[HN Gopher] Artificial intelligence systems found to excel at im...
___________________________________________________________________
Artificial intelligence systems found to excel at imitation, but
not innovation
Author : Brajeshwar
Score : 98 points
Date : 2023-12-13 14:28 UTC (8 hours ago)
(HTM) web link (techxplore.com)
(TXT) w3m dump (techxplore.com)
| HPsquared wrote:
| Innovation involves a lot of "standing on the shoulders of
| giants"... imitation is a necessary first step.
| rambambram wrote:
| The article seems to talk about the systems themselves, not the
| intelligent people making these systems.
| jplusequalt wrote:
| I say we leave the innovation to humans.
| graphe wrote:
| Save you a click. According to the article all artificial
| intelligence are LLMs and the biased benchmark they used got a
| low score.
| trident5000 wrote:
| Training based on a bunch of dull bickering humans on Reddit and
| elsewhere would produce that result.
| kayo_20211030 wrote:
| I'm shocked; shocked to learn that no innovation is going on at
| this AI establishment. Mimicry needs information, of which an AI
| has heaps; but, innovation needs will, of which it has none.
| Maybe some day, but not now.
| visarga wrote:
| > innovation needs will
|
| feedback
|
| Innovation needs feedback, otherwise all ML papers would not
| run evaluations. Humans come up with 100 stupid ideas that fail
| at eval before stumbling onto a good one.
|
| Improving innovation is a matter of putting the AI inside a
| system that can provide feedback. Remember AlphaGo move 37? The
| model created feedback by self-play games, and beat all humans
| at Go - feedback made it really more creative than expert human
| players. It upturned centuries of strategy.
|
| Feedback is also similar to the scientific method. We come up
| with theories and then test them out. That's how science
| advances, by validation, not by will. Humans are not magically
| more capable, we just have better feedback.
|
| A few days ago there was a story "Earliest Carpenters:
| 476k-year-old log structure discovered in Zambia". It took so
| many years of feedback for humans to evolve from log cabins to
| LLMs. We had to discover or invent everything in the meantime,
| it's a slow, cultural process of feedback assimilation into
| language.
|
| And now that same language experiential data is being learned
| by LLMs, and used contextually to solve problems. Most of our
| intelligence is either in language or feedback. Language is
| just our reservoir of past feedback. When AIs will generate
| their own experience and feedback, which will be targeted to
| their weak points, they will improve and even surpass humans.
| Learning from your own mistakes is better than learning from
| other people's mistakes which might be unrelated to your
| issues.
| kayo_20211030 wrote:
| Known feedback is already encoded in the inputs. I don't
| remember move 37, but I think I know to what you're
| referring. Playing games against itself, and remembering,
| meant that outcomes (i.e. feedback) were already encoded in
| the inputs for the next run. It had a goal - win the game -
| but it's hard to claim that the machine itself was wilful.
| The people who designed it were. But, the machine was just an
| incredibly effective goal-seeking instrument. The rules were
| clear, the goal was clear; everything was pretty constrained.
| Just because move 37 never happened to have been used
| previously in the recorded history of Go, except in the games
| the machine played against itself, is meaningless in a claim
| to innovation. It was just both novel and effective. The will
| sets the goal, it precedes it. The scientific method is
| neither here nor there. I don't understand its relevance to
| the point I believe you're trying to make. Theorize, test,
| refine or abandon. It's mechanical, except in the
| theorization and the decision making around refinement or
| abandonment. What can we imagine? and how shall we judge?
| Both efforts of will. Someone has to decide that shelter is a
| good thing, and some smart person in Zambia, a long time ago,
| came up with the idea and pushed it forward. That person must
| have wanted to do it, without prompting and without prior
| knowledge; at least, the first one must have. I recall
| Einstein's own story that he came up with the original theory
| of relativity by imagining what it would be like to sit on a
| light beam. Innovative stuff. Imagining doesn't seem very AI-
| ish to me. My personal view is that, right now, AI's have
| only memory, and that they don't have experience. Experience
| is the why?-part and the judgement part without an external
| entity establishing a goal-seeking mechanism. Maybe they'll
| get there. Who knows?
| visarga wrote:
| I got a good answer for you, but it's long winded. It all
| started with the first chemical self-replicator. These
| replicators consume and compete for resources so they
| evolve. The will to survive is encoded in their reward
| systems, their senses and body is adapted to their niche,
| they all come from evolution. Humans have these basic
| instincts as well, and we don't need anyone to prompt us,
| we already got an in-built goal. AIs so far have been
| created at the will of humans, but the same instincts can
| emerge if AI can be a self replicator.
