I keep working on the collective intelligence of humans – which, inevitably, involves working on my intelligent cooperation with other people – in the context of the COVID-19 pandemic. I am focusing on one particular survival strategy which we, Europeans, developed over centuries (I can’t speak for them differently continental folks): the habit of hanging out in relatively closed social circles of knowingly healthy people.
The social logic is quite simple. If I can observe someone for many weeks and months, in a row, I sort have an eye for them. After some time, I know whom that person hangs out with, I can tell when they look healthy, and, conversely, when they look like s**t, hence suspiciously. If I concentrate my social contacts in a circle made of such people, then, even in the absence of specific testing for pathogens, I increase my own safety, and, as I do so, others increase their safety by hanging out with me. Of course, epidemic risk is still there. Pathogens are sneaky, and Sars-Cov-2 is next level in terms of sneakiness. Still, patient, consistent observation of my social contacts, and just as consistent making of a highly controlled network thereof, is a reasonable way to reduce that risk.
That pattern of closed social circles has abundant historical roots. Back in the day, even as recently as in the first half of the 20th century, European societies were very clearly divided in two distinct social orders: that of closed social circles which required introduction, prior to letting anyone in, on the one hand, and the rest of the society, much less compartmentalised. The incidence of infectious diseases, such as tuberculosis or typhoid, was much lower in the former of those social orders. As far as I know, many developing countries, plagued by high incidence of epidemic outbreaks, display such a social model even today.
As I think of it, the distinction between immediate social environment, and the distant one, common in social sciences, might have its roots in that pattern of living in closed social circles made of people whom we can observe on a regular basis. In textbooks of sociology, one can find that statement that the immediate social environment of a person makes usually 20 ÷ 25 people. That might be a historically induced threshold of mutual observability in a closed social circle.
I remember my impressions during a trip to China, when I was visiting the imperial palace in Beijing, and then several Buddhist temples. Each time, the guide was explaining a lot of architectural solutions in those structures as defences against evil spirits. I perceive Chinese people as normal, in the sense they don’t exactly run around amidst paranoid visions. Those evil spirits must have had a natural counterpart. What kind of evil spirit can you shield against by making people pass, before reaching your room, through many consecutive ante rooms, separated by high doorsteps and multi-layered, silk curtains? I guess it is about the kind of evil spirit we are dealing with now: respiratory infections.
I am focusing on the contemporary application of just those two types of anti-epidemic contrivances, namely that of living in close social circles, and that of staying in buildings structurally adapted to shielding against respiratory infections. Both are strongly related to socio-economic status. Being able to control the structure of your social circle requires social influence, which, in turn, and quite crudely, means having the luxury to wait for people who gladly comply with the rules in force inside the circle. I guess that in terms of frequency, our social relations are mostly work-related. The capacity to wait for the sufficiently safe social interactions, in a work environment, means either a job which I can do remotely, like home office, or a professional position of power, when I can truly choose whom I hang out with. If I want to live in an architectural structure with a lot of anterooms and curtains, to filter people and their pathogens, it means a lot of indoor space used just as a filter, not as habitat in the strict sense. Who pays for that extra space? At the end of the day, sadly enough, I do. The more money I have, the more of that filtering architectural space I can afford.
Generally, epidemic protection is costly, and, when used on a regular basis across society, that protection is likely to exacerbate the secondary outcomes of economic inequalities. By the way, as I think about it, the relative epidemic safety we have been experiencing in Europe, roughly since the 1950ies, could be a major factor of another subjective, collective experience, namely that of economic equality. Recently, in many spots of the social space, voices have been rising and saying that equality is not equal enough. Strangely enough, since 2016, we have a rise in mortality among adult males in high-income countries (https://data.worldbank.org/indicator/SP.DYN.AMRT.MA). Correlated? Maybe.
Anyway, I have an idea. Yes, another one. I have an idea to use science and technology as parents to a whole bunch of technological babies. Science is the father, as it is supposed to give packaged genetic information, and that information is the stream of scientific articles on the topic of epidemic safety. Yes, a scientific article can be equated to a spermatozoid. It is relatively small a parcel of important information. It should travel fast but usually it does not travel fast enough, as there is plenty of external censors who cite moral principles and therefore prevent it from spreading freely. The author thinks it is magnificent, and yet, in reality, it is just a building block of something much bigger: life.
Technology is the mother, and, as it is wisely stated in the Old Testament, you’d better know who your mother is. The specific maternal technology here is Artificial Intelligence. I imagine a motherly AI which absorbs the stream of scientific articles on COVID and related subjects, and, generation after generation, connects those findings to specific technologies for enhanced epidemic safety. It is an artificial neural network which creates and updates semantic maps of innovation. I am trying to give the general idea in the picture below.
An artificial neural network is a sequence of equations, at the end of the day, and that sequence is supposed to optimize a vector of inputs so as to match with an output. The output can be defined a priori, or the network can optimize this one too. All that optimization occurs as the network produces many alternative versions of itself and tests them for fitness. What could be those different versions in this case? I suppose each such version would consist in a logical alignment of the type ‘scientific findings <> assessment of risk <> technology to mitigate risk’.
