I am changing the path of my writing, ‘cause real life knocks at my door, and it goes ‘Hey, scientist, you economist, right? Good, ‘cause there is some good stuff, I mean, ideas for business. That’s economics, right? Just sort of real stuff, OK?’. Sure. I can go with real things, but first, I explain. At my university, I have recently taken on the job of coordinating research projects and finding some financing for them. One of the first things I did, right after November 1st, was to send around a reminder that we had 12 days left to apply, with the Ministry of Science and Higher Education, for relatively small grants, in a call titled ‘Students make innovation’. Honestly, I was expecting to have 1 – 2 applications max, in response. Yet, life can make surprises. There are 7 innovative ideas in terms of feedback, and 5 of them look like good material for business concepts and for serious development. I am taking on giving them a first prod, in terms of business planning. Interestingly, those ideas are all related to medical technologies, thus something I have been both investing a lot in, during 2020, and thinking a lot about, as a possible path of substantial technological change.
I am progressively wrapping my mind up around ideas and projects formulated by those students, and, walking down the same intellectual avenue, I am making sense of making money on and around science. I am fully appreciating the value of real-life experience. I have been doing research and writing about technological change for years. Until recently, I had that strange sort of complex logical oxymoron in my mind, where I had the impression of both understanding technological change, and missing a fundamental aspect of it. Now, I think I start to understand that missing part: it is the microeconomic mechanism of innovation.
I have collected those 5 ideas from ambitious students at Faculty of Medicine, in my university:
>> Idea 1: An AI-based app, with a chatbot, which facilitates early diagnosis of cardio-vascular diseases
>> Idea 2: Similar thing, i.e. a mobile app, but oriented on early diagnosis and monitoring of urinary incontinence in women.
>> Idea 3: Technology for early diagnosis of Parkinson’s disease, through the observation of speech and motor disturbance.
>> Idea 4: Intelligent cloud to store, study and possibly find something smart about two types of data: basic health data (blood-work etc.), and environmental factors (pollution, climate etc.).
>> Idea 5: Something similar to Idea 4, i.e. an intelligent cloud with medical edge, but oriented on storing and studying data from large cohorts of patients infected with Sars-Cov-2.
As I look at those 5 ideas, surprisingly simple and basic association of ideas comes to my mind: hierarchy of interest and the role of overarching technologies. It is something I have never thought seriously about: when we face many alternative ideas for new technologies, almost intuitively we hierarchize them. Some of them seem more interesting, some others are less. I am trying to dig out of my own mind the criteria I use, and here they are: I hierarchize with the expected lifecycle of technology, and the breadth of the technological platform involved. In other words, I like big, solid, durable stuff. I am intuitively looking for innovations which offer a relatively long lifecycle in the corresponding technology, and the technology involved is sort of two-level, with a broad base and many specific applicational developments built upon that base.
Why do I take this specific approach? One step further down into my mind, I discover the willingness to have some sort of broad base of business and scientific points of attachment when I start business planning. I want some kind of horizon to choose my exact target on. The common technological base among those 5 ideas is some kind of intelligent digital cloud, with artificial intelligence learns on the data that flows in. The common scientific base is the collection of health-related data, including behavioural aspects (e.g. sleep, diet, exercise, stress management).
The financial context which I am operating in is complex. It is made of public financial grants for strictly speaking scientific research, other public financing for projects more oriented on research and development in consortiums made of universities and business entities, still a different stream of financing for business entities alone, and finally private capital to look for once the technology is ripe enough for being marketed.
I am operating from an academic position. Intuitively, I guess that the more valuable science academic people bring to their common table with businesspeople and government people, the better position those academics will have in any future joint ventures. Hence, we should max out on useful, functional science to back those ideas. I am trying to understand what that science should consist in. An intelligent digital cloud can yield mind-blowing findings. I know that for a fact from my own research. Yet, what I know too is that I need very fundamental science, something at the frontier of logic, philosophy, mathematics, and of the phenomenology pertinent to the scientific research at hand, in order to understand and use meaningfully whatever the intelligent digital cloud spits back out, after being fed with data. I have already gone once through that process of understanding, as I have been working on the application of artificial neural networks to the simulation of collective intelligence in human societies. I had to coin up a theory of intelligent structure, applicable to the problem at hand. I believe that any application of intelligent digital cloud requires assuming that whatever we investigate with that cloud is an intelligent structure, i.e. a structure which learns by producing many alternative versions of itself, and testing them for their fitness to optimize a given desired outcome.
With those medical ideas, I (we?) need to figure out what the intelligent structure in action is, how can it possibly produce many alternative versions of itself, and how those alternative thingies can be tested for fitness. What we have in a medically edged digital cloud is data about a population of people. The desired outcome we look for is health, quite simply. I said ‘simply’? No, it was a mistake. It is health, in all complexity. Those apps our students want to develop are supposed to pull someone out of the crowd, someone with early symptoms which they do not identify as relevant. In a next step, some kind of dialogue is proposed to such a person, sort of let’s dig a bit more into those symptoms, let’s try something simple to treat them etc. The vector of health in that population is made, roughly speaking, of three sub-vectors: preventive health (e.g. exercise, sleep, stop eating crap food), effectiveness of early medical intervention (e.g. c’mon men, if you are 30 and can’t have erection, you are bound to concoct some cardio-vascular s**t), and finally effectiveness of advanced medicine, applied when the former two haven’t worked.
