Breathe
By Pola Strauss
Day by day the discussion about the relevance in multiple research fields regarding the impact and importance of artificial intelligence (AI) grows stronger. Over time, humanity has undergone multiple large-scale transformations that have caused revolutions in more than one industry.
These technologies are essential for scientists to understand the phenomena they study and thus see beyond what they can only see with the naked eye. Hence, within the international debate on AI, there are genuine concerns about the potential dangers of the massive adoption of algorithms and how they could cause the disappearance of different jobs, including many of them trained on data with discrimination biases that could make this selective process even more regressive. However, hand in hand with my background as an economist, I think of this adoption from a risk vs. return approach. Therefore, if in addition to risks we think about returns, AI can help humanity to solve highly complex problems, shortening the time it takes to execute certain processes in multiple fields of science and other sectors. So the relevant question for me is: Can AI unusually accelerate humanity's scientific progress and usher us into a new golden age of discovery?
AI algorithms learn and use large volumes of information and apply them in a variety of ways including:
i. perform specific tasks and decision making functions;
ii. predict behaviors and make suggestions based on historical and current data;
iii. interact with people in an intelligent manner and, ideally, without the biases associated with data manipulation.
In this way, the use of AI will make processes and tasks more efficient by decreasing their time, improving their planning and prediction with more accurate results, as well as increasing the productivity and satisfaction of the members of a work team.
Under this premise, the relevant question becomes: Are all companies in all industries ready to ride the wave of fervor presented by AI? The answer is no. Even within each sector, different companies stand out from their competitors, as they are affected by different circumstances such as idiosyncratic factors, hence a relatively standardized algorithm requires some specific adjustments to achieve generalization. In my experience, adoption starts from the minimum order and structure required by the information, a transition that metaphorically would be equivalent to moving from a dirt road to an asphalt road. Only through an adequate environment is it possible to reach new latitudes with the efficient use of the technological resources available to them.
The transition will take time, but it is expected to eventually materialize, because the more AI tools are applied to a process, the more their own architecture will allow them to generalize to more diverse and changing environments. More structured data with greater variety leads to improved predictive capability of algorithms. With better trained algorithms, it is possible to provide better service, which is key to a company's success. These, in turn, lead to more and better use of tools, which should help close the virtuous cycle.
For those of us who have decided to undertake in this sector, contributing from the private sector to the AI revolution, the advantage of those who lead the adoption will be fundamental. At Daat Analytics we are convinced that the faster the implementation of AI in an organization to provide solutions to its problems, its execution will increase in pace and with it its predictive capacity. It is to be expected that over time the success of certain companies will grow exponentially; it is also to be expected that the risks for their competitors of not adopting these tools will increase.
In the past, the irruption of various technological changes has led to various errors, such as falsely claiming them as panaceas or attributing to them side effects that were not observed. Suffice it to recall that in its time the electric telegraph was praised in the 1850s as a herald of world peace, as were airplanes in the 20th century and later useful tools for aggressors and aggressed in the great wars of the last century. Different experts in the 1990s mentioned that the use of the Internet would reduce inequality and eradicate controls over various societies, something that happened in exactly the opposite direction. On the other hand, the mechanism by which AI could solve different problems in the world seems to have a more solid historical basis: under new approaches and tools it is possible to generate a boom of discoveries and scientific innovation that will change the world. It will be essential not to lose the realistic perspective.
We can find several examples of this type of AI breakthroughs, such as one that I personally find fascinating: literature-based discovery (LBD), which analyzes existing scientific literature, using large language models (LLM), which discover new hypotheses, connections or ideas based on their training with a processing capacity superior to that of humans. This tool's goal is to identify new experiments to be tested and even suggest possible research collaborations. This could boost interdisciplinary work and foster innovation across fields. It is hard to believe that such systems can identify "blind spots" in a given field and even predict future discoveries and who will make them. It sounds like science fiction; however, science is already evolving in this direction, fiction would be to deny it.
Therefore, the idea that AI could transform and accelerate certain processes in different areas is now a reality. However, there is a sociological barrier: all this transformation can happen if, as a human species, we are willing to implement it in an intelligent and sustainable way. Having a huge majority of people who will require education and training to know these tools better, the big focus is on the fear that causes some jobs to disappear, but I would bet on stating on the question that gives title to this article, "Can you really show an old dog new tricks?"
The transition that AI is undergoing for now is gradually moving from being a utopia (machine learning) to a reality for a still limited percentage of the population, generally specialists in various scientific fields.
Governments can collaborate by funding more research on the integration of AI in different productive sectors that are not exclusively for the private sector. If this is achieved, by including this type of tools, humanity will experience changes of proportions comparable to the Industrial Revolution, because of the technological change, but it should also be a new Enlightenment, because of the humanistic perspective that we should imbue it with.
To use a trite phrase dating back to June 1980: "If Japan Can...Why Can't We", if not before Silverpreneur has decided to enter fully into this revolution, what are you going to do about it?
The opinions expressed are the responsibility of the authors and are absolutely independent of the position and editorial line of the company. Opinion 51.
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