Presentation: Computational Foundations of Minds and the Universe

Wolfram's recent work on the foundations of physics, mathematics, biology and machine learning introduces a major new framework for thinking about fundamental philosophical questions.  This talk will provide a non-technical, philosophically oriented survey of these directions.
https://writings.stephenwolfram.com/category/philosophy/

Prof. Dr. Andrew Adamatzky
University of the West of England, Bristol, UK.

Presentation: Origin of Intelligence

What is intelligence, and how deeply is it rooted in the fabric of life itself? In this talk, I explore the fundamental emergence of intelligent behaviours far beyond the animal kingdom. Beginning with spiking electrical activity observed in slime moulds, plants, and fungi, I will demonstrate how simple biological networks exhibit complex decision-making and adaptive behaviours — without neurons or brains. These organisms challenge our traditional notions of cognition, revealing that intelligence may not require a nervous system at all. I will also present recent work from our laboratory where we build and study proto-brains: experimental ensembles of proteinoid microspheres that spontaneously generate spiking patterns, coordinate actions, and process environmental information. By examining these synthetic and natural minimal systems, we gain insights into the deep origins of intelligence, suggesting that cognition may emerge wherever matter organises to sense, decide, and act upon the world.

Prof. Dr. Selmer Bringsjord
Rensselaer Polytechnic Institute, Rensselaer Artificial Intelligence and Reasoning Laboratory, New York, USA

Presentation: Variegated Common Sense Versus the Science of Universal Intelligence, When the Aliens Arrive

Aliens have arrived on Earth. Two of them, of exceedingly strange appearance (floating, glowing orbs of bright light; nothing more) have emerged from a glistening, spherical spaceship whose only marking is “CTC3” and, hovering about six feet above the ground, speak this aloud to the UN Secretary General and five scientific advisors standing with him in New York City, in crisp English:

“We come in peace to quickly solve your biggest problems (silly wars and use of silly power sources being two of them), and then loop back home before going to yet another primitive planet in need of assistance. We are on a very tight schedule so as to hit three black holes in correct sequence. As an indicator of our coming handiwork, here” — a small book is handed over, along with a datastick — “is the cure for all forms of what you refer to as ‘cancer.’ The material also includes the number that is the \Sigma function applied to input 7, with an accompanying proof of correctness in the system your computational logicians know as ‘Athena.’ Now, so that we can serve you rapidly, please immediately grant us access to every corner of your world, including its cyber dimension. Our renovation should take at most one of your hours, after which we’ll be on our way. We await your rapid response, within one hour max. At the expiration of that period, if you decline, we shall depart immediately, leaving you to your own (primitive) devices. We are of course aware that you have hand-picked the quintet standing with you, so in the interests of efficiency and cleanliness, the one hour of deliberation should be confined to you and them, here, on the spot, in this very room, which is large enough for some side-meetings as needed. Of course, use of your little Internet during deliberation is permissible. The countdown starts … now.”

The science of universal intelligence (SUI) is a field that has arisen in the 21st century out of AI and — especially — AGI. It is concerned with theories providing a rigorous definition of and measurement scheme for intelligence in agents short of, at, and beyond the human level. These agents can be natural or artificial, or a mixture thereof. One such theory, from Marcus Hutter (2005) and Shane Legg (& Hutter) (2007), is their theory universal intelligence; it’s a purely numerical/

Keywords: Variegated Common Sense Versus the Science of Universal Intelligence, When the Aliens Arrive

Dr. David Gamez
Department of Computer Science at Middlesex University, London, UK.

Presentation: Intelligence and Consciousness in Natural and Artificial Systems

Considerable progress has been made with the development of systems that can drive cars, play games, predict protein folding and generate natural language. These systems are described as intelligent and there has been a great deal of talk about the rapid increase in artificial intelligence and its potential dangers. However, our theoretical understanding of intelligence and ability to measure it lag far behind our capacity for building systems that mimic intelligent human behaviour. There is no commonly agreed definition of the intelligence that AI systems are said to possess, nor has anyone developed a practical measure that would enable us to compare the intelligence of humans, animals and AIs on a single scale. This talk addresses these problems by clarifying the nature of intelligence and outlining a new algorithm for measuring intelligence that can be applied to any system.

