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The story of sciencebite: an internet marketplace for scientific expertise

Sciencebite was an internet startup in operation from April 2014 to March 2015, which aimed to provide an open marketplace for scientific expertise to industrial R&D. I led the startup from its conception; we won the support of a prominent angel investor in Berlin: the team numbered five people by the time it closed. Although we did not succeed in realising a successful marketplace, we learned much through building and launching two web applications that reached many scientists and R&D-led companies. The aim of this article is to share some of these learnings: especially about the ways in which scientists are bound to their conventional working practices, despite a desire to advance their careers in an open way.

The Sciencebite hypothesis: an open marketplace that would add value to the interface between industrial R&D and scientific experts

We started with the ideal of founding an “open expertise” platform that incentivized scientists to promote themselves based on their expertise, and take R&D out of secretive corporate relationships and onto the open internet. Until now there is little open exchange on the internet between industrial R&D and scientific expertise. The best examples are the “open innovation” crowdsourcing platforms, including InnoCentive and NineSigma, which are well established but serve only a tiny proportion of the world’s R&D needs. Rather, the main exchange between R&D and scientific expertise, which includes the job market, is fairly secretive.

A great source of inspiration for us was the recent trend of open internet platforms for other professions, such as StackExchange for programming knowhow, and Elance for software / web design freelancing. We admired the way software engineers could build their careers, make connections, and even sell their services using online tools, by being open about their expertise and contributing to open-source projects. Was there any reason why this culture should not also work for scientific R&D?

The following figure shows sciencebite’s space as we see it. A great variety of scientific expertise is used in industrial R&D, and there are many successful examples of open internet marketplaces for different professions. However there is little in the way of an open internet connection between scientific expertise and industrial R&D (X on the diagram). On the other hand, there are many successful service companies providing R&D expertise secretively (Y on the diagram), and there are successful open platforms providing open sharing in academic science like ResearchGate, Mendeley and Academia, but do not provide expertise to industry (Z on the diagram).

Our first product iteration: an idealistic communication tool for scientists to share knowhow, consult privately and refer colleagues

Our first product was full of idealism and hope for better communication between scientists. Launched July 2014 (private beta), the web application allowed users (mainly from Industry) to find scientists, ask them questions, and invite them to share publicly, consult privately (under our standard NDA and Service Contract) or refer colleagues for a share of the benefit. A sketch is shown in Appendix A. We built the platform using the current best practices for rapidly prototyping a web app: a Ruby-on-Rails back-end, Bootstrap front-end, hosted on Heroku, and connecting to external identity services (LinkedIn), external data sources (Pubmed and BASE), and an external search service (based on Solr), and email based workflows.

We launched this product to hundreds of relevant target users recruited mainly through our extended network of R&D professionals (applied scientists, engineers, project managers and executives in R&D). A considerable number of users came from our TechCrunch Disrupt San Francisco exposure, the related Wall Street Journal Germany article and subsequent events in Berlin and Europe where we took part (e.g. WebSummit Dublin, Slush Helsinki). We also reached a variety of potential experts for one-on-one interviews, including academic scientists, employees in R&D companies and independent consultants.

The results were that a small number of our industrial contacts soon placed requests with us, to source expertise for immediate problems that they had. We found that, when we had these requests, it was easy to recruit junior scientists who were willing to consult. However, senior scientists or established consultants tended not to respond. The other functions we built were trickier to get right: few scientists made referrals and none shared knowhow openly on our site. We discovered many assumptions about our users were incorrect, with regard to their working practices and the user experience. For a start, we embraced the use open identities through LinkedIn as a way of verifying our user base, however most target customers wanted to deal with experts anonymously – therefore our use of open identities was a serious discouragement to many corporate R&D users. On the other side, scientists were not motivated to share publicly because they would have all the exposure of a journal article, and none of the academic benefits. We also found that users took too much care choosing scientists, and too little care to ask a good question. Regarding the search functionality, we thought that a minimal viable product would be to search open academic data with Solr, a state-of-the-art open source search engine. Unfortunately, this was not enough, as it did not give us a competitive advantage on Pubmed / Google Scholar, even though ours focused on people rather than papers. It was particularly problematic to deal with all the kinds of search terms that users threw at us, which were very diverse: from discipline expertise (e.g. microbiology) to full questions (e.g. how to estimate lifetime of a plastic part under stress?) or specific keywords (e.g. molecular drug targets).

