watson artificial intelligence into any device or thing, from robots to walls. Its latest effort to get Watson in the hands of developers is Project Intu, an experimental framework to help developers equip devices more easily with Watson's conversation, language and visual-recognition services. IBM wants developers to use Intu to add so-called cognitive capabilities to machines, such as getting them to understand questions and gestures, or for example, monitor the weather and the time of day. The company has released software developer kits for Intu on GitHub and its Intu gateway, offering the tools to build Watson features for any platform, including Raspberry Pi, Windows, Linux and macOS.

InfiniBand is set to hit 200Gbps (bits per second) in products that were announced Thursday, potentially accelerating machine-learning platforms as well as HPC (high-performance computing) systems. The massive computing performance of new servers equipped with GPUs calls for high network speeds, and these systems are quickly being deployed to handle machine-learning tasks, Dell'Oro Group analyst Sameh Boujelbene said. So-called HDR InfiniBand, which will be generally available next year in three sets of products from Mellanox Technologies, will double the top speed of InfiniBand. It will also have twice the top speed of Ethernet. But the high-performance crowd that's likely to adopt this new interconnect is a small one, Boujelbene said. Look for the top 10 percent of InfiniBand users, who already use 100Gbps InfiniBand, to jump on the new stuff, she said.

That there's little love lost between Donald Trump and Silicon Valley is hardly a new revelation. But while a Trump Administration's relationship with the tech elite is bound to be more distant and chillier than a Clinton Administration's would have been, its policies may simply be sub-optimal for the tech sector rather than disastrous -- and with some notable silver linings. On one hand, a lack of the close ties prominent tech execs formed with the Obama Administration, and appeared set to maintain with a Clinton Administration, could cause some problems. Obama, who recently guest-edited an issue of Wired magazine and gave the publication a thoughtful interview on various tech topics, has been willing to lend an ear to Silicon Valley's views on issues such as autonomous driving regulations, encryption keys, artificial intelligence research and STEM education funding. Trump, who appears to know much less about tech and briefly called for a boycott of Apple (AAPL) in response to the company's unwillingness to help break the encryption on an iPhone used by one of the San Bernardino shooters, probably won't be holding talks with the likes of Tim Cook and Facebook's (FB) Mark Zuckerberg and Sheryl Sandberg as frequently.

But that is not to say it is perfect by any stretch of the imagination. "Deep learning has led to some big advances in computer vision, natural language processing, and other areas," Tommi Jaakkola, a Massachusetts Institute of Technology professor of electrical engineering and computer science, told Digital Trends. "It's tremendously flexible in terms of learning input/output mappings, but the flexibility and power comes at a cost. That is it that it's very difficult to work out why it is performing a certain prediction in a particular context." This black-boxed lack of transparency would be one thing if deep learning systems were still confined to being lab experiments, but they are not.

Almost every action we do on the Internet or on mobile applications is recorded in files known as web logs. These logs can be very voluminous, providing a classic example of Big Data. Data science and Machine Learning algorithms can provide a way of extricating value from web logs. At the OVH Summit on the 11th of October, I presented a workshop on getting value out of web logs through Machine Learning with Dataiku DSS. In this article, I will run through the aspects of that presentation.

Machine learning is a very hot topic for many key reasons, and because it provides the ability to automatically obtain deep insights, recognize unknown patterns, and create high performing predictive models from data, all without requiring explicit programming instructions. This is a summary (with links) to an article series that's intended to be a comprehensive, in-depth guide to machine learning, and should be useful to everyone from business executives to machine learning practitioners. It covers virtually all aspects of machine learning (and many related fields) at a high level, and should serve as a sufficient introduction or reference to the terminology, concepts, tools, considerations, and techniques in the field. The first chapter of the series starts with both a formal and informal definition of machine learning. This is followed by a discussion of the machine learning process end-to-end, the different types of machine learning, potential goals and outputs, and a categorized overview of the most widely used machine learning algorithms.

Science fiction has, for many years, looked to a future in which robots are intelligent and cyborgs are commonplace. The Terminator, The Matrix, Blade Runner and I, Robot are all good examples of this vision. But until the last decade, consideration of what this might actually mean in the future was unnecessary because it was all science fiction, not scientific reality. Now, however, science has not only done some catching up; it's also introduced practicalities that the original story lines didn't appear to include (and, in some cases, still don't include). What we consider here are several different experiments linking biology and technology together in a cybernetic way--essentially ultimately combining humans and machines in a relatively permanent merger.

Google has built machine learning systems that can create their own cryptographic algorithms -- the latest success for AI's use in cybersecurity. But what are the implications of our digital security increasingly being handed over to intelligent machines? Google Brain, the company's California-based AI unit, managed the recent feat by pitting neural networks against each other. Two systems, called Bob and Alice, were tasked with keeping their messages secret from a third, called Eve. None were told how to encrypt messages, but Bob and Alice were given a shared security key that Eve didn't have access too.