Category Archives: R&D

Physna: Google and GitHub for 3D

Source: Protocol, Aug 2020

a public search engine called Thangs (a name that DeMeyere was quick to point out he inherited rather than chose).

The team is thinking of the product as a cross between Google and GitHub for anything with volume. It could well end up being the stepping stone that sets Physna, or someone like it, up to do with three-dimensional objects what Google did to words and images on the internet.

On its surface, Thangs looks rather like the Thingiverse, Yeggi or other sites that 3D-printing enthusiasts use to upload and search for models of things to print out. And Thangs definitely has that functionality — a search for “Pikachu” will show you several models of the adorable Pokémon that users have uploaded — as well as searching every publicly available 3D model database.

What it also shows you is a graphical, collaborative history of each model, kind of like going back in the edit history on Google Docs or version controlling on GitHub.

And if a model is actually part of another model, it can have its own page with its own edit history, allowing you to change each requisite part without having to reupload the entire thing.

You can set up a profile and comment on or collaborate on any public model, as well as store as many models as you would like.

Say you’re working with some friends on pulling together a massive 3D-printed Iron Man costume for the next time you can go to a Comic-Con; each person could take a different piece of the armor and work on it separately then have all their work ladder up into one project folder, without anyone having to act like a project manager and keep track of each individual piece.

Geometry is a universal language,” DeMeyere said.

Physna’s search product allows companies to search by 3D models, rather than by text.

Thangs is just the start of what DeMeyere envisions for the volumetric data on Physna. “I want to build a geometric cloud,” he said. It’s a tough sell right now, given that so much of this is still conceptual. “The problem is, nobody has anything to compare this to,” DeMeyere said.

But if you accept that the way we interact with the internet in the future is going to evolve, perhaps it’s not that crazy an idea. The internet to date has primarily been experienced on computer screens of varying sizes, which is fine for text and flat images.

But it’s possible that the future will have far more depth — whether that’s through augmented-reality headsets the likes of which Microsoft and others are developing, or VR that Facebook is building, or even volumetric displays — and we’ll need companies built to understand that 3D data.

Picking Tech Sides: US vs China

Source: WSJ, Aug 2020

The rest of this century seems likely to be an experiment to determine whether China can develop (or otherwise acquire) all it needs to maintain a modern technocratic surveillance state—not to mention expand its Chinese customer base to replace lost sales from beyond its borders.

The U.S. and its allies, meanwhile, face the loss of potentially huge markets in China. Eventually, it’s also likely to mean that American firms will manufacture less of their tech in China, though that could take decades. For internet companies, there isn’t as much at risk, as China’s Great Firewall long ago blocked Facebook, Google, Twitter, YouTube, Netflix, Wikipedia and most other sites and services of the mainstream Western internet.

In the current standoff, the U.S. “has a lot of power here,” says Dan Wang, technology analyst at Gavekal Dragonomics, a Beijing-based research firm, because in addition to banning these apps from the U.S., it has the power to prevent U.S. firms, and foreign companies that use technology from the U.S., from selling to Chinese tech companies.

China, meanwhile, has less leverage, says Patrick Moorhead, president of the technology research firm Moor Insights & Strategy, because it already blocked so many U.S. tech companies. “I think China played their card years ago,” he adds. Beijing could retaliate by other means, for example by canceling airplane orders with Boeing.

China is on its way to achieving not just a separate internet from the U.S., but a completely separate technology stack, from apps and services all the way down to the silicon microchips on which they run.

 

R&D Social Return is 4X of its Private Return

Source: Wiley, Feb 2020

use our estimates to conduct a rough welfare analysis which suggests the marginal social return to R&D exceeds the private return to R&D by a substantial amount, about 44 percentage points or a factor of about four.5 This suggests substantial underinvestment in R&D and an important role for government support.

…implies that ideas growth urn:x-wiley:01435671:media:fisc12195:fisc12195-math-0051 is proportional to a measure of research effort .24 π can be thought of as a measure of research productivity – it is the degree to which an absolute given amount of research effort translates into growth. These models imply that constant research effort should lead to constant exponential growth. Unfortunately, equation 12 is not easily reconcilable with the data as the number of US researchers has increased substantially over time whereas US TFP growth rates have not.

