[HN Gopher] What Will Transformers Transform?
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What Will Transformers Transform?
Author : KKKKkkkk1
Score : 104 points
Date : 2023-03-23 07:43 UTC (15 hours ago)
(HTM) web link (rodneybrooks.com)
(TXT) w3m dump (rodneybrooks.com)
| 0xdeadbeefbabe wrote:
| > But perhaps that says something about how we humans relate to
| language and the world, and that language really only, perhaps,
| has a tenuous connection to the world in which we live; a scary
| thought.
|
| Why is that a scary thought? Is this related to business speak
| https://www.atrixnet.com/bs-generator.html and how it is scary?
| layer8 wrote:
| I think he means language in general, in which case it would be
| scary in a similar way as not being able to trust your senses.
| k__ wrote:
| Human: write a scientist skill classifier in Python.
|
| GPT: is blatantly racist and makes syntax errors.
|
| Other human: It writes a Python program without getting the
| indentation right!!!
|
| Made my day, thanks.
| jxy wrote:
| I guess it's joke, but I had to try. GPT-4's interpretation of
| the prompt is "on point": class
| ScientistSkillClassifier: def __init__(self):
| self.skill_domains = { "biology":
| ["genetics", "ecology", "microbiology", "evolution"],
| "chemistry": ["analytical", "organic", "inorganic",
| "physical"], "physics": ["quantum",
| "relativity", "thermodynamics", "mechanics"],
| "computer_science": ["algorithms", "machine_learning",
| "artificial_intelligence", "programming"],
| "earth_science": ["geology", "meteorology", "oceanography",
| "climatology"], "mathematics": ["calculus",
| "statistics", "geometry", "algebra"] }
| def classify(self, skill): for domain, skills
| in self.skill_domains.items(): if
| skill.lower() in skills: return domain
| return "unknown"
| zvonimirs wrote:
| It might be a hype like many other things ( _khm_ crypto _khm_ )
| but I think this one is just different. It might change the world
| really drastically.
| wsgeorge wrote:
| > but I think this one is just different. It might change the
| world really drastically.
|
| FWIW crypto did change the world in a number of ways:
|
| 1. CBDCs actively being explored.
|
| 2. Increased concern about the environmental cost of compute.
|
| 3. It probably raised the public's level of bullshit
| detection...
| antibasilisk wrote:
| not to mention the fact that we now have a way of transacting
| completely anonymously online, the value of which cannot be
| understated
| topaz0 wrote:
| Given the discourse around chatgpt (3) seems questionable, or
| at least not general.
| otabdeveloper4 wrote:
| > It might change the world really drastically.
|
| Or it might not.
|
| _I want to believe_ , like the meme says.
| janalsncm wrote:
| Machine learning was used long before ChatGPT came around. It's
| not a fad or a bubble.
| neilellis wrote:
| It can only predict words, is like saying all neuron's are just
| switches, all matter is just protons, electrons and neutrons.
|
| It's just reductive. What we're seeing from LLMs is simply
| amazing considering what they are supposed to just do.
|
| Whatever LLMs are capable of, big or small it exceeds such
| reductive thinking as 'it predicts words'.
| janalsncm wrote:
| Exactly. These kinds of reductive arguments always bother me
| because they ignore emergent effects. Chess engines are "just"
| running minimax? Well guess what, they're evaluating millions
| of positions per second.
|
| If this whole intelligence thing can be reduced to "just"
| predicting the next word, maybe we're not as special as we
| thought. In fact anyone who reads the Wikipedia article on
| cetacean intelligence will quickly realize that we aren't the
| hot shit we once thought we were.
| clarge1120 wrote:
| One expects the academic community to step in and provide this
| kind of analysis, namely that an LLM combined with a great deal
| of computing capacity exhibits capabilities greater than the
| sum of its parts.
| ftxbro wrote:
| I'm a long time LLM enjoyer and by far the best analysis I've
| seen for that is https://generative.ink/posts/simulators/ but
| it's way "too much" for normal people who are learning about
| stochastic parrots and blurry jpegs instead.
