Quantum computing is not magic. It is not going to change how your favorite game, word processor or accounting application operate. It can be thought of as a highly specialized type of computing resource that will be used much as we use GPUs today. For relevant applications that will mean an operating flow along the lines of a big chunk of computing using classical machines; followed by a highly targeted operation on the quantum machine; in turn followed by more computing to interpret the results from the quantum machine and integrate into the larger task.
With this, we can expect to push a range of very significant boundaries in our collective computing capabilities. In the near term bringing significant speed up to today’s operations, in the longer term allowing explorations into presently computationally impossible tasks.
There are some excellent resources out there for getting a technical flavor of quantum computing. That said, this requires a very different mindset to conventional computing. Here are some of the starting points I’ve used:
I’m am so far underwhelmed by the resources available for a quick take on what quantum computing brings to both computing and to business for less technical folk. I’ll collect interesting takes on this as edits to this blog, and I am in the process of building a walk-though that will provide analogies, user-models and a sense of the real world and business impacts for the rest of us.
Despite the hype, quantum computing is imminent, and will drive new competitive realities for a huge number of industries.
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Michael Harries is a technologist and partner with The Robotics Hub focused on AI and emerging technologies. One of Michael’s present roles is as acting CTO of The Robotics Hub portfolio company, Black Brane, who are commercializing innovations including a highly scalable virtual quantum computer.
The Robotics Hub approach is to work very closely with portfolio companies, often taking operational roles during the initial build up from seed to A.
Recently I was debating the push to raise minimum wages to $15 with a conservative friend. He was taking a straightforward market view – if something is priced higher – less will be purchased – hence loss of jobs, business challenges and more. He was also convinced that any data to the contrary was likely to be tainted by people moving out of the area, and indeed that the only places to have tried it had thriving economies due to tech bubbles, so not good examples for the whole.
I mentioned an article I’d seen recently in Washington Post that took very much the other position, that raising minimum wages could be transformative. I’d like to believe this to be true. More fundamentally, I think it’s unconscionable to pay people less than a living wage.
We wrapped with my agreeing to take a closer look at the article and underlying research, given the progressive bent of the Washington Post, and the frequent misrepresentation of research by members of the press. Hence, here’s a quick analysis – hopefully to be continued at some point in the future. In summary, I was impressed by the underlying research and would like to think that it’s the start of a more positive view of minimum wage in general.
I have had undergraduate exposure to the field economics, but none professionally, so my read on this material should be taken as that of an informed amateur.
The central question in the minimum-wage debate has shifted. Where economists once asked, “Will raising the wage floor kill jobs?” they now ask, “Just how transformative could a higher minimum wage be?”
Apparently the CBO is relying upon older/discredited research.
“According to Berkeley economist Michael Reich, the CBO appears to have picked a grab bag of high-quality and now-discredited studies, and weighted them all the same in their analysis. It “reveals an unwillingness to recognize the major differences in scientific quality among studies,” Reich said.”
This seems to be the type of mud flinging that one expects to see in academic circles from time to time as new understandings/common truths percolate slowly through a field. It’s also clearly complicated by ideological preferences – and economics is a field that’s rife with these.
On closer review, the CBO document is an interesting read, and in particular digging into some of the references was illuminating. One that they cited explores neo-classical publication selection bias in the economics literature that biases toward a negative association between minimum wages and employment.
At a later stage I’m looking forward to taking a closer look at the CBO references, to see if the claim about mixed academic quality stands up – a couple that I looked at seem to be a bit basic in premise around absolute $ increases versus relative to local conditions, etc. The document is worth reviewing in any case.
Back to the Washington Post piece.
The first paper cited in opposition to the CBO is fascinating and looks to be a substantial step forward in understanding the space. “Minimum Wage Effects in Low-Wage Areas”, Godoey and Reich, 2019. This study uses a new methodology to dramatically enrich source data and looks at relative minimum wage (compared to the median) as a way to normalize these data. Washington Post was good enough to provide a high level visualization of their findings:
The white paper:
Starts by noting that much of the prior research looks at minimum wage only to $10 level, but relatively little of this shows reductions in employment, etc.
They use data is by county & PUMAs (local areas based on census-defined Public Use Microdata Areas — areas of about 100,000 people) – including many that don’t have heavy high tech sector:
They focus their work on where the minimum wage sits versus median wage for the local area. This seems to be an important normalization that substantially improves their analytical power.
We use sub-state variation in median wages to array local areas according to the likely effects of minimum wages. Doing so substantially expands the range of relative minimum wages and minimum wage bites beyond the levels observed with state-level data. Our sample of relative minimum wages in low-wage areas encompasses relative minimum wages as high as .82. This ratio is comparable to the highest state-level relative minimum wages that would obtain if the federal minimum wage was gradually increased to $15 by 2024. It lies well above the .59 ceiling in previous minimum wage research.
Using American Community Survey data from 2005 to 2017, we estimate both event study and generalized difference-in-difference models to analyze the effects of minimum wages on wages, employment and poverty in areas with low and high relative minimum wages (low median wages) and with low and high minimum wage bites. We conduct these analyses among a range of high-exposure groups (those with high school education or less, and teens). The results are similar across all these groups. We find that minimum wages increase wages more in the high impact areas, validating our methodological approach. We do not detect that minimum wages decrease employment or hours in low or high impact areas. Minimum wage increases do, however, reduce poverty rates among households and children. Overall, these results close the gap between current minimum wage policy and evidence-based research.