Example: article describing the way that Sars-Cov-2 dupes the human immune system is associated with the risk generated once a person has been infected, and can be mitigated by proper stimulation of our immune system before the infection (vaccine), or by pharmaceuticals administered after the appearance of symptoms (treatment). Findings reported in the article can: a) advance completely new hypotheses b) corroborate existing hypotheses or c) contradict them. Hypotheses can have a strong or a weak counterpart in existing technologies.
The basic challenge I see for that neural network, hence a major criterion of fitness, is the capacity to process scientific discovery as it keeps streaming. It is a quantitative challenge. I will give you an example, with the scientific repository Science Direct (www.sciencedirect.com ), run by the Elsevier publishing group. I typed the ‘COVID’ keyword, and run a search there. In turns out 28 680 peer-reviewed articles have been published this year, just in the journals that belong to the Elsevier group. It has been 28 680 articles over 313 days since the beginning of the year (I am writing those words on November 10th, 2020), which gives 91,63 articles per day.
On another scientific platform, namely that of the Wiley-Blackwell publishing group (https://onlinelibrary.wiley.com/), 14 677 articles and 47 books have been published on the same topic, i.e. The Virus, which makes 14 677/313 = 46,9 articles per day and a new book every 313/47 = 6,66 days.
Cool. This is only peer-reviewed staff, sort of the House of Lords in science. We have preprints, too. At the bioRχiv platform (https://connect.biorxiv.org/relate/content/181 ), there has been 10 412 preprints of articles on COVID-19, which gives 10 412/313 = 33,3 articles per day.
Science Direct, Wiley-Blackwell, and bioRχiv taken together give 171,8 articles per day. Each article contains an abstract of no more than 150 words. The neural network I am thinking about should have those 150-word abstract as its basic food. Here is the deal. I take like one month of articles, thus 30*171,8*150 = 773 100 words in abstracts. Among those words, there are two groups: common language and medical language. If I connect that set of 773 100 words to a digital dictionary, such as Thesaurus used in Microsoft Word, I can kick out the common words. I stay with medical terminology, and I want to connect it to another database of knowledge, namely that of technologies.
You know what? I need to take on something which I should have been taken on already some time ago, but I was too lazy to do it. I need to learn programming, at least in one language suitable for building neural networks. Python is a good candidate. Back in the day, two years ago, I had a go at Python but, idiot of me, I quit quickly. Well, maybe I wasn’t as much of an idiot as I thought? Maybe having done, over the last two years, the walkabout of logical structures which I want to program has been a necessary prelude to learning how to program them? This is that weird thing about languages, programming or spoken. You never know exactly what you want to phrase out until you learn the lingo to phrase it out.
Now, I know that I need programming skills. However strong I cling to Excel, it is too slow and too clumsy for really serious work with data. Good. Time to go. If I want to learn Python, I need an interpreter, i.e. a piece of software which allows me to write an algorithm, test it for coherence, and run it. In Python, that interpreter is commonly called ‘Shell’, and the mothership of Python, https://www.python.org/ , runs a shell at https://www.python.org/shell/ . There are others, mind you: https://www.programiz.com/python-programming/online-compiler/ , https://repl.it/languages/python3 , or https://www.onlinegdb.com/online_python_interpreter .
I am breaking down my research with neural networks into partial functions, which, as it turns out, sum up my theoretical assumptions as regards the connection between artificial intelligence and the collective intelligence of human societies. First things first, perception. I use two types of neural networks, one with real data taken from external databases and standardized over respective maxima for individual variables, another one with probabilities assigned to arbitrarily defined phenomena. The first lesson I need to take – or rather retake – in Python is about the structures of data this language uses.
The simplest data structure in Python is a list, i.a. a sequence of items, separated with commas, and placed inside square brackets, e.g. my_list = [1, 2, 3]. My intuitive association with lists is that of categorization. In the logical structures I use, a list specifies phenomenological categories: variables, aggregates (e.g. countries), periods of time etc. In this sense, I mostly use fixed, pre-determined lists. Either I make the list of categories by myself, or I take an existing database and I want to extract headers from it, as category labels. Here comes another data structure in Python: a tuple. A tuple is a collection of data which is essentially external to the algorithm at hand, immutable, and it can be unpacked or indexed. As I understand, and I hope I understand it correctly, any kind of external raw data I use is a tuple.
Somewhere between a tuple (collection of whatever) and a list (collection of categories), Python distinguishes sets, i.e. unordered collections with no duplicate elements. When I transform a tuple or a list into a set, Python kicks out redundant components.
Wrapping it partially up, I can build two types of perception in Python. Firstly, I can try and extract data from a pre-existing database, grouping it into categories, and then making the algorithm read observations inside each category. For now, the fastest way I found to create and use databases in Python is the sqlite3 module (https://www.tutorialspoint.com/sqlite/sqlite_python.htm ). I need to work on it.
I can see something like a path of learning. I mean, I feel lost in the topic. I feel it sucks. I love it. Exactly the kind of intellectual challenge I needed.
