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.

Not a day goes by when we do not hear about the threat of AI taking over the jobs of everyone from truck drivers to accountants to radiologists. An analysis coming out of McKinsey suggested that "currently demonstrated technologies could automate 45 percent of the activities people are paid to perform." There are even online tools based on research from the University of Oxford to estimate the probability that various jobs will be automated. This concern that progress in AI will make most human labor obsolete has led some to call for a (universal) basic income, in which all citizens periodically and unconditionally receive money from the state (see "Basic Income: A Sellout of the American Dream"). Y Combinator, a prominent startup incubator in Silicon Valley, will run a pilot study of basic income in Oakland, California, and its president has stated that "at some point in the future, as technology continues to eliminate traditional jobs and massive new wealth gets created, we're going to see some version of this at a national scale."

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If pop culture is to be believed, society is quickly heading toward a highly automated future ruled by artificial intelligence. Take Iron Man's trusty sidekick, J.A.R.V.I.S. Within the Marvel franchise, the artificial intelligence system is able to think, act, and feel like a human. The supporting character is even sarcastic and witty -- both trademark human characteristics. In some ways, J.A.R.V.I.S. seems like a better human than most humans. With the release of AI technologies like IBM Watson and Salesforce Einstein, in addition to the recent buzz about the "Partnership on AI," which has brought together some of the world's biggest tech companies to advance research in the sector, it might seem like that fantasy is quickly turning into reality.

Remember when artificial intelligence was supposed to be a good thing? When we thought we would, in our old age, each be tended to by a personalised robotic nurse? When we thought that all our jobs would be made obsolete, allowing us to live lives of unbroken leisure? That glorious future might still be on the horizon, but for now AI is rubbish. We live in a world where stupid robots and gormless algorithms are incompetently conspiring to make our lives much more difficult than they need to be.

Artificial intelligence and machine learning systems are made up of code and algorithms, and as such, they work as fast as computers can process them. Often this means massive amounts of learning can be accomplished every second without stop 24x7x365. Code doesn't need to take weekends off, holidays, or sick time. It can recognize complex patterns, areas of potential improvement and problems in real-time (aka digital-time). Given these available computing capabilities and speeds, what are executives to do with AI and machine learning, when we live and operate in relatively slow human-time, and work within organizations that work at an even slower pace of organizational-time.