Chennai: Buying on Amazon, Flipkart or Snapdeal always comes with the prompt Do you want to save your card details?' A question that poses a lot of risks -at the merchant gateway, bank and customer end -irrespective of whether it is a large retailer or your neighbourhood florist and confectioners.For banks, customers using e-commerce sites poses the debate of convenience versus security . With convenience comes lesser steps and faster buying, but higher security would mean multiple authentications and more time -so to strike a fine balance between the two -Indian banks are now looking at software products like FSS' Access Control, geo-fencing of accounts and rule-based access. "What it means is, if you use your card at an ATM in Chennai and in the next 5 minutes your husband tries to buy a product online from an ecommerce site from Delhi -the card could get blocked or ask for further authentication. With artificial intelligence, the system could sense this is an unusual transaction because the same card is being used within 5 minutes at locations more than 1,000 kilometres apart," says Suresh Raja gopalan, president products, FSS. Banks now have the capability to set rules to ensure that any unusual transaction gets blocked or requires multiple authenticators.

It's not just gigantic search engines that are looking to acquire tech and talent around emerging areas like speech recognition. Even design and craft marketplaces can use a little machine learning and artificial intelligence to make their wheels turn a little better. Today, the popular handmade-goods site Etsy announced it has acquired a startup called Blackbird Technologies, which has developed algorithms that can help Etsy develop better and more advanced search with features like natural language processing, image recognition and analytics. Financial terms of the deal have not been disclosed, but we're asking and will update if we learn more. The company's employees -- it appears there were around 10 -- will all be joining Etsy.

Beginning in September 2016 in Cambridge, the BM3C collaboration will bring together leading brain, cognitive, and computer scientists to conduct research in the field of unsupervised machine understanding of audio-visual streams of data, using insights from next-generation models of the brain to inform advances in machine vision. The vision is that this integrated cross-disciplinary research will lead to advances that are likely to change both our personal and professional lives - from helping clinicians improve elderly and disabled care to helping organizations maintain and repair complex machinery as well as a host of cross-industry applications. "In a world where humans and machines are working together in increasingly collaborative relationships, breakthroughs in the field of machine vision will potentially help us live healthier more productive lives," said Guru Banavar, Chief Scientist, Cognitive Computing and VP at IBM Research. "By bringing together brain researchers and computer scientists to solve this complex technical challenge, we will advance the state-of-the-art in AI with our collaborators at MIT." The BM3C will be led by Professor James DiCarlo, head of the Department of Brain & Cognitive Sciences (BCS) at MIT, who will be supported by a team of faculty members, researchers, and graduate students from both the Brain & Cognitive Sciences department and the MIT Computer Science and Artificial Intelligence Lab (CSAIL).

About a decade ago, the author Alexander Weinstein suffered an unexpectedly affecting loss: His computer died, taking with it years of his creative work. "I was deeply upset," says the 39-year-old writer and instructor. "Around the same time, a lot of my students were getting iPhones, and talking about how much they loved them--saying that, if they lost anything, please don't let it be their iPhones. I got the sense that we were all starting to forge this very deep emotional connection with technology." Weinstein's response came in the form of a short story, "Saying Goodbye to Yang," which is now the first entry in his future-shocked, surprisingly moving, thoroughly excellent new speculative-fiction collection, Children of the New World.

Obama administration officials are previewing long-awaited guidance that attempts to bring self-driving cars to the nation's roadways safely -- without creating so many roadblocks that the technology can't make it to market quickly. Traditional automakers and tech companies have been testing self-driving prototypes on public roads for several years, with a human in the driver's seat just in case. The results suggest that what once seemed like a technology perpetually over the horizon appears to be fast approaching, especially with car companies announcing a string of investments and acquisitions in recent months. Federal officials have been struggling with how to capitalize on the technology's promised safety benefits -- the cars can react faster than people, but don't drink or get distracted -- while making sure they are ready for widespread use. The new guidance represents their current thinking, which they hope will bring some order to what has been a chaotic rollout so far.

We @ProjectJunoAI are big fans of landscapes. That's why we've created a machine intelligence landscape focused entirely on Europe [1]. Europe deserves a landscape of its own to highlight its talent and expertise. Until recently, its contribution to the innovation and commercialisation of machine intelligence technologies has been under-appreciated. We now see growing self-confidence borne of the success, and continued presence, of local acquired startups like VocalIQ, Swiftkey, Deepmind, Magic Pony Technology, and PredictionIO.

Google's DeepMind announced the WaveNet project, a fully convolutional, probabilistic and autoregressive deep neural network. It synthesizes new speech and music from audio and sounds more natural than the best existing Text-To-Speech (TTS) systems, according to DeepMind. Speech synthesis is largely based on concatenative TTS, where a database of short speech fragments are recorded from a single speaker and recombined to form speech. This approach isn't flexible and can't be adjusted to new voice inputs easily, often resulting in the need to completely rebuild a dataset when there's a desire to drastically alter existing voice properties. DeepMind notes that while previous models typically hinge around a large audio dataset from a single input source, or single person, WaveNet retains its models as sets of parameters that can be modified based on new input to an existing model.

If you are able to read and sufficiently comprehend any text in just a few seconds, you have reached the zenith of a prime human endeavor. For other readers who clock near the average of 230 words per minute and often find yourselves in situations where time is not a luxury, you may find Textdiver very useful. Textdiver is a free online tool developed by Scope Labs that uses Natural Language Processing (NLP), Text Statistics and Data Mining to uncover hidden trends and insights within any text. While the true meaning of a text is very difficult to ascertain with even the most advanced NLP techniques, Textdiver offers readers a content summary based on the text structure and word distribution, from which keywords and core concepts can be inferred. What sets Textdiver apart from many tools is that it goes a step further in offering more comprehensive metrics beyond Word and Character Count. These include: Word Frequencies, Average Word Length, Average Number of Syllables Per Word, Sentence Count, Sentence Length Distribution, Average Sentence Length, Readability (years of formal education needed to understand a text as estimated by the SMOG index), Lexical Density (percentage of content words), Lexical Diversity (percentage of unique lexical words), Estimated Reading Time, Estimated Speaking Time, Average Typing Speed and Webpage Scraping functionalities.

The artificial intelligence offshoot of Google has paired up with University College London Hospitals NHS Foundation Trust for a research project. DeepMind Health announced that it will be receiving anonymised data from the trust for a research partnership into head and neck cancer. The five-year collaboration will use around 700 anonymised CT and MRI scans of former patients, dating back to 2008, and additional data relating to approximate age, anatomy location, cancer type and radiotherapy received. Currently before radiotherapy can be administered, clinicians take up to four hours to identify and differentiate between cancerous and healthy tissues on CT and MRI scans of head and neck cancer patients. This process is called segmentation.

D-RAFT Corporate Demo Days are designed to connect best ready-to-scale, late stage startups in one area with corporations that want to buy, partner or invest in disruptive solutions. We invite interested companies from the portfolios of leading incubators, accelerators and VCs in Europe. Ten selected startups will receive the opportunity to present their solutions (including: AI & digital disruption, big data & machine learning, human to technology, exponential technologies) in a 8 min.