This headline violates Betteridge’s Law of Headlines — Yes, boring speakers do drone on longer than interesting ones.
Although brief, the article contains some good pointers to keep in mind for my upcoming presentation on Managing IT in 2019.
“Dull talks at conferences can feel interminable.”
— Read on www.nature.com/articles/d41586-018-06817-z
Doug Tedder has accurately captured the general trend away from managing IT as distinct services published through a service catalog, captured from users as service requests, that deliver value to customers. These may still be useful as conceptual models, but relatively few organizations publish a service catalog in such a literal manner. What happens to “service management” frameworks like ITIL? I don’t have an answer, but in the short-term, perhaps not much.
Most users of ITIL never made the conceptual leap from processes to services. Therefore, ITIL remains the definitive framework for commonly used processes, such as Incident Management, Problem Management, and Change Management. I am not suggesting ITIL captures the state of the art even in these areas. ITIL’s shortcomings are well documented, and we hope they will be addressed in the upcoming refresh of ITIL.
IT and business thinking of itself respectively as a “service provider” and “customer” may have been a good idea at one time, but that time has passed. How does IT and the business need to act in the digital age? Doug Tedder discusses.
— Read on www.dougtedder.com/2018/04/01/lets-stop-playing-service-provider-and-customer/
Malicious AI Report
— Read on maliciousaireport.com/
This will prove to be some interesting weekend reading material. I have no comments at the moment as I have read it yet.
It’s hard to miss the news on quantum computing. Breakthroughs in the last few years have demonstrated the opportunities and potential of quantum computing. The question is whether it will scale to more qbits while maintaining the stability of quantum entanglement. There are detractors, but it is too promising and far-reaching to ignore.
The work that Huang and co have done is to run this algorithm on a quantum computer in a proof-of-principle experiment. The team uses a six-photon quantum processor to analyze the topological features of Betti numbers in a network of three data points at two different scales. And the outcome is exactly as expected.
Of course, this example is not so hard for classical computers or even human brains to analyze. But the key point is that the Chinese have made it work on a quantum computer, a device that is set to dramatically outperform conventional computers in the coming years.
Article in Technology Review
Midway through 2017, researchers at Google announced that they hoped to have demonstrated quantum supremacy by the end of the year. (When pressed for an update, a spokesperson recently said that “we hope to announce results as soon as we can, but we’re going through all the detailed work to ensure we have a solid result before we announce.”)
It would be tempting to conclude from all this that the basic problems are solved in principle and the path to a future of ubiquitous quantum computing is now just a matter of engineering. But that would be a mistake. The fundamental physics of quantum computing is far from solved and can’t be readily disentangled from its implementation.
Apple has a lot of work to do if it wants to compete with other companies in the self-driving car industry. Tesla already sells vehicles with semi autonomous systems, while automakers like General Motors are already giving rides in their self-driving cars.
Meanwhile, Google and Waymo are testing their autonomous Chrysler Pacifica Minivan in San Francisco, and have plans to launch their own ride-hailing service. It won’t be the only autonomous taxi service around, however, as Uber will be joining the race for driverless cabs in 2019. Even a few Lyft-branded vehicles were making the rounds around CES 2018.
Within AI, deep learning (DL) represents the area of greatest untapped potential. (For more information on AI categories, see sidebar, “The evolution of AI”). This technology relies on complex neural networks that process information using various architectures, comprised of layers and nodes, that approximate the functions of neurons in a brain. Each set of nodes in the network performs a different pattern analysis, allowing DL to deliver far more sophisticated insights than earlier AI tools. With this increased sophistication comes greater needs for leading-edge hardware and software.
Well aware of AI’s massive potential, leading high-tech companies have taken early steps to win in this market. But the industry is still nascent and a clear recipe for success hasn’t emerged. So how can companies capture value and see a return on their huge AI investments?
The tech giant is pulling the plug on support for Windows Vista. All folks still using the product now have a strong incentive to upgrade.
If You’re Somehow Still on Windows Vista, Upgrade Right Now – WIRED
I don’t use Apple Pay because my iPhone’s fingerprint verification is so unreliable.
Apple Pay Promised to Make Plastic Obsolete. Then Came Wary Shoppers, Confused Clerks – The Wall Street Journal