Cognitive technologies are increasingly being used to solve business problems; indeed, many executives believe that AI will substantially transform their companies within three years. But many of the most ambitious AI projects encounter setbacks or fail. A survey of 250 executives familiar with their companies' use of cognitive technology and a study of 152 projects show that companies do better by taking an incremental rather than a transformative approach to developing and implementing AI, and by focusing on augmenting rather than replacing human capabilities. Broadly speaking, AI can support three important business needs: automating business processes (typically back-office administrative and financial activities), gaining insight through data analysis, and engaging with customers and employees. To get the most out of AI, firms must understand which technologies perform what types of tasks, create a prioritized portfolio of projects based on business needs, and develop plans to scale up across the company.

SingularityNET, is an ambitions project to create a decentralized marketplace for AI, has raised a lot of money in its token sale. In around 60 seconds after opening the sale to the public, it sold out of the whole amount of available tokens (the AGI token), bringing the total to $36 million. However, a startup raising a lot of money in a token sale is not really of interest to me. This is part and parcel of this crazy unregulated, crypto word these days. But was IS interesting to me is what SingularityNET actually is.

If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.

This chapter is based on lectures from the 2016 Park City Mathematics Institute summer school on The Mathematics of Data, and it appears as a chapter [1] in the edited volume of lectures from that summer school.

New details regarding Apple's efforts in autonomous car technology were revealed in a patent published this week, spotted by Autoblog. The patent, called "Autonomous Navigation System," was filed by Apple in 2015, about a year after the company reportedly started working on self-driving technology. The paperwork filed with the U.S. Patent and Trademark Office details a navigation system with sensors installed in the vehicle that provides "updates to a virtual characterization" of a route drivers have traveled on. The patent also mentions a " database of characterizations," where information on traveled roads can be stored in. "Some embodiments provide an autonomous navigation system which enables autonomous navigation of a vehicle along one or more portions of a driving route based on monitoring, at the vehicle, various features of the route as the vehicle is manually navigated along the route to develop a characterization of the route."

In recent years, many tech giants (Google, Microsoft Azure, IBM) invested heavily in the general-use of Machine Learning and Deep Learning. In 2018, more SME businesses will learn how to use their solutions and full service platforms. They have managed to optimize Computer Vision and Natural Language Processing in such a way that it will most likely outperform any other (smaller) player in this field. With help of API's they will take over (market share up to 85%) the general-use machine learning industry in 2018. In 2017 there has been an exponential use of so called'click – drag and drop' tools.

Mayor of London's TechInvest Programme is an exclusive event series supported by the Mayor of London, Sadiq Khan, in partnership with UK Business Angels Association. This four-year London-based series will showcase the Capital as a global hub for tech innovation by inviting London's tech entrepreneurs to pitch their ground-breaking tech businesses to industry leading investors – increasing their potential to access investment to support their journey to high growth and global success. Applications are open for the first of the Mayor of London's TechInvest events for 2017-18 which will take place on January 18, 2018 At each event, the top 10 entrepreneurs selected from a panel of industry experts, will be given the opportunity to present their innovative technologies to a group of over 100 leading investors, drawn from the Angel and VC community. The selected businesses will also get one to one support and guidance with pitching to and engaging with investors. The first in the TechInvest event series will focus on Artificial Intelligence.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

The first element of building a successful IoT strategy is that it should be based on some business value such as percentage increase in revenue or reduction in cost or improvement in productivity/efficiency. The second element is that the commitment should be top-down: What is the leadership's commitment to the success of the IoT project? Third, it needs to have an ecosystem play, involving all the key ecosystem participants while the strategy is being built. The fourth one is the right set of skills and the capability to consume IoT. And the fifth element is having an end-to-end perspective that touches everyone in the organization for whom the project is relevant.

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