| kenjackson wrote:
| And "predicting words" given a huge corpus of interesting text
| by itself seems like it could be amazing. Predicting words
| effectively can mean that it has internalized all the logic
| used to generate the original texts. At some extrema simply
| predicting words could mean more than just "knowing facts", but
| actually having the accumulated smarts of those that made large
| contributions to the corpus.
|
| Allow me to participate in some hyperbole here, but "predicting
| words" could result in a model that is "smarter" in most
| capacities than most humans.
| blueorange8 wrote:
| It's basically creating an accurate world view
| ay wrote:
| I tried the example about the table not fitting into the car
| using GPT4. Not only it answered it correctly, but it actively
| (but politely) argued with me when I tried to imply a different
| target for "it":
|
| https://mobile.twitter.com/ayourtch/status/16388720452817879...
|
| (Edit: it did of course end up in a nonsense, so one can argue
| the point of not having the model of the world still stands;
| there is an "edge of bullshit", but it seems to be moving further
| with each update)
|
| At this point the arguments about about LLMs need to be tagged,
| which exact version they are talking about. (Probably most are
| talking about GPT3.5)
|
| My (small) experience playing with both indicates there is a
| significant gap between the two.
| yorwba wrote:
| The answering style matches the usual presentation of a
| Winograd schema, which is a bit of a problem when you actually
| want to test its general reasoning ability, because it could be
| that someone already posted your go-to test question and the
| answer on the internet and the model was trained on that.
|
| Maybe we need to start obfuscating this stuff
| import base64;import hashlib;f=lambda s:bytes(k^v for k,v in zi
| p(hashlib.shake_256(b'wOmGLuIVp70').digest(len(s)),s));print(f(
| base64.decodebytes(b'Iq6VQOBRifQwwwO6gluzzWEnGIICFKKFwM1oMWmBsT
| rIhMj5AseeNmNUwtEZkthcz8m8v8qKmVIx7nEjPOsqOUimKaTIJ8OKk2STdo/SR
| ZGLAOsBSmgGaNTYEgT3KaayJWmGVf7K/UN06VyosEHfyFZlsS+PHDS6B3bN94qr
| zdnOA9f12FwWuaTPNJhLGcXFX7r5H8mtWyt9uWq6n5AItEcRXId04ssR8jfvNra
| y2fwFIh5qPHTdQvZ9ogKLJ4Y+nAro7ecRSXXgskAj5EBmo2YobRkfE26er/Tj9D
| ZHNx81N64ujWvN8jiS7aNcYs/oaEyN0oqnZvia9qocrv6CfBr+wGGSG1oxk5mbh
| AgkQhfuyR6c8MVKNFKp8HFo6SR7auju8vLnYjcObcII88vRbbua/jQmakiWwmS6
| 8Y1e1Gqmqg==')).decode())
|
| to avoid burning test questions. (GPT-3.5 gave the answer I
| expected on the 4th try, didn't test with GPT-4.)
| jxy wrote:
| GPT-4 answered correctly and explained the chemical reaction.
| yorwba wrote:
| That's good to hear.
| HarHarVeryFunny wrote:
| I tried the car-table example too (via Bing), using the exact
| same wording, and got a totally different confused reply, so it
| must think the two parsings of the sentence are closely
| probable (and isn't deterred by the semantics).
|
| I tried it with Bard too, which also thought the table was too
| small to fit in the car, and doubled down on this when pressed
| by explaining how the table might be too narrow or deep or
| heavy(!) to fit in the car.
|
| I was a bit surprised to see GPT-4 get this wrong, even it it's
| only doing so some of the time (sampling temperature
| randomness?).
| HarHarVeryFunny wrote:
| Just tried again with GPT-4 (exactly as before), and reply
| this time was simply "The table didn't fit in the car because
| it was too big. Do you have any other questions?".
|
| Bing/GPT bolded the words "too big".