Following our participation in the Google LaunchPad Berlin in October 2014, we reviewed our assumptions by conducting additional persona interviews with 5 target groups: Junior Scientists, R&D Managers, PhDs, PostDocs and Freelance Consultants.

We shifted our focus to where we found the traction – the need for short consultations with experts, and depreciate the other features. Our literature search function was replaced with a curated database of scientists that we recruited. (We planned to bring back the literature search when we had more resources to improve the search technology and data sources.) From the persona interviews, we decided to target specifically R&D managers on the industry side (most direct need out of our users in industry), and specifically junior scientists on the expert side (most available for flexible consulting, easiest to recruit). Instead of approaching all industries, we decided to focus on biotech (large investment in R&D, multidisciplinary, very scientific).

Our second product iteration: a freelance consulting marketplace, for scientists to promote their profiles along with their availability for consulting

Thus came our second product, launched January 2015. This product was more realistic and based on what our target users had asked for: a two-sided marketplace for expertise consultations. A sketch is shown in Appendix B. Scientists could register to present their profiles and availability (on our standard contract). Companies could browse our scientists, send them messages and request a consultation. We built a signup process to import profile data from Mendeley and LinkedIn, and profiles that emphasized a scientist’s skills.

We launched an outreach campaign, to assess the expertise needs of business owners and chief scientists at innovative biotech startups and SMEs, in Germany, Europe and the US. This was a great success – around 35 executives gave us in-depth interviews, and we offered each of them publicity in the form of a feature on our blog which we decided to focus on the biotech scene. We also got into Wired Germany’s feature “15 Ideas for a Better World” in February 2015. We experimented with social media advertising, leading to successful campaigns on Facebook and LinkedIn to recruit experts. We also recruited scientists and executives at academic conferences and trade shows – these events were a high investment for the number of users, but they allowed us high-quality face-to-face feedback. We attempted to form university partnerships via several tech transfer / career offices of German universities, and joined BioDeutschland (the industry association for German biotech) to promote ourselves to its member companies.

Our earlier successes with finding companies to request consultations were unfortunately not repeated, perhaps because the new contacts were from cold contact. Biotech startups all said they would find expertise within their networks (and not pay for it), and none placed a request with us. Large companies said the same and commented that they invest in managing their expertise networks, and therefore doubted our service would work for large companies. Our social media ads showed many scientists to be curious about this type of work, and enthused by our idea, but focusing on freelance consulting was not an option for them – they were much more worried about finding permanent jobs. Our newly recruited scientists differed widely in how much data they would put on their profiles – only a few wrote a lot about their skills. However, none were contacted by target companies, either via our own contacts or organically from their profile as an internet landing page. Content marketing on our blog resulted in bursts of visits, with around 5% of visits converted into use of website (i.e. search on web app).

Many scientists showed they were unsure about whether they were even allowed to do paid work outside their research institution; contacts at university commercialization offices also expressed these concerns. A couple of companies reported that they do use flexible labor from junior scientists, but not as short consultations, rather as occasional project work lasting weeks or months. University commercialisation / career offices showed polite interest in our idea but opposed their own PhDs consulting without their involvement (fear of IP being shared, legal regime not authorizing such consulting engagements, etc.)

Our main learning was that, unfortunately, the market for junior scientist skills on a consultation basis is very limited. Junior scientists were difficult for us to sell to biotech companies, because these companies already have practices of acquiring knowhow, and what we offered them was not powerful enough to make them change their practices.

