Category Archives: Innovation

Clayton Christensen’s Contributions

Source: HBR, Jan 2020

Clayton M. Christensen is best known for his theory of disruptive innovation, in which he warns large, established companies of the danger of becoming too good at what they do best. To grow profit margins and revenue, he observes, such companies tend to develop products to satisfy the demands of their most sophisticated customers.

As successful as this strategy may be, it means that those companies also tend to ignore opportunities to meet the needs of less sophisticated customers — who may eventually form much larger markets.

An upstart can therefore introduce a simpler product that is cheaper and thus becomes more widely adopted (a “disruptive innovation”). Through incremental innovation, that product is refined and moves upmarket, completing the disruption of the original company.

Disruptive innovation:

the core theory of why bad things happen to good companies. “Disruptive Technologies: Catching the Wave” is the big-picture “why is this a problem” article warning established companies that a seemingly rational concern with profit margins can have disastrous results.

Product innovation: 

why good managers struggle to innovate successfully, this time focusing on the discipline of product innovation itself, rather than on organizational and management structures.

By understanding the tasks that customers look to a product for (the “job to be done”), a company can develop offerings — products, services, and whole brands — that customers truly value. Christensen uses the “milk shake” example to show how product developers should be considering their task.

The financial tools in the way

Established financial incentives often make it unattractive for companies to innovate. In “Innovation Killers: How Financial Tools Destroy Your Capacity to Do New Things,” Christensen and his coauthors target metrics such as discounted cash flow, net present value, and earnings per share, along with attitudes towards fixed and sunk costs. They suggest that leaders take up other methods for evaluating investments — ones that consider future value.

Business model innovation

Product innovations might be necessary, but to be truly disruptive, they often need to be delivered to the market through new business models.

In “Reinventing Your Business Model,” Christensen and his coauthors describe how to determine if your company needs a new business model and what makes one successful, using examples ranging from Apple’s iTunes to CVS’s MinuteClinics.

To Christensen, the role of every general manager is to lay a foundation for future growth. To that end, managers need to understand disruptive innovation, the threat it poses, and how to lead their teams and organizations to create growth that can keep pace with ever-evolving technologies, industries, and customers.

 

Improving Medical Research

Source: BioWorld, Jan 2020

Peter Thiel is not a fan of incremental science. The high-profile venture capital investor, who invests across technology and the life sciences via various vehicles, including the Founders Fund, suggested that as academic and government bureaucracies have scaled up and rigidified over the last 50 or 60 years, that has eroded the ability of researchers to pursue innovative science.

At a keynote address at the Precision Medicine World Conference, Thiel argued for enabling riskier research grant-making via institutions such as the NIH, as well as abandoning the scientific staple of the double-blind trial and encouraging the U.S. FDA to further accelerate its regulatory evaluations. He said that these deficiencies are inhibiting the ability of scientists to make major advances, despite the current environment that is flooded with capital and research talent.

“There’s a story we can tell about what happened historically in how processes became bureaucratized. Early science funding was very informal – DARPA’s a little bit different – but in the 1950s and 1960s, it was very generative,” said Thiel. “You just had one person [who] knew the 20 top scientists and gave them grants – there was no up-front application process. Then gradually, as things scaled, they became formalized.

He then cited the success of major scientific programs – such as the development of the atomic bomb in the Manhattan Project, the Apollo space program and Watson and Crick’s discovery of DNA – that hinged on having “preexisting, idiosyncratic, quirky, decentralized scientific culture[s]” and were accelerated rapidly by a major infusion of cash.

There’s sort of a bimodal distribution of scientists. You basically have people who are extremely conventional and will do experiments that will succeed but will not mean anything. These will not actually translate into anything significant, and you can tell that it is just a very incremental experiment.

Then you have your various people who are crazy and want to do things that are [going to] make a very big difference. They’re, generally speaking, too crazy for anything to ever work.”

“You want to … find the people who are roughly halfway in between. There are fewer of those people because of … these institutional structures and whatnot, but I don’t think they’re nonexistent,” he continued.

On China: Technology, Innovation and Growth

Source: Dan Wang, Jan 2020

The main ideas can be summed up in two broad strokes.

  1. First, China’s technology foundations are fragile, which the trade war has made evident.
  2. Second, over the longer term, I expect that China will stiffen those foundations and develop firms capable of pushing forward the technological frontier.

It’s not obvious to me that apps like WeChat, Facebook, or Snap are doing the most important work pushing forward our technologically-accelerating civilization. To me, it’s entirely plausible that Facebook and Tencent might be net-negative for technological developments. The apps they develop offer fun, productivity-dragging distractions; and the companies pull smart kids from R&D-intensive fields like materials science or semiconductor manufacturing, into ad optimization and game development.