Equation 19 shows that the MSR is determined by the degree of diminishing returns to the ideas stock (σ ), the fundamental growth rate of new ideas (gA , which in semi‐endogenous growth models is not affected by R&D in the long run) and the R&D to output ratio (urn:x-wiley:01435671:media:fisc12195:fisc12195-math-0061).

Back‐of‐the‐envelope welfare calculations confirm the earlier paper’s findings of a sizeable wedge between the social and private returns to R&D, suggesting $4 of social benefit to every $1 of R&D spent.

Although the evidence here does suggest that greater public policy support for R&D is justified, it does not tell us exactly what the precise form of policy intervention should be.

One option is through the tax system and, in line with the previous literature, we do find that R&D tax credits are effective in raising firm‐level R&D spending.

However, tax credits or more direct R&D subsidies through industrial grants may both have a bigger influence on the price of R&D (mainly wages of R&D researchers) than on the volume of R&D if the supply of researchers is inelastic.

This motivates policies that may act on the supply side of R&D through increasing the quantity and quality of potential inventors.

This may be through increasing the number of students studying science, technology, engineering and maths (STEM subjects) or more radically by increasing the exposure of individuals from low‐income families, minorities and women to the chance of becoming a future inventor.39, 40

TFP Declines

Source: HBR, Nov 2019

Productivity Growth

Source: Obama White House archives, Jul 2015

The third level of mystery is explaining the conceptual drivers of productivity growth. Even if we agreed on the facts of historical productivity growth, explaining those facts is more difficult still. Moses Abramovitz famously called TFP a “measure of our ignorance,” the unexplained gap between input and output.1 And a rigorous conceptual understanding of that gap continues to elude economists

Figure 2—and all subsequent references to annual U.S. labor productivity in these remarks—references real output per hour worked in the private nonfarm business sector (excluding government enterprises) as reported by the Bureau of Labor Statistics (BLS). In other contexts, I have referenced the BLS’ labor productivity series for the nonfarm business sector (including government enterprises). The two series are closely correlated and exhibit the same trends, but excluding government enterprises permits the analysis of total factor productivity (TFP) that follows.

a simple thought experiment provides a sense of how important productivity is to incomes: what if productivity growth from 1973 to 2013 had continued at its pace from the previous 25 years? In this scenario, incomes would have been 58 percent higher in 2013. If these gains were distributed proportionately in 2013, the median household would have had an additional $30,000 in income. Had income inequality and labor force participation not worsened markedly, middle-class incomes would be nearly twice as high.

Virtually all the variation in labor productivity growth is accounted for by variation in TFP.

Stagnant Productivity Growth

Source: IMF, Apr 2017

The paper reiterates many of the arguments concerning advanced economies referenced in this post, such as total factor productivity (TFP) hysteresis due to the boom-bust financial cycle and resulting capital misallocation, “an adverse feedback loop of weak aggregate demand, investment, and capital-embodied technological change”, elevated economic and policy uncertainty.

The Politicization of Science (Research)

Source: City-Journal, Autumn 2016

  1. there’s the Left’s opposition to genetically modified foodsTo preserve their integrity, scientists should avoid politics and embrace the skeptical rigor that their profession requires. They need to start welcoming conservatives and others who will spot their biases and violate their taboos. Making these changes won’t be easy, but the first step is simple: stop pretending that the threats to science are coming from the Right. Look in the other direction—or in the mirror., which stifled research into what could have been a second Green Revolution to feed Africa.
  2. Second, there’s the campaign by animal-rights activists against medical researchers, whose work has already been hampered and would be devastated if the activists succeeded in banning animal experimentation.
  3. Third, there’s the resistance in academia to studying the genetic underpinnings of human behavior, which has cut off many social scientists from the recent revolutions in genetics and neuroscience.

Each of these abuses is far more significant than anything done by conservatives, and there are plenty of others. The only successful war on science is the one waged by the Left.

… two huge threats to science are peculiar to the Left—and they’re getting worse.

