I can recall the experience of getting in on a cold morning, pumping the throttle pedal three times to activate the semi-automatic choke, starting it, and getting out to clear frost off the window while it warmed up a little. The tactile feeling and squeak of the throttle linkage, the sound of the starter motor, the hollow sound of the door closing, and the noxious exhaust from the cold start (which I haven't smelled in 30 years). I remember how my little plastic window scraper sounded when scraping the glass, and even how the defrost vents made two regions which were always easier to scrape. But, I cannot really remember a specific episode of this on a certain date or leading to a particular trip.

On the other hand, I do have an episodic memory on my final trip in this truck. It was sliding off an icy road, rolling over, and sledding down a steep slope. I remember the ruptured, snow-filled windshield, and the sound of the engine idling upside-down. I remember the slow motion way the whole crash unfolded, and the clothes I was wearing as I crawled back to the roadway.

Ironically, I have more emotional context with the generic cold-start memory. It brings with it some vignette of teenage eagerness and pride in having this vehicle and the freedom it represented. The crash is more dissociated, a movie I was watching from within myself. I can meta-remember that I was very distressed afterward, but those feelings are not connected with the memory during recall.

* Continuously updates its state based on sensory data

* Retrieves/gathers information that correlates strongly with historic sensory input

* Is able to associate propositions with specific instances of historic sensory input

* Uses the above two points to verify/validate its belief in said propositions

Describing how memories "feel" may confuse the matter, I agree. But I don't think we should be quick to dismiss the argument.

It's pretty obvious that an LLM not knowing what it does or does not know is a major part of it hallucinating, while humans do generally know the limits of their own knowledge.

Perhaps in 1999 it seemed reasonable to think that passing the Turing Test, or maximally compressing/predicting human text makes for a good AI/AGI test, but I'd say we now know better, and more to the point that does not appear to have been the motivation for designing the Transformer, or the other language models that preceded it.

The recent history leading to the Transformer was the development of first RNN then LSTM-based language models, then the addition of attention, with the primary practical application being for machine translation (but more generally for any sequence-to-sequence mapping task). The motivation for the Transformer was to build a more efficient and scalable language model by using parallel processing, not sequential (RNN/LSTM), to take advantage of GPU/TPU acceleration.

The conceptual design of what would become the Transformer came from Google employee Jakob Uzkoreit who has been interviewed about this - we don't need to guess the motivation. There were two key ideas, originating from the way linguists use syntax trees to represent the hierarchical/grammatical structure of a sentence.

1) Language is as much parallel as sequential, as can be seen by multiple independent branches of the syntax tree, which only join together at the next level up the tree

2) Language is hierarchical, as indicated by the multiple levels of a branching sytntax tree

Put together these two considerations suggests processing the entire sentence in parallel, taking advantage of GPU parallelism (not sequentially like an LSTM), and having multiple layers of such parallel processing to hierarchically process the sentence. This eventually lead to the stack of parallel-processing Transformer layers design, which did retain the successful idea of attention (thus the paper name "Attention is all you need [not RNNs/LSTMs]").

As far as the functional capability of this new architecture, the initial goal was just to be as good as the LSTM + attention language models it aimed to replace (but be more efficient to train & scale). The first realization of the "parallel + hierarchical" ideas by Uzkoreit was actually less capable than its predecesssors, but then another Google employee, Noam Shazeer, got involved and eventually (after a process of experimentation and ablation) arrived at the Transformer design which did perform well on the language modelling task.

Even at this stage, nobody was saying "if we scale this up it'll be AGI-like". It took multiple steps of scaling, from early Google's early Muppet-themed BERT (following their LSTM-based ELMo), to OpenAI's GPT-1, GPT-2 and GPT-3 for there to be a growing realization of how good a next-word predictor, with corresponding capabilities, this architecture was when scaled up. You can read the early GPT papers and see the growing level of realization - they were not expecting it to be this capable.

Note also that when Shazeer left Google, disappointed that they were not making better use of his Transformer baby, he did not go off and form an AGI company - he went and created Character.ai making fantasy-themed ChatBots (similar to Google having experimented with ChatBot use, then abandoning it, since without OpenAI's innovation of RLHF Transformer-based ChatBots were unpredictable and a corporate liability).