I can see at least one salient, scientific hurdle to jump over: that outcome vector of health. In my own research, I found out that artificial neural networks can give empirical evidence as for what outcomes we are really actually after, as collectively intelligent a structure. That’s my first big idea as regards those digital medical solutions: we collect medical and behavioural data in the cloud, we assume that data represents experimental learning of a collectively intelligent social structure, and we make the cloud discover the phenomena (variables) which the structure actually optimizes.
My own experience with that method is that societies which I studied optimize outcomes which look almost too simplistic in the fancy realm of social sciences, such as the average number of hours worked per person per year, the average amount of human capital per person, measured as years of education before entering the job market, or price index in exports, thus the average price which countries sell their exports at. In general, societies which I studied tend to optimize structural proportions, measurables as coefficients in the lines of ‘amount of thingy one divided by the amount of thingy two’.
Checkpoint for business. Supposing that our research team, at the Andrzej Frycz – Modrzewski Krakow University, comes up with robust empirical results of that type, i.e. when we take a million of random humans and their broadly spoken health, and we assume they are collectively intelligent (I mean, beyond Facebook), then their collectively shared experimental learning of the stuff called ‘life’ makes them optimize health-related behavioural patterns A, B, and C. How can those findings be used in the form of marketable digital technologies? If I know the behavioural patterns someone tries to optimize, I can break those patterns down into small components and figure out a way to utilize the way to influence behaviour. It is a common technique in marketing. If I know someone’s lifestyle, and the values that come with it, I can artfully include into that pattern the technology I am marketing. In this specific case, it could be done ethically and for a good purpose, for a change. In that context, my mind keeps returning to that barely marked trend of rising mortality in adult males in high-income countries, since 2016 (https://data.worldbank.org/indicator/SP.DYN.AMRT.MA). WTF? We’ll live, we’ll see.
The understanding of how collective human intelligence goes after health could be, therefore, the kind of scientific bacon our university could bring to the table when starting serious consortial projects with business partners, for the development of intelligent digital technologies in healthcare. Let’s move one step forward. As I have been using artificial neural network in my research on what I call, and maybe overstate as collective human intelligence, I have been running those experiments where I take a handful of behavioural patterns, I assign them probabilities of happening (sort of how many folks out of 10 000 will endorse those patterns), and I treat those probabilities as instrumental input in the optimization of pre-defined social outcomes. I was going to forget: I add random disturbance to that form of learning, in the lines of the Black Swan theory (Taleb 2007; Taleb & Blyth 2011).
I nailed down three patterns of collective learning in the presence of randomly happening s**t: recurrent, optimizing, and panic mode. The recurrent pattern of collective learning, which I tentatively expect to be the most powerful, is essentially a cycle with recurrent amplitude of error. We face a challenge, we go astray, we run around like headless chickens for a while, and then we figure s**t out, we progressively settle for solutions, and then the cycle repeats. It is like everlasting learning, without any clear endgame. The optimizing pattern is something I observed when making my collective intelligence optimize something like the headcount of population, or the GDP. There is a clear phase of ‘WTF!’(error in optimization goes haywire), which, passing through a somehow milder ‘WTH?’, ends up in a calm phase of ‘what works?’, with very little residual error.
The panic mode is different from the other two. There is no visible learning in the strict sense of the term, i.e. no visible narrowing down of error in what the network estimates as its desired outcome. On the contrary, that type of network consistently goes into the headless chicken mode, and it is becoming more and more headless with each consecutive hundred of experimental rounds, so to say. It happens when I make my network go after some very specific socio-economic outcomes, like price index in capital goods (i.e. fixed assets) or Total Factor Productivity.
Checkpoint for business, once again. That particular thing, about Black Swans randomly disturbing people in their endorsing of behavioural patterns, what business value does it have in a digital cloud? I suppose there are fields of applied medical sciences, for example epidemiology, or the management of healthcare systems, where it pays to know in advance which aspects of our health-related behaviour are the most prone to deep destabilization in the presence of exogenous stressors (e.g. epidemic, or the president of our country trending on Tik Tok). It could also pay off to know, which collectively pursued outcomes act as stabilizers. If another pandemic breaks out, for example, which social activities and social roles should keep going, at all price, on the one hand, and which ones can be safely shut down, as they will go haywire anyway?
 Taleb, N. N. (2007). The black swan: The impact of the highly improbable (Vol. 2). Random house.
 Taleb, N. N., & Blyth, M. (2011). The black swan of Cairo: How suppressing volatility makes the world less predictable and more dangerous. Foreign Affairs, 33-39.