“… showed the fall in jobs paying less than the new minimum wage had been fully offset by the jump in new jobs paying just over it.” “also confirmed findings that a rising minimum wage ripples through the organizational chart, helping workers who earn as much as $3 an hour more than the new minimum wage. About 40 percent of its wage benefits go to workers who aren’t directly affected.”
Washington Post summary of “The Effect of Minimum Wages on Low-Wage Jobs”
I didn’t have access to full text of this article, so have not yet assessed the methodology. At the highest level, they conclude no impact on number of low-wage jobs from minimum wage increases, spillover to others at the low end of wage distribution, but some impact on jobs in the ‘tradeable sector’.
Here’s their abstract – with bold sections added by me:
We estimate the effect of minimum wages on low-wage jobs using 138 prominent state-level minimum wage changes between 1979 and 2016 in the United States using a difference-in-differences approach. We first estimate the effect of the minimum wage increase on employment changes by wage bins throughout the hourly wage distribution. We then focus on the bottom part of the wage distribution and compare the number of excess jobs paying at or slightly above the new minimum wage to the missing jobs paying below it to infer the employment effect. We find that the overall number of low-wage jobs remained essentially unchanged over the five years following the increase. At the same time, the direct effect of the minimum wage on average earnings was amplified by modest wage spillovers at the bottom of the wage distribution. Our estimates by detailed demographic groups show that the lack of job loss is not explained by labor-labor substitution at the bottom of the wage distribution. We also find no evidence of disemployment when we consider higher levels of minimum wages. However, we do find some evidence of reduced employment in tradeable sectors. We also show how decomposing the overall employment effect by wage bins allows a transparent way of assessing the plausibility of estimates.
Other research is noted that shows:
Reductions in recidivism
Older folk working longer
Job hopping falls
Benefits not cut
Productivity rises
Reduced suicides
Given that this is based around a Washington Post article, it can be expected to be somewhat biased, however the reading so far of underlying research does imply a shift in economic/data driven thinking for this area.
There remain questions about the level to which minimum wage should be pushed (my friend asked – “if $15 why not $100”), but I suspect there are likely to be good analyses or certainly reasoning around pegging around X standard deviations below the median, or some such similar.
Researchers were able to find new indicators of heart disease risk present in pictures of retinas by analyzing them with artificial intelligence, according to a paper published Thursday by researchers at Google and Verily, which has not been peer reviewed. Doctors today rely heavily on blood tests to determine risk of heart disease; a potential test based on retinal images would be less invasive, easier to obtain, and faster to analyze with AI.
Chomsky critiqued the field of AI for adopting an approach reminiscent of behaviorism, except in more modern, computationally sophisticated form. Chomsky argued that the field’s heavy use of statistical techniques to pick regularities in masses of data is unlikely to yield the explanatory insight that science ought to offer. For Chomsky, the “new AI” — focused on using statistical learning techniques to better mine and predict data — is unlikely to yield general principles about the nature of intelligent beings or about cognition.
Very few occupations will be automated in their entirety in the near or medium term. Rather, certain activities are more likely to be automated, requiring entire business processes to be transformed, and jobs performed by people to be redefined, much like the bank teller’s job was redefined with the advent of ATMs.
More specifically, our research suggests that as many as 45 percent of the activities individuals are paid to perform can be automated by adapting currently demonstrated technologies.4 In the United States, these activities represent about $2 trillion in annual wages. Although we often think of automation primarily affecting low-skill, low-wage roles, we discovered that even the highest-paid occupations in the economy, such as financial managers, physicians, and senior executives, including CEOs, have a significant amount of activity that can be automated.
Conceptual graphs (CGs) are a formalism for knowledge representation. In the first published paper on CGs, John F. Sowa (Sowa 1976) used them to represent the conceptual schemas used in database systems. The first book on CGs (Sowa 1984) applied them to a wide range of topics in artificial intelligence, computer science, and cognitive science.
Well worth taking a look at Conceptual Graphs — in large part, Kyndi’s (http://kyndi.com) technology uses conceptual graphs as one of the pillars for natural language understanding.
This post is an attempt to bridge the gap between the elementary and advanced understandings of tensors. We’ll start with the elementary (axiomatic) approach, just to get a good feel for the objects we’re working with and their essential properties. Then we’ll transition to the “universal” mode of thought, with the express purpose of enlightening us as to why the properties are both necessary and natural.
1. Loop Support is human-powered AI for customer support. Existing tickets and conversations provide the data to train AI how to handle easy requests and then humans in the middle handle the tougher tasks. I like that there is a closed loop for machine learning and a clear ROI story in a big existing market. For now, Loop Support differentiates itself from a crowded landscape (Digital Genius, ASAPP and many others) by bringing the reps as opposed to just powering the software.
By unifying what data scientists, data engineers,
and the business are doing, we enable enterprises
to focus on the actual problems they want to solve.
In the near future, you might ask your email client how to get an introduction to a new professional contact. That’s the idea behind the latest updates to Trove, a service that’s designed to analyze users’ email messages and provide them with insights about their professional network.