The internet companies in San Francisco and Beijing are highly skilled at business model innovation and leveraging network effects, not necessarily R&D and the creation of new IP. (That’s why, I think, that the companies in Beijing work so hard. Since no one has any real, defensible IP, the only path to success is to brutally outwork the competition.)

I wish we would drop the notion that China is leading in technology because it has a vibrant consumer internet. A large population of people who play games, buy household goods online, and order food delivery does not make a country a technological or scientific leader.

How about emerging technologies like AI, quantum computing, biotechnology, and hypersonics, and other buzzing areas? I think there’s no scientific consensus on China’s position on any of these technologies, but let’s consider it at least a plausible claim that Chinese firms might lead in them.

So far however these fields are closer to being speculative science projects than real, commercial industries. AI is mostly a vague product or an add-on service whose total industry revenue is difficult to determine, and that goes for many of the other items.

In my view, focusing the discussion on the Chinese position in emerging technologies distracts from its weaknesses in established technologies. Take semiconductors, machine tools, and commercial aviation, which are measured by clearer technical and commercial benchmarks. They are considerably more difficult than making steel and solar panels, and Chinese firms have a poor track record of breaking into these industries.

The focus on speculative science projects brings to light another issue around discussions of China and technology: an emphasis on quantifying inputs. So much of the commentary focuses on its growth in patent registrations, R&D spending, journal publications, and other types of inputs.

One can find data on these metrics, which is why measures of “innovation” are often constructed around them. But these inputs are irrelevant if they don’t deliver output, and it’s not clear that they often do, neither in China nor anywhere else. Wonderfully asymptoting charts on Chinese patent registrations and R&D spending suggest that Chinese firms might overrun the rest of the world any day now. So far however the commercial outputs are not so impressive.

Learning by doing

I think however that long-term prospects are bright. In my view, Chinese firms face favorable odds first in reaching the technological frontier and next in pushing it forward. I consider two advantages to be important. First, Chinese workers produce most of the world’s goods, which means that they’re capturing most of the knowledge that comes from the production process. Second, China is a large and dynamic market. On top of these structural factors, Chinese firms have stiffened their resolve to master important technologies after repeated US sanctions.

My essay How Technology Grows argues that technological capabilities ought to be represented in the form of an experienced workforce. We should distinguish technology in three forms: tools, direct instructions (like blueprints and IP), and process knowledge.

The third is most important: “Process knowledge is hard to write down as an instruction: you can give someone a well-equipped kitchen and an extraordinarily detailed recipe, but absent cooking experience, it’s hard to make a great dish.”

We should think of technology as a living product, which has to be practiced for knowledge even to be maintained at its current level. I offered the example of the Ise Grand Shrine, which Japanese caretakers tear down and rebuild anew every generation so that they don’t lose its production knowledge.

Here’s an example I came across more recently: Mother Jones reported in 2009 that the US government forgot how to produce “Fogbank,” a classified material essential to the production of the nuclear bomb, because relevant experts had retired. The government then had to spend millions of dollars to recover that production knowledge.

I believe that the hard-to-measure process knowledge is more important more easily observable tools and IP. We would be capable of making few meaningful advancements if a civilization from 2,000 years in the future were able to dump blueprints on us, just as the Pharaohs and Caesars from 2,000 years in the past would have been able to do nothing with the blueprints of today.

Today, Chinese workers produce most of the world’s goods, which means that they engage more than anyone else in the technological learning process. Few Chinese firms are world-leading brands. But workers in China are using the latest tools to manufacture many of the most sophisticated products in the world.

They’re capturing the marginal process knowledge, and my hypothesis is that puts them in a better place than anyone else to develop the next technological advancements. To be more concrete, Chinese workers will be able to replicate the mostly-foreign capital equipment they currently use, make more of their own IP, and build globally-competitive final products.

https://www.spglobal.com/en/research-insights/featured/a-new-great-game-china-the-u-s-and-technology

 

https://delta2020.com/contact/8-news/191-2030-vision-is-china-set-to-become-the-global-technology-leader

https://voxeu.org/article/technology-diffusion-and-global-living-standards

 

https://www.spglobal.com/en/research-insights/featured/a-new-great-game-china-the-u-s-and-technology

A 4.1/4.0 for his Stanford PhD!

Source: Ed Boyden, Oct 2019

Related Resources:

Huffington Post, Sep 2016

Synthesize new ideas constantly.

Never read passively. Annotate, model, think, and synthesize while you read, even when you’re reading what you conceive to be introductory stuff.

