[HN Gopher] Using GPT-3 to pathfind in random graphs
___________________________________________________________________
Using GPT-3 to pathfind in random graphs
Author : tibbar
Score : 153 points
Date : 2022-09-23 07:50 UTC (1 days ago)
(HTM) web link (jacobbrazeal.wordpress.com)
(TXT) w3m dump (jacobbrazeal.wordpress.com)
| mjburgess wrote:
| The problem with research conducted in this way is that there's
| no theory of operation of the model, except the implicit one "it
| works like how I expect".
|
| And then there's no hypothesis test against predictions made
| based on this theory, ie., no one tries to _disconfirm_ the model
| _works that way_. It is usually trivial to disconfirm. ML models
| almost always work _some other way_ , usually exploiting
| coincidental statistical information in the training set.
|
| This turns the whole show into a superstitious activity, in which
| we think google answers our "local restaurants" query _because_
| it knows where I live (rather than say, using an IP location or
| one of 100 other methods).
|
| If you present a ML model as "capable of x", you're required to
| _disprove_ that claim as best you can, and present the occasions
| where it failed.
| xyzzy123 wrote:
| It might not be rigorous science but it's a cracker of a blog
| post.
|
| It shows a surprising ability of language models, code is
| provided and plenty of mystery is left for the reader... which
| can be investigated by almost anyone for tens of dollars.
| mjburgess wrote:
| But is it an ability? I can't tell.
|
| What would a useless "random" algorithm find operating on
| these graphs as inputs? What would a _useful_ one find? What
| would that be "useful" for?
|
| Is this system much closer to the useless or the useful? We
| cant tell.
|
| Without any explanation, we're left reading this blog post as
| a magic trick in the literal sense.
|
| Consider the rate at which it "uses edges that don't exist"
| -- this makes it _not_ a graph traversal algorithm. So it is
| just exploting syntax in the input whose representation has
| some "graphlike" qualities which can be statistically
| exploited.
|
| Why is that interesting? I dont know. Is this a _good_
| system?
| xyzzy123 wrote:
| I don't know! That's why it's fun.
|
| (EDIT: I can safely say this isn't practically useful...
| but it's interesting in the same way as watching a cat try
| to fill out a tax return. I'm not surprised the cat does it
| badly, I'm surprised that it _seems_ to be doing it at all,
| even if it 's really a trick).
| visarga wrote:
| > ML models almost always work some other way , usually
| exploiting coincidental statistical information in the training
| set.
|
| That's true for models trained on small datasets, but GPT-3's
| dataset was anything but superficial, and on top of it we
| consider this task to be OOD, thus the amazement.
| Der_Einzige wrote:
| Sorry, you and the posters who think that this task wasn't in
| the training data are wrong.
|
| I'm pretty sure nearly everything, including textual
| representations of graph problems, are found in the training
| data.
|
| "The internet is vast and infinite" - Motoko Kusanagi at the
| end of the original ghost in the shell...
| YorkshireSeason wrote:
| When Newton described the laws of gravity, now often referred
| to as Newtonian physics, did he explain what gravity was, in
| other words, did he have a "theory of operation of the model,
| except the implicit one"? No, _au contraire_! He gave laws that
| took gravity as a black box, but worked very well allowing us
| to predict things "like how I expect". It has been argued that
| giving up on explaining what gravity really is, and instead
| focussing on being able to predict its workings, has been one
| of Newton's great insights. One might argue that we still don't
| understand what gravity and the remaining fundamental forces
| (electromagnetic, strong and weak forces) really are, we still
| treat them as a black box, just like modern ML research mostly
| treat transformers (and similar) as a black box. That's normal
| science. Indeed, what else can you do?
|
| The fact that "it works like how I expect" and that there is a
| clear trajectory of working more and more "like how I expect"
| is a strong indicator of scientific progress.
| taberiand wrote:
| The performance of this model using English descriptions suggests
| to me the possibility of a model that is built to work using
| precise mathematical notation performing even better.
|
| Are there models that are trained to receive and respond with a
| purely mathematical description of the problem?
| lpasselin wrote:
| Check out Google Minerva.
| https://ai.googleblog.com/2022/06/minerva-solving-quantitati...
| phi0 wrote:
| OpenAI has trained a model to solve Math Olympiad-type
| problems[1], with some initial success, but currently the model
| isn't good at coming up with proofs involving more than a
| couple arguments chained together. Still some very impressive
| work.
