Business analytics software provider FusionOps has launched a platform that uses artificial intelligence (AI) to identify opportunities to improve supply chain performance for customers such as manufacturers and enterprise corporations, the company said Tuesday. The new "cognitive applications" product is an extension of Mountain View, Calif.-based FusionOps' existing suite of supply chain solutions, which use database analysis and machine-learning tools to help companies improve their forecasts, control costs, and hone inventory levels. The platform uses embedded AI and machine learning to connect the dots between complex signals such as changes in demand, manufacturing, inventory, orders, and external factors. The system then suggests solutions and can even take corrective action in real time, without requiring human input. The new suite comes in three main parts, with a search feature and a cognitive application to be available in Q4 2016 and a supply chain actions tool arriving later in 2017.

Cyberspace is an increasingly hostile environment. In 2015, a PwC study of U.S. organizations found that 79 percent of respondents had detected a security incident during the year. Today, malicious hackers continue to wage on business networks and systems. Their aim - to extract data to sell on the black market. Making use of the latest technology, these criminals exert huge pressure on businesses to defend its assets.

"We are currently working on machine learning to pick up early signals of ill health. My current role is to ensure that this is implemented in line with national recording guidance which does not cover machine learning. This is currently in pilot phase in the A&E in Salford." "We could use machine learning where we currently have manual intervention in aspects of our workflows, and we could even get to the stage where we use a lot of machine learning in our underwriting algorithms," says Rob Harding, Capital One Europe CIO. The company has already implemented some aspects of machine learning into its data science team, but Harding believes there is much more to be explored in the area." "JLL has partnered with Leverton, a Berlin-based'proptech' start-up company, as we are seeking to increase automation in our lease management operations to ensure improved efficiency and swifter delivery of outputs for our clients.

As the tech world pushes forward with the development of artificial intelligence, the Institute of Electrical and Electronics Engineers (IEEE) is asking everyone to pause and consider the ethical ramifications. It's kicked off that process by publishing the first draft of a document that explores a range of ethical challenges posed by AI. The document, "Ethically Aligned Design: A Vision for Prioritizing Human Wellbeing with Artificial Intelligence and Autonomous Systems (AI/AS)," was drafted by committees of the IEEE's Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems. More than 100 global thought leaders and experts on AI, ethics, and related issues are contributing to the initiative. With the first draft published, the IEEE is inviting public comment on the document. "We need to make sure that these technologies are aligned to humans in terms of our moral values and ethical principles," the report's executive summary says.

It looks like 2017 is shaping up to be an interesting year in the embedded-technology space--for a variety of reasons. We're coming off a year that saw a number of large mergers, and the change in the U.S. political climate is significant. Tom Starnes, analyst with Objective Analysis, notes, "One country's loss is another's gain as corporations try to hold on to as much of their revenues as they can in very complex and competitive markets for sophisticated technology. Meantime, the very-global chip industry has been consolidating at a rate and with a mass not seen before, with all but a few companies struggling for profitability." Jim Handy says, "Objective Analysis expects the memory business to be highly profitable in 2017, followed by a collapse beginning in mid-2018."

Morgan Stanley upgraded Xilinx (NASDAQ: XLNX) from Equalweight to Overweight with a price target of $67.00 (from $46.00). Analyst Joseph Moore thinks machine learning could change the way investors look at the stock. He also anticipates revenue share gains. Moore explained, "A year ago, the focus of some Xilinx bulls was the high strategic value of the company's franchise in the context of a consolidating industry; the stock has modestly underperformed the substantial semiconductor rally since then, as M&A activity has focused on other subsectors. And yet, the fundamental outlook for the company has improved, we think, for 3 reasons: 1) as machine learning continues to evolve, we see the market for'inference' moving away from microprocessors and towards specialty solutions, including Xilinx FPGAs (as well as graphics and other specialty chips); 2) potential for share gains is improving as we have seen the company stretch its lead over Intel/Altera in the two most recent manufacturing nodes; and 3) we generally see the overall growth in FPGAs modestly improving, as base stations fade in importance and new technologies start to penetrate autos, factories and data centers."

Autonomous machines shining lasers at ants -- and spraying water at bewildered cats -- for the amusement of cackling grandchildren. Hobbyists are just getting started with deep-learning technologies that give them cheap, off-the-shelf capabilities that put Ronald Reagan's Star Wars program to shame. In the latest edition of the AI Podcast, NVIDIA engineer Bob Bond and Make: Magazine Executive Editor Mike Senese explain to host Michael Copeland how they've taken the once esoteric technology of deep learning and put it to work on offbeat projects that can be tackled on budgets of a few hundred bucks. "One of the big things that's happening -- and it's happening in real time right now -- is the technology is finally hitting a point where we, as consumers, have access to this type of capability," Senese says.

Palantir CEO Alex Karp Says Going Public Is'A Possibility' Big data is ubiquitous because it can provide valuable insight that is unavailable without it. Analyzing big data sets can pose problems, however. For starters, big data is big, sometimes too big to be handled effectively by commonly used analysis tools. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory and the University of Haifa in Israel have developed a solution to this problem that turns big data into manageable data. Data analysis tools such as low-rank approximation, singular-value decomposition, principal-component analysis, and nonnegative matrix factorization are commonly used to reduce the number of variables in a dataset.

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