Confessions of a Biological Scientist – Part II: The Organic Engine of Science

Scientific advancement is the greatest endeavour that humans have ever undertaken, but this is not to say it is perfect. Science as a system was evolved, not designed, and thus it suffers all of the warts, inefficiencies and limitations of any organic system. In the near future something as important as science will no longer be left to imperfect and inefficient biological scientists, but will become be the realm of digital scientists. In this second part of Confessions of a Biological Scientist, I will discuss the imperfections of the scientific system as and why artificial intelligence may be necessary to overcome our collective limitations. 

Despite what the current dogma of scientific boosterism might say about it, I do not believe that humans are born as scientists. Yes, we are naturally curious. Yes, we naturally ask questions and recognize patterns. But taking these raw abilities and turning them into scientific thought requires a set of tools that are not necessarily natural to the human mind. It is for this reason that we have developed elaborate systems of education, research and publication in order to propagate scientific thought.

The modern system of scientific education is a long and arduous process. To become a scientist, one must first have a broad base of knowledge from which to build towards scientific thought. A good understanding of math, physics, and chemistry is an absolute must for any scientist. After this, a student must immerse themselves in a specific field, and eventually an even more specific discipline, for many years before they are adequately educated in the technical and academic knowledge necessary to work in a given field. All told, it takes at least 25 years and hundreds of thousands to turn a single curious child into a PhD graduate. 

While it does succeed in producing more new scientific grads every year, the scientific education system is far from efficient. In today’s world, the age at which a scientist can expect to get their first post as an independent academic researcher has been steadily increasing. As we pour an increasing amount of energy goes into training scientists, it would seem that the scientific education system as a whole is actually becoming less efficient over time. Is there any way this process will realistically be able to compete if an AI emerged with even a low level of scientific ability?

This process whereby a system can actually lose efficiency over time is a characteristic flaw of organic systems. If there is inadequate selective pressure to maintain or increase efficiency, then over time the system may tend towards inefficiency, accruing errors which are never eliminated. Just as the human population has accrued costly maladaptations over time such as poor eyesight, obesity, and other genetic diseases, the scienctific system also carries with it negative traits which are copied from generation to generation of scientists. 

And educational inefficiency is not the only, or even the worst, of these maladaptations found within scientific system.

If we hold that the ideal of science is the quest for pure truth, then it should be the ideas that best fit the data which are held to be the best. Unfortunately, this is not the case. Human communication has evolved from story-telling, and science is no exception. Scientists are often more interested with what provides the most captivating story and resonates with the current scientific paradigms rather than what is the simplest truth or best-fit model.

This tendency of scientists to converge on popular scientific notions is worsened by the publication arm of the scientific machine. Peer-reviewed scientific journals are ranked according to their impact factor, which is a metric based on the average number of citations that a paper published would get. Just as in politics or business, the goal of the science game is to be popular.

Journals want to publish work that is up to scientific rigor, yes, but even more importantly than this, they want to publish work that will be popular. But what determines what science is going to be most cited? It is not necessarily what is the best for the advancement of science, but simply what is most interesting to the scientists. This applies pressure to scientists to try to make scientific reports more interesting to the scientific audiences, skewing scientific writing towards grandstanding. On some level, science is simply a form of highly controlled entertainment, serving a very specific audience a very specific product.  

Whereas, science might be best served through pairing carefully observed data with simple conclusions and measured insights. All too often it is expected that scientific reports present striking new data with exciting conclusions and deep new insights. Rather than letting the data speak for itself, science must be packaged up neatly and sold one powerpoint slides and scientific manuscript at a time. 

This bombastic style leaves little room for unanswered questions, encouraging scientists to avoid discussing the potential pitfalls of their research, glossing over holes rather than addressing them. No longer it the scientist expected to impartially report data, but we must be salesmen shilling our own observations. We are encouraged to be as lawyers, advocating for our own stories in a battle between ideas.

Through this process of selling and re-selling science we are perpetuating the false perception that our scientific data is perfect, our conclusions unquestionable, and our insights complete. Yes, science is making steps forwards, but it is in shuffling steps not leaps and bounds. 

The funding system for science also further exacerbates the problems created by the scientific publication system, because it is in effect simply an extension of the scientific publication system. Grant applications are ranked by scientists on their merit, but merit is really a function of how well the ideas of the grant resonate with current scientific thinking, and how interesting the ideas are to the panel. The idea that a scientists could slave away for decades on a niche problem which may or may not be of interest to the scientific community at large seems a quaint idea of a lost generation.

In the end, we really lack of any objective measure for the value of a scientific idea. This means that it is the one with the best story who wins. Even with our shiny armour of raw skepticicsm, we are still just as vulnerable to a good story as the rest of humanity. In my mind this is the fundamental limit we face today, human science can only advance as quickly as scientific groupthink can haltingly step from one paradigm to the next (see Kuhn).

In the end, it may be that these negative traits are not just inefficiencies which can be cut away from the scientific system, but they may be an expression of our human imperfection. We are inefficient, political and error-prone beings, thus by extension our science, and our scientific systems are inescapably inefficient, political, and error-prone. Human science is an organic machine, complete with imperfections and limitations. 

Perhaps the only way to overcome the current limits to scientific advancement will be to remove the humans from science altogether. With the advancement of artificial intelligence it may soon be possible to create a new type of science, wherein our collective advancement is not limited to the whims of human minds. Next week I will discuss  the first baby steps of robot scientists and how I imagine these scientists will begin to replace us, and ultimately the entire scientific system over the next two decades.


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