They are. GPT3 doesn't have a lot of common sense. But, language models that large have lots of general intelligence due to their size, and are an incredible basis for doing stuff, if trained on the task at hand. eg (mostly non-vetted):
https://metaphor.systems/ (which I used to find everything in this list besides the first three items; note that you can drop any of these links into metaphor and walk the semantic relatedness web near these sites! I probably didn't even have to paste this big list, but y'all consider link clicks a trivial inconvenience, turn up your clickthrough-and-close rate to match your usage of mindless recommenders and retake your agency over what websites you use! or something! uh anyway)
https://github.com/features/copilot (which you know about, of course)
https://summarize.tech/ (also gpt3)
general research ai tools:
https://www.semanticscholar.org/ - the paper recommender is wonderful, add your favorite safety papers to your feeds! my strongest recommendation on this list besides metaphor.
https://iris.ai/ - looks very cool but kinda expensive; probably not even available to individual researchers outside institutions
https://scite.ai/home - looks like a general related-work finding tool like semanticscholar, may have some tastefully chosen small ML models like "does this citation support or contrast?". [costs. $16/mo individual. personally, that means SKIP]
https://www.scinapse.io/ looks cool, found via metaphor.systems semantic search seeded with semantic scholar
https://scholarmagic.com/ looks cool, claims to have cite-as-you-write tool, I wonder how it compares to galactica
https://www.onikle.io/ looks cool, claims to compete with semanticscholar, just try semanticscholar though lol,
https://www.resolute.ai/ looks cool but not free
https://www.causaly.com/ bio papers only and also not free I think
https://www.hebbia.ai/ not sure if this is for science or internal tools for companies or what
https://www.academiclabs.com/ looks xpensive
https://consensus.app/search/ tries to summarize, doesn't do as well as the classic and locally crafted..
...https://elicit.org/! which is incredible and is the only one of these things I actually use already besides semanticscholar
https://ohmytofu.ai/ general site recommender based on contextual relevance, seems similar to metaphor in that respect [edit: ohmytofu appears nonfunctional]
https://www.albera.com/ suggests plausible research trees to learn about a subject [edit: tried! sorta works. feels like just another elicit prompt]
https://www.scholarcy.com/ paper summarizer
https://www.genei.io/ another paper summarizer, this one is actually gpt3, like you asked for
https://www.prophy.science/ not sure if this uses real ai or not but it looks maybe cool
https://www.scinapse.io/ who knows if this one is any good
https://brevi.app/ another research summarizer
https://keenious.com/ yet another paper recommender
legal:
https://www.spellbook.legal/ (custom trained?)
https://www.advocat.ai/ (might be gpt3?)
https://skritswap.com/ (looks meh)
https://legalrobot.com/ (tba?)
https://www.darrow.ai/ (idk)
https://www.puzzlelabs.ai/ (this one looks mildly cooler)
https://loio.com/ (ai powered contract linter)
https://www.uncoverlegal.com/ (legal semantic search)
https://www.dellalegal.com/ (contract review)
https://www.lexcheck.com/ (contract review and editing)
https://www.legartis.ai/ (contract review and editing)
https://zuva.ai/ (some blend of the above)
https://www.donna.legal/ (contract linter)
https://motionize.io/ (yet another linter addon for word)
https://www.amplified.ai/ patent recommender
not ai, but came up and looks cool: https://lawya.com/
chem & bio (cell culture go foom!) - I'm even less qualified to evaluate most of these than the legal text stuff:
https://www.noble.ai/
https://www.epistemic.ai/
https://www.benchsci.com/
https://genoml.com/
https://www.mendel.ai/
https://braininterpreter.com/?q=thinking out loud <- wtf!
https://www.nextmol.com/
https://paige.ai/
https://crystals.ai/
https://www.vant.ai/
https://chemintelligence.com/
https://www.aqemia.com/
https://www.asimov.com/
https://www.abzu.ai/
https://www.biorelate.com/
https://allchemy.net/
https://deepchem.io/
https://www.benchsci.com/
https://pentavere.ai/
https://kantify.com/
https://www.ersilia.io/
https://www.atomwise.com/
https://www.sorcero.com/
https://www.chemify.io/
https://www.anagenex.com/
https://www.menten.ai/
https://pending.ai/
https://www.pharm.ai/
https://teselagen.com/
https://www.benevolent.com/research (actual ai bio lab)
https://atomic.ai/ (lab, no public usability)
valence discovery goes here, as do deepmind and standford medai
sus:
https://www.aktana.com/ <- warning, this one looks like a manipulation tool
this one is specifically military; I'm sure that it, and many others like it, will detect this comment and categorize it somewhere:
https://primer.ai/public-sector/ai-in-warfare-a-race-the-u-s-cant-afford-to-lose/
honorable mentions:
https://www.semion.io/ <- amazing looking paper relationships tool, but not actually based on deep learning
https://flywire.ai/ actually a game, not an ai
https://www.aicrowd.com/ just an ai research competition site
https://www.journalmap.org/ GIS + paper discovery? no ai tho
https://tools.kausalflow.com/ this is a list of tools made by people who like lists almost as much as I do. similar list to the stuff you find browsing my profile here - big list of tools, mildly curated but significant shopping still remains.
and of course, my purpose in sharing these link floods is to give people seeds to find stuff on the webbernet. you asked for how ai has been productive; the answer is, it's a bit of a mess, but here's a big list of starting points to comment on. if anyone browses through these, please share which ones were worth more than a few seconds to your intuition - I spent an hour and a half on this list and barely skimmed any of them!
Indeed it works great for babble. OpenAI suggests the prompt of "Write a tagline for an ice cream shop" as an example. Using it I was able to generate the following three examples:
I would have assumed that you could do pruning just by prompting it to generate an evaluation and a rating. Evaluating the above taglines using the prompt "Explain the advantages and disadvantages of the following tagline for an ice cream shop, and then rate the tagline on a scale from 1 to 10.", I get:
The best ice cream in town!:
A scoop of happiness.:
Delicious ice cream for all!:
😅I suppose this doesn't tell us much. The evaluations are all very repetitive, but they seem to correctly apply to the generated ideas, it's just that the original ideas are not very distinct.
To try and generate more distinct ideas, I asked GPT-3 to come up with some themes for ice cream shops. It gave the following themes:
This yielded:
This critique doesn't seem great. For instance, ice cream is disproportionately eaten in the summer, yet it praises "Frosty's" for being "appropriate for the winter season". Possibly my prompt is bad though.
But at a first look, it seems like you might be right? Idk.