Variation of the second product: a freelance jobs marketplace

Without developing a new product, we investigated whether our platform could be adapted for jobs, particularly freelance/flexible jobs, i.e. engagements lasting from a few weeks to a few months. This idea was marketed from February 2015. We set up meetings with several HR managers in large and medium-sized German biotech companies, who were very open to discussing the market with us. The HR managers were generally friendly to the idea of an open marketplace for scientific labor. However, they were unconcerned with freelance jobs. Most said that, whenever they have an opening, they have no problem having scientists come to them. We also targeted recruiters who had recently posted entry-level scientific jobs. The response from this group was disappointing: despite being lower in the corporate hierarchy, they were very difficult to reach, probably because they are used to being chased by job applicants and staffing agents. The few we reached were unenthusiastic, because the balance of power is already in their favour, they did not need to actively look for candidates. Moreover, there were many criteria they wished to see in a candidate outside what was provided in our profiles. Some feedback we got from scientists suggested their availability for flexible jobs was likely to be scarcer than for short consultations.

We were forced to acknowledge that there simply isn’t a significant market for short term scientific jobs in R&D, and the current Sciencebite profiles were not sufficient to convince companies to try hiring junior scientists in this way.

Conclusion – we did not succeed in developing a model that connects industrial clients to scientific expertise in a viable way

After careful review of alternative options, we have to admit we have exhausted the product options available to us without finding a model that connects industrial clients to scientific experts in a viable way. Each model has met roadblocks that are at present difficult to overcome.

Model	Roadblock
Freelance marketplace	insignificant market for consultations or flexible scientific work in R&D
Expertise search	insignificant market for expertise of junior scientists (and far behind competitors as a general expertise search platform)
Q&A (open knowhow)	little incentive for scientists to share outside academic system; inhibition to ask open questions in R&D
Promotional platforms	unclear competitive advantage of the current Sciencebite profiles, no particular evidence that developing a better profile would be profitable
Crowdsourcing	far behind competitors, no particular inspiration for original product
Networking platforms
Job marketplaces

We believe that the failure of our initiatives cannot be attributed to execution. We worked marvelously as a team, built and launched the products in a relatively short time, and promoted them to many people. Even if we had reached 10x as many target companies; even if we had spoken to 10x as many people within those companies; even if we had recruited 100x as many scientists, or scientists only from top institutions; even if our web application had behaved slicker; even if there had been more ways for scientists to import their data; even if we had had higher search rankings, etc., it is difficult to imagine that the market reaction to our products would be any different.

Hypothetical technological advances could perhaps have given us a viable product: e.g. a radical new search technology or data source, but these were not the vision of the business – it was more about a marketplace than a technology.

Likewise, we cannot blame lack of finance. Our pre-seed fund has sufficed for us to build two web applications, and try all the marketing and business models that we conceived. If we had succeeded in raising a seed round, we could have built more product features and a slicker product, and scaled up the marketing, but it would not have helped with the roadblocks above.

So we could not prove the Sciencebite hypothesis because (1) the product vision was insufficient, (2) the market that we are going after hardly exists, and (3) we have not brought a radical advance that is able to quickly change R&D working practices.

Outlook: there is hope

Nevertheless, there are signs of hope. We discovered a universal interest from R&D professionals about connecting openly – many people told us that they can’t imagine why our concept did not exist already. There was a widespread need for expertise among companies, despite the skepticism that high quality knowhow could be provided via a website. We found a genuine desire among scientists to sign up, share their skills, and a curiosity that they could earn money this way. I believe that someday, someone will figure out a way to build a platform that caters for these needs, whether it is through a technology advance or an amazing insight about scientists’ behavior. I look forward to that day.

A. First product: share, refer and consult

1. Users were invited to enter a search query to find relevant scientists.

2. The user could select one or more scientists to invite an answer.

3. The recipient had three options: share publicly, creating a public page linked to their profile for self-promotion…

… or advise privately in the Sciencebite project room, which provided standard contracts and payment services for R&D consulting…

… or refer a colleague and take a share of the rewards if that colleague earned money through Sciencebite.

