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by Marshferm
211 days ago
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The problem is none of these approaches to intelligence are related to biointelligence. These are intuited, folk psychological/scientific, in a sense pseudoscientific graftings from machine vision principles. While there is spatial computing in brains, there is no metric or need to claim there is such a thing as spatial intelligence, it's like saying intelligence intelligence. The brain is spatial, so what? And the spatial this approach uses is mechanically formulaic, without any connection to how we grow intelligence. Biology does not see 3-D, we integrate two 2-D inputs and make a 2.5-D from them in varying degrees of resolution. Within the brain our mapping systems and sensory/emotional/memory architectures range from no D (they are affinities of exploding areas) to 2-D topology, which integrate from whole brain to the finer aspects of mapping in what we understand so far are unique combinations of allo and egocentric scales of space. Also as thought and words (and symbols, representations, metaphors, add anything arbitrary here in our weak externals) are divergent and unrelated, the manifest idea these approaches link them as a route to an unsupportable and additonal layer of "world modelling" is inferior innovation (if can even be called innovation). These are all steps back from the integrations we see biointelligence operating with. This is synthetically deciding that intelligence can be simplified with limited general processes and then form fitting them into a binary code to mimic it. It's really poor ideation. None of this requires models (maps are not models), and the entire idea that a model is being used as a gateway to intelligence as redundant and oxymoronic as in any "world model". In essence, this is the last range of disability to achieve intelligence from binary, which in itself is a poor form to interpolate the oscillatory dynamical nature of consciousness/intelligence. It was very premature to develop this phase of code like this. |
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To follow completely in your footsteps would require recreating the totality of physical evolution that led to intelligence within a fraction of the time. This isn't feasible to an investor expecting returns within a reasonable timeline.
Getting to this point leaped from text generation to image generation to video generation, and now these three approaches are experimenting on what the next step should be. These three approaches did not come from an isolated vacuum and are the result of iterative progress.
The general idea now is to take the video model and give it 3D spatial capabilities to better model the implicitly symbolic and virtual worlds it is depicting and reason what would happen next. Fei-Fei Li wants to produce a 3D scene asset. DeepMind wants to simulate what can happen in that 3D scene. Yann LeCun wants to expand upon the symbolic reasoning by adding another layer of intelligence.
Traditional AI balk at the lack of inherent agentic purpose and goals, but LLMs separately evolved from pattern matching and statical analysis of the digital output of human labor.
A number of people in the LLM field have accepted that recreating the animal brain is not the point. Instead they work on a unique digital intelligence as if it was select fragments of the human brain existing in a digital world, informed by neuroscience research.
I think LLMs may not reason like a human with skin in the game, but humans are rather flawed in making sense of the nihilistic stage of history we find ourselves in. It is difficult for at least half of humanity on an IQ level to reason with what we have created. I think there is a case for a separate digital intelligence to analyze and make sense of the digital world which only seems to merge further with reality. Maybe this is a transhumanist singularity not in a technological term, but in terms of human idealism in creating values for ourselves.