... it is said that he [Babbage] sent the following letter to Alfred, Lord Tennyson about a couplet in "The Vision of Sin":
Every minute dies a man,
Every minute one is born
I need hardly point out to you that this calculation would tend to keep the sum total of the world's population in a state of perpetual equipoise, whereas it is a well-known fact that the said sum total is constantly on the increase. I would therefore take the liberty of suggesting that in the next edition of your excellent poem the erroneous calculation to which I refer should be corrected as follows:
Every minute dies a man,
And one and a sixteenth is born
I may add that the exact figures are 1.167, but something must, of course, be conceded to the laws of metre.
"""
Charles Babbage and his Calculating Engines
zahlman•3mo ago
Wouldn't "one and a sixth" be more accurate in both respects?
cbhl•3mo ago
Shouldn't it be the other way around if the population is increasing? Every minute one is born = 1440 born/day, every minute and a sixteenth ~= 1335 dead/day for a net population increase of 105/day.
BrenBarn•2mo ago
It means that in every minute, one and a sixteenth of a man is born.
behnamoh•3mo ago
how do you decompress all those 4 words from one token?
estebarb•3mo ago
Not from one token, from one embedding. Text contains a low amount of information: it is possible to compress a few token embeddings into a single tiken embedding.
The how is variable. The calm paper seems to have used a MLP to compress from and ND input (N embeddings of size D) into a single D embedding and other for decompress them back
HarHarVeryFunny•3mo ago
The mechanism would be prediction (learnt during training), not decompression.
It's the same as LLMs being able to "decode" Base64, or work with sub-word tokens for that matter, it just learns to predict that:
<compressed representation> will be followed by (or preceded by) <decompressed representation>, or vice versa.
floodfx•3mo ago
Why are completion tokens more with image prompts yet the text output was about the same?
Garlef•3mo ago
"Thinking" Mode
nunodonato•3mo ago
it doesn't say that anywhere.
cma•3mo ago
Some multimodal models may have a hidden captioning step that may take completion tokens, others work on a fully native representation, and some do both I think.
ashed96•3mo ago
In my experience, LLMs tend to take noticeably longer to process images than text.
psadri•3mo ago
I wonder if these stay in the prefix cache?
weird-eye-issue•3mo ago
It has to get the image data first, basically just IO time before processing it
ashed96•3mo ago
IIRC there's pre-processing (embedding/tokenization?) before feeding images to LLMs?
Hit this issue optimizing LLM request times. Ending up lowering image resolution. Lost some accuracy but could bear that.
bikeshaving•3mo ago
https://en.wikipedia.org/wiki/A_picture_is_worth_a_thousand_...
heltale•3mo ago
https://arxiv.org/abs/2010.11929
estebarb•3mo ago
bikeshaving•3mo ago
pastor_williams•3mo ago
"""
"""zahlman•3mo ago
cbhl•3mo ago
BrenBarn•2mo ago
behnamoh•3mo ago
estebarb•3mo ago
The how is variable. The calm paper seems to have used a MLP to compress from and ND input (N embeddings of size D) into a single D embedding and other for decompress them back
HarHarVeryFunny•3mo ago
It's the same as LLMs being able to "decode" Base64, or work with sub-word tokens for that matter, it just learns to predict that:
<compressed representation> will be followed by (or preceded by) <decompressed representation>, or vice versa.