B. Second product: two-sided marketplace

1. Users could search for a scientist.

2. Scientists could present their profiles and availability for consulting.

3. A user could request consultations from a scientist and buy their time.

How do scientists share on academic social networks like ResearchGate?

A data analysis of user habits shows open sharing is mostly limited to publications; very few scientists are liberal with knowhow

A key aim of Sciencebite is to create a open platform for scientific expertise online. At the moment, Sciencebite is much smaller than the academic social networks, academia.edu, researchgate.net and mendeley.com. So I wondered how scientists behave on these networks, how they are promoting their identities, publications and expertise, and how they are connecting with each other.

Without wishing to comment on the competitive positions of the three largest academic networks, in this part of the world – Berlin – where I am based, ResearchGate is the one that we hear about the most, and with a claimed user count of > 6m academics from whitelisted institutions, it has certainly achieved an amazing penetration of the world’s academics. Speaking personally as someone with a background in academic science, I can confirm that every scientist that I know now seems to have a ResearchGate profile, although many do not actively engage with it.

So how are scientists behaving online, taking ResearchGate as the source of data? I wrote a script to browse ResearchGate profiles at random, and ran it twice, to look at the change over a year: firstly in November 2013 (sampling 3028 profiles), and secondly in February 2015 (sampling 3407 profiles). The sample represents some 0.1% of ResearchGate profiles, and leads to some pretty interesting conclusions about how scientists behave on the new internet platforms.

Scientists are increasingly sharing their professional identities online

ResearchGate has 6m users as of January 2015, and is currently growing at around 10k users per day. A growing minority of users share their professional identity on the site (Figure 1): 36% shared a profile picture in 2013, 43% in 2015. Fewer have updated their profiles with their current positions, but this too is growing: 7% in 2013 and 24% in 2015. It’s interesting that fewer have filled in a current position than uploaded a profile picture, which perhaps reflects that many users register with Facebook, or have left academia since joining ResearchGate. As of February 2015, 18% have both uploaded a profile picture and filled in their current position. If we consider this as a definition of an active user, we can estimate ResearchGate’s active user base as 1.1m.

Figure 1: identity sharing on ResearchGate

A growing number of scientists are sharing full texts of their publications

We see an impressive engagement with sharing full texts of publications online (Figure 2). Some 41% of ResearchGate users had uploaded at least one full text by Nov 2013, growing to 80% by Feb 2015. Considering the rapid growth of ResearchGate signups in this time, it reflects an impressive rate of sharing among new users.

Figure 2: publication uploading on ResearchGate has acheived an impressive penetration.

Figure 2: publication uploading on ResearchGate has achieved an amazing penetration of the academic community. Now 80% of RG users have uploaded at least one full text.

It’s remarkable that so many more users have shared their publications (~3m) than filled in their profile information (~1m). However, given the importance of publications in the academic career path, it is perhaps less surprising: academics find it more important to share publications than to display their current position. This would surely be the opposite of user behavior on mainstream professional networks like LinkedIn.

In addition to the sampling of users, I sampled 1000 publications randomly on ResearchGate, looking at the relationship between publication date and sharing. In the past 3 years, a great number of new publications have been listed on ResearchGate, reflecting its deep penetration of the scientific community. We can also see that new publications are increasingly shared on ResearchGate – almost half of new publications in the past three years are uploaded (Figure 3).

Figure 3: the growth in shared full texts on ResearchGate reflects the amazing penetration of the academic community in recent years. Now almost 50% of new publications are shared openly as full text.

Almost nobody is using ResearchGate’s Open Reviews

ResearchGate launched a new feature in March 2014: Open Reviews, for scientists to openly peer-review each others’ papers after publication, for quality, originality, reproducibility, etc. Unfortunately almost nobody is using it. Only 4 users in my sample of 3407 have given an Open Review, and none came back to give a second.

The reason for this low adoption is probably that scientists get little career advancement from an Open Review. If they give a negative review, they may make enemies, and they gain none of the influence over a journal that they do in the traditional peer review system. Moreover, if they give a positive review, it does not help their publication and citation record, which are their main criteria to advance their careers.