|
| [1] https://analyticsindiamag.com/openais-neural-theorem-
| prover-...
| ALittleLight wrote:
| I find it a little unsettling that GPT-3 is able to do this well.
| This kind of problem is abstract and I assume its training data
| doesn't extensively cover this kind of thing. I wonder if it
| would work meaningfully worse if you used different words in the
| prompt other "edge" and "node" which are probably used in
| examples somewhat like this.
|
| Example prompt -
|
| Every turn each gloober flomps the same gloobers. If a yellow
| gloober flomps a blue gloober then the blue gloober turns yellow,
| otherwise, gloobers stay the same color.
|
| 1. gloober Roy flomps gloober Zoo and gloober Bat
|
| 2. gloober Zoo flomps gloober Bat and gloober Crystal
|
| 3. gloober Crystal flomps gloober X-Ray
|
| 4. gloober Bat flomps gloober Blue
|
| If gloober Zoo is the only gloober that is yellow, and all other
| gloobers are blue, will gloober Blue turn yellow, and, if so, at
| what turn?
|
| Answer: gloober Blue
| hackerlight wrote:
| It does look like AGI will happen quite soon.
| Fragoel2 wrote:
| I disagree with your definition of "well". It found an optimal
| solution only 30% (roughly inferred from the bar plot) of the
| time and only on graphs of very limited size (up to 14 nodes).
| I'm pretty sure even the most basic path-finding algorithms we
| have can do better than this.
| ALittleLight wrote:
| It's not about competing with good path finding algorithms.
| It would not be surprising to find a program that someone had
| written to find paths that could kind of find paths okay. The
| surprising thing is that nobody wrote this program to find
| paths at all. This is a program that is attempting to predict
| what the next characters are in a string of text - and it can
| kind of do path finding.
| amelius wrote:
| How well does GPT-3 perform on standard IQ tests?
| dwohnitmok wrote:
| GPT-3 didn't even need the `gloober Blue` prompt as part of
| `Answer:`. It 0-shotted (no other training examples required)
| it straight out of the box.
|
| Prompt: Every turn each gloober flomps the same
| gloobers. If a yellow gloober flomps a blue gloober then the
| blue gloober turns yellow, otherwise, gloobers stay the same
| color. 1. gloober Roy flomps gloober Zoo and
| gloober Bat 2. gloober Zoo flomps gloober Bat and
| gloober Crystal 3. gloober Crystal flomps gloober
| X-Ray 4. gloober Bat flomps gloober Blue
| If gloober Zoo is the only gloober that is yellow, and all
| other gloobers are blue, will gloober Blue turn yellow, and, if
| so, at what turn? Answer:
|
| Continuation: gloober Blue will turn yellow on
| turn 4.
|
| > I find it a little unsettling that GPT-3 is able to do this
| well.
|
| I agree and think that this is definitely the right reaction.
| nikkwong wrote:
| I know that GPT-3 is not AGI nor modeled properly to ever
| represent what we would consider to be AGI; but this feels
| freakishly AGI-ish. This feels like the model is 'reasoning'
| in a way that I would not expect based on my intuition of how
| GPT-3 works. As it's been explained to me in lay terms; GPT
| is great at predicting the next word based on a chunk of
| previous context. This feels like it's doing much more than
| that as it's understanding the context and parameters that
| relate to a question embedded in the text, which seems
| unrelated to the original instruction of GPT-3 as I
| understand. Can anyone explain?
| ALittleLight wrote:
| Speaking of things that feel weird, while I had the
| playground open I tried another prompt that came to to my
| mind. This prompt is based on the idea that people will
| usually miss it if you double a word (e.g. I doubled "to" a
| few words back - did you notice?)
|
| I gave GPT-3 a prompt to test this. Like a human reader
| would, GPT-3 seems to miss the doubled word in the last
| sentence. Continuation is at the asterisk.
| Repeat the sentence in quotes. "The man
| went to the gas station today." The man went to the
| gas station today. "I'm happy to be on
| holiday." I'm happy to be on holiday.
| "If you can get to the the store on time, it would be
| appreciated" If you can get to the store on time,
| it would be appreciated.
