Ryan Greene
@rabrg
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Pinned Tweet
Excited for my first public contribution at
@OpenAI
— Embeddings v2: it unifies text search, text similarity, and code search, outperforms our best v1 models at most tasks, while being priced 99.8% cheaper!
Our new embedding model is significantly more capable at language processing and code tasks, cost effective, and simpler to use.
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i have great respect for Ilya, and trust in his intentions. but i can’t comprehend the lack of transparency in his decision
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born too late to explore the world, born too early to explore the universe. born just in time to explore latent space
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Project Gutenberg, with a better UX for reading and discovering its contents, would rival Wikipedia in accessibility to humanity's knowledge
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love the phrasing of "world simulators": imagine the scale of intelligence needed to accurately model relationships between complex entities in a physical environment
the beautiful aesthetics are just a cherry on top
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@stephenbalaban
"Behind it all is surely an idea so simple, so beautiful, that when we grasp it – in a decade, a century, or a millennium – we will all say to each other, how could it have been otherwise?"
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The most popular use case of our new embedding model is, by far, retrieval-augmented question-answering. Checkout
@isafulf
's new plugin that makes this a native experience with ChatGPT!
We've written plugins for browsing & Python code execution (amazing for data science use-cases), launched with 11 partners, and have open-sourced a high-quality plugin for retrieval over any data you'd like to make accessible to ChatGPT:
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the best, for free. love that
@OpenAI
's values are aligned to do things like this
Not only is this the best model in the world, but it's available for free in ChatGPT, which has never before been the case for a frontier model.
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one of the biggest lessons i've learned from my first year doing research is that, when someone comes up with an idea, it's almost certain that someone else has either already done it or is about to do it
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i wish more recommendation algorithms gave users control of the diversity of their feeds, like the temperature slider of a language model
give me entropy. help me discover the unknown
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i can’t wait for content recommendation algorithms to get augmented with natural language feedback, letting users granularly and iteratively critique and refine their feeds in real-time
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brain-computer interfaces will enable communication through embeddings instead of tokens, increasing the fidelity to thought by OOMs
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reading these lines in a metacognition text has me even more deeply appreciating the current state of LLMs
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@OpenAI
huge thanks to
@arvind_io
,
@lilianweng
,
@sherwinwu
,
@sandersted
, and many more people that i don't have the twitter handles of for all of the help with this release!
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reading the writings of my favorite authors sometimes feels like reading an articulation of my subconscious
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the simplest of changes, like going to a park, or turning on some music, can make a piece of writing resonate so much more deeply with me. like my comprehension is limited by my environment. and it's empowering to remember how easy it is to shape it
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it is of no coincidence that "comprehension" and "compression" spring from the same root
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writing is a noisy serialization of the mind. language models are trained to reproduce the noise (as opposed to directly reducing it)
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passing copies of books between friends, seeing each other’s notes and highlights, is one of the cutest things ever
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@bcjordan
@OpenAI
i'm excited to see how such a cheap ($0.40 per 1m tokens), easy-to-use model (versatile representational space, reliable API / don't need to self-host) enables more innovative retrieval-based apps like
@omniscience42
to be created and scaled :)
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i can’t help but have fomo of all the spirit going on in the arena though. looking forward to getting back :)
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Borges' Funes is a prophetic meditation on large language models: the mapping of words to numbers, the idea of something that has a greater capacity of memory than all of mankind, and contrasting that capacity with that of "thinking"
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mere mortals is an existential romance between humanity and knowledge
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being in tune with your tastes compounds: knowing what you like makes it easier to find things you do; the more you discover them, the more opportunities you have to understand what draws you to them
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@_smileyball
Higashiyama Jisho-ji (Kyoto), Maruyama Park (Kyoto), and Shinjuku Gyoen Garden (Tokyo)!
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@ilyas121_real
@mattshumer_
@OpenAI
some inspiration:
-
@rileytomasek
's
-
@ultrasoundchad
's
-
@CedricMakes
's
-
@itsandrewgao
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a beautiful implementation of a beautiful idea
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ilya words it a lot better: i had overestimated the role of new ideas, and underestimated the significance of their execution
Many believe that great AI advances must contain a new “idea”. But it is not so: many of AI’s greatest advances had the form “huh, turns out this familiar unimportant idea, when done right, is downright incredible”
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@Muennighoff
@openais
@hongjin_su
@WeijiaShi2
@MSFTResearch
Props to the authors of INSTRUCTOR and E5! They were some of my favorite embeddings papers from the past year, along with MRL, Promptagator, Relative Representations, and, of course, your MTEB :)
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@rememberlenny
@OpenAI
going to miss seeing you around, Lenny. best of luck with your next adventure
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@yoheinakajima
memory is the outcome of learning. novelty is a prerequisite for learning (you can’t learn what you already know)
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@sociaIrate
this is an example of why "realize" is one of my favorite polysemes
realize
1. become fully aware of (something) as a fact; understand clearly
2. cause (something desired or anticipated) to happen
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@dwarkesh_sp
i've gotten a lot of incredible recommendations this year by telling ChatGPT my favorite ideas and asking for thinkers / writings who've thought about similar things
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@yeetgenstein
don't forget his boy Funes
Borges' Funes is a prophetic meditation on large language models: the mapping of words to numbers, the idea of something that has a greater capacity of memory than all of mankind, and contrasting that capacity with that of "thinking"
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Maria Popova's curations and musings in The Marginalian have been some of my favorite reads of the year. this is a particularly beautiful one
This week's highlightable delights, in one place: William James on love, Marie Howe's stunning hymn to humanity, Nick Cave reads an animated poem
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the amount you are able to take away from an experience is scaled by your interest in it; in this sense, being in tune with your tastes is a type of meta-learning
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@SarahChieng
it's recently been a lot of transcendentalists: Emerson, Thoreau, and Whitman. going through a bit of an individualistic era :)
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@arankomatsuzaki
the new text-embedding-ada-002 should actually be pretty good at multilingual tasks; we removed this as a limitation in our documentation a while back!
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@sociaIrate
i keep a log of experiences that give me aesthetic chills (commonly reading certain books or listening to music)
i've been feeding this list to base GPT-4, and having it continue the generation, suggesting future experiences for me. some of my favorite muses have come from this
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@AkiyaCollective
i happened to stumble upon it being rented out on Airbnb last year and had such a lovely stay there. happy to hear there are communities like this set out to build more!
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@highchinar1
it's usually better to embed short texts tbh. returning a paragraph of just the most important information, instead of hiding it in pages of boilerplate
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@melodaysong
do you let your attention completely wonder, or guide it to particular topics but without censoring?
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@willdepue
@SHL0MS
@springpard
@IgorBrigadir
@metaphorsystems
unarXive is begging for a builder to create this using a hybrid of its embedded text and citation graph
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@lifeonmarsspace
one of my favorite embedding papers is a study of this: despite different models embedding concepts in completely different positions, the relation between concepts is largely universal, allowing for communication
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it is only called reasoning if a meat brain does it, otherwise it’s just retrieval of approximate algorithms present in the pre-training data mixture
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