|
| But as I said before, the fact that it sometimes gets it
| wrong must mean it doesn't see one parsing as much to be
| favored over the other, which is surprising given how
| competent it generally is.
| thefreeman wrote:
| The biggest takeaway from the article for me was GPT 4 has 100
| trillion parameters, 500x more then GPT 3. that type of
| exponential scaling is going to hit an upper bound really
| quickly. So when you say it "moves further with each update"
| you should realize that the improvements are coming from
| throwing massively more scale at the problem and not some
| underlying improvement in the technique, and also balance the
| amount of improvement with the scale itself.
|
| obviously gpt-4 is nowhere near 500x better than gpt-3. let's
| say it's 20% better (very generous imo). can they realistically
| 500x the model again? and if so, is that going to be worth an
| additional 4% gain to the original model quality? numbers are
| completely made up and math is probably wrong but i think i'm
| hopefully making my point, that diminishing returns will
| quickly become a blocker with this type of scaling.
| cinntaile wrote:
| We shouldn't forget that at the same time effort is being
| spent to achieve the same results with a lot fewer
| parameters.
| carbocation wrote:
| I would hesitate to assume that is true. The 100 trillion
| param number has not been confirmed by anyone who could know
| if it's true.
| og_kalu wrote:
| Gpt-4 doesn't have 100 trillion parameters. It's not much
| larger than 3. It just has a lot more data according to
| Altman.
| qualudeheart wrote:
| Altman himself has said it doesn't have 100T parameters. It
| has more data in line with the Chinchilla laws.
| blueorange8 wrote:
| I don't believe they spent 7 months just "making gpt-4
| safer" - I think they spent a very long time doing human
| reinforcement learning to make it better and now hope to
| speed that process up the next time using gpt-4 itself as
| the reinforcement.
| sparsevector wrote:
| I also tried this out and the balloon example as well.
|
| User: Barbara, Mary, and Harry had red balloons. Ted had a blue
| balloon. Sam also had a red balloon. How many children have red
| balloons?
|
| ChatGPT (GPT-4): Barbara, Mary, Harry, and Sam each had a red
| balloon. That means there are 4 children with red balloons.
|
| User: What about Ted?
|
| ChatGPT (GPT-4): Ted had a blue balloon, not a red one. So, he
| is not counted among the children with red balloons. Therefore,
| the total number of children with red balloons remains 4.
|
| User: Why do you think I asked my original question?
|
| ChatGPT (GPT-4): It seems that you asked your original question
| to test my ability to understand and process information
| provided in a text. You wanted to see if I could correctly
| identify and count the number of children with red balloons
| based on the details given.
|
| User: Can you explain what mistakes a simpler model may have
| made on this question?
|
| ChatGPT (GPT-4): A simpler language model may have made the
| following mistakes when answering your original question: ...
|
| I like trying these sort of puzzles and "gotcha" prompts and
| I've found GPT-3.5 and lower versions are indeed not hard to
| confuse, but it's _much_ harder to find simple examples that
| confuse GPT-4. Often times GPT-4 will even be able to explain
| the puzzle and why it 's interesting. I think this is part of
| why GPT-4 feels qualitatively different. I understand (roughly)
| how transformers work, but GPT-4 does seem to have some
| emergent ability to reason, at least in the sense of making
| short chains of logical inferences, and this does seem to
| inform text completions not just on these sort of puzzles. That
| said I do think there's real danger of overconfidence and over
| reliance.
| usaar333 wrote:
| Agreed GPT4 is better, but there's still simple world
| modeling issues:
|
| > I have a chair in front of the TV and one in front of the
| computer. I put a watch under the chair in front of the TV. I
| swap the chairs. Where is the watch?
|
| > After swapping the chairs, the watch is now under the chair
| that is in front of the computer.
|
| Interestingly, GPT3 got it correct.
| zootreeves wrote:
| Question isn't clear if the watch is stuck to the bottom of
| the chair.
| blueorange8 wrote:
| If I ask it like this, if gets it right:
|
| I have a chair in front of the TV and one in front of the
| computer. I placed a watch on the floor beneath the chair
| in front of the TV. Then, I swapped the positions of the
| chairs. Where is the watch now?