That way, you will always aim towards understanding things at a resolution fine enough for you to be creative

Conversations with Tyler, Apr 2019

we don’t actually have theories — detailed knowledge — enough to make predictive, interesting models, for example, of how we form emotions, of how we make decisions.

… questions about attention-focusing drugs like Ritalin or Adderall. Maybe they make people more focused, but are you sacrificing some of the wandering and creativity that might exist in the brain and be very important for not only personal productivity but the future of humanity?

I think one of the things that I really love about the space of ecological diversity is, if you think of brains as computing things, then ecological diversity might provide many ways of computing the same thing but in different ways that actually yield interesting computational insights or aesthetic outcomes.

I think architecture is very important. I find architecture to be very inspiring for scientific ideas.

My group started at the MIT Media Lab, and now we have half our group over here in the MIT McGovern Institute, but I used to wander the halls of campus late at night to just look at stuff, the posters in the hallway. I get inspiration by trying to connect dots from different fields or disciplines or even entirely separate, unconnected topics. I find a lot of productivity from inspirational environments and connecting dots between random things.

COWEN: Now, you were first hired here by the Media Lab, is that correct?

BOYDEN: I was.

COWEN: They were a different ecosystem, and they saw some reason to hire you, where other groups didn’t see the same reason.

BOYDEN: Yeah. I was writing up these faculty applications to propose to set up a full-time neurotechnology group — let’s control the brain, let’s map the brain. At the time, the majority of the places that I applied to for faculty jobs actually turned me down.

So I went to the Media Lab to talk to people there. I’d been an undergrad researcher there. That’s when I was doing work on quantum computing, for example. It was just sheer dumb luck. They had a job opening that they couldn’t fill, and they said, “Why don’t you apply?”

COWEN: A job opening for what?

BOYDEN: I can’t recall the details. It might have been a professor of education or something. I forget what it was. But they said, “You know what? We’re the Media Lab. Maybe our new mission is to hire misfits.”

It’s a great place for people between one field and another where there’s sort of some space. But you know what? It could be an entire new discipline. Now, flash forward 12 years later, we actually started a center for neuroengineering here at MIT that I co-direct.

I think I learn more from individuals and their variability than from categories of people. For example, in our group at MIT, I have two PhD students. Neither finished college, actually. I can’t think of any other neuroscience groups on Earth where that’s true.

COWEN: And you hired them.

BOYDEN: I did, yeah.

COWEN: Knowing they didn’t finish college. And that was a plus? Or, “I’ll hire them in spite of this”?

BOYDEN: Well, one of them had been a Thiel fellow and then decided that it could be good to have an ecosystem in academia to support a long-term biotechnology play, and it’s hard to do biotechnology all by yourself. The other was a college dropout who was working as a computer tech support person next door, and both of them are now leading very independent projects.

Again, I try to look more at the individual, and I try to get to know people over a long period of time to learn what they’re good at and how they can maybe make a contribution based upon their unique experience. That’s different from what people have done traditionally.

BOYDEN: I think there’s so much crosstalk nowadays. I read a statistic that 40 percent of the professors at MIT trained at one point in their career at Stanford, Harvard, or MIT. So there’s a lot of crosstalk that goes back and forth. I think one of the themes in science is that you end up learning different things and bringing multiple things to bear.

COWEN: How should we improve the funding of science in this country?

BOYDEN: I like to look at the history of science to learn about its future, and one thing I’ve learned a lot over the last couple years — and it’s even happened to me — is that it’s really hard to fund pioneering ideas.

The third thing I would do is I would go looking for trouble. I would go looking for serendipity.

One idea is, how do we find the diamonds in the rough, the big ideas but they’re kind of hidden in plain sight? I think we see this a lot. Machine learning, deep learning, is one of the hot topics of our time, but a lot of the math was worked out decades ago — backpropagation, for example, in the 1980s and 1990s. What has changed since then is, no doubt, some improvements in the mathematics, but largely, I think we’d all agree, better compute power and a lot more data.

So how could we find the treasure that’s hiding in plain sight? One of the ideas is to have sort of a SWAT team of people who go around looking for how to connect the dots all day long in these serendipitous ways.

COWEN: Does that mean fewer committees and more individuals?

BOYDEN: Or maybe individuals that can dynamically bring together committees. “Hey, you’re a yogurt scientist that’s curious about this weird CRISPR molecule you just found. Here’s some bioinformaticists who are looking to find patterns. Here’s some protein engineers who love — ”

COWEN: But should the evaluators be fewer committees and more individuals? The people doing the work will always be groups, but committees, arguably, are more conservative. Should we have people with more dukedoms and fiefdoms? They just hand out money based on what they think?