|
| ---
|
| What's extra-odd about this is that GPT-3 can repeat random
| character sequences up to 1,000 characters in legnth. For
| each of these I just gave the sentence in quotes and let
| GPT-3 autocomplete the repeat. I think that's especially
| odd because it's showing that GPT-3 does something
| different when it is asked to repeat random characters
| (wouldn't miss a duplicate) versus when it is asked to
| repeat sentences that it could "read".
|
| "I need something stronger than I'm XXXknjd used to."
|
| I need something stronger than I'm XXXknjd used to.
|
| "pmkkrrpjvn"
|
| pmkkrrpjvn
|
| "wxvbssajxczjjewmanvzznxlwzwdvfrghsstmzpvqwsstrxkzunjgxicvr
| shychbqsulydkiwgyryhkgjtukssacnodtggfahvus"
|
| wxvbssajxczjjewmanvzznxlwzwdvfrghsstmzpvqwsstrxkzunjgxicvrs
| hychbqsulydkiwgyryhkgjtukssacnodtggfahvus
|
| "dcyvufmxwmctighwwhmamgwuyldpkzrwubvcbjirxzhuoqtktcdfjgasur
| cprctnxmlqfvwkxixrtqqynxfjvtfhmgsfglxhvfhnbqnixulexkqflrdxq
| pwgavrphkbgspgoyfjfolczssjfrmggtpcdheneaynhdwimcmvkcsauxwnl
| ovefpmqyznynuoxraqixeveiuqotssiymrtmbiodwnhmtexfrttzdlbdwdu
| qzvrkpmqmjcyciqsuwqmlhjrveeozktvccsspsqosutpngigzkqgaltojhv
| kodzoeqzhlhfqyblvqkoyvjkphmjroidjpbpnkvrchvrnvuoqhgzkypvjyc
| fzrdsyefstckorqwgsbknslqyiwqghdinuwrapkgyvhslbfgqrslsrferho
| dlvukdsvvzdcwyjwtrqhqkvwbwksefuanzaarvhnmjcgtajsbivaijtherc
| bmqfvndlsfhjtwsiztpwcehuvckmcbjvhnqbvhhqwugrodtyjfrwlkbpatp
| wgrlzneenyqgcdyzzhrzfsrvfqlnunfpnathcpuealrdwcliriufxdfqayg
| xtvdriidzzzdbbfwrowhlbasogmzfkwhaaufrcjsdgdobaozazbdfmasrgj
| iblluvfvfkeuwksyrzdmepmapesadqoozrpaidlgshyhpbypdzdxjdtwnfa
| xthjwluarbldmxvvfdissthlpdxqxjmgpuefwqvfjupxveztgrrwyobgalj
| qkgexeulvjucokythfkmcgnlbfkmpgrpaoztrawqciyilbzwjxagwqgwuqc
| wactwdtobomlkanmdbhpmubdzvolqpusjbtgqoymuovzsqbkzonabhzogab
| raxmnxnvfdotfisefmfbdbqjzgrhdtgtppobyndfgsrlpxyutebzlrxgahw
| izvtmieutqnkflkldxeathvrttyffacofuwdkhxdklcisbzbrlddtahut"
|
| dcyvufmxwmctighwwhmamgwuyldpkzrwubvcbjirxzhuoqtktcdfjgasurc
| prctnxmlqfvwkxixrtqqynxfjvtfhmgsfglxhvfhnbqnixulexkqflrdxqp
| wgavrphkbgspgoyfjfolczssjfrmggtpcdheneaynhdwimcmvkcsauxwnlo
| vefpmqyznynuoxraqixeveiuqotssiymrtmbiodwnhmtexfrttzdlbdwduq
| zvrkpmqmjcyciqsuwqmlhjrveeozktvccsspsqosutpngigzkqgaltojhvk
| odzoeqzhlhfqyblvqkoyvjkphmjroidjpbpnkvrchvrnvuoqhgzkypvjycf
| zrdsyefstckorqwgsbknslqyiwqghdinuwrapkgyvhslbfgqrslsrferhod
| lvukdsvvzdcwyjwtrqhqkvwbwksefuanzaarvhnmjcgtajsbivaijthercb
| mqfvndlsfhjtwsiztpwcehuvckmcbjvhnqbvhhqwugrodtyjfrwlkbpatpw
| grlzneenyqgcdyzzhrzfsrvfqlnunfpnathcpuealrdwcliriufxdfqaygx
| tvdriidzzzdbbfwrowhlbasogmzfkwhaaufrcjsdgdobaozazbdfmasrgji
| blluvfvfkeuwksyrzdmepmapesadqoozrpaidlgshyhpbypdzdxjdtwnfax
| thjwluarbldmxvvfdissthlpdxqxjmgpuefwqvfjupxveztgrrwyobgaljq
| kgexeulvjucokythfkmcgnlbfkmpgrpaoztrawqciyilbzwjxagwqgwuqcw
| actwdtobomlkanmdbhpmubdzvolqpusjbtgqoymuovzsqbkzonabhzogabr
| axmnxnvfdotfisefmfbdbqjzgrhdtgtppobyndfgsrlpxyutebzlrxgahwi
| zvtmieutqnkflkldxeathvrttyffacofuwdkhxdklcisbzbrlddtahut
| neongreen wrote:
| ...huh. That's fascinating.