|
| After swapping the positions of the chairs, the watch is
| now on the floor beneath the chair that was initially in
| front of the computer, since that chair is now in front of
| the TV.
| layer8 wrote:
| This is not a competition in getting it to answer right,
| though. It's that the instances where it answers wrong
| demonstrate its lack of inner mental model of what it is
| supposedly reasoning about, as well as a lack of meta-
| awareness. I think we tend to underestimate what mere
| linguistic correlation is capable of producing, and are
| too quick to attribute intelligent reasoning and an inner
| mental model to it.
| kenjackson wrote:
| I just tried it and it says it's under the chair in front
| of the computer. I used the Bing model.
| letmevoteplease wrote:
| I asked GPT 4 (Bing) your question, but added "(Please give
| a plausible explanation for why this question could be
| answered either way.)"
|
| > This question could be answered either way depending on
| how you interpret the phrase "under the chair". If you mean
| under the chair as in attached to the bottom of the chair,
| then the watch would move with the chair when you swap
| them. In that case, the watch would be in front of the
| computer. However, if you mean under the chair as in on the
| floor below the chair, then the watch would stay on the
| same spot when you swap the chairs. In that case, the watch
| would be in front of the TV. So, it depends on what you
| mean by "under".
| usaar333 wrote:
| Yes, but that's a huge hint. GPT4 naively doesn't
| understand this.
|
| Question:
|
| > I have a chair in front of the TV and one in front of
| the computer. I put a watch under the chair in front of
| the TV. I swap the chairs. Where is the watch?
|
| > Work out step by step. Is this question ambiguous?
|
| Answer:
|
| This question is not ambiguous, but let's break it down
| step by step to understand the situation and find the
| location of the watch.
|
| You have a chair in front of the TV (Chair A) and one in
| front of the computer (Chair B). You put a watch under
| Chair A (the one in front of the TV). You swap the
| chairs, meaning Chair A is now in front of the computer
| and Chair B is in front of the TV. After swapping the
| chairs, the watch remains under Chair A. Since Chair A is
| now in front of the computer, the watch is under the
| chair in front of the computer.
| blueorange8 wrote:
| Ya gpt-4 is still confused: (it's interesting cause it
| does realize the watch doesn't move but still gets it
| wrong)
|
| Model: GPT-4
|
| I have a chair in front of the TV and one in front of the
| computer. I put a watch under the chair in front of the
| TV. I swap the chairs. Where is the watch?
|
| Work out step by step. Also define "under the chair". Is
| the question ambiguous?
|
| Step 1: A chair with a watch under it is in front of the
| TV. Step 2: Another chair is in front of the computer.
| Step 3: The chairs are swapped.
|
| To clarify the term "under the chair," let's define it as
| a watch placed beneath the chair, such that the chair is
| directly above the watch.
|
| Now let's analyze the situation after swapping the
| chairs.
|
| After Step 3:
|
| The chair that was initially in front of the TV (with the
| watch under it) is now in front of the computer. The
| chair that was initially in front of the computer is now
| in front of the TV. As the question is about the location
| of the watch, it is important to note that the watch was
| not mentioned to be moved along with the chair.
| Therefore, the watch remains in its original position,
| which is under the chair that was initially in front of
| the TV.
|
| The question could be considered ambiguous if one assumes
| that the watch was also moved along with the chair.
| However, since the watch's movement was not explicitly
| mentioned, it is reasonable to assume that the watch
| remained in its original position during the chair swap.
|
| So, the watch is still under the chair that was initially
| in front of the TV, which is now in front of the
| computer.
| [deleted]
| imjonse wrote:
| I thought the gotcha was that nowhere was it stated which of
| them are actually children.
| kzrdude wrote:
| How it just takes these implicit assumptions in stride
| makes me worry that in the future these chatbots will be
| fine with assumptions and me - human - has a too square
| brain to go on without definitions. :)
| K2L8M11N2 wrote:
| > There will be no viable robotics applications that harness the
| serious power of GPTs in any meaningful way.
