Cortical circuits exhibit intricate recurrent architectures that are remarkably similar across different brain areas. Such stereotyped structure suggests the existence of common computational principles. However, such principles have remained largely elusive. Inspired by gated-memory networks, namely long short-term memory networks (LSTMs), we introduce a recurrent neural network in which information is gated through inhibitory cells that are subtractive (subLSTM). We propose a natural mapping of subLSTMs onto known canonical excitatory-inhibitory cortical microcircuits. Our empirical evaluation across sequential image classification and language modelling tasks shows that subLSTM units can achieve similar performance to LSTM units. These results suggest that cortical circuits can be optimised to solve complex contextual problems and proposes a novel view on their computational function. Overall our work provides a step towards unifying recurrent networks as used in machine learning with their biological counterparts.

Researchers at an Oxford hospital have developed artificial intelligence (AI) that can diagnose scans for heart disease and lung cancer. The systems will save billions of pounds by enabling the diseases to be picked up much earlier. The heart disease technology will start to be available to NHS hospitals for free this summer. The government's healthcare tsar, Sir John Bell, has told BBC News that AI could "save the NHS". "There is about £2.2bn spent on pathology services in the NHS. You may be able to reduce that by 50%. AI may be the thing that saves the NHS," he said.

She presented the most popular methods of interpreting Machine Learning classifiers, for example, feature importance or partial dependence plots and Bayesian networks. Finally, she introduced Local Interpretable Model-Agnostic Explanations (LIME) framework for explaining predictions of black-box learners – including text- and image-based models - using breast cancer data as a specific case scenario. Kasia Kulma is a Data Scientist at Aviva with a soft spot for R. She obtained a PhD (Uppsala University, Sweden) in evolutionary biology in 2013 and has been working on all things data ever since. For example, she has built recommender systems, customer segmentations, predictive models and now she is leading an NLP project at the UK's leading insurer. In spare time she tries to relax by hiking & camping, but if that doesn't work;) she co-organizes R-Ladies meetups and writes a data science blog R-tastic (https://kkulma.github.io/).

Munich-based Picsure (formerly known as Snapsure) is live with its AI platform as of January 1 . The startup, established in 2017 by Enrico Bolloni and Florian Bischof, allows insurers to generate insurance proposals based on images, while also offering AI solutions for fraud detection and customer identification. Here's a list of brands interested in the startup's tech: One use case shared by the startup is Wert14 by SkenData that allows customers to obtain a home insurance quote with a picture of their home.

Artificial intelligence (AI) and machine learning are causing excitement all over the world. Recent reports, such as one from Accenture, claim it has the potential to revolutionise the future of all businesses operations. For instance, research tasks that take hundreds of hours, such as candidate profiling, can now be performed by an AI within seconds. It's no wonder that many businesses are tapping into this trend – the potential savings, in both time and money, are extraordinary. However, what are the consequences of programming AI in today's environment?

CENTRAL bankers around the world have set up or are creating departments to embrace Big Data in the quest for deeper insight into the economies they manage. David Hardoon, chief data officer at the Monetary Authority of Singapore, in a recent speech said: "Isaac Asimov once said, 'I do not fear computers. I fear the lack of them.'" "We are now starting to put in place the necessary tools, infrastructure and skillsets to harness the power of data science to unlock insights, sharpen surveillance of risks, enhance regulatory compliance and transform the way we do work." Authorities like Mr Hardoon are tapping publicly available sources such as Google Trends and jobs websites to help "nowcast" their economies, and confidential data like credit registers that can help identify a stressed bank.

Sophia, a robot integrating the latest technologies and artificial intelligence developed by Hanson Robotics is pictured during a presentation at the "AI for Good" Global Summit at the International Telecommunication Union (ITU) in Geneva, Switzerland June 7, 2017. LONDON -- Banks are getting excited about the potential of artificial intelligence in finance, with hopes that AI could both cut costs and boost revenues. Artificial intelligence has advanced in recent years and financial services companies are now looking at its potential applications in both investment banking and retail banking. Advocates tout AIs potential in everything from bond markets to savings accounts. "Based on our UBS Evidence Lab survey of 86 banks, an optimal scenario of limited disruption suggests AI technology could potentially lead to a 3.4% revenue uplift and cost savings of 3.9% over the next three years," UBS strategist Philip Finch wrote a recent note titled "Is AI the next revolution in retail banking?" "I think the future of financial services is AI," Barnaby Hussey-Yeo told Business Insider. Hussey-Yeo is the CEO and founder of Cleo, a "chatbot" app that uses artificial intelligence to give people advice on how to optimise their finances.

Robotic surgeons are on the march. Across the NHS they are taking over thousands of operations from their human counterparts for prostate cancer or kidney and bladder surgery. Science fiction has become science fact. The machines, with their pinpoint-accurate computer-controlled arms, are being introduced in the belief that they can perform minute surgical tasks such as cutting and stitching far more effectively than quiver-fingered humans, and with less risk of bleeding from excessive incisions or poor suturing. There are now around 60 such robots, of a type called'da Vinci', in NHS hospitals.

In certain sunny climes, self-driving cars are multiplying. Dressed in signature spinning sensors, the vehicles putter along roads in California, Arizona and Nevada, hoovering up data that will one day make them smart enough to run without humans. Besides perennial sunshine, those places share other common traits: wide, well-manicured roads, functional traffic enforcement, and agreeable local governments. That's how Chandler, Arizona – a Phoenix suburb on nobody's radar as of a few weeks ago – became the first US town to host autonomous cars on public streets without human safety drivers. Courtesy of Waymo, they're expected to start carrying passengers within the next few months.

Hackers are constantly finding new targets and refining the tools they use to break through cyberdefenses. The following are some significant threats to look out for this year. The cyberattack on the Equifax credit reporting agency in 2017, which led to the theft of Social Security numbers, birth dates, and other data on almost half the U.S. population, was a stark reminder that hackers are thinking big when it comes to targets. Other companies that hold lots of sensitive information will be in their sights in 2018. Marc Goodman, a security expert and the author of Future Crimes, thinks data brokers who hold information about things such as people's personal Web browsing habits will be especially popular targets.