New research has shown that grocery retailers are struggling to optimise stock replenishment processes, with almost half saying that their decisions are still based on'gut feeling'. Retail applications provider Blue Yonder surveyed 750 grocery managers and directors in the US, UK, Germany and France. It found that, in spite of a rise in accurate algorithms for automated replenishment and demand planning, 46% of surveyed directors in the UK say that replenishment is still an entirely manual process and the same amount saying that it was fully automated. A further 30% believed that instinct-based decision making was slowing them down. Of the four countries involved in Blue Yonder's survey, Germany had the highest proportion of respondents using manual or partially automated systems, with just one-third of managers who had fully automated their stock replenishment processes.

This story was delivered to BI Intelligence Apps and Platforms Briefing subscribers. To learn more and subscribe, please click here. Huawei's R&D arm, Noah's Ark Laboratory, announced on Tuesday a partnership with the University of California, Berkeley's artificial intelligence (AI) lab focused on researching AI in all its forms. Initially, the Chinese smartphone company will fund UC Berkeley 1 million as it covers areas like deep learning, reinforcement learning, machine learning, natural language processing (NLP), and computer vision. Huawei is likely investigating AI in order to ensure its not left behind by rival smartphone companies like Samsung, Apple, and Google, all of which have begun implementing AI into their devices. As hardware shipments begin to decelerate, hardware companies are looking at AI as the next growth platform.

Washington, D.C. - IBM, a pioneer in the advancement of artificial intelligence, today welcomed the release of a White House report on the future and promise of this exciting technology: "We commend the Administration, and the Office of Science and Technology Policy (OSTP) in particular, for launching an open and inclusive dialogue that helped shape today's report. The document recognizes what IBM has believed all along, that artificial intelligence (AI), or cognitive computing systems like IBM Watson, will jump start economic opportunity and help solve some of humanity's biggest challenges. Embedding ethical training into computer science education, as the report recommends, is a positive way to prepare the next generation of technology experts to appropriately guide the advancement of AI systems. Cybersecurity is one area in particular where IBM agrees that AI can be a true game-changer, and one where we are actively preparing IBM Watson to make a real and tangible difference in the push to better defend America's digital networks.

About GilPress I launched the Big Data conversation; writing, research, marketing services; https://whatsthebigdata.com/ & http://infostory.com/ Image This entry was posted in AI, Chatbots, robots and tagged Futurism.

The White House sees artificial intelligence as an increasingly critical technology that can fight cyberattacks, upgrade weapons of war, improve health care and even unclog traffic for the commute home. However, the Obama administration also expects that A.I. will reduce low-level jobs and create security and ethical issues. With critical pros and cons looming, the federal government wants to work with the private sector and academia to guide A.I. in a positive direction, according to a recently released report, Preparing for the Future of Artificial Intelligence. The report was prepared by the National Science and Technology Council's subcommittee on Machine Learning and Artificial Intelligence. "A.I. can be a major driver of economic growth and social progress, if industry, civil society, government, and the public work together to support development of the technology, with thoughtful attention to its potential and to managing its risks," the report states.

We make a major error when we think of AI as a technology, or perhaps as just another technology. This error can turn out to be a costly mistake. Specifically, using the term "technology" to describe artificial intelligence can influence us to ignore the perils of AI, to accept it as we have accepted other technologies, and to incorrectly assume that in the past 3 centuries we have acquired enough experience to deal with technological revolutions. AI is different and hence it should be approached completely differently. Most importantly it should be approached humbly – and with an understanding that this is not something we have dealt before.

Can artificial intelligence improve government accountability? Last year Bass provided an excellent overview of the role big data can play in increasing government accountability. His analysis covered the current state of affairs and the problems with achieving the goal of a transparent government (Bass, 2015). In an amazing and eye-opening manner, he showed that while data can increase government accountability, numerous unresolved issues remain that must be overcome before we can have higher transparency. One of the major issues identified by Bass is the fact that many stakeholder (including journalists, agencies, members of congress, and others) don't want greater transparency.

Todoist is one of the more full-featured to-do services out there, and it's available on nearly every platform out there. Whether you use Mac or Windows, iOS or Android, Todoist has you covered. It works much like you'd expect: you can ask Alexa to add items to the various lists that you have in your Todoist account, and you can also ask it to tell you everything that's on your to-do list for that day. And Alexa works with Todoist's natural language processing, so you can ask it to add things to your list "tomorrow" or "next Wednesday" and it'll know just what you're asking it for. It's not clear if you'll be able to tell Alexa to add items to specific projects or to-do lists in your account -- they probably get added to whatever your default list is for you to sort out on your phone or computer.

I hear a lot of complaints about machine learning from business clients. It's too complicated is one of them. Machine learning is complex by nature. The more that complexity spills over to users and executive management, the more turned off to machine learning they become. I've seen it happen in real time.

On January 7, 2016, I attended University of Toronto's machine learning conference entitled, "Machine Learning and the Market for Intelligence". It was a tremendously informative and exhilarating experience! Geoff Hinton, one of the world's foremost researchers in this field, presented his ideas, as did several other top-notch authors and researchers. Jerry Kaplan, the author of "Humans Need Not Apply", provided an energetic and impassioned view of why he believes AI-regulation is not as important as execution at this stage of ML (machine learning) technology. Venture Capitalists from the valley made the trip up north, to discuss how they were assessing startups which use ML to build applications for diverse industries such as HR, logistics, healthcare, and medical research.