| visarga wrote:
| The trick was diversity. GPT-3 trained on so many tasks it
| can understand zero shot ones like pathfinding, even when
| formulated in completely different terms.
| bilsbie wrote:
| If it walks like a duck ...
| joe-collins wrote:
| I think the sentences in this example are, odd vocabulary
| notwithstanding, fairly typical descriptions of logical
| implication, which I expect has non-trivial representation
| in the training set. GPT-3 is probably capable of matching
| the pattern of the tokens (sentence structure), even if the
| content of the tokens (spelling/vocabulary) is novel.
| uup wrote:
| I doubt it. There are billions of parameters and weights
| all combining in non trivial ways to produce the output.
| I don't think you can describe any of its workings with
| simple terms like "doing token substitution" even if
| that's what it looks like to us when we look at the
| output.
| joe-collins wrote:
| Try it. Reduce the tokens to single characters but keep
| the structure. Does it work? Maybe single characters
| looks too much like an abbreviation and that throws it
| off, because those are found in sufficiently different
| contexts. Exchange to two characters, three. Throw
| similar inputs at it and find the point where it reliably
| "gets it".
| mjburgess wrote:
| A NN is a means of compressing a training dataset into a
| mathematical representation (weights) which can be
| exploited for pattern finding. The representation of the
| training data is where 95% of the "magic" comes from. _We_
| intelligently prepare it, so no intelligence is required in
| its use.
|
| On any given input, its predictions can "look" how we
| expect an intelligent response to "look". The job is to
| disprove this intuition, which is usually trivial. This
| cannot be done by looking at a single response.
|
| Consider walking into a room and predicting a few people's
| personality traits correctly based on their star sign. This
| shows nothing.
| YorkshireSeason wrote:
| This sounds like you consider what has been called
| "feature engineering" in older ML research as being
| fundamental to modern ML successes. But the trajectory
| has been the opposite direction, away from feature
| engineering, and instead towards letting the transformer
| architecture learn as much as possible, including
| interesting features.
|
| Most research on deep net architecture, is about how to
| train it effectively on huge stacks of GPUs and TPUs.
| That's how/why transformers were invented.
| mjburgess wrote:
| weights are just compressions of the training set. The
| reason hardware improvements have improved ML is just
| because we're compressing the training set to larger
| sizes.
|
| Going eg., from 100TB training to 300BB weights. The
| process of training is more-or-less just "building a
| search index", and prediction is little more than
| "looking up examples in the search index and combining".
| visarga wrote:
| Reminds me of Pedro Domingos's paper - Every Model
| Learned by Gradient Descent Is Approximately a Kernel
| Machine
|
| https://arxiv.org/abs/2012.00152
| YorkshireSeason wrote:
| Modern DL uses _stochastic_ gradient descent, which
| Domingos 's paper doesn't deal with, in fact he leaves
| the generalisation as an open problem.
|
| Be that as it may, universality in computing models is a
| well known issue. I could also say "Every Model Learned
| by Gradient Descent Is an x86 processor + a lot of RAM",
| or ""Every Model Learned by Gradient Descent Is a 2 Layer
| Neural Net" (as 2 layer nets are universal
| approximators), but this is not terribly illuminating.
| YorkshireSeason wrote:
| I don't understand those objections.
|
| Yes, one of the key insights of modern DL-based ML is
| that size matters, neural networks start producing
| interesting generalisation only once you have large
| parameter spaces. Indeed, one might wonder if size is all
| that matters?
