Bill Benzon, BAM! Bootstrapping Artificial Minds

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Loony Tunes

@MarkChangizi Your people, no?

@sebkrier Does that mean you've partnered with Mazda and are all driving Mazdas?

Important news: GDM has chosen the side of Zoroastrianism. More to come soon.

People misinterpret the Ramanujan and Einstein examples and assume this just means AGI has to be a super genius otherwise it doesn't count. To me this doesn't seem to be the point; they're illustrations of categories more than thresholds. Demis previously described creativity as falling into three buckets: Interpolation, i.e. averaging many data points and recombining them, e.g. an image model producing a new photo that would have not been in a dataset before. Extrapolation, i.e. going beyond the convex hull of the training data to produce something experts recognise as genuinely new - we have a lot of examples of this with existing systems, like Move 37 and all the recent maths examples. And thirdly, invention: actually coming up with the game of Go in the first place. It's the third type that existing systems, at least as currently scaffolded/used, appear to lack. There may be a difference between solving existing conjectures or problems, and actually coming up with the theory in the first place. Language models produce all sorts of new outputs, but the outputs differ qualitatively in kind even if they're novel to varying degrees. Does the new output live inside the conceptual space defined by the training data (interpolation), push outside it along existing dimensions (extrapolation), or reframe the space itself by proposing a new conceptual structure (invention)? Ramanujan illustrates the depth of intuition and innovation that the third 'true creativity/invention' category represents. There may be recombination and extrapolation as part of that process, but at least so far they don't seem to be sufficient. Of course you can argue that most humans don't do this - when was the last time you invented a new abstraction? My uneducated view is that this third type of creativity doesn't have to lead to crazy new inventions - obviously inventing the theory of relativity is both more impressive and more *valuable* (and thus depends also on a social aspect and utility), but I think individuals do this third type of creativity in many smaller/micro ways too. For example a teenager on TikTok warping a meme format into something the format didn't previously allow/cater for. This kind of type-3 operation should be observable at small scales, frequently, with low stakes too. I think it's very reasonable to argue that this is *just* interpolation/extrapolation, but I personally don't think this is all there is. If it was then it should be feasible for e.g. Talkie (the model trained up to 1930s data) to be prompted/scaffolded to create a new abstraction entirely. I haven't seen this, except by teaching it basic coding through fine-tuning - but that's not the same thing, since you're showing it the new abstractions (coding) to start with! I'm not sure if this is because (a) language models lack the cognitive operation entirely, given the architecture; or (b) it has the operation but lacks the appropriate scaffolding, memory, and process. Maybe it's (b), though I lean towards (a): language models are better at paradigm exploitation than paradigm generation. Of course practically speaking, you don't strictly need this third type of creativity for the technology to be transformative and revolutionize fields; but it's a difference that is still worth highlighting and accounting for, since it also implies certain ceilings determined by the shape of the existing conceptual space.

NEW SAVANNA: The Vatican News presents Magnifica humanitas in 4 minutes and 37 seconds new-savanna.blogspot.com/2026/05/the-va…

Major difference in my mind: - an engineer, given a problem, invents and tries multiple solutions and stops when the solution is good enough. The goal is product innovation and shipping. - a scientist asks new questions, proposes various new solutions, compares them (sometimes with old ones), and writes about it. The methodology must be sound or else peers will sneer. The goal is scientific breakthroughs and technological progress. Both can be called "researchers". Many people can do both: these are activities, not identities. Importantly, most product innovations are built on scientific breakthroughs and technological innovations that happened 2, 5, 10, or 20 years earlier.

NEW SAVANNA: The old religion (Christianity) vs. the new (A.I.) new-savanna.blogspot.com/2026/05/the-ol…

Finally, a big name has the courage to tell it: we are nowhere near AGI. Demis Hassabis, CEO of Google DeepMind and Nobel laureate for AlphaFold, put it neat and clear: "Today's systems are nowhere near [AGI]. Doesn't matter how many Erdős problems you solve… I think it's far, far from what a true invention, or someone like Ramanujan, would have been able to do." This is the elephant in the room that many AI enthusiasts prefer not to see, or are actively trying to hide. Erdős problems are well defined, often combinatorial, on finite spaces. They are exactly the kind of problems on which current AI can achieve spectacular performance with a lot of compute and knowledge. A neural network can search a huge graph of possibilities. It can recombine existing knowledge at unprecedented scale. It can discover surprising solutions inside an already defined conceptual space. But true invention is something else. True invention is not only solving a problem. It is inventing new objects, new dimensions, new connections. It is inventing new problems. From resolving to inventing there is a discontinuity that we don't know how to bridge. We are making extraordinary tools. But we are nowhere close to AGI.

On Method: Computational Compressibility in Complex Natural and Cultural Phenomena, Version 2 academia.edu/166054951/On_M… via @academia

“Kubla Khan” and the Embodied Mind academia.edu/8810242/_Kubla… via @academia

neurosymbolic by @swarat et al for the Erdos win, with much more careful, quantitative work than openai’s in hindsight i wonder whether OpenAI rushed theirs out, knowing this was coming?

Ring Composition: Some Notes on a Particular Literary Morphology academia.edu/8529105/Ring_C… via @academia

Monday 25 May Today’s Daily Picture Theme is ‘Pano' with @PanoPhotos RT or reply with your own photo Tomorrow’s theme will be ‘Abanonded' #DailyPictureTheme A lovely pano of a sunset in San Diego, California!

NEW SAVANNA: Pope Leo presents Magnifica Humanitas while standing next to a tech founder new-savanna.blogspot.com/2026/05/pope-l…

@amazing_physics Edison

Who will you choose?⚡

The Eternal Sloptember geohot.github.io//blog/jekyll/u…

GPT-3: Waterloo or Rubicon? Here be Dragons, Version 4.2 academia.edu/43787279/GPT_3… via @academia

Breaking: Young Princeton prof beats OpenAI at its own Erdos game, 3 days later. arxiv.org/abs/2605.20579…

Engineering a science are different kinds of discipline. Engineers design things and build them. Scientists analyze things and create causal models. Both activities, however, tend to be technical and to involve math. But otherwise, VERY different.

BFFs