Artificial Intelligence, Chaos, Prediction and Understanding in Science

Sanjuán, Miguel A. F.

doi:10.1142/s021812742150173x

Cited by 10 publications

(4 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But AGI is not around the corner, and we are not alone with this assessment. The limits of current AI applications have been questioned by others, emphasizing that these systems lack autonomy and understanding capabilities, which we conversely find in natural intelligence (Nguyen et al, 2015;Broussard, 2018;Hosni and Vulpiani, 2018;Marcus and Davis, 2019;Mitchell, 2019;Roitblat, 2020;Sanjuán, 2021;Schneier, 2021). The true danger of AI lies in the social changes and the disenfranchisement of our own agency that we are currently effecting through targetspecific algorithms.…”

Section: Discussionmentioning

confidence: 95%

How Organisms Come to Know the World: Fundamental Limits on Artificial General Intelligence

2022

View full text Add to dashboard Cite

Artificial intelligence has made tremendous advances since its inception about seventy years ago. Self-driving cars, programs beating experts at complex games, and smart robots capable of assisting people that need care are just some among the successful examples of machine intelligence. This kind of progress might entice us to envision a society populated by autonomous robots capable of performing the same tasks humans do in the near future. This prospect seems limited only by the power and complexity of current computational devices, which is improving fast. However, there are several significant obstacles on this path. General intelligence involves situational reasoning, taking perspectives, choosing goals, and an ability to deal with ambiguous information. We observe that all of these characteristics are connected to the ability of identifying and exploiting new affordances—opportunities (or impediments) on the path of an agent to achieve its goals. A general example of an affordance is the use of an object in the hands of an agent. We show that it is impossible to predefine a list of such uses. Therefore, they cannot be treated algorithmically. This means that “AI agents” and organisms differ in their ability to leverage new affordances. Only organisms can do this. This implies that true AGI is not achievable in the current algorithmic frame of AI research. It also has important consequences for the theory of evolution. We argue that organismic agency is strictly required for truly open-ended evolution through radical emergence. We discuss the diverse ramifications of this argument, not only in AI research and evolution, but also for the philosophy of science.

show abstract

Section: Discussionmentioning

confidence: 95%

How Organisms Come to Know the World: Fundamental Limits on Artificial General Intelligence

2022

View full text Add to dashboard Cite

show abstract

“…Sanjuan affirmed in a recent work, "Machine learning and deep learning techniques are contributing much to the advancement of science. Their powerful predictive capabilities appear in numerous disciplines, including chaotic dynamics, but they miss understanding" [30] (pp. [1][2][3][4][5].…”

Section: Computing and Chaosmentioning

confidence: 99%

Overview and Perspectives of Chaos Theory and Its Applications in Economics

Fernández-Díaz

2023

Mathematics

View full text Add to dashboard Cite

Starting from the contribution of such thinkers as the famous Giordano Bruno (1583) and the great mathematician and physicist Henri Poincaré (1889) and the surprising discovery of the meteorologist Edward Lorenz (1963), we consider the expansion of the mathematics of chaos in this article, paying attention to topology, qualitative geometry, and Catastrophe Theory, on the one hand, and addressing the possibilities derived from the new Computer Science as Quantum Algorithms and the advances in Artificial Intelligence, on the other. We especially highlight the section on computing chaos, which we consider to be new calculation and analysis instruments, such as machine learning and its algorithm called reservoir computing, through which we can know the dynamics of a chaotic system. With past data, with equations like Karamoto–Sivashinsky, one can improve predictions of the system eight times further ahead than in previous methods. Integrating the machine learning approach and traditional model-based prediction, one could obtain accurate predictions twelve Lyapunov times. As we know, in the framework of chaos theory, it is habitually accepted that the idea of long-term prediction seems impossible because we live under a veil of uncertainty. But with technological advances, the landscape begins to change, both in chaos theory and in its applications, especially in the field of economics, to which we devote particular attention, carrying out as an example the analysis of the evolution of the Madrid Stock Exchange in the 2006–2013 crisis. Above all this, a reflection of a general nature is necessary to enlighten us on the possibility of opening a new horizon.

show abstract

“…Now understanding M.A.F. Sanjuán [10] among others has discussed the status of 'understandability' . is one of the 'philosophical' concepts which involves introspective reference to the functioning of the human brain.…”

Section: What Is Human Mathematics?mentioning

confidence: 99%

The nature of properly human mathematics

Ruelle

2023

Adv Cont Discr Mod

View full text Add to dashboard Cite

We claim that human mathematics is only a limited part of the consequences of the chosen basic axioms. Properly human mathematics varies with time but appears to have universal features which we try to analyse. In particular, the functioning of the human brain privileges concept naming and short formulations. This leads to organising mathematical knowledge structurally. We consider briefly the problem of non-mathematical sciences.

show abstract

Artificial Intelligence, Chaos, Prediction and Understanding in Science

Cited by 10 publications

References 42 publications

How Organisms Come to Know the World: Fundamental Limits on Artificial General Intelligence

How Organisms Come to Know the World: Fundamental Limits on Artificial General Intelligence

Overview and Perspectives of Chaos Theory and Its Applications in Economics

The nature of properly human mathematics

Contact Info

Product

Resources

About