The first threat is confirmation bias, the well-documented tendency of people to seek out and accept information that confirms their beliefs and prejudices. In a classic study of peer review, 75 psychologists were asked to referee a paper about the mental health of left-wing student activists. Some referees saw a version of the paper showing that the student activists’ mental health was above normal; others saw different data, showing it to be below normal. Sure enough, the more liberal referees were more likely to recommend publishing the paper favorable to the left-wing activists. When the conclusion went the other way, they quickly found problems with its methodology.

Scientists try to avoid confirmation bias by exposing their work to peer review by critics with different views, but it’s increasingly difficult for liberals to find such critics. Academics have traditionally leaned left politically, and many fields have essentially become monocultures, especially in the social sciences, where Democrats now outnumber Republicans by at least 8 to 1. (In sociology, where the ratio is 44 to 1, a student is much likelier to be taught by a Marxist than by a Republican.) The lopsided ratio has led to another well-documented phenomenon: people’s beliefs become more extreme when they’re surrounded by like-minded colleagues. They come to assume that their opinions are not only the norm but also the truth.

Groupthink has become so routine that many scientists aren’t even aware of it. Social psychologists, who have extensively studied conscious and unconscious biases against out-groups, are quick to blame these biases for the underrepresentation of women or minorities in the business world and other institutions. But they’ve been mostly oblivious to their own diversity problem, which is vastly larger. Democrats outnumber Republicans at least 12 to 1 (perhaps 40 to 1) in social psychology, creating what Jonathan Haidt calls a “tribal-moral community” with its own “sacred values” about what’s worth studying and what’s taboo.

The narrative that Republicans are antiscience has been fed by well-publicized studies reporting that conservatives are more close-minded and dogmatic than liberals are. But these conclusions have been based on questions asking people how strongly they cling to traditional morality and religion—dogmas that matter a lot more to conservatives than to liberals.

A few other studies—not well-publicized—have shown that liberals can be just as close-minded when their own beliefs, such as their feelings about the environment or Barack Obama, are challenged.

Social psychologists have often reported that conservatives are more prejudiced against other social groups than liberals are. But one of Haidt’s coauthors, Jarret Crawford of the College of New Jersey, recently noted a glaring problem with these studies: they typically involve attitudes toward groups that lean left, like African-Americans and communists. When Crawford (who is a liberal) did his own study involving a wider range of groups, he found that prejudice is bipartisan. Liberals display strong prejudice against religious Christians and other groups they perceive as right of center.

the second great threat from the Left: its long tradition of mixing science and politics. To conservatives, the fundamental problem with the Left is what Friedrich Hayek called the fatal conceit: the delusion that experts are wise enough to redesign society. Conservatives distrust central planners, preferring to rely on traditional institutions that protect individuals’ “natural rights” against the power of the state. Leftists have much more confidence in experts and the state.

… The Right cited scientific work when useful, but it didn’t enlist science to remake society—it still preferred guidance from traditional moralists and clerics. The Left saw scientists as the new high priests, offering them prestige, money, and power. The power too often corrupted. Over and over, scientists yielded to the temptation to exaggerate their expertise and moral authority, sometimes for horrendous purposes.

These same sneer-and-smear techniques predominate in the debate over climate change. President Obama promotes his green agenda by announcing that “the debate is settled,” and he denounces “climate deniers” by claiming that 97 percent of scientists believe that global warming is dangerous. His statements are false.

While the greenhouse effect is undeniably real, and while most scientists agree that there has been a rise in global temperatures caused in some part by human emissions of carbon dioxide, no one knows how much more warming will occur this century or whether it will be dangerous. How could the science be settled when there have been dozens of computer models of how carbon dioxide affects the climate? And when most of the models overestimated how much warming should have occurred by now? These failed predictions, as well as recent research into the effects of water vapor on temperatures, have caused many scientists to lower their projections of future warming. Some “luke-warmists” suggest that future temperature increases will be relatively modest and prove to be a net benefit, at least in the short term.

The most vocal critics of climate dogma are a half-dozen think tanks that together spend less than $15 million annually on environmental issues. The half-dozen major green groups spend more than $500 million, and the federal government spends $10 billion on climate research and technology to reduce emissions.