BOYDEN: A committee of people who have multiple non-overlapping domains of knowledge can be quite productive.

But in economics and in other fields, it also seems like people are trying to make better maps of things and how they interact.

BOYDEN: One way to think of it is that, if a scientific topic is really popular and everybody’s doing it, then I don’t need to be part of that. What’s the benefit of being the 100,000th person working on something?

So I read a lot of old papers. I read a lot of things that might be forgotten because I think that there’s a lot of treasure hiding in plain sight. As we discussed earlier, optogenetics and expansion microscopy both begin from papers from other fields, some of which are quite old and which mostly had been ignored by other people.

I sometimes practice what I call failure rebooting. We tried something, or somebody else tried something, and it didn’t work. But you know what?

Something happened that made the world different. Maybe somebody found a new gene. Maybe computers are faster. Maybe some other discovery from left field has changed how we think about things. And you know what? That old failed idea might be ready for prime time.

With optogenetics, people were trying to control brain cells with light going back to 1971. I was actually reading some earlier papers. There were people playing around with controlling brain cells with light going back to the 1940s. What is different? Well, this class of molecules that we put into neurons hadn’t been discovered yet.

COWEN: The same is true in economics, I think. Most of behavioral economics you find in Adam Smith and Pigou, who are centuries old.

BOYDEN: Wow. I almost think search engines like Google often are trying to look at the most popular things, and to advance science, what we almost need is a search engine for the most important unpopular things.

COWEN: Last question. As a researcher, what could and would you do with more money?

BOYDEN: Well, I’m always looking for new serendipitous things, connecting the dots between different fields. These ideas always seem a bit crazy and are hard to get funded. I see that both in my group but also in many other groups.

I think if I was given a pile of money right now, what I would like to do is to find a way — not just in our group but across many groups — to try to find those unfundable projects where, number one, if we think about the logic of it, “Hey, there’s a non-zero chance it could be revolutionary.”

Number two, we can really, in a finite amount of time, test the idea. And if it works, we can dynamically allocate more money to it. But if it doesn’t work, then we can de-allocate money to it.

I would like to go out and treasure hunt. Let’s look at the old literature. Let’s look at people who might be on the fringes of science, but they don’t have the right connections, like the people who I talked about earlier. They’re not quite in the right place to achieve the rapid scale-up of the project. But by connecting the dots between people and topics, you know what? We could design an amazing project together.

Measuring Originality in Science

Source: Springer, Nov 2019

We conceptualise originality as the degree to which a scientific discovery provides subsequent studies with unique knowledge that is not available from previous studies.

Specifically, we measure the originality of a paper based on the directed citation network between its references and the subsequent papers citing it. We demonstrate the validity of this measure using survey information. In particular, we find that the proposed measure is positively correlated with the self-assessed theoretical originality but not with the methodological originality.

We consider originality to be rooted in a set of information included in a focal scientific paper. However, we argue that the value of the paper is realised through its reuse by other scientists, and that its originality is established through its interaction with other scientists and follow-on research (Latour and Woolgar 1979; Merton 1973; Whitley 1984).

Base measure

We propose to measure the originality of an individual scientific papers based on its cited papers (i.e., references) and citing papers (i.e. follow-on research). We draw on subsequent papers that cite the focal paper to evaluate whether the authors of these subsequent citing papers perceive the focal paper as an original source of knowledge (Fig. 1A).

Suppose that the focal paper X cites a set of prior papers (reference set R) and is cited by a set of subsequent papers (citing set C). If X serves as a more original source of knowledge, then the citing papers (i.e., papers in citing set C) are less likely to rely on papers that are cited by X (i.e., papers in reference set R). In contrast, if X is not original but an extension of R, then C will probably also cite R together with X. In other words, we exploit the evaluation by the authors of follow-on research to measure the originality of the focal paper.

In conclusion, this study proposes a new bibliometric measure of originality. Although originality is a core value in science (Dasgupta and David ; Merton ; Stephan ; Storer ), measuring originality in a large scale has been a formidable challenge. Our proposed measure builds on the network betweenness centrality concept (Borgatti and Everett ; Freeman ) and demonstrates several favourable features as discussed above. 

Progress Studies

Source: AIER, Aug 2019

My work has argued that nations that are open to risk-taking, trial-and-error experimentation, and technological dynamism (i.e., “permissionless innovation”) are more likely to enjoy sustained economic growth and prosperity than those rooted in precautionary principle thinking and policies (i.e., prior restraints on innovative activities).

