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

Analytic Hierarchy Process

AHP Background

One model I want to explore today is the Analytic Hierarchy Process (AHP) that was developed by Thomas L. Saaty in the 1970’s as a tool for choosing an option using a set of weighted criteria.

For example, we may choose a software package on the basis of criteria such as supported features or functions, scale-ability, quality (fitness for purpose, fitness for use), security, availability and disaster recovery. AHP provides a mechanism for weighting the criteria by interviewing several members of staff for one-by-one assessments of relative importance, which can then be transformed into relative weightings using an eigenvector transformation.

The idea of using multiple criteria to assess multiple options is not new. AHP enhances the ability to weight the assessment criteria using feedback from multiple stakeholders with conflicting agendas. Rather than determining a “correct” answer it assesses the answer most consistent with the organization’s understanding of the problem.

Other use cases can include project portfolio optimization, vendor selection, plant location, hiring, and risk assessment. More information can be found at the International Journal of the Analytic Hierarchy Process (free registration).

Simple AHP hierarchy with associated default priorities.

Applications in ITSM

In the field of ITSM there a examples of papers that describe the instances in which AHP was used.

The paper “EDITOR-IN-CHIEF ENRIQUE MU USES AHP TO HELP CITY OF PITTSBURGH MOVE TO THE CLOUD” (free registration) briefly discusses Professor Enrique Mu’s application of the AHP for the City of Pittsburgh’s efforts to migrate IT functions to cloud providers. The decision period spanned several months and was considered strategic for the city.

Another paper “The critical factors of success for information service industry in developing international market: Using analytic hierarchy process (AHP) approach” (paywall) discusses the use of AHP for analyzing critical success factors in international market diversification for information service providers in Taiwan. They interviewed 22 participants (CEO’s, experts, consultants) to generate pairwise comparisons of CSF’s, with which the AHP method was able to distill into factor weighting. These factor weightings could be used by specific information service providers to determine whether or not they should consider entering specific markets.

In “A Method to Select IT Service Management Processes for Improvement” (free access to PDF) Professors from School of Management & Enterprise at University of Southern Queensland used AHP as part of a method for ranking ISO2000 process priorities for improvement. This particular paper is worth exploring in much greater detail because, in my experience, the prioritization or process or service improvement initiatives can be very painful at organizations, particularly those with multiple influential stakeholders with incompatible or conflicting requirements.

Last but not least, In “DECISION SUPPORT IN IT SERVICE MANAGEMENT: APPLYING AHP METHODOLOGY TO THE ITIL INCIDENT MANAGEMENT PROCESS” (free registration) Professors at the FH JOANNEUM University of Applied Sciences in Graz, Austria discuss the use of AHP in prioritizing Incidents. In their specific implementation they used four decision criteria to prioritize Incidents:

  1. Number of customers affected
  2. Whether “important” customers are affected
  3. Penalty or cost of the outage
  4. Remaining time until violation of the service level target

IT organizations typically use simplified “rules of thumb” methods for prioritizing Incidents based on Impact and Urgency. Notably three of these four factors are typically included inside variants of the schema. Please see my discussion in Incident prioritization in detail (which also avoids the explicit use of SLAs in evaluating Incident resolution).

I don’t find the prioritization of Incidents to be a particularly strong candidate for AHP analysis. High priority incidents are relatively rare and are generally handled one at a time or by non-overlapping resources. Lower priority incidents (routine break-fixes for Services Desk) can be handled first-come-first-service or using the relatively crude but quick methods described in ITIL.

Prioritization of Problems seems a more suitable candidate for AHP because a) Problem resolution can require days or weeks, b) multiple Problems may be active and contending for resources, and c) the resolution or Problem can deliver much greater long-term financial impacts for organizations. The principles and underlying support system would be similar.

Other uses of AHP that merit further investigation include:

  • Prioritization of service and process improvement initiatives
  • Selection of ITSSM tools
  • Selection of vendors (in support of the Supplier Management function / process of Service Design) and/or cloud providers
  • Activity prioritization in environments of resources in multi-queuing environments (leveling of activities across multiple processes and/or projects)