Add it up, and it’s clear that scientists face tremendous pressure to support the “consensus” on reducing carbon emissions, as Judith Curry, a climatologist at Georgia Tech, testified last year at a Senate hearing.

“This pressure comes not only from politicians but also from federal funding agencies, universities and professional societies, and scientists themselves who are green activists,” Curry said. “This advocacy extends to the professional societies that publish journals and organize conferences. Policy advocacy, combined with understating the uncertainties, risks destroying science’s reputation for honesty and objectivity—without which scientists become regarded as merely another lobbyist group.”

To preserve their integrity, scientists should avoid politics and embrace the skeptical rigor that their profession requires. They need to start welcoming conservatives and others who will spot their biases and violate their taboos. Making these changes won’t be easy, but the first step is simple: stop pretending that the threats to science are coming from the Right. Look in the other direction—or in the mirror.

2 Consecutive Years of Productivity Declines

Source: ZeroHedge, Nov 2016

After a recession-signalling three straight quarters of decline, Q3 prleminary productivity data showed a huge 3.1% surge QoQ – the biggest jump since Q3 2014. However, the jump was not enough to regain annual gains as year-over-year productivity declined 0.04%. This is the first consecutive annual decline since 1993.

Probability of Finding ET Life within 10 Years

Source: Harvard, Mar 2013

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Does Digital Tech Lower Income & Reduce Jobs?

Source: HBR, May 2015

 Brynjolfsson and McAfee explain that while digital technologies will help economies grow faster, not everyone will benefit equally—as the latest data already shows. Compared with the Industrial Revolution, digital technologies are more likely to create winner-take-all markets.

once you adjust for inflation, an American household at the 50th percentile of income distribution earns less today than it did in 1998, even after accounting for changes in household size.

… the Great Decoupling. The two halves of the cycle of prosperity are no longer married: Economic abundance, as exemplified by GDP and productivity, has remained on an upward trajectory, but the income and job prospects for typical workers have faltered.

Workers’ prospects are deteriorating in the developing world, too. A recent study by Loukas Karabarbounis and Brent Neiman found that labor’s share of GDP had declined in 42 out of 59 countries, including China, Mexico, and India. The researchers concluded that as advances in information technology caused the price of plants, machinery, and equipment to drop, companies shifted investment away from labor and toward capital.

The net effect has been to decrease the demand for low-skilled information workers while increasing the demand for highly skilled ones. … skill-biased technical change. By definition, it favors people with more education, training, or experience.

What if we were to reframe the situation? What if, rather than asking the traditional question—What tasks currently performed by humans will soon be done more cheaply and rapidly by machines?—we ask a new one: What new feats might people achieve if they had better thinking machines to assist them? Instead of seeing work as a zero-sum game with machines taking an ever greater share, we might see growing possibilities for employment. We could reframe the threat of automation as an opportunity for augmentation.

Brynjolfsson: You could break the Second Machine Age into stages. In stage II-A, humans teach machines what we know painstakingly, step-by-step. That’s how traditional software programming works. Stage II-B is when machines learn on their own, developing knowledge and skills that we can’t even explain. Machine learning techniques have had some success doing that in areas as diverse as understanding speech, detecting fraud, and playing video games.

Is there a third stage?

Brynjolfsson: Maybe. It might be when machines understand emotions and interpersonal reactions, an area where humans still have the edge.

humans are still far superior in three skill areas. One is high-end creativity that generates things like great new business ideas, scientific breakthroughs, novels that grip you, and so on. Technology will only amplify the abilities of people who are good at these things.

The second category is emotion, interpersonal relations, caring, nurturing, coaching, motivating, leading, and so on. Through millions of years of evolution, we’ve gotten good at deciphering other people’s body language…

Brynjolfsson: …and signals, and finishing people’s sentences. Machines are way behind there.

The third is dexterity, mobility.

The intellectually easy thing to do is to look at an existing process and say, How can I have a machine do part of that job? It does take a certain amount of creativity and a little bit of work to do that, and it does create value. However, it takes a lot more creativity to say, How can I have this machine and this human work together to do something never done before and create something that will be more valuable in the marketplace?