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".

It was one of those amazing "we're living in the future" moments. In an October 2013 press release, IBM declared that MD Anderson, the cancer center that is part of the University of Texas, "is using the IBM Watson cognitive computing system for its mission to eradicate cancer." Well, now that future is past. The partnership between IBM and one of the world's top cancer research institutions is falling apart. The project is on hold, MD Anderson confirms, and has been since late last year.

Many people have fond memories of using the Game Boy Camera, but to call its low-resolution black-and-white shots "photos" would be... generous. Don't tell that to Roland Meertens, though. He recently devised a neural network that turns Game Boy Camera images into more presentable pictures. He trained the AI to clean up, colorize and fill in details for images by feeding it thousands of photos reduced to Game Boy-level image quality. The results aren't exactly good enough to frame for posterity, but they're far easier on the eyes.

The use of artificial intelligence and the "next-generation" of virtual learning environments (VLEs) are two areas of technology that have been forecast to have a major impact on higher education in the future, according to the expert panel of a major new report. The NMC Horizon Report: 2017 Higher Education Edition is produced by the New Media Consortium – a community of hundreds of universities, colleges, museums and research organisations driving innovation across their campuses – and is the flagship publication of the NMC Horizon Project, which analyses emerging technology uptake in education. Artificial intelligence, the report notes, has the "potential to enhance online learning, adaptive learning software, and research processes in ways that more intuitively respond to and engage with students". Samantha Adams Becker, senior director of publications and communications at NMC and the report's editor, said that the higher education world was already seeing the initial benefits of AI, which was "very much driving" the adaptive learning field. "If you think about online courses where there may be hundreds of students, it's currently very difficult for a professor or instructor to maybe get a good grasp on how students not only are performing, but are feeling about the material…as they're lecturing or a video's playing," she said. "Virtual avatars and chatbots…have the ability to assess that on an individual level, and if the student seems stuck then maybe you can replay part of the video.