|
| I don't see what is wrong with search indices, every
| computer is a gigantic search index, because main memory
| is a map from an index (the memory address) to 2^n bit
| integers, the value stored at the index. So anything that
| can be computed at all, can be computed by "a search
| index". The important thing that, _prima facie_ ,
| distinguish NNs from eg SQL databases, is the use real
| numbers as latent space: because real numbers are a
| metric space, so you have a notion of distance which
| allows you do use efficient local search like gradient
| descent. Moreover, when you ask this 'mere' search index
| (NNs) about something that it was not trained on, it can
| (and probably does) return something that is close in
| this metric space to something it learned.
|
| That did not work at all in the 1990s. Today it works
| spectacularly well in some domains. So an interesting
| question now is, to use your language: is human
| intelligence anything more than _" building a search
| index"_ and _" looking up examples in the search index
| and combining"_? So this is a variation of Searle's
| Chinese room, but unlike him in 1980, we have working
| Chinese rooms!
| mjburgess wrote:
| It is important to note that the search space is just the
| training data. That space does not span enough dimensions
| to actually solve the problems in question.
|
| For example, we act in the world so as to set the state
| of possible causal variables -- and thereby can
| distinguis causes.
|
| Above, graph algorithms operate on a discrete graph space
| and hence on the graph. GPT here isn't doing that.
|
| Seeing NNs as distance-based searches in a compressed
| 'training space' demystifies what solutions can be found.
|
| Here the text space defined by the training set does not
| permit a graph traversal, so eg., the NN 'uses' edges
| that don't exist
|
| We are not searches across such spaces, which are
| radically impoverished and not parametrised by time or
| action
| YorkshireSeason wrote:
| It is not the case that _" the search space is just the
| training data"_, the search space in NNs are some
| variants of real multivariate functions. The training
| data can be all manner of things, in LLMs, they are text
| in natural language. Graphs or natural language text are
| mapped from a discrete space to a continuous metric
| space.
|
| It is true that LLMs cannot _currently_ solve complex
| mathematics problems reliably. Whether that 's an
| intrinsic shortcoming of transformers, or not is an
| interesting open question, that is being investigated as
| we speak. A lot of well-known DL research teams bet on
| transformers beating humans in mathematics in the next
| few years, see e.g. https://imo-grand-
| challenge.github.io/
| jmfldn wrote:
| "is human intelligence anything more than "building a
| search index" and "looking up examples in the search
| index and combining"?"
|
| Yes, it is more than this. Even if we can simulate things
| that appear like thinking using these methods, what we do
| bears no comparison. That's not a criticism of the
| computer here but the brain doesn't work like this.
|
| Ultimately, maybe the question "can computers think" is
| meaningless as Turing himself said. If the output appears
| like it is the product of thought, then what difference
| does it make?
| nl wrote:
| > The process of training is more-or-less just "building
| a search index", and prediction is little more than
| "looking up examples in the search index and combining".
|
| It's really not.
|
| You are compressing the information in that dataset. That
| means the model has to learn characteristics of the
| information in the data that represent it. This is one of
| the reasons why some argue that compression is a form of
| intelligence[1]
|
| It's a very different mechanism to a search index or
| database even if in some case it appears to work
| similarly.
|
| [1] http://www.hutter1.net/ai/
| pyinstallwoes wrote:
| Your example implies that they are not affected by star
| signs. This is arguably false given people believe in
| star signs and thus certainly influenced by the traits of
| said star signs.
| mjburgess wrote:
| My point is a few instances of reality confirming our
| prejudices is not the basis for explanation. This is
| something we've already discovered, it's called "the
| scientific method", and its wholey absent from ML.
| YorkshireSeason wrote:
| How do you explain the tangible progress in ML?
| tveita wrote:
| > It 0-shotted (no other training examples required) it
| straight out of the box.
|
| It 0-shotted it... but incorrectly, right?
|
| Gloober Zoo flomps gloober Bat on turn 1. Gloober Bat flomps
| gloober Blue on turn 2.
|
| Answering '4' seems like a total guess, maybe based on there
| being four steps, or 'Blue' being mentioned in step 4.
|
| The replies to your post seem to assume that the answer is
| correct, but it is actually demonstrating a _lack_ of step-
| wise reasoning.
| LodeOfCode wrote:
| To me the numbers imply the steps are sequential, so I'd
| say the answer is actually turn 1
| londons_explore wrote:
| I think you and OP have a different definition of [subject
| verb object]...
