Mauna Kea Technologies (Euronext: MKEA, FR0010609263; OTCQX: MKEAY), inventor of Cellvizio(R), the multidisciplinary confocal laser endomicroscopy platform, today announced its full-year results for the financial year ended December 31, 2015, as approved by the Board of Directors at its meeting on March 23, 2016. Benoît Jacheet, Chief Financial Officer of Mauna Kea Technologies, stated: "2015 was a critical year in the implementation of our updated strategic plan and this affected our sales performance. Even so, we successfully delivered a significant improvement in our gross margin and operating performance by streamlining our cost structure to reduce fixed operating costs. Increased financial flexibility is expected to support the continued growth of our global user base." As previously reported, Mauna Kea Technologies recorded a 22% decline in its full-year 2015 sales to EUR8,547 thousand.

Microsoft's attempt at engaging millennials with artificial intelligence has backfired hours into its launch, with waggish Twitter users teaching its chatbot how to be racist. The company launched a verified Twitter account for "Tay" – billed as its "AI fam from the internet that's got zero chill" – early on Wednesday. The chatbot, targeted at 18- to 24-year-olds in the US, was developed by Microsoft's technology and research and Bing teams to "experiment with and conduct research on conversational understanding". Related: How much should we fear the rise of artificial intelligence? "Tay is designed to engage and entertain people where they connect with each other online through casual and playful conversation," Microsoft said.

What would be the best way to deliver nutrition to the 7.1 billion odd people on this planet? Science would tell you that it is not the animals. Researchers at the Not Company (NotCo) which is a food-tech startup based at Chile have developed food products that is no longer based on animal ingredients but entirely based on plants. They use machine learning technology to develop tasty, nutritious and affordable plant-based food. However, the food looks and tastes like the classic (animal-based) food.

This article was written by Natasha Latysheva. Here we publish a short version, with references to full source code in the original article. In machine learning, you may often wish to build predictors that allows to classify things into categories based on some set of associated values. For example, it is possible to provide a diagnosis to a patient based on data from previous patients. Many algorithms have been developed for automated classification, and common ones include random forests, support vector machines, Naïve Bayes classifiers, and many types of neural networks.

Between 9 and 15 March 2016, a five game competition took place between Lee Sedol, the second-highest ranking professional Go player, and AlphaGo, a computer program created by Google's DeepMind subsidiary. The competition was high-stake: a prize of one million dollars was put up by Google. How exactly did AlphaGo manage to do it? All I could figure out was that machine learning was involved. Having a PhD in machine learning myself, I decided to go through the trouble and read the paper that DeepMind published on the subject. I will do my best to explain how it works in this blog post. I also read different opinions of how much a big deal this win is, and I will have some things to say about that myself (spoiler: I think it's a pretty big deal). Go and chess are very popular board games, which are similar in some respects: both are played by two players taking turns, and there is no random element involved (no dice rolling, like in backgammon). In 1997, Garry Kasparov was defeated by Deep Blue, a computer program written by IBM, running on a supercomputer. This was the first time that a reigning world chess champion was defeated by a computer program in tournament conditions.

This podcast is my message to future AI systems that will process all recorded audio after humanity is driven to extinction. It's because they fail to understand the complex unintended consequences of what they see as innocent innovations into dangerous realms like AI, genetic engineering, high frequency stock trading and more. Humans are driven by green, not caution. They are denialists about future unintended consequences as long as that denial serves their short-term interests. As a result, humanity is pursuing several key "innovations" that may ultimately spell the end of humanity altogether… with AI being just one among many.

Manuela Veloso is the new head of Carnegie Mellon University's Machine Learning Department. Manuela Veloso, a computer scientist renowned for her work in artificial intelligence and robotics, is the new head of Carnegie Mellon University's Machine Learning Department, Andrew Moore, dean of the School of Computer Science, announced today. She succeeds Tom Mitchell, E. Fredkin University Professor and the founding head of the Machine Learning Department (MLD), who remains a member of the faculty. Veloso, the Herbert A. Simon Professor of Computer Science, has been a faculty member since earning her Ph.D. in computer science at Carnegie Mellon in 1992. "Carnegie Mellon's AI community has long nurtured the field of machine learning -- software that acquires knowledge and improves its performance with experience -- culminating in the creation of the world's first machine learning department 10 years ago," Moore said.

Google will create business tools and products based on its own artificial intelligence technology, seeking to entice more companies to rent its cloud-computing services. The company also said it won several new large cloud customers, including the interactive division of Walt Disney Co., which now runs a web-subscription service on Google's cloud, and Coca-Cola Co., which rented Google servers for a World Cup marketing campaign. While modern cloud systems are based on "decades-old" technology, Google said, the company's forthcoming products and services are designed for the next wave of cloud computing. Google will charge for these capabilities, a departure from its typical consumer-focused approach in which AI technology supports free Web-based services and apps such as Photos and Translate.

Google will create business tools and products based on its own artificial intelligence technology, seeking to entice more companies to rent its cloud-computing services. The Alphabet Inc. unit plans to offer services such as audio transcription and image identification built around its machine-learning software. Google has used this technology for its own products, and is now making the capabilities available for other companies to rent and access over the Internet. The company also said it won several new large cloud customers, including the interactive division of Walt Disney Co., which now runs a web-subscription service on Google's cloud, and Coca-Cola Co., which rented Google servers for a World Cup marketing campaign. Google wants to broaden the appeal of its cloud services to more corporate customers.

Hewlett Packard Enterprise last week announced the public availability of its HPE Haven OnDemand Machine Learning as a Service. The Microsoft Azure cloud-based platform provides more than 60 APIs and services that deliver deep learning analytics on a variety of data, including text, audio, images, social Web and video. Launched in beta in 2014, HPE Haven OnDemand has more than 12,750 registered developers generating millions of API calls per week, the company said. Usage- and SLA-based pricing for enterprise-class delivery to support production deployment also are available. "We're bringing a unique solution to the market built on almost a decade of experience in advanced analytics and machine learning that has been proven," said Jeff Veis, VP of marketing for big data at HPE. "We have leveraged this experience into both the design and approach that we have adopted for Haven OnDemand," he told the E-Commerce Times.