|
| That's a weird prediction to make, considering that PaLM-E does
| exactly that: https://palm-e.github.io/
| InfiniteRand wrote:
| What will Transformers Transform? Transformation!
| [deleted]
| ftxbro wrote:
| Rodney Brooks, famous for arguing that that in order for robots
| to accomplish everyday tasks in an environment shared by humans,
| their higher cognitive abilities, including abstract thinking
| emulated by symbolic reasoning, need to be based on the primarily
| sensory-motor coupling (action) with the environment,
| complemented by the proprioceptive sense which is a key component
| in hand-eye coordination, is unsurprisingly bearish on GPT
| capabilities especially for robotics. He concludes with a
| prediction that there will be new categories of pornography. So
| he will certainly get at least one prediction right.
| blueorange8 wrote:
| And I would argue that just language is enough
| flangola7 wrote:
| It might be, and it might be even better with both.
|
| GPT-4 language performance improved after adding vision
| training.
| topaz0 wrote:
| Interestingly, he keeps a record of his past predictions and
| how they are faring. The link is at the bottom of TFA. Seems
| like he has been reasonably successful at this kind of
| prediction.
| oh_sigh wrote:
| Has he predicted that his future predictions will be as
| successful as his previous predictions?
| ftxbro wrote:
| He makes nine predictions at the end which he calls
| 'specific'. Four of them are most directly about GPT, and of
| these four, three are of the type "GPT is powerful and
| potentially dangerous or bad and might make bad or unexpected
| or scary things happen."
|
| (This follows the pattern I've noticed where the term "AGI
| skeptic" will soon, if not already, mean "I don't trust AGIs
| in positions of authority or power" rather than "I don't
| think the technology is capable of matching our level of
| cognition.").
|
| So let's grant him 8/9 of his predictions, and turn our
| attention to the one that seems to be his 'real' prediction.
| This specific and direct prediction about GPT is that "There
| will be no viable robotics applications that harness the
| serious power of GPTs in any meaningful way." which I mean,
| maybe he's trying to use the words "viable" or "serious" or
| "meaningful" to weaken his claim so that it's never wrong. If
| we assume he's not just making a vacuously weakened
| prediction, then I wonder if he has seen
| https://palm-e.github.io/ for example. You could say the
| prediction is wrong already, or that it will obviously be
| wrong before 2030, or that his phrasing makes it impossible
| for the prediction to ever be wrong.
|
| Speaking of when or if his predictions can be shown to be
| wrong, I thought it was weird that he only says "These
| predictions cover the time between now and 2030." whereas for
| his earlier predictions he seems to have made a more
| formalized way of incorporating his dates into his
| predictions which he's not using for his GPT predictions for
| some reason:
|
| ---
|
| I specify dates in three different ways:
|
| NIML meaning "Not In My Lifetime, i.e., not until after
| January 1st, 2050
|
| NET some date, meaning "No Earlier Than" that date.
|
| BY some date, meaning "By" that date.
|
| Sometimes I will give both a NET and a BY for a single
| prediction, establishing a window in which I believe it will
| happen.
| transfire wrote:
| Well Mr. Brooks, I am certain you have at least one thing
| right... #9.
| Sunhold wrote:
| This article repeats the rumor that GPT4 has 100 trillion
| parameters, which Sam Altman has called "complete bullshit."[1]
|
| [1] https://www.youtube.com/watch?v=ebjkD1Om4uw
| neilellis wrote:
| I think people are getting confused withe the human brain's
| number of synapses. That's why that number is a 'magic' number.
| zvonimirs wrote:
| exactly
| aaroninsf wrote:
| Evergreen:
|
| Ximm's Law: every critique of AI assumes to some degree that
| contemporary implementations will not, or cannot, be improved
| upon.
|
| Lemma: any statement about AI which uses the word "never" to
| preclude some feature from future realization is false.
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(page generated 2023-03-23 23:02 UTC)