Hi Bill,
Interesting article. I had similar thought process of comparing LLM to holographic images, based on similarities between Fast Fourier Transform digital algorithm, FFT and some optical systems doing the same in optical domain. I wonder if there is any formal value (or possible applications) in comparing the two domains except just aesthetical.
- Greg
gdabrowski@autograf.pl
I strongly suspect there is, but don't have to tools for it myself. Have you seen my post, Toward a Theory of Intelligence: Did Miriam Yevick know something in 1975 that Bengio, LeCun, and Hinton did not know in 2018?
Also, check out the quotation from Francois Chollett near the end of this: The role of philosophical thinking in understanding large language models: Calibrating and closing the gap between first-person experience and underlying mechanisms.
These ideas weren't unfamiliar to Hinton. For example, see the following paper on "Holographic Reduced Representations" by a PhD student of his from 1991: https://www.ijcai.org/Proceedings/91-1/Papers/006.pdf
"Everyone" has known about holography since "forever." That's not the point of the article. Yevick's point is that there are two very different kinds of objects in the world and two very different kinds of computing regimes. One regime is well-suited for one kind of object while the other is well-suited for the other kind of object. Early AI tried to solve all problems with one kind of computing. Current AI is trying to solve all problems with a different kind of computing. If Yevick was right, then both approaches are inadequate. She may have been on to something and she may not have been. But as far as I know, no one has followed up on her insight.
Cross-posted from New Savanna.
That’s been on my mind for the last week or two, ever since my recent work on ChatGPT’s memory for texts [1]. On the other than, there’s a sense in which it’s been on my mind for my entire career, or, more accurately, it’s been growing in my mind ever since I read Karl Pribram on neural holography back in 1969 in Scientific American [2]. For the moment let’s think of it as a metaphor, just a metaphor, nothing we have to commit to. Just yet. But ultimately, yes, I think it’s more than a metaphor. To that end I note that cognitive psychologists have recently been developing the idea of verbal memory as holographic in nature [3].
Note: These are quick and dirty notes, a place-holder for more considered thought.
Holography in the mind
Let’s start with an article David Hays and I published on neural holography as the neural underpinning of metaphor [4]. Here’s where we explain the holographic process:
The 175 billion weights that constitute the LLM at the core of ChatGPT, that’s the holographic memory. It is the superposition of all the texts in the training corpus. The training procedure – predict the next word – is a device for calculating a correlation (entanglement [5]) between each word in context, and every other word in every other text, in context. It’s a tedious process, no? But it works, yes?
When one prompts a trained memory, the prompt serves as a reference beam. And the whole memory must be ‘swept’ to generate each character. Given the nature of digital computers, this is a somewhat sequential process, even given a warehouse full of GPUs, but conceptually it’s a single pass. When one accesses an optical hologram with a reference beam, the beam illuminates the whole holograph. This is what Miriam Yevick called “one-shot” access in her 1975 paper, Holographic or Fourier Logic [6]. The whole memory is searched in a single sweep.
Style transfer
So, that’s the general idea. Much detail remains to be supplied, most of it by people with more technical knowledge than I’ve got. But I want to get in one last idea from the metaphor paper. We’ve been explaining the concepts of focal and residual schemas:
That’s a mouthful, I know. Notice our emphasis on style. That’s what’s got my attention.
One of the more interesting things LLMs can do is stylistic transfer. Take a piece of garden variety prose and present it in the style of Hemingway or Sontag, whomever you choose. Hays and I argued that that’s how metaphor is created, deep metaphor, that is, not metaphor so desiccated we no longer register its metaphorical nature, e.g. the mouth of the river. We made our argument about visual scenes: Achilles in batter, a lion in battle. LLMs apply the same process to texts, where style is considered to be a pattern of residuals over the conceptual content of the text.
More later.
References
[1] Discursive Competence in ChatGPT, Part 2: Memory for Texts, Version 3, https://www.academia.edu/107318793/Discursive_Competence_in_ChatGPT_Part_2_Memory_for_Texts_Version_3
[2] I recount that history here: Xanadu, GPT, and Beyond: An adventure of the mind, https://www.academia.edu/106001453/Xanadu_GPT_and_Beyond_An_adventure_of_the_mind
[3] Michael N. Jones and Douglas J. K. Mewhort, Representing Word Meaning and Order Information in a Composite Holographic Lexicon, Psychological Review, 2007, Vol. 114, No. 1, 1-37. DOI: https://doi.org/10.1037/0033-295X.114.1.1
Donald R. J. Frankin and D. J. K. Mewhort, Memory as a Holograpm: An Analysis of Learning and Recall, Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale, Association 2015, Vol. 69, No. 1, 115–135, https://doi.org/10.1037/cep0000035
[4] Metaphor, Recognition, and Neural Process, https://www.academia.edu/238608/Metaphor_Recognition_and_Neural_Process
[5] See posts tagged with “entangle”, https://new-savanna.blogspot.com/search/label/entangle
[6] Miriam Lipschutz Yevick, Holographic or Fourier Logic, Pattern Recognition 7, 197-213, https://sci-hub.tw/10.1016/0031-3203(75)90005-9