The first part of the talk starts with a discussion of previous definitions of intelligence. It then argues for a close link between prediction and intelligence and addresses two misconceptions about intelligence. The first is that people often think that humans have a general form of intelligence that has the same level in all environments. This belief motivates the idea that we could develop machines with artificial general intelligence (AGI). However, human intelligence often fails when it is confronted with environments that are significantly different from the natural world, such as high-dimensional numerical spaces. A second issue is that people naively assume that they directly apply their intelligence to the physical world. However, we can only be intelligent about things that are revealed to us through our senses, and people, animals and artificial systems have very different sensory experiences. So, it is much more accurate to say that agents apply their intelligence to their perceived environment, or umwelt.

The second part of the talk explores the measurement of intelligence. Previous work in this area includes IQ, g and universal measures, such as compression tests and algorithms based on goals and rewards. To address the limitations of previous measures, I have developed a new algorithm for measuring predictive intelligence that is based on an agent’s internal state transitions. Experiments have been done to test this algorithm, and it has many potential applications in AI safety and the comparative study of intelligence.

The talk concludes with some reflections on the relationships between intelligence and consciousness. It is commonly assumed that there is a close relationship between intelligence and consciousness in biological systems, However, this correlation might not exist in artificial systems, who could be highly intelligent with low levels of consciousness, or highly conscious with low levels of intelligence. In the future we might be able to use algorithmic measures of consciousness, such as information integration theory (IIT), and universal measures of intelligence to systematically study the relationships between intelligence and consciousness in natural and artificial systems.

Keywords: Intelligence and Consciousness in Natural and Artificial Systems

Presentation: Mind everywhere: recognizing and communicating with unconventional intelligence

In this talk I will describe my framework for an engineering approach to recognizing, communicating with, and ethically relating to unconventional intelligences. I will begin with some conceptual clarification of intelligence and embodiment. I will then describe a number of surprising examples of intelligence, using the collective intelligence of cells navigating anatomical space as a model system, and show data on our approach to use the bioelectric interface as a means of communicating with the agential material of life. I will show how re-setting the memories, goals, and cognitive light cone of cell groups drives applications in birth defects, regenerative medicine, cancer, and bioengineering. I will describe our efforts to develop tools to expand our native mind-blindness to large regions of the cognitive spectrum, including the emergent cognition of very minimal models (both living and computational) whose capabilities are not explained by evolutionary history. I will end with some speculative implications of these ideas for the future of science and philosophy of mind.

Presentation: The Role of Eco-Cognitive Openness and Situatedness

I will use my studies on abductive intelligence in an eco-cognitive framework to demonstrate the concept of “locked and unlocked strategies” in deep learning systems, indicating different inference routines for creative results. Higher forms of creative abductive intelligence are involved in unlocked human cognition, whereas locked abductive strategies are characterized by weak hypothetical creative intelligence because of the absence of what I refer to as eco-cognitive openness and situatedness. The fundamental nature of the human brain as an open system that is continuously coupled with the environment - a so-called “open” or dissipative system - is the physical basis for this special type of “openness”. The brain's activity is the continuous attempt to achieve equilibrium with its environment, and this interaction can never be turned off without seriously harming the brain. It is impossible to imagine the brain lacking its physical essence, which is its openness. In the brain, ordering is the direct result of an “internal” open dynamical process of the system rather than being generated from the outside, as I have described in my latest book Eco-Cognitive Computationalism (2022), “computational domestication of ignorant entities”.