Sharing of knowhow and advice is still lacking

Far fewer users engage with the Q&A forum on ResearchGate, in comparison with their sharing of publications (Figure 4). In November 2013, only 6.3% had ever asked, commented on, or answered a question. Even of those who had engaged with Q&A, very few were repeat users: 2.3% had ever asked a question, only 0.56% had come back to ask a second question, and only one in the entire sample could be really classified as an active user by asking more than 10 questions.

The situation had not improved much by February 2015. 6.6% had even asked/commented/answered. 2.8% had ever asked, and 1.1% had ever come back for a second. No users in the sample had asked more than 10 questions.

Why do so few scientists request and share practical knowhow and advice online? Perhaps it is the secretive culture of research, and the importance of publications versus other types of dissemination. Perhaps it is also because of the highly specialized nature of knowhow in science – valuable knowhow is often much more specialized than in other professions, for example in software development, where programmers enjoy a culture of sharing all kinds of practical information, through open source software, blogs and Q&A sites.

Figure 4: knowhow sharing on ResearchGate is still minimal. Only a very small proportion of users use the Q&A, and this proportion has changed little in 2013 – 2015

Conclusion: scientists are changing some of their habits, but only as far as the academic career path allows them

Great numbers of scientists are now sharing online, and in the past couple of years, it has become common to share publications openly on academic social networks like ResearchGate. However, not many scientists have changed their behavior beyond this – other types of sharing are still lacking, In particular, sharing of expertise and knowhow is still minimal.

I believe this represents a great challenge and opportunity to scientists who are interested in working differently. The trend of online sharing among scientists still overwhelmingly reflects the traditional academic career path, because it so strongly anchored to journal publications and citations. For all the frustrations of collaborating and making progress in science, there are perhaps still a great number of undiscovered solutions that would allow us to work together better.

The individuality of popcorn

or, why life can be both random and destined

There’s a widespread delusion that life is a like a lottery, and that events in the real world are determined by chance. I notice it when people talk about the future using the language of statistics: what are the chances of X, e.g. getting rich, becoming famous, having an accident? The unique case of the individual is replaced with a vague idea about the population.

This delusion is very common: we see it in popular culture, politicians, journalists and advertisers use it. I hardly see anyone in the public eye oppose it – a notable exception being Peter Thiel in his recent book, From Zero to One.

Perhaps scientists share this attitude most of all. I encountered it at a recent popular science event in Berlin. A professional scientist at one of the local research institutes constructed an experiment “popcorn decay”. The aim of this experiment was to measure the popping of a population of popcorn kernels, and therefore show that popcorn popping was like radioactive decay, which is an intrinsically unpredicatable (i.e. random) process, and presumably therefore uncaused and meaningless. But it wasn’t really about popcorn – it was about everything in life. He wanted to convey an approach to thinking about all events in life – not just the popping of a popcorn.

Every one counts

What is randomness? What is probability?

What are we talking about when we say something is random? Basically it means that we cannot predict it. For example, when one flips a coin, it is very hard to predict how many times the coin will turn before it lands, and therefore one is completely ignorant about whether it turns out heads or tails.

The important thing to realise is that randomness is subjective. Just because you cannot predict an event, does not mean that nobody else can predict it, or that you could not predict it if you were given extra information or allowed to look at it in retrospect.

What is random for one person, may not be random for another. Imagine your dog barks some mornings for no apparent reason. On a given morning, you don’t know whether it will bark. This is random. Then you notice that the dog only barks when a certain postman delivers the mail. It is not random any more: you ask the postman for his schedule, and now you know when the dog is going to bark.

So, randomness means unpredictability. We call a variable random if we cannot predict it. It does not mean that it is not determined by some hidden cause or that it is meaningless. Only that we cannot predict it. Randomness is subjective. Randomness is not a physical property, it is a statement of ignorance.

