Neuro-Techno-Philosophy

What I found quite compelling is how he frames the evolving relationship between philosophy and science. According to him, rapid advances in neuroscience and technology, such as human enhancement, AI, and the emergence of human-machine hybrids, are not just changing our world, they are also challenging the very foundations of how we understand human nature. As a result, philosophers should also

Take the debate between free active inference and embodies enactivist approaches in neuroscience.

What is "free active inference"? Neither would I say these views are having a debate; they are perfectly mutually consistent. One thing I like about active inference is that emphasis on formality helps clarify, imo, some of these philosophical positions on biology that are often stated by other authors in ways which can comes across as vague or imprecise or over-inflated.

The fact that fep can apply to anything also is perfectly consistent with embodied and extended cognition, especially when you consider that the enactive nature of fep as instantiated by Markov blankets with active states can be nested - i.e. Markov Blankets within Markov blankets - cells, neural populations, brains, social systems, eco-niches, etc, etc.

In the contrasts between Predictive coding Predictive processing and Predictive engagement, there are not just vocabulary differences; there is a basic conceptual problem about how the different models understand brain function. These seem to be philosophical differences more than neuroscientific ones—differences that concern our understanding of concepts like representation, inference, embodiment, engagement, attunement, affordance, etc. We think that further progress on these issues will depend to some extent on sorting out the very basic issue of defining the unit of explanation (brain vs. brain-body-environment) and controversies about the vocabulary of the explanans. If, for example, for an enactivist PE account, inference and representation are not key parts of the explanans, it needs to provide further explanation of how precisely processes like adjustment, attunement, and accommodation work as part of predictive engagement.

Notwithstanding the strong contrasts, there is clearly some common ground on which the differences among predictive models can be clarified (Active inference, enactivism and the hermeneutics of social cognition, Shaun Gallagher and Micah Allen 2016).

This turns so much of 20th century analytic philosophy on it head (goodbye later Wittgenstein, goodbye Dummett).

Back in 1938, Bertrand Russell wrote: “love of power, like lust, is such a strong motive that it influences men’s actions more than they think it should”, and that “the psychological conditions for the taming of power are in some ways the most difficult”. Contemporary neuroscience has demonstrated this in scientific terms, showing how power is neurochemically represented in the brain through a release of dopamine, the same neurochemical involved in the reward circuitry and largely associated with generating the feeling of pleasure, and the motivation to repeat those actions that are conducive to dopamine releases. In other words, power-seeking is akin to other addictive processes, producing ‘cravings’ at the neurocellular level and generating a high much like other drugs. Power, including political power, therefore, will lead to an increase in dopamine levels, which will make those in positions of power to do anything to maintain or enhance their powers.

Wikipedia:Early work on Frames was inspired by psychological research going back to the 1930s that indicated people use stored stereotypical knowledge to interpret and act in new cognitive situations.[11] The term Frame was first used by Marvin Minsky as a paradigm to understand visual reasoning and natural language processing.[12] In these and many other types of problems the potential solution space for even the smallest problem is huge. For example, extracting the phonemes from a raw audio stream or detecting the edges of an object. Things that seem trivial to humans are actually quite complex. In fact, how difficult they really were was probably not fully understood until AI researchers began to investigate the complexity of getting computers to solve them.

The initial notion of Frames or Scripts as they were also called is that they would establish the context for a problem and in so doing automatically reduce the possible search space significantly.

Anyway it seems to me that the psychologists in the 1930s, later Wittgenstein in the 1940s and 1950s, AI researchers in the 1970s, and Fedorenko in the 2020s are all broadly compatible. I emphasise broadly: I'm sure there are many devils in the details.