Understanding computers and cognition

What Hacker News says

1. NobleLieApr 2026
   The boiling - cooking - is the bias here. Winograds Understanding Computers and Cognition is the most excellent resource in 2026, written over three decades ago by now.
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2. robwwilliamsJan 2025
   Yes! What fun to read and I think you are pointing in a smarter direction.

   Memory as recursion and the key to step toward AGI.

   The trick is recursive self-control of attention while being battered by 1001 input streams. Perhaps better to think about “input” as interrupt requests that a “self” must evaluate and usually ignore to stay on tasks: Making all of those sandwiches!

   Bodies may be essential soon along with all of the hard knocks of selection as motivation to memorize, learn, adapt.

   Here is a great book that Terry Winograd and Fernando Flores read very carefully:

   “Autopoiesis and Cognition: The Realization of the Living” by Maturana and Valera (1980).

   This intense book is an axiomatic approach to life and cognition. Enactivist philosophers are now extending their original insights:

   1: Terrence W. Deacon: Incomplete Nature

   2. Evan Thompson: Mind in Life

   3. Luis Favela: The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment

   4: Alva Noë: Out of Our Heads

   5: Terry Winograd and Fernando Flores: Understanding Computers and Cognition

   6: Douglas Hofstadter: I Am a Strange Loop

   Pretty sure that Demis Hassabis, Rich Sutton, and Karl J. Friston. Like Maturana and Valera, all three of them have strong backgrounds in neuroscience.

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3. robwwilliamsAug 2024
   At some level the fundamental assumptions of a “color” space as a real representation of the human perceptual space is almost comical. The color yellow has been an issue for many decades and diverse combinations of wavelengths evoke identical conscious yellow percepts.

   The fundamental “not-geometry” of color space is the output of a very complex deep neural net.

   Thinking about color space as “real” world is almost nonsensical ;-). Trying to map “real” world into brain now has a deservedly bad wrap. Modern philosophy has largely given up on Descartes, sharp subject-object dichotomies, and the brain and mind as a warped mirror of nature.

   If puzzling at first then check out Humberto Maturana and F. Valera (both experts in color vision electrophysiologist and philosophers), Martin Heidegger, Richard Rorty, and Daniel Dennett have powerful arguments against representationalism of the type implicit in most neuroscience.

   Valera, Rorty, Dennett are all readable. Heidegger and Matura are more cryptic unless you are a philosopher.

   https://en.wikipedia.org/wiki/Francisco_Varela

   Valera’s “The Embodied Mind: Cognitive Science and Human Experience”, co-authored with Evan Thompson and Eleanor Rosch, is a good intro on this and related concepts.

   But for Hacker News readers by far the best intro to this topic are chapters 4 and 5 of is the CS classic by Terry Winograd and Fernando Flores that is often highlighted on HN. It is a key text in understanding both what goes on in our brains and what is and may go on in machine brains.

   1986. Understanding Computers and Cognition: A New Foundation for Design (with Fernando Flores) Ablex Publ Corp

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4. robwwilliamsMar 2024
   Yes, the word “represented” is too widely used and abused in neuroscience—to the point where a frog has “fly detector” neurons. Humberto Maturana pushed back against this pervasive idea. Chapter 4 of Terry Winograd’s and Francesco Valera’s Understanding Computers and Cognition has a good overview of common presumptions.

   Given that CNS is a 700 million year hack, there will be lots of odd tricks used to generate effective behaviors.

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5. mindgam3Jun 2019
   Fair question. I can't claim a definite answer as to when this general/commonsensical ability develops, but I think to some degree we are born with it/preprogrammed as you point out. Certainly by toddler phase most humans have figured out visual pattern recognition better than the world's most advanced machine learning algorithms.

   My own thinking on this has been very much influenced by an early AI researcher-turned-philosopher and HCI practitioner, Terry Winograd. His book Understanding Computers and Cognition has some solid arguments for seeing cognition as a trait evolved as a form of "structural coupling" between a living organism and its environment. Highly recommended for anyone looking for an alternate perspective on AI from someone who was doing it way, way before it was cool.

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6. mindgam3May 2019
   One day we will look back on this talk as a high water mark of the AI religion craze. This whole AGI discourse that OpenAI/Altman are evangelizing is like a giant skyscraper they are trying to build on a foundation of quicksand.

   1. The foundational issue is not even that AGI "does not yet exist, with even AI's top researchers far from clear about when it might". It's way worse than that. There is a strong argument made by one of the grandfathers of AI research that AGI cannot exist, at least in the sense of common sense intelligence as attributed to humans. (see Winograd "Understanding Computers & Cognition" 1985). I was first introduced to these ideas taking a class from Winograd in undergrad.

   Winograd asks why we attribute mind properties to computers but not to, say, clocks. The dominant view of mind assumes that cognition is based on systematic manipulation of representations, but there is another, non-representational way of looking at it as a form of "structural coupling" between a living organism and its environment. "The cognitive domain deals with the relevance of the changing structure of the system to behavior that is effective for its survival."

   I won't try to summarize a book-length argument in a few paragraphs. I just want to point out that this whole AGI conversation rests on a premise that has been seriously challenged.

   The fact that Altman can get away with saying stuff like "Once we build a generally intelligent system... we will ask it to figure out a way to make an investment return" is an indication of just how insane the mainstream AI discussion has gotten. At this point it sounds like straight-up religion being prophesied from on high.

   2. The whole "capped profit" positioning at 100x return is absurd, as the author points out. Altman's argument for why it makes sense involves invoking the possibility that the AGI opportunity is so incomprehensibly enormous that if OpenAI manages to crack this particular nut, it could “maybe capture the light cone of all future value in the universe". Repent, ye sinners, for the kingdom of heaven is at hand!

   3. Most troubling, perhaps, is OpenAI's transparent ploy to attempt to generate buzz and take the ethical high ground with their alarmist PR strategy. Altman's justification for OpenAI's fear-mongering, which I'll paraphrase as "look at what happened with Facebook", just doesn't hold up to scrutiny. To begin with, Facebook was a real product from day one; AGI is currently a fantasy.

   But there's a deeper problem with invoking Facebook. The lesson to be learned from Facebook's failure is that the real danger with tech isn't algorithms but the people that design them. Algorithms have no agency. They just do what they're supposed to do. But hiding behind the algorithm seems to be the preferred way for tech oligarchs to avoid taking responsibility for the problems they created.

   The reason why I'm so troubled by OpenAI sounding the alarm bells about destructive AGI is that they are shifting the discussion away from the real threat: people. Especially people in power with virtually unlimited technological power and massive blind spots about the consequences of their actions. Give the algorithms a break!

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