The first person to make all this clear was Bayes. Bayesian statistics is a buzzword now (especially in this age of big data), so it is unfortunate that even most statisticians use him without really appreciating what he was talking about.

Probability is just a quantification of that ignorance. A probability of 50% means that one is completely ignorant about whether something will happen or not. So when the weather forecasters say 50% chance of rain, they are actually saying it might rain, and perhaps it often rains in circumstances that seem similar, but they are completely ignorant about whether it will rain or not.

Even famous scientists use the language of probability for deceptive purposes. Astronomer Royal, Prof Martin Rees caused a media sensation when he claimed in his book, that humanity has a 50% chance of extinction in this century. For most people, that conjures up a mental image of flipping a coin – heads: humanity lives, tails: humanity dies out. But it is not really like that. Most events in real life happen only once – and the 21^st century will only happen once. There is no meaningful analogy to flipping a coin. And Martin Rees is not privy to any secret knowledge about the dangers of human extinction that millions of other educated people have also studied. If he wanted to speak honestly, he would have said “I think there are real dangers facing us in the near future. I don’t know the future better than anyone else, but I feel completely unsure of whether humanity is going to survive this century.”

But what about quantum physics? Is there some level at which events are random in a physical sense, not just a subjective sense?

I studied physics at Oxford, which was a real traditional academic discipline, like almost no other subject. Of course we learned the Copenhagen interpretation of quantum physics, that a system is in a superposition of states until an observer “rolls the dice”, and the system randomly “collapses” into one or other state. This is of course famously illustrated by Schrödinger’s cat, which is killed if a radioactive nucleus decays. The fate of the cat is unknowable in advance, and its death is therefore somehow causeless and meaningless.

We learned that lots of people were once unhappy with this picture of things, and they came up with “hidden variable theories”, to reassure themselves that there was a hidden cause behind the cat’s fate. Einstein, was of course one of them, “God does not play dice,” he asserted. And we got a mental image of Einstein in the desert, like some biblical prophet, alone and mocked by his people. We learned that nowadays any self-respecting physicist ignores all that stuff. We got the vague impression that hidden variable theories were disproven by von Neumann. And even if they weren’t disproven, we should ignore them on the principle of Occam’s Razor.

As scientists we were also taught to think of all things objectively – that we would never learn the truth about an object if we failed to remove ourselves from the picture. Although we learned probability theory and the physics of stochastic (random) processes, the understanding that we gained was flawed. Random processes cannot be seen completely objectively, because randomness itself is a subjective experience.

The Copenhagen interpretation inspired the “many worlds” interpretation. In some ways, many worlds is a logical extension of the Copenhagen interpretation. It basically holds that everything in this world is without cause – that for every effect, the opposite effect might just as well have occurred, because both exist in parallel worlds that diverged from each other when the event took place.

All this is vastly removed from our everyday experience. Of course in real life too, many events have an uncertain outcome. I recently founded a start-up company with a couple of others in Berlin. Now, whether a start-up company succeeds or not is uncertain, even to its founders. However, the probability of it succeeding is perhaps conceptually different to the probability of a nucleus decaying. In a nuclear decay, the probability of decay in a given period is the same for all observers with access to a table of half-lives. However, in judging whether a company will succeed, all observers are not equal. My co-founders, investors and myself have privileged information. Other industry insiders and competitors may have other privileged information. Each observer’s understanding and judgement of the company, the service, the competitive situation and the industry varies immensely. Every observer may assign his own subjective probability of the company succeeding. Several years from now, when the fate of the company is clear, we may retrospectively analyse the history of the company and understand why it succeeded or failed. So at no point is there a roll of the dice. Eventually when we look back, we will understand the fate of the company, and conclude that given the environment and our actions, there could not have been the opposite outcome. The universe never divides into two parallel universes. There is only the gradual transition from unknown to known.