Assoc. Prof. Marcin Milkowski
Associate professor in the Institute of Philosophy and Sociology of the Polish Academy of Sciences, Warsaw, Poland

Presentation: No Intelligence without Representation

This talk focuses on the indispensable role of representation in constituting intelligence. Central to the inquiry into intelligence is understanding the relationship between mind, world, and action. I argue, first, that even basic embodied intelligence, often cited as evidence against representation, relies fundamentally on contentful states. Drawing on analyses of intentionality and procedural memory, skills are shown to possess directive content (a world-to-mind direction of fit) defined by satisfaction conditions. This representational layer is crucial for explaining the normativity, learning, and potential failures (e.g., apraxia) inherent in skilled action. Second, I argue that directive content alone is insufficient. Genuine intelligence requires the capacity for descriptive representation—states with a mind-to-world direction of fit, capable of being true or false in a way that corresponds to reality. This commitment to representational realism and truth is not merely a feature of high-level cognition but a foundational requirement for systems that can learn about, understand, and flexibly adapt to the complexities of the world beyond immediate interaction loops. Intelligence, therefore, is inextricably linked to the dual representational capacities to both shape the world according to goals and accurately reflect its state.

Prof. Dr. Vincent C. Müller
A. v. Humboldt Professor, Philosophy & Ethics of AI, University of Erlangen-Nürnberg, Germany

Presentation: AI Philosophy: A New Philosophical Method (Vincent C. Müller with Guido Löhr)

On the background of the general problem of philosophical methodology, we identify a new tool for the philosophical toolbox: AI. We propose that not only can AI learn from philosophy, but philosophy can learn from AI, too: It is both ways. This applies particularly to conceptual analysis, which can be advanced by asking what would be required for an AI system to fall under the concept we are discussing. This method it avoids anthropocentrism and gives us a new way of testing our philosophical theories. Given the wide range of features we can consider for AI systems, this method allows us to cover a wide range of philosophical issues, especially in the philosophy of mind, language, epistemology, and ethics. In the first section, we present two salient examples of this new method (consciousness and free will) and in the second section, we analyse what the method is. Finally, we defend this new method against anticipated objections.

Prof. Dr. Diane Proudfoot
Professor of Philosophy, University of Canterbury - TE WHARE WĀNANGA O WAITAHA, New Zealand.

Presentation: The concept of intelligence in Turing, Wittgenstein, and Anscombe

In discussing his imitation game, Alan Turing did not distinguish between intelligence and thinking: his test of intelligence in machines is also a ‘criterion for thinking’. Taking intelligence in this broad sense, I argue that a consistent approach to the concept of intelligence is developed in the work of Turing, Ludwig Wittgenstein, and Elizabeth Anscombe.

For this inquiry, three features of Turing’s proposal (that success in his imitation game is sufficient for thinking) are distinctive. First, he used the term ‘thinking’ as it was used in everyday conversation; he devoted considerable space to countering objections to the possibility that machines can think, in the ordinary sense of ‘think’. Second, he denied that, for a computer to be judged as intelligent, it is necessary that it have a certain architecture or internal construction—the machine’s success in the game is not nullified by observation of its innards. Third, according to Turing, the concept of intelligence is an ‘emotional concept’. I interpret an ‘emotional concept’ as, in modern terminology, a response-dependent concept: seeing a machine as intelligent is at least as important to its being intelligent as its processing speed, storage capacity, and suchlike.

In his later philosophy, Wittgenstein argued for the same approaches to the mind and added two further claims. In his view, fourth, the context of a behaviour determines its significance: if an entity’s behaviour fits into ‘the bustle of life’ in such a way that we are ‘inclined to speak of inner and outer human states’, we can say that the being thinks. And fifth, if we ask what are these ‘inner’ states of thinking (understanding, and so on), Wittgenstein suggests that that they are in some sense linguistic fictions—for example, when explicating mental content, he said ‘It is in language that it’s all done’.

