BofE uses accelerator to gain real-world fintech experience

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The Bank of England is using its new fintech accelerator to work with startups on developing proof-of-concepts (POCs) in data analytics, information security and distributed ledgers. Launched in June, the accelerator is designed to boost the BofE's practical experience with fintech, with firms invited to apply to work with it on POCs that address challenges unique to the central bank. Speaking at Web Summit in Lisbon this week, BofE COO Charlotte Hogg invited new applications and gave an update on the project, revealing that the bank is working with BMLL Technologies on a POC that uses a machine learning platform, applied to historic limit order book data, to spot anomalies and facilitate the use of new tools in analytical capabilities. A second POC, with Enforcd, uses an analytic platform designed specifically to share public information on regulatory enforcement action. Meanwhile, two firms - Anomali and ThreatConnect - are working on technologies to collect, correlate, categorise and integrate cyber security intelligence data.


How Machine Learning is Making for Better IT Security - insideBIGDATA

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In this special guest feature, Cecilia Pizzurro, Senior Director, Strategic Data Projects at LOGICnow, discusses the convergence of data/machine learning and cybersecurity, and the idea that these two are playing off of each other in a more meaningful way than ever before. Cecilia leads a team of data scientists and software engineers in Cambridge (US) and Newcastle (UK). These teams use machine learning and big data analytics to find business value in the vast amount of customer data gathered from LOGICnow's products. She was also the co-founder and CTO of the The Dolomite Group, a South American mining consortium, pioneering machine learning and big data analyses to improve mining efficiency and reduce environmental impact in Peru. This company is currently finalizing its acquisition by a Chilean mining company.


An Introduction to Deep Learning and it's role for IoT/ future cities

@machinelearnbot

This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things (modelled on the course I teach at Oxford University and UPM – Madrid).


Can deep learning help solve lip reading?

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Lip reading is a tricky business. Test results vary, but on average, most people recognize just one in 10 words when watching someone's lips, and the accuracy of self-proclaimed experts tends to vary -- there are certainly no lip-reading savants. Now, though, some researchers claim that AI techniques like deep learning could help solve this problem. After all, AI methods that focus on crunching large amounts of data to find common patterns have helped improve audio speech recognition to near-human levels of accuracy, so why can't the same be done for lip reading? The researchers from the University of Oxford's AI lab have made a promising -- if crucially limited -- contribution to the field, creating a new lip-reading program using deep learning.