Even though the brain takes in billions of bits per second in raw information through the ocular nerves, touch, etc, there is a serial embedding bottleneck that constrains the retained information to a much smaller amount. The conscious mind probably runs at about 3-4 tokens per second, some numbers to help justify this claim:

- Various studies of memory retention find the brain has a serial embedding bottleneck of about 40-60 bits per second (source: Silicon Dreams: Information, Man, Machine, by Robert Lucky), which if we assume a roughly LLM sized token dictionary would be somewhere in the realm of 3-4 tokens a second

- "A typical speaking rate for English is 4 syllables per second." (https://en.wikipedia.org/wiki/Speech_tempo)

- Humans have a reaction time ranging from around 1/4 to 1/3 of a second, or an implied action-token production rate of 3-4 per second

This implies that a human trains on about 1.6 billion tokens during their lifetime:

>>> 60 * 60 * 16 * 365 * 79

1,660,896,000

While from a raw parameter count perspective a human brain is something on the order of 89 trillion parameters (89 billion bioneurons with 1000 synapse each) it is vastly undertrained according to data scaling rules. Relatively small models like LLaMa 3 70B get close to the linguistic complexity of a human being with a brain the size of a mouse. This implies that the human mind pattern is much more compressible than previously believed. The existence of GPT itself implies that a human mind pattern, or at least substantial and productive fractions of a human mind pattern can be learned from textual evidence alone. Scaling language models also improves their internal alignment with human neural representations: https://arxiv.org/html/2312.00575v1

The practical outcome of this is that while "grief bots" that mimic the dead are currently a novelty, as AI researchers get over chatbots and look towards the larger potential of these systems we will begin to realize that a high fidelity simulacrum of a productive researcher or intellectual can multiply that researchers productivity. Furthermore the digital copies can continue to work after their biological death. This question concerns that scenario.

It resolves YES if before the end of 2027 there is any published paper or major editorial credited in whole or in part to an AI simulacrum of a deceased author and this is generally accepted as an addition to that authors corpus. "General acceptance" means it's listed on things like bibliographies for that author or the editorial appears under their name without qualifications or excuses for quality.

To be notable, the scientist or public intellectual must have one of:

1. An established Wikipedia article

2. Twitter account or similar with over 10k followers

3. Held a professorship, endowed chair, fellowship, etc at a major university or industrial research laboratory[0].

4. A prestigious award or recognition in their field, such as a Nobel Prize, Fields Medal, Turing Award, or equivalent[1].

5. 90th percentile h-index for their field or subdomain.

Their paper must be published in either a peer reviewed journal, conference communication, or commonly accepted venue for established knowledge in that field or discourse. If it is an editorial it must be published in a major newspaper or functional equivalent.

If no such paper or editorial exists by the closing date it resolves NO.

CLARIFICATION/UPDATE: I won't resolve it YES if the inclusion is clearly a joke, since that wouldn't be "general acceptance", by which I mean that the people immediately involved in the publication consider it an extension of that persons work. How they feel about it 'being' that person is out of scope. e.g. They could consider the bot to be a work by the person which produces new works, rather than literally them.

[0]: Claude wrote this bullet.

[1]: Ibid.