Anscombe’s account of intention—which can plausibly be taken as a model for a more general account of the mind—shares at least four of the five positions just listed: it investigates the everyday concept, denies that intention is an ‘interior act’, emphasizes the role of context, and ‘define[s] intentional action in terms of language—the special question “Why?”’. Anscombe developed the idea of the concept of intention’s being language-dependent, in two ways: by providing a taxonomy of different senses of the ‘Why?’ question; and (in a separate discussion of intentional objects of thought) explicating an intentional object on the model of the direct object in grammar, and then arguing that questions about its ontological status are misconceived.

Combining these ingredients amounts to (a recipe for constructing) an approach to the concept of intelligence in the broad sense. Two questions arise: how might such an approach relate to more recent philosophical theories of intelligence? And, are there current debates for which this approach might be useful?

Keywords: The concept of intelligence in Turing, Wittgenstein, and Anscombe

Prof. Dr. Oron Shagrir
Vice-President for International Affairs; Schulman Chair in of Philosophy, The Hebrew University of Jerusalem, Israel

Presentation: The mathematical objection to artificial (machine) intelligence

Turing develops the idea of machine intelligence in a series of lectures and papers between 1947 and 1952. In some of them he addresses the mathematical objection (his term) whose gist is the claim that humans can assert some mathematical truths that exceed the abilities of computing machines. We first ask why Turing took so seriously the mathematical objection. After all, even if some humans surpass machines in their mathematical abilities, this by itself does not undermine the project of machine intelligence. Our answer is that the mathematical objection raises a dilemma with respect to Turing’s core claims about machine intelligence and forces him to relinquish at least one of them. We then clarify Turing’s reply to the mathematical objection. Based on the textual evidence, we argue that, according to Turing, the machine that plays against the human in the Turing test is not a static machine but an enhanced machine.

Keywords: The mathematical objection to artificial (machine) intelligence

Prof. Dr. Jordi Vallverdú
Professor & ICREA Acadèmia researcher, Universitat Autonoma de Barcelona, Catalonia, Spain

Presentation: Cognitive Romanticism: Humans Are Worse than Stochastic Parrots!

Most Humans Are Stochastic Parrots, and LLMs Reveal Our Intellectual Mediocrity. While critics often dismiss large language models (LLMs) as mere "stochastic parrots," I argue that this accusation misunderstands both machine intelligence and, more importantly, human cognition itself. Most human thought is not creative, critical, or unpredictable; it is rote, imitative, and driven by deeply ingrained social, religious, and cognitive biases. The dominance of myth, ideology, and irrational belief systems across cultures reveals that humans themselves function largely as stochastic parrots — endlessly repeating patterns they neither question nor understand. Rather than exposing the limitations of artificial intelligence, LLMs expose the uncomfortable truth about human intellectual mediocrity. In this talk, I will attack the myth of human cognitive exceptionalism, dismantle the romantic notions attached to human "creativity" and "autonomy," and propose a radical redefinition of intelligence beyond humanist illusions. Intelligence, whether in biological or artificial systems, must be seen not as the privilege of a superior species, but as an emergent property of patterned interaction with environments — often stochastic, occasionally innovative, but rarely transcendental.

Keywords: Cognitive Romanticism: Humans Are Worse than Stochastic Parrots!

Dr. Hector Zenil
Associate Professor / Senior Lecturer, School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences & Medicine & King’s Institute for Artificial Intelligence, King’s College London, UK.

Presentation: Why LLMs Can't Escape the Pattern-Matching Prison if They Don't Learn Recursive Compression

In this talk we will introduce and discuss SuperARC, a new proposed open-ended test based on algorithmic probability to critically evaluate claims of Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI), challenging standard metrics grounded in statistical compression and human-centric tests. By leveraging Kolmogorov complexity rather than Shannon entropy, the test measures fundamental aspects of intelligence, such as synthesis, abstraction, and planning or prediction. Comparing state-of-the-art Large Language Models (LLMs) against a hybrid neurosymbolic approach, we identify inherent limitations in LLMs, highlighting their incremental and fragile performance, and their optimisation primarily for human-language imitation rather than genuine model convergence. These results prompt philosophical reconsideration of how intelligence—both artificial and natural—is conceptualised and assessed.