Randomness does not mean uncaused (an event can be unpredictable, but still have causes) – the de Broglie-Bohm theory and Grete Hermann

I believe that the understanding of probability and randomness which is normally taught to scientists is actually rather shallow. I think a much deeper view is that offered by Bayesian statistics: randomness is a property of the observer (ignorance), not a property of the system. When I first heard about Bayes, I thought that quantum mechanics must be the exception. So, shortly after I left academic science, I was fascinated to discover that there was an accepted interpretation of quantum mechanics – the de Broglie-Bohm interpretation – in which probability is once again a property of the observer and not of the system, and therefore compatible with Bayesian probability theory.

My understanding of de Broglie-Bohm is that a particle has both a definite position and a definite momentum. However its trajectory is determined not by Newton’s equation of motion, but by an equation of motion that depends on the wavefunction. The de Broglie-Bohm theory of the double slit experiment can be appreciated in a single figure:

The double-slit diffraction pattern according to the de Broglie-Bohm interpretation. The particle always has a definite position and momentum, and moves deterministically according to a function of the wave function. However its initial conditions are unknowable.

Thus the “randomness” is from initial conditions, not from collapse of the wavefunction. It can therefore be called a “non-local” hidden variable theory, as unlike the “local” hidden variable theories. It is not disproven by Bell’s theorem. In other words, there’s no getting away from the wavefunction, but the wavefunction is deterministic: there’s no roll of dice.

But the trouble with the de Broglie-Bohm theory is that, even if it formally just a flavour of canonical quantum mechanics, it’s mathematically rather complicated, and therefore fails the test of Occam’s razor. When I cast my mind back to the electron-in-trap or the hydrogen atom, I wouldn’t dream of applying the de Broglie-Bohm theory. It’s just a more complicated version of the same quantum mechanics.

Recently I heard of Grete Hermann’s work. In the 1920s, Hermann disproved von Neumann’s theorem on hidden variables, as Bell did 20 years later, although her work remained obscure. Hermann also argued that although hidden variable theories are not forbidden, they are actually unnecessary for causality in quantum mechanics. The key is to make the difference between two concepts as we talk about randomness: causation and prediction. So it was impossible to predict the fate of Schrödinger’s cat, because that depended on the decay of a nucleus, which was physically unpredictable. But that doesn’t mean it was not caused: on the contrary, in quantum mechanics, the measurement itself gives us the deterministic cause. It is always possible to explain an event with classical physics after the fact, even if it is impossible before the fact.

Conclusions

1. Life is both random (hard for us to predict) and destined (there is no effect without cause)

I must admit that I have an ideological agenda in this. I want to believe that life is not “random”. All events in a person’s life happen only once: the fact that we can’t predict the future is because of our ignorance, not because the future is meaningless. And although we can learn a lot from statistics of a population, it does not show us the secrets of our own lives.

2. Proving the individuality of popcorn

All this gave me inspiration for an experiment to follow “popcorn decay”. Instead of showing that popcorn popping (like nuclei decay) is random, I would like to investigate whether popcorn popping can be predicted. Using as reproducible a heating method as I can find, and using a single variety of popcorn from a single batch, I’ll also measure everything that’s easy to measure about each kernel before popping it: weight, dimensions, density, color, defects, etc, and see how much of the variation in popping time can be explained by these parameters, using simple machine learning – i.e. multivariate linear regression.

Afterword: the meaning of a popcorn’s life

A criticism of all this discussion, normally given to me by scientists, is that all this is of no practical value. Popcorn individuality is therefore to be scorned in the same way that scientists are supposed to scorn all discussions of philosophy or faith. Belief in “truth” is useless – all we need is a practical theory, not “truth”.

Regarding the practical point of view, I have an additional perspective. I have been out of academic science now for 6 years, and I’m a start-up entrepreneur. I can say from experience that belief is very important practically for an entrepreneur: I have to have complete faith in the purpose of what I’m doing, because if I don’t, nobody else will, not investors, not colleagues, not customers. But isn’t doing science also an enterprise? Looking back, I feel that doing science requires conviction, even if the character of natural science itself is only to offer observable explanations, and not a deeper truth.