Apple revealed its fourth quarter financial results during a conference call with investors on Tuesday, but perhaps the most interesting narrative -- aside from the company's first annual revenue decline since 2001 -- was the tech giants avowed focus on machine learning. "Today, machine learning drives improvement in countless features across our products," Apple CEO Tim Cook boasted during his initial remarks. Cook then offered a state of machine learning at Apple, by running down areas where machine learning is already helping to improve the iPhone user experience, noting that machine learning "enables the proactive features in iOS 10, and that cameras employ it in face recognizing software. Machine learning is also a key aspect of Apple's fitness offerings. "Machine learning continually helps Siri get smarter in areas, including understanding natural language," Cook added.

"SAS has morphed from a pure tools-based analytics company to solutions-based company. That is driving SAS to get more involved with other technologies and ecosystems." SAS is the latest long-time analytics firm to enter the era of'open' and'cloud-based' analytics. SAS Viya, revealed last spring and rolling out now, aims to take businesses into the new-gen of analytics offering full list of lifecycle support features and capabilities, SAS' chief customer officer Fritz Lehman told IDN. "With SAS Viya, we have a complete rewrite [of the popular SAS analytics platform] for today's new business challenges. New ways to access and build analytics apps are key for so many workers inside businesses today," Lehman said.

Who within organizations should be responsible for robotics security? Today's security threats have expanded in scope and seriousness. There can now be millions -- or even billions -- of dollars at risk when information security isn't handled properly. As robotics becomes increasingly intertwined with other facets of IT such as the cloud, mobile devices, data analytics and the Internet of Things (IoT), concerns about the risk of data theft and other negative impacts are legitimate. But the security issue goes beyond traditional risks such as the loss or theft of information or the interruption of services.

Like many technology companies, Microsoft is pinning a lot on AI -- including the areas of speech and image recognition. To help speed up development, and to enable others to start working on their own projects, the company has released an updated, open source version of the Microsoft Cognitive Toolkit. This is a deep learning toolkit, previously known as the Computational Network Toolkit (CNTK), and it's available for anyone to use completely free of charge. The toolkit has applications far beyond speech recognition, and it has already been used in Bing, and the latest version includes support for Python and C . Microsoft Artificial Intelligence and Research has already used the toolkit to develop technology capable of recognizing words in a sentence just as a well as human beings.

A team of researchers in the UK have developed an artificial intelligence (AI) program that can predict the outcome of human rights cases involving torture, degrading treatment, and privacy. The AI -- developed by researchers at University College London (UCL) and the University of Sheffield, alongside Dr Daniel Preoţiuc-Pietro from the University of Pennsylvania -- successfully predicted the verdicts for 79% of 584 cases at the European Court of Human Rights (ECtHR). In order to reach a decision, the AI analysed case text using a machine learning algorithm, the researchers said. The algorithm looked for patterns in the text and was able to classify each case either as a "violation" or a "non-violation". To prevent bias and mislearning, the team selected an equal number of violation and non-violation cases.

MOUNTAIN VIEW, CA--(Marketwired - Nov 1, 2016) - Today, Redis Labs, the home of Redis, introduced an open source project Redis-ML, the Redis Module for Machine Learning that accelerates the delivery of real-time recommendations and predictions for interactive apps, in combination with Spark Machine Learning (Spark ML). Machine learning is fast becoming a critical requirement for modern smart applications. Redis-ML accelerates the delivery of real-time predictive analytics for use cases such as fraud detection and risk evaluation in financial products, product or content recommendations for e-commerce applications, demand forecasting for manufacturing applications or sentiment analyses of customer engagements. Spark ML (previously MLlib) delivers proven machine learning libraries for classification and regression tasks. Combined with Redis-ML, applications can now deliver precise, re-usable machine learning models, faster and with lower execution latencies.

UK-based Graphcore announced that it's raised USD 30 million from investors in a Series A financing round after two years in stealth mode. The investors include Bosch, Samsung, Amadeus Capital, C4 Ventures, Draper Esprit, Foundation Capital & Pitango Capital. Graphcore is developing processors aimed at improving machine learning. The technology looks to reduce the cost of accelerating AI applications in the cloud and bring AI to low-power consumer devices. GPUs have been used in deep learning to date because they are the only commercially-available parallel processors that have come close to delivering the high compute density required, according to the company.

Symantec Endpoint Protection 14 fuses essential endpoint technologies with advanced machine learning and memory exploit mitigation in a single agent, delivering a multi-layered solution able to stop advanced threats and respond at the endpoint regardless of how the attack is launched. The solution delivers powerful protection in a lightweight package, building on 99.9% efficacy, low false positives and a 70% reduced footprint over the previous generation through new advanced cloud lookup capabilities. Mike Fey, president and chief operating officer at Symantec, said: "Multi-layered protection, enabled by artificial intelligence, backed by the world's largest and most powerful threat intelligence force, and powered by the cloud, this is literally the smartest choice in endpoint technologies. Symantec Endpoint Protection 14 is an essential element of an integrated cyber defence strategy that enterprises require to combat today's advanced threats." Powered by combined threat intelligence capabilities by integrating Symantec and Blue Coat's security telemetry, Symantec now protects 175 million consumer and enterprise endpoints, 163 million email users, 80 million web proxy users, and processes nearly eight billion security requests across these products every day.

Ars is excited to be hosting this online debut of Sunspring, a short science fiction film that's not entirely what it seems. You know it's the future because H (played with neurotic gravity by Silicon Valley's Thomas Middleditch) is wearing a shiny gold jacket, H2 (Elisabeth Gray) is playing with computers, and C (Humphrey Ker) announces that he has to "go to the skull" before sticking his face into a bunch of green lights. It sounds like your typical sci-fi B-movie, complete with an incoherent plot. Except Sunspring isn't the product of Hollywood hacks--it was written entirely by an AI. To be specific, it was authored by a recurrent neural network called long short-term memory, or LSTM for short. The AI named itself Benjamin. Knowing that an AI wrote Sunspring makes the movie more fun to watch, especially once you know how the cast and crew put it together. Director Oscar Sharp made the movie for Sci-Fi London, an annual film festival that includes the 48-Hour Film Challenge, where contestants are given a set of prompts (mostly props and lines) that have to appear in a movie they make over the next two days. Sharp's longtime collaborator, Ross Goodwin, is an AI researcher at New York University, and he supplied the movie's AI writer, initially called Jetson. As the cast gathered around a tiny printer, Benjamin spat out the screenplay, complete with almost impossible stage directions like "He is standing in the stars and sitting on the floor."

CSIRO researchers are using visions of the undead and the human face to teach a machine what terrifies us most. Nightmare Machine is an algorithm-based piece of artificial intelligence, or AI, created by a team of researchers at CSIRO and the Massachusetts Institute of Technology (MIT) that spontaneously generates zombie faces out of human ones and transforms images of places into visions of the inferno. Dr Manuel Cebrian Ramos, a research scientist at Data61, the CSIRO's digital and data innovation group, and his colleagues fed 200,000 images of normal human faces into the machine's neural network to teach it to recognise faces. The algorithm was then able generate faces at random according to what it had learnt. They then added a single zombie face, giving it slightly more weight in the neural network than the others to turn human faces into zombies.