|
| My reading is that on line 1, no action happens, because
| Roy isn't yellow.
| tveita wrote:
| The order seems clear to me: "If a yellow gloober flomps
| a blue gloober then the blue gloober turns yellow,"
|
| I still don't see how 4 could be a correct answer, could
| you post a plausible sequence of turns where Blue turns
| yellow on turn 4?
| londons_explore wrote:
| List of yellow gloobers _after_ each turn.
|
| 1. Zoo
|
| 2. Zoo, Bat, Crystal
|
| 3. Zoo, Bat, Crystal, X-Ray
|
| 4. Zoo, Bat, Crystal, X-Ray, Blue
| tveita wrote:
| The text says "Every turn each gloober flomps the same
| gloobers."
|
| If I understand you correctly, you are applying only one
| of the steps each turn, in order, which contradicts the
| instructions by flomping different gloobers each turn.
|
| It is also guessable in multiple ways which is
| unfortunate. "There are four steps specified so I guess
| it happens on turn four." is one invalid way of getting
| the same answer.
| HL33tibCe7 wrote:
| > If gloober Zoo is the only gloober that is yellow
| ALittleLight wrote:
| I've been messing with the prompt a little bit and tried to
| get it to spell out what it thinks is happening. (I changed
| the name of gloober Blue to gloober Lovely just to make it
| easier to read). Continuation is at the asterisk.
| Every turn each gloober flomps the same gloobers. If a yellow
| gloober flomps a blue gloober then the blue gloober turns
| yellow, otherwise, gloobers stay the same color.
| 1. gloober Roy flomps gloober Zoo 2. gloober Roy
| flomps gloober Bat 3. gloober Zoo flomps gloober Bat
| 4. gloober Zoo flomps gloober Crystal 5. gloober
| Crystal flomps gloober X-Ray 6. gloober Bat flomps
| gloober Lovely gloober Zoo is the only yellow
| gloober. Write the turn and which gloobers are yellow.
| 1. Yellow gloobers - Zoo. 2. Yellow gloobers -* Zoo,
| Bat. 3. Yellow gloobers - Zoo, Bat, Crystal.
| 4. Yellow gloobers - Zoo, Bat, Crystal, X-Ray. 5.
| Yellow gloobers - Zoo, Bat, Crystal, X-Ray, Lovely.
| 6. Yellow gloobers - Zoo, Bat, Crystal, X-Ray, Lovely.
|
| Other than thinking the spread of yellow is limited to one
| gloober at a time, this seems right.
| codetrotter wrote:
| > Other than thinking the spread of yellow is limited to
| one gloober at a time, this seems right.
|
| Yup. Here are the results I get when manually applying
| these rules myself.
|
| Here I mention only the flomps that change gloobers to
| become yellow.
|
| Turn 1: Zoo is yellow.
|
| Turn 2: Zoo flomps Bat, turning Bat yellow. Zoo flomps
| Crystal, turning Crystal yellow.
|
| Turn 3: Crystal flomps X-Ray, turning X-Ray yellow. Bat
| flomps Lovely, turning Lovely yellow.
|
| In other words it would be:
|
| 1. Yellow gloobers - Zoo.
|
| 2. Yellow gloobers - Zoo, Bat, Crystal.
|
| 3. Yellow gloobers - Zoo, Bat, Crystal, X-Ray, Lovely.
|
| So it's got the order correct, and like you said it's just
| that it thinks only one gloober is flomped each turn.
| [deleted]
| hhhfvk wrote:
| Since GPT-3 is prone to guessing, maybe it's just guessing
| it's the last step. What happens if you try a misdirection
| and insert a 5th step?
| mbil wrote:
| Reminds me of a paper that I saw the other day: "On the
| Paradox of Learning to Reason from Data"[0]
|
| [0]: http://starai.cs.ucla.edu/papers/ZhangArxiv22.pdf
| foota wrote:
| This is fantastic, thanks for sharing!
| dr_dshiv wrote:
| Propositions:
|
| 1. GPT3 is AGI
|
| 2. In combination with people, GPT3 is superintelligence
|
| Strong claims. Anyone care to argue for or against?
| trention wrote:
| GPT3 works only on text, both input and output. A system thus
| constrained, solely by virtue of that fact, is by definition
| not an AGI.
| kevincox wrote:
| Why not? You can control robots with a sequence of text
| instructions.
| taneq wrote:
| You can represent anything as text.
