Within the silos of incumbent financial services, so-called fintech companies are good at picking off one thing only and doing it well. This approach is also taken within data science where a lot of the properly intelligent work is about understanding the domain (problem) and how best to use the information/data for the problem you have. In doing so, a fintech approach -- collaboration, open-sourcing code -- is helping to gradually change the culture of finance, even in some hitherto heavily guarded domains. "Without this specialization and domain knowledge, it's very hard to rise above the noise. However, the algorithms themselves are often applicable in many areas or problems, and we are probably seeing decreasing specialization here," said Dr. Tristan Fletcher, research director of Thought Machine.

Tech giants and startups that use machine learning--especially cutting-edge deep learning algorithms--will need to grapple with the potential biases in their AI systems sooner rather than later. So far there seems to be more growing awareness and discussion of the problem rather than any systematic agreement on how to handle bias in machine learning AI, Friedler explains. One approach involves scrubbing any biases out of the datasets used to train machine learning AI. But that may come at the cost of losing some useful linguistic and cultural meanings. People will need to make tough ethical calls on what bias looks like and how to proceed from there, lest they allow such biases to run unchecked within increasingly powerful and widespread AI systems. "We need to decide which of these biases are linguistically useful and which ones are societally problematic," Friedler says. "And if we decide they're societally problematic, we need to purposely decide to remove this information."

There's currently a shortage of over seven million physicians, nurses and other health workers worldwide, and the gap is widening. Doctors are stretched thin -- especially in underserved areas -- to respond to the growing needs of the population. Meanwhile, training physicians and health workers is historically an arduous process that requires years of education and experience. Fortunately, artificial intelligence can help the healthcare sector to overcome present and future challenges. Here's how AI algorithms and software are improving the quality and availability of healthcare services.

Machine learning, especially its subfield of Deep Learning, had many amazing advances in the recent years, and important research papers may lead to breakthroughs in technology that get used by billions of people. The research in this field is developing very quickly and to help our readers monitor the progress we present the list of most important recent scientific papers published since 2014. The criteria we used to select the 20 top papers are by using citation counts from three academic sources: scholar.google.com; Since the number of citations varied among sources and are estimated, we listed the results from academic.microsoft.com For each paper we also give the year it was published, a Highly Influential Citation count (HIC) and Citation Velocity (CV) measures provided by semanticscholar.org.

Netflix uses AI to go from recommendations based on what you've seen, to what you like

There's currently a shortage of over seven million physicians, nurses and other health workers worldwide, and the gap is widening. Doctors are stretched thin -- especially in underserved areas -- to respond to the growing needs of the population. Meanwhile, training physicians and health workers is historically an arduous process that requires years of education and experience. We're inviting 250 to exhibit at TNW Conference and pitch on stage! Fortunately, artificial intelligence can help the healthcare sector to overcome present and future challenges.

There's currently a shortage of over seven million physicians, nurses and other health workers worldwide, and the gap is widening. Doctors are stretched thin -- especially in underserved areas -- to respond to the growing needs of the population. Meanwhile, training physicians and health workers is historically an arduous process that requires years of education and experience. Fortunately, artificial intelligence can help the healthcare sector to overcome present and future challenges. Here's how AI algorithms and software are improving the quality and availability of healthcare services.

This is a notebook, a place to record what I learn as I explore a new frontier of applied artificial intelligence. My aim is to explain the key concepts in simple language, because complexity doesn't have to mean complicated. The format of this glossary is inspired by Marvin Minsky's AI classic The Society of Mind, a collection of short posts, where I can record what I've encountered, and perhaps teach what I've found. Being a glossary, there's no need to read posts in chronological order, just read whatever topics catch your interest. As there's no narrative, this post will serve as the index, and will be updated as new posts are added.

This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch. You can raise an issue or submit a pull request, whichever is more convenient for you. The guideline is simple: just follow the format of the previous bullet point.

The world of online banking is heading towards a digital reality where questions are no longer answered by humans, but by computers. In the last year alone the number of banks focusing on developing technology to create realistic conversations between humans and robots has risen exponentially. As chatbot technology gets smarter, thanks to machine learning techniques, banks are developing new ways to interact with customers, that can understand human emotions and recognise intentions, all whilst engaging in conversation. For the development of truly intelligent virtual assistants and chatbots, deep learning and natural language processing (NLP) technologies are key. Adam McMurchie, SME Technician Subject Matter Expert at Lloyds Banking Group, has a good understanding of where the gaps and challenges are currently within deep learning, with a background in Financial Services, Technological Development and Neurocomputing.