Demonstration of Data Analysis with Quantum Computing

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

The Era of Quantum Computing Is Here. Outlook: Cloudy | Quanta Magazine

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.

In a Bid to Compete, Apple Grows Fleet of Self-Driving Cars

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.

Artificial intelligence: The time to act is now | McKinsey & Company

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 varied responses to this question are interesting

The responses vary among:
1. This guy is incompetent and should be fired (fairness seeking).
2. This guy is incompetent and will be fired anyway (realism).
3. This guy is incompetent and you should help him to acquire the skills he needs for his job (compassion).
4. Talk to your manager (practical).
5. It depends on the organization and culture (consultant speak).

In general the correct answer is to address any concerns with your immediate manager, while being willing and able to offer suggestions should he/she request them.

Incompetent new employee-should I advise HR/ethics? – Best Practices – Spiceworks

Bill Gates and Steve Jobs agreed on little

But both agreed that healthcare was ripe for disruption. That is still true, but the pace is slower than we envisioned a decade ago.

One reason is the high cost of certification. Consumer-grade equipment produces interesting data for casual self-analysis. Producing data to be used in medical diagnoses requires greater confidence in the accuracy of the data and the consistency of the devices used to produce it. In the case of robotics, makers have to demonstrate in clinical trials that the equipment is safer and produces tangibly better outcomes.

Another reason for the slow pace of disruption is maintaining the confidentiality of patient data. Device makers collect and store patient data, but need mechanisms authorize and interface with medical providers on behalf of the patients. Extending the value chain requires complex protocols and interfaces, while there is little incentive for any single party to develop them.

These are some random musings on research I performed a few years ago. If I have overlooked any recent developments, please feel free to leave feedback.

A robotic revolution in healthcare – BBC News