I remember being presented Word2Vec as an undergrad in 2016 and I could not predict the sci-fi reality that is today

faizshah 3 hours ago | parent | next [-]

I think when I saw word2vec I realized that semantic meaning can be encoded in machines just from prediction. So then when seq2seq etc came around I saw that this encoding could be used to make increasingly more convincing imitations of thought and language.

I think what wasnt obvious was when Transformers came around it wasn’t obvious that generating text could get comparable results to thinking. So when Chain of Thought etc came around it was really surprising but ReAct arch made it obvious that some control loops would be the next stage.

I think now working with big multi agent systems with Opus 5.5 its clear once we develop the right mainframe-y sorta infra (virtual filesystems, deeper tool permissions, file locking, agent metrics, etc) there is a clear path emerging to what the future of the industry will look like (imo).

▲

__atx__ 5 hours ago | parent | prev | next [-]

Indeed. I remember taking a machine learning course as an undergrad in 2018 and scratching my head how could systems like these possibly scale to meaningfully interacting with computers let alone the real world. Well, here we are :)

▲

resters 7 hours ago | parent | prev [-]

The idea that a word is defined by the company it keeps was decades old. Mikolov’s insight was that you could learn it at scale with a very simple model, and that meaning would fall out as geometry. Obvious in hindsight, foundational in practice.

▲

microtonal 5 hours ago | parent [-]

Not to downplay his accomplishments (I have sung my praises in another comment), but even vector models were already around and somewhat popular before. Much of it started in Information Retrieval with things like TF-IDF vectors and later PPMI matrices that were converted to small, dense matrices with SVD. Of course, later Yoshua Bengio et al. introduced neural language models for training embeddings.

There is even a paper by Levy & Goldberg from 2014 that shows that skip-gram with negative sampling implicitly factorizes a PPMI word-context matrix.

But Mikolov's formulation is very nice in that it is a simple linear model that allows for a very simple Hogwild optimization that worked very well/fast on multi-core CPUs of the time.

	▲	DC-3 5 hours ago \| parent [-]
		Just FYI: You sang his praises. If you "sang my praises", that means you celebrated yourself.