The primary application of artificial intelligence in retail is customer service chatbots, intelligent search tools, and personalisation. Despite knowing where to put their money, only a handful of retailers in the UK have trialled AI due to it being prohibitively expensive. Most AI investment in 2017 will be targeted towards e-commerce -- though Amazon is something of an exception. Amazon intends to use AI to replace in store cashiers in its Amazon Go stores to detect products shoppers have picked up. Over the next five years a growing number of retailers will buy into AI as it becomes more affordable. Here's where some big players are putting their money: Most retailers use algorithms to suggest similar items or items bought by other customers already, but in 2016 John Lewis was one of the first retailers in the UK to implement an artificially intelligent visual search tool for its iPad app.

Microsoft CEO Satya Nadella spoke at a public event in India on Monday, stressing upon the immense potential of artificial intelligence (AI), calling it the "ultimate breakthrough" in technology. "Because for all the advances in computer interface, there is nothing to beat language [the ability to do human-level speech recognition]," he said during a fireside chat with Nandan Nilekani -- India's premier technocrat and the brain behind the Aadhaar identification system. The chat was streamed live on the Microsoft Developer page on Facebook. Nadella and Nilekani were later joined by Binny Bansal, CEO of Flipkart, India's largest e-commerce company that announced a cloud partnership with Microsoft's Azure. Calling AI "the third run time", Nadella said, "If the operating system was the first run time, the second run time you could say was the browser, and the third run time can actually be the agent. Because in some sense, the agent knows you, your work context, and knows the work. And that's how we are building Cortana. We are giving it a really natural language understanding."

BENGALURU: Microsoft chief executive officer Satya Nadella said "an enlightened immigration policy" has enabled him to live the American dream, even as he emphasised that governments had the right to determine immigration and trade policies. "American values have always been about inclusion and diversity. It's a land of immigrants," said Nadella, who was born in Hyderabad and completed his bachelor's degree in engineering from the Manipal Institute of Technology, before moving to the US in 1988. "American technology reaching me in India is what made it even possible to dream the dream. And then, the enlightened American immigration policy is what led me to live the dream. And I think those are things we will always advocate for... the American dream and the American enlightened immigration policy, especially for high-skilled workers, is something that I'm optimistic about," Nadella told ETin an interview.

Investment in seafood and aquaculture technology increased 271% compared to the $52 million raised across both 2014 and 2015, but there is still a huge need for technology to bring efficiency and sustainability to the industry.

If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.

This repository contains the TSFRESH python package. "Time Series Feature extraction based on scalable hypothesis tests". The package contains many feature extraction methods and a robust feature selection algorithm. Data Scientists often spend most of their time either cleaning data or building features. While we cannot change the first thing, the second can be automated.

Want to receive a weekly deep dive into all things auto, transportation, & logistics tech? Click here to subscribe to our auto tech newsletter. Private companies working in auto tech are on pace to attract record levels of deals and funding in 2016, with autonomous driving startups leading the charge. As expectations around self-driving vehicles have risen, major corporations have ramped up their own initiatives, racing to deploy technology onto public roads. Using CB Insights' investment, acquisition, and partnership data, we identified 33 corporate groups involved in the development of advanced driver assistance systems and self-driving vehicles. They are a diverse group of players, ranging from automotive industry stalwarts to leading technology brands. The list is organized alphabetically (companies working on industrial autonomous vehicles were not included in this analysis).

One can be book-smart, street-smart, emotionally gifted, wise, rational, or experienced; it's rare and difficult to be intelligent in all of these ways. Intelligence has many sources and our brains don't respond to them all the same way. Thus, the quest to develop artificial intelligence begets numerous challenges, not the least of which is what we don't understand about human intelligence.

Artificial intelligence, machine learning and neural networks-based deep learning are concepts that have recently come to dominate venture capital funding, startup formation, promotion and exits and policy discussions. The highly-publicized triumphs over humans in Go and Poker, rapid progress in speech recognition, image identification, and language translation, and the proliferation of talking and texting virtual assistants and chatbots, have helped inflate the market cap of Apple (#1 as of February 17), Google (#2), Microsoft (#3), Amazon (#5), and Facebook (#6). While these companies dominate the headlines--and the war for the relevant talent--other companies that have been analyzing data or providing tools for analysis for years are also capitalizing on recent AI advances. A case in point are Equifax and SAS: The former developing deep learning tools to improve credit scoring and the latter adding new deep learning functionality to its data mining tools and offering a deep learning API. Neural network created in SAS Visual Data Mining and Machine Learning 8.1 Both companies have a lot of experience in what they do.

American business publication Fast Company has released its list of the most innovative companies of 2017. The annual list ranks enterprises that "tap both heartstrings and purse strings and use the engine of commerce to make a difference in the world" according to its website. Amongst the top ten artificial intelligence and machine learning companies are tech giants Google and IBM and startup Iris AI. AI companies also dominated the top 10 global businesses across all sectors with Amazon at number one. Amazon was selected as the leading company for "offering even more, even fast and even smarter".