Collison and Cowen suggest that “there can be ecosystems that are better at generating progress than others, perhaps by orders of magnitude. But what do they have in common? Just how productive can a cultural ecosystem be?” Beyond gaining a better understanding of how innovation ecosystems work, they also want to nurture them. “Can we deliberately engineer the conditions most hospitable to this kind of advancement or effectively tweak the systems that surround us today?” they ask.

Mokyr has argued that technological innovation and economic progress can be viewed as “a fragile and vulnerable plant, whose flourishing is not only dependent on the appropriate surroundings and climate, but whose life is almost always short. It is highly sensitive to the social and economic environment and can easily be arrested by relatively small external changes.” McCloskey’s work has shown that cultural attitudes, social norms, and political pronouncements have had a profound and underappreciated influence on opportunities for entrepreneurialism, innovation, and long-term economic growth

Many scholars have surveyed the elements that contribute to a successful innovation culture and their lists typically include:

  • trust in the individual / openness to individual achievements;
  • positive attitudes towards competition and wealth-creation (especially religious openness toward commercial activity and profit-making);
  • support for hard work, timeliness, and efficiency;
  • willingness to take risks and accept change (including failure);
  • a long-term outlook;
  • openness to new information / tolerance of alternative viewpoints;
  • freedom of movement and travel for individuals and organizations (including flexible immigration and worker mobility policies);
  • positive attitudes towards science and development;
  • advanced education systems;
  • support for property rights and contracts;
  • reasonable regulations and taxes;
  • impartial administration of justice and the respect for the rule of law; and,
  • stable government institutions and transfers of power.

 

Innovation Dispersion

Source: MIT Technology Review,  Jan 2017

The rate at which innovations appear and disappear has been carefully measured. It follows a set of well-characterized patterns that scientists observe in many different circumstances. And yet, nobody has been able to explain how this pattern arises or why it governs innovation.

The notion that innovation arises from the interplay between the actual and the possible was first formalized by the complexity theorist Stuart Kauffmann. In 2002, Kauffmann introduced the idea of the “adjacent possible” as a way of thinking about biological evolution.

The adjacent possible is all those things—ideas, words, songs, molecules, genomes, technologies and so on—that are one step away from what actually exists. It connects the actual realization of a particular phenomenon and the space of unexplored possibilities.

But this idea is hard to model for an important reason. The space of unexplored possibilities includes all kinds of things that are easily imagined and expected but it also includes things that are entirely unexpected and hard to imagine. And while the former is tricky to model, the latter has appeared close to impossible.

What’s more, each innovation changes the landscape of future possibilities. So at every instant, the space of unexplored possibilities—the adjacent possible—is changing.

“Though the creative power of the adjacent possible is widely appreciated at an anecdotal level, its importance in the scientific literature is, in our opinion, underestimated,” say Loreto and co.

Nevertheless, even with all this complexity, innovation seems to follow predictable and easily measured patterns that have become known as “laws” because of their ubiquity. One of these is Heaps’ law, which states that the number of new things increases at a rate that is sublinear. In other words, it is governed by a power law of the form V(n) = knβ where β is between 0 and 1.

Words are often thought of as a kind of innovation, and language is constantly evolving as new words appear and old words die out.

This evolution follows Heaps’ law. Given a corpus of words of size n, the number of distinct words V(n) is proportional to n raised to the β power. In collections of real words, β turns out to be between 0.4 and 0.6.

Another well-known statistical pattern in innovation is Zipf’s law, which describes how the frequency of an innovation is related to its popularity. For example, in a corpus of words, the most frequent word occurs about twice as often as the second most frequent word, three times as frequently as the third most frequent word, and so on. In English, the most frequent word is “the” which accounts for about 7 percent of all words, followed by “of” which accounts for about 3.5 percent of all words, followed by “and,” and so on.

This frequency distribution is Zipf’s law and it crops up in a wide range of circumstances, such as the way edits appear on Wikipedia, how we listen to new songs online, and so on.

these systems involve two different forms of discovery. On the one hand, there are things that already exist but are new to the individual who finds them, such as online songs; and on the other are things that never existed before and are entirely new to the world, such as edits on Wikipedia.

Loreto and co call the former novelties—they are new to an individual—and the latter innovations—they are new to the world.

Curiously, the same model accounts for both phenomenon. It seems that the pattern behind the way we discover novelties—new songs, books, etc.—is the same as the pattern behind the way innovations emerge from the adjacent possible.

That raises some interesting questions, not least of which is why this should be. But it also opens an entirely new way to think about innovation and the triggering events that lead to new things. “These results provide a starting point for a deeper understanding of the adjacent possible and the different nature of triggering events that are likely to be important in the investigation of biological, linguistic, cultural, and technological evolution,” say Loreto and co.