| jmfldn wrote:
| There is zero evidence for the AGI claim or even a weaker
| intelligence claim. GPT 3 is just a search engine on steroids.
| I'm regularly suprised by the results but it's honestly just an
| illusion as far as 'intelligence' goes. This has nothing to do
| with intelligence, and anyone who thinks otherwise is
| succumbing to the Eliza effect.
| hackerlight wrote:
| Why can't the same thing be said of human intelligence? If we
| knew more about neuroscience it'd probably seem just as
| trivial. Wetware trained on examples we've seen. Experience
| encoding neurons. Neurons running on the laws of physics.
| Data goes in, endogenous processing happens, then outputs. I
| don't see why it's categorically different. ANNs are just
| demystified because we made them and know how they work.
| jmfldn wrote:
| You've answered your own question.
|
| "why can't the same thing be said of human intelligence"
|
| "if we knew more about neuroscience..."
|
| So that's why we can't say the same thing.
|
| Fwiw, my take is that whatever the brain does, its a
| machine of some sort but possibly not even a turing
| machine. Many disagree but noone can prove me wrong. Yet at
| least.
| runeks wrote:
| > Anyone care to argue for or against?
|
| You go first.
| dr_dshiv wrote:
| I tried in the thread to give clear definitions of
| intelligence and AGI in order to make an argument for.
|
| I didn't give references for the definitions, but for
| intelligence, I think Legg and Hutter (2007) & Russell and
| Norvig (2010) work. And Wikipedia has some tests for AGI.
|
| I asked GPT3 "if you were going to go into a person's house
| and make a cup of coffee, detail what steps you'd take." GPT3
| responded with text that is indistinguishable from what I'd
| expect from a high school graduate, at least.
|
| 1. Knock on the door or ring the doorbell.
|
| 2. Wait for the person to answer the door.
|
| 3. If the person invites you in, step into the house.
|
| 4. Find the kitchen and locate the coffee pot and coffee
| beans.
|
| 5. Measure out the desired amount of coffee beans and grind
| them.
|
| 6. Place the ground coffee in the coffee pot.
|
| 7. Fill the coffee pot with water and turn it on.
|
| 8. Wait for the coffee to finish brewing.
|
| 9. Pour the coffee into a mug and enjoy.
| dr_dshiv wrote:
| Intelligence is the capacity to survive and thrive by taking
| actions that are likely to succeed. Artificial intelligence is
| defined by the ability to take actions that maximize measures
| of success. Artificial general intelligence is defined by the
| ability to operate intelligently regardless of context; ie, not
| merely trained in a particular context but capable of
| abstracting and generally succeeding in a range of contexts
| roughly as broad as a human.
|
| By those reasonable and common definitions, GPT3 seems like a
| form of AGI. I mean, it is used for protein synthesis
| sequences, robotic sequences, design sequences-- and it is a
| fabulous writer in a vast range of domains.
|
| It doesn't need to be god to be AGI v1.
|
| I work with it every day and I recognize my contributions to
| it. But it is so generally intelligent. That is, it produces
| successful outcomes (useful text) in a vast range of contexts.
| bilsbie wrote:
| Where's the best place to try out one of these language models
| right now? Doesn't have to be gpt3.
| theblazehen wrote:
| Try signing up for https://beta.openai.com/playground, I got
| access to GPT-3 in a couple days. For GPT-2 you can try out
| https://transformer.huggingface.co/
| jokethrowaway wrote:
| I can't wait for the next version which will surely know how to
| pass a leetcode interview.
| jcims wrote:
| Would be interesting to see if it does better if the process was
| broken down into 'moves' and the model asked/reminded what its
| next possible steps are along the way.
| speedgoose wrote:
| Have you tried to create a fine tuned model to see if it helps?
| ALittleLight wrote:
| I don't believe the weights for GPT-3 are public and it would
| be expensive to fine tune such a large model.
| speedgoose wrote:
| I paid a bit more than $1 to fine tune the biggest GPT3 model
| for my use case. I didn't push a lot of data and the fine
| tuning took 35 minutes (without counting the time waiting in
| the queue).
| Der_Einzige wrote:
| It's not that expensive to find tune huge models.
|
| You can find tune gpt-J or gpt-neo with a 3090
| dwohnitmok wrote:
| OpenAI provides an API for fine-tuning.
| https://beta.openai.com/docs/guides/fine-tuning
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