Reuters – A former Google engineer, whose speech recognition software is used in more than a billion Android smartphones, has launched a company that uses blockchain technology to build a new operating system for banks. Paul Taylor, a Cambridge University academic with an expertise in artificial intelligence, speech synthesis and machine learning, started working on the system, called Vault OS, two years ago in a basement in London's Shoreditch district, known for being a tech startup hub. Blockchain technology has captured the imagination of the financial industry, with advocates saying it has the potential to shake up how markets operate. The technology, which underpins the digital currency bitcoin, creates a shared database in which participants can trace every transaction ever made. The ledger is tamper-proof and transparent, meaning that transactions can be processed without the need for third-party verification.

Baidu has made its second investment in a U.S. fintech company inside a month after it put an undisclosed sum of money into ZestFinance, a big data firm specializing in credit scoring. Baidu, which operates China's dominant search platform, took part in a 60 million round for payments firm Circle in June. The deal is part of an agreement that will see Baidu use ZestFinance's technology to develop a credit scoring platform that is based on its search data. That's important in a market like China because traditional credit systems are broken there. There's precious little formalized credit history data while many people don't use banks heavily or are unbanked.

"Aapo did it again!" - I exclaimed while reading this paper yesterday on the train back home (or at least I thought I was going home until I realised I was sitting on the wrong train the whole time. This gave me a couple more hours to think while traveling on a variety of long-distance buses...) Aapo Hyvärinen is one of my heroes - he did tons of cool work, probably most famous for pseudo-likelihood, score matching and ICA. Time-contrastive learning (TCL) is a technique for learning to extract nonlinear representations from time series data. First, the time series is sliced up into a number of non-overlapping chunks, indexed by \tau . Then, a multivariate logistic regression classifier is trained in a supervised manner to look at a sample taken from the series at an unknown time and predict \tau, the index of the chunk it came from.

You may start watching the following video "Bicentennial Man", courtesy of Touchstone Pictures.

A major concern for our rights as consumers is the way that machines direct us according to their interests and not ours. Experts such as Dr Jonathan Cave warn about the growing influence of software machines on our lives. Cave says that software machines will make use of what they know about us to present information to us which may not be to our advantage. Because the search engines that we have used know a certain amount about us and our previous buying decisions, they are keen to exploit that by turning us into a buyer of something, by a process known as'filter bubbles' – a feedback loop where recommendations only reinforce existing patterns. As Dr Rupp states'if you are not paying then you are not the customer'. Thus if you are not paying for an internet technology such as Google or Facebook it is not acting in your interests, but rather in the interests of the customers who are paying to present information to you.

The satisfiability problem for SPARQL 1.0 patterns is undecidable in general, since the relational algebra can be emulated using such patterns. The goal of this paper is to delineate the boundary of decidability of satisfiability in terms of the constraints allowed in filter conditions. The classes of constraints considered are bound-constraints, negated bound- constraints, equalities, nonequalities, constant-equalities, and constant-nonequalities. The main result of the paper can be summarized by saying that, as soon as inconsistent filter conditions can be formed, satisfiability is undecidable. The key insight in each case is to find a way to emulate the set difference operation. Undecidability can then be obtained from a known undecidability result for the algebra of binary relations with union, composition, and set difference. When no inconsistent filter conditions can be formed, satisfiability is decidable by syntactic checks on bound variables and on the use of literals. Although the problem is shown to be NP-complete, it is experimentally shown that the checks can be implemented efficiently in practice. The paper also points out that satisfiability for the so-called ‘well-designed’ patterns can be decided by a check on bound variables and a check for inconsistent filter conditions.

I'm co-founder and CTO at Overleaf, a successful SaaS startup based in London. From August 2014 to December 2015, I manually tracked all of my work time, minute-by-minute, and analysed the data in R. Like most people who track their time, my goal was to improve my productivity. It gave me data to answer questions about whether I was spending too much or too little time on particular activities, for example user support or client projects. The data showed that my intuition on these questions was often wrong. There were also some less tangible benefits. It was reassuring on a Friday to have an answer to that usually rhetorical question, "where did this week go?" I feel like it also reduced context switching: if I stopped what I was doing to answer an chat message or email, I had to take the time to record it in my time tracker. I think this added friction was a win for overall productivity, perhaps paradoxically. This post documents the (simple) system I built to record my time, how I analysed the data, and the results.

For more than a decade, Silicon Valley's technology investors and entrepreneurs obsessed over social media and mobile apps that helped people do things like find new friends, fetch a ride home or crowdsource a review of a product or a movie. Now Silicon Valley has found its next shiny new thing. And it does not have a "Like" button. The new era in Silicon Valley centers on artificial intelligence and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.

At this year's Annual Meeting of the New Champions, hosted by the World Economic Forum in Tianjin, China, the Meta-Council on Emerging Technologies choose the top ten technologies of 2016, based on their potential to transform industry and society. A diverse range of breakthrough technologies, including autonomous vehicles, natural language artificial intelligence, and next generation batteries, were examined in collaboration with Scientific American, highlighting advances that have the power to improve lives, transform industries, and safeguard the planet. The report also provides an opportunity to debate human, societal, economic, or environmental risks and concerns that the technologies may pose prior to widespread adoption. "The global community needs to come together and agree on common principles if our society is to reap the benefits and hedge the risks of these technologies," said Dr. Bernard Meyerson, Chief Innovation Officer of IBM, and Chairman of the Meta-Council. In raising awareness about these technologies, moreover, the Council aims to address challenges in investment and regulation.

Though deals to private artificial intelligence companies -- excluding incubator/accelerator rounds -- fell 10% in Q2'16, dollar funding reached an all-time high. Q2'16 saw 3 100M mega-rounds by companies using AI: a 154M Series A round to China-based healthcare startup iCarbonX (backed by Tencent, Vcanbio), a 100M growth equity round raised by New Jersey-based Fractal Analytics (backed by Khazanah Nasional Berhad) and a 100M Series D round raised by California-based cybersecurity unicorn Cylance (backed by investors including Blackstone Group, Insight Venture Partners, and Khosla Ventures). Our AI category includes companies applying AI solutions to verticals like healthcare, security, advertising, and finance as well as those developing general-purpose AI tech. Nearly 70% of the deals went to startups in the United States in Q2'16. A majority of the startups raising funds were still in their early-stages: Nearly 60% of the deals went to startups raising seed/angel and Series A rounds, while mid-stage startups (Series B and C) received 12% of the deals.