Few industries are as competitive as ecommerce. Not only are online retailers competing with other online stores and brick-and-mortar locations, but also the overall noise that is the Internet. We live in a world where consumer attention span is getting shorter and shorter: 40 percent of people abandon a website that takes more than three seconds to load, and the average shopping cart is abandoned more than 68 percent of the time. I'm hard pressed to find an ecommerce site that is not constantly scrambling to engage more and drive more sales. Technology is finally helping with those efforts in a big way.

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Today on In Case You Missed It: MIT's Computer Science and AI Lab have cooked up another autonomously driving vehicle, but this one is a disability scooter. In this newly posted video, you can watch as the scooter navigates around human obstacles when taking a person on the way to their destination. In other AI news, Google and Blizzard Entertainment are teaming up to use Deepmind to train the system to autonomously play Starcraft II. If you, too, have a fondness for Big Mouth Billy Bass, the singing fish trophy, you need to see how one was hacked to be the voice of Alexa. And if you haven't yet played the New York Times' Voter Suppression Trail, you're missing out on both nostalgia and maybe sadness.

The automotive market is seeing accelerated growth and rapid adoption of vision applications that will lead the way to autonomous vehicles. With the complexity of these systems, Tier-1 suppliers, OEMs, and the entire automotive industry are utilizing artificial intelligence and deep learning algorithms to identify objects, determine free space for vehicles and plan the vehicle movement. As companies explore these deep learning algorithms and shift from R&D labs to the realization and deployment of low power embedded solutions, it is important to have a sound foundation in the form of an efficient HW and SW platform that is optimized for CNN workloads and other deep learning approaches.

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data.

In conjunction with the Web Summit conference in Lisbon today, Facebook is unveiling artificial intelligence (AI) software that it's using in order to let users apply and switch artistic styles for live video streams on Android and iOS. After demonstrating the technology at a conference last month, Facebook is now testing the video style transfer technology on mobile in a few countries, and it will be deployed more widely in the near future. The Caffe2go technology Facebook developed in the past three months is an implementation of a hot type of AI called deep learning, which typically involves training on lots of data, like images, and then making inferences about new data. In this case, Facebook has developed pre-trained neural networks that can then make inferences about new data on the fly on mobile. Google did something similar with a part of Google Translate last year, but Google also recently demonstrated neural style transfer technology of its own, although it's not yet been shown to run on mobile devices.

The researchers, working with Google's artificial intelligence division DeepMind, trained the software on more than 30,000 videos of test subjects speaking sentences. Over time, it would match certain words with particular lip movements to learn what words were being spoken. The researchers then played it further videos of people speaking sentences and the LipNet software succeeded with 93.4 per cent accuracy. This compares to 52.3 per cent for hearing impaired students, and surpassed other lip-reading programs. Unlike previous software, LipNet digested the phrases as full sentences, and allowing it to put words in context rather than decipher them individually allowed much greater accuracy.

Dublin's quarterly meetup event for product focused: Machine Learning – Natural Language Processing – Deep Learning – Machine Vision – Augmented Intelligence – Neural Networks – Cognitive – Machine Ethics, in short… Artificial Intelligence. Dublin AI is a community that brings startups, corporates and academics in the AI sphere together to understand the current state of these technologies, its value add for businesses, and what is in store for the near future. Talks aim to reveal trends, industry insights and practical approaches of applying AI technologies. While some of our talks will take a technical deep dive, the forum is open to all those with a strong interest in the field.

Note: This is a short version of "Deep Learning -- The Unreasonable Effectiveness of Randomness". The paper submissions for ICLR 2017 in Toulon France deadline has arrived and instead of a trickle of new knowledge about Deep Learning we get a massive deluge. This is a gold mine of research that's hot off the presses. Many papers are incremental improvements of algorithms of the state of the art. I had hoped to find more fundamental theoretical and experimental results of the nature of Deep Learning, unfortunately there were just a few.

Even professional lip-readers can figure out only 20% to 60% of what a person is saying. Slight movements of a person's lips at the speed of natural speech are immensely difficult to reliably understand, especially from a distance or if the lips are obscured. And lip-reading isn't just a plot point in NCIS: It's an essential tool to understand the world for the hearing-impaired, and if automated reliably, could help millions. A new paper (pdf) from the University of Oxford (with funding from Alphabet's DeepMind) details an artificial intelligence system, called LipNet, that watches video of a person speaking and matches text to the movement of their mouth with 93.4% accuracy. The previous state of the art system operated word-by-word, and had an accuracy of 79.6%.