The limits of biology

The limits of biology are practical, rather than theoretical, although some biological problems are so difficult, that it seems unlikely that we’ll be ever able to solve them. Among these problems, I will select the following:

·         The origin of life. The possibility of replicating an experiment is one of the fundamental principles of scientific method, as it is applied in the experimental sciences. No discovery is considered valid until it has been confirmed by an independent team. If an experiment cannot be replicated, it is not considered scientific.

The origin of life took place only once in the history of the Earth. Obviously, it cannot be replicated. Therefore, it is not a scientific fact, but a historical fact. Historic facts are treated in a different way than scientific facts: documents are sought that confirm that the fact did happen and describe how it happened. The credibility of these documents is then estimated. In the case of the origin of life, the documents would be fossil remains, but it’s practically impossible to find them, so it’s very likely that this problem will never be solved.

The limits of physics

There are two kinds of limits in scientific research:

1.      Theoretical or intrinsic limits: when these limits exist, no matter how many scientific discoveries may be made in the future, they won’t be exceeded.

2.      Practical limits: they appear when, in theory, a problem can have a solution, but there are practical reasons that make it impossible, at least for the time being. In these cases, we cannot affirm that the problem won’t be solved in the future.

Sometimes we don’t know if a given limit is theoretical or practical. In these cases, what will happen in the future is open. If the limit turns out to be theoretical, it will never be exceeded. If it is practical, it will be exceeded if our technical capabilities exceed the technical needs for its resolution, being possible that this will never happen. Take, as an example, the inherently difficult math problems I mentioned in the previous post.

The limits of mathematics

Kurt Gödel

In the last decades of the nineteenth century, Friedrich Ludwig Gottlob Frege, a professor in the university of Vienna, undertook an ambitious goal: formalizing the arithmetic in a set of axioms and deduction rules, in such a way that every true theorem would be deductible from the axioms by a finite number of applications of the deduction rules. The result was a monumental book, Grundgesetze der Arithmetike (1893-1903), which introduced, among other things, a basic formalization of set theory and a cumbersome notation, quickly replaced by Peano’s, which we are using now.

Unfortunately for Frege, when the second volume of his book was about to be published, he received a letter from Bertrand Russell, proving that his formulation of set theory entails an inconsistency. In Frege’s set theory, some sets are not member of themselves (as the set of all integers, which is not an integer), while other sets are members of themselves (as the set of all infinite sets, which is an infinite set). Russell then defined this set: the set of all sets that are not members of themselves. It is easy to see that this set leads to a paradox: if it is a member of itself, it cannot be a member of itself, and vice versa. Russell’s paradox wreaked havoc with Frege’s work, who had to add a hasty appendix to his book and then abandoned his research on the fundamentals of mathematics.

Conscience, Self-conscience and Artificial Intelligence

Ramón López de Mántaras

In an article published on 3/22/2021, these words are attributed to Josu Bilbao, head of the ICT area of ​​IKERLAN:

In three years, artificial intelligence will make an intelligent dialogue with machines possible.

We have seen many similar predictions since the term Artificial Intelligence (AI) was invented over 60 years ago. In most cases (if not all) those predictions have been too optimistic. Is the same going to happen here?

I have consulted with one of the world's leading experts in the field of the automatic analysis of natural language, a technique used to implement the type of applications referred to in the article I am commenting, and he told me this:

It depends on how "intelligent dialogue" is defined. If it is restricted to a specific domain, for example, controlling your home automation systems using Google Home, that can be done now with a success rate above 70%, while ten years ago it was science fiction. If you want to have a philosophical conversation where your interlocutor understands what he is saying, rather than generating texts from a language model, this is too optimistic.