As manifestly obvious from model distillation/quantization "parameters" is
a weasel-word.  At least their failure to even estimate the conversion
factor between "tokens" and "parameters" didn't take it to the reductio ad
absurdum of a single, arithmetically code "parameter" for their entire set
of "tokens".

But this is actually skirting the real issue, which is that until the
models are actually dynamical rather than statistical, they will be
incapable of reasoning.  If they would at least _try_ to make their models
dynamical -- ie: actually exploit the power of algorithmic information  --
I'd be more interested.

On Sat, Dec 11, 2021 at 5:59 PM Matt Mahoney <[email protected]>
wrote:

> GPT-3 is like a super brain that memorizes the internet. RETRO is like a
> normal brain (1B parameters) that can look up stuff on Google. This
> requires less computation for the same performance.
>
> The data is interesting. More parameters always helps and more data always
> helps. Even the worst models are predicting at about human level, 1 bit per
> byte. If there is an upper limit on prediction accuracy then we still don't
> know what it is, and this goes far beyond my own research.
>
> On Sat, Dec 11, 2021, 12:08 PM James Bowery <[email protected]> wrote:
>
>> My apologies if the image didn't come through.
>>
>> The reference is to:
>> https://deepmind.com/research/publications/2021/improving-language-models-by-retrieving-from-trillions-of-tokens
>>
>> On Sat, Dec 11, 2021 at 10:22 AM <[email protected]> wrote:
>>
>>> *?*
>>>
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