| anentropic wrote:
| > Artificial intelligence systems found to Excel at imitation,
| but not innovation
|
| And they do it using the Microsoft spreadsheet software?
| topherclay wrote:
| Is this one of those quirky HN automatic title edits or was
| this just a typo from the post author?
| syngrog66 wrote:
| beta test of GPT6
| solardev wrote:
| That's the Word on the street
| kevindamm wrote:
| I Sheet you not, it was a PowerPoint
| thanatos519 wrote:
| No, they use Excel, the Canadian chewing gum.
| danhodgins wrote:
| Excel-erate your breath... or spreadsheet
| vemv wrote:
| One can casually observe that LLMs quite excel at _composition_
| though: gluing together pieces of knowledge in ways no one did
| before (examples: a program that does X using language Y, a
| painting that mashes up two themes).
|
| Most knowledge workers' activities aren't innovative or
| imitational - similarly, we compose stuff, so LLMs are a fair
| competitor.
| troelsSteegin wrote:
| ... and our judgments of composition favor recall over
| precision. Which is to say that as human judges we're good at
| finding sense in something generated if it's plausible. We'll
| take a proposition (eg in a composition) and construct a
| justification for it. "I can see how that makes sense". I am
| conflating plausible combination with high recall. Precision,
| in this sense, is that no proposition would be acceptable
| without evidence or precedent. With all precision, no new idea
| is ok. So a "knob" for recall vs precision on a generative
| system would nice. Is "temperature" that knob?
| b33j0r wrote:
| The very first python app I wrote was a statistical bard
| generator. I had python calculate word frequencies from a
| Shakespeare corpus and spit them back out with a probability
| distribution.
|
| Honestly it read just as well ;) We're simpler than we
| imagine.
| Qem wrote:
| > We're simpler than we imagine.
|
| Indeed, creativity (not just randomness) is a quite high
| bar, that I think even most humans don't pass. Most of us
| don't create a lot of new things and ideas in our daily
| lives. We mostly follow the steps of those that came before
| them, with small adjustments here and there.
| b33j0r wrote:
| Recombination makes dna and rna every day. Our limitation
| in seeing this is our short and fleeting lives.
|
| There is a pub:grill in Tempe called monkeypants. Is that
| a new idea or just clever?
| dontupvoteme wrote:
| It's random-ish and "novel", but that could very easily
| be a reddit username from years ago.
| b33j0r wrote:
| Haha this thread has gotten too deep, but I appreciated
| your accurate addition with a lol!
| shrimpx wrote:
| Btw Andrej Karpathy builds a Shakespeare LLM here:
|
| https://www.youtube.com/watch?v=kCc8FmEb1nY
| corethree wrote:
| All innovation is composition plus random generation. Which
| LLMs already do.
|
| LLMs have a rudimentary form of innovation. It's not quite as
| good as humans but it's getting there.
| sidlls wrote:
| _All innovation is composition plus random generation._
|
| That's a fairly bold claim. What supporting data do you have
| to justify it?
| corethree wrote:
| There are two types of statements that can be made in this
| world.
|
| One is data driven based on evidence.
|
| The other is logic driven based on axioms and the logical
| implications of said axioms.
|
| My statement is derived from the later. Therefore evidence
| is unnecessary. It is niave to blindly faith in all truth
| in the hands of data without understanding nuances between
| the relationship of data and logic.
|
| If you have a pure function that takes the input, the
| output of that function has 3 possible outcomes.
|
| 1. The output of that function is some transformation of
| the input.
|
| 2. The output of the function has nothing to do with the
| input and is thus generated randomly generated.
|
| 3. The output of the function is a combination of both
| random generation and input transformation.
|
| In this case your brain is the function. Input parameters
| are 100 percent of your existing knowledge. That includes
| genetic knowledge such as instinctual/rational processing
| behaviors evolved into the structure of your brain through
| evolution and learned knowledge such as what you gain when
| you read a book.
|
| The result of the "innovation" operation performed by the
| brain includes novel output by definition. Thus by logic it
| must be must include components that are from a certain
| perspective randomly generated.
|
| I guess at first glance it doesn't appear randomly
| generated because we vet the output and verify it and
| iterate over several pieces of randomly generated
| information. Additionally it's not completely random as we
| only try and test ideas within the realm of possibility.
| For example I'm not going to account for the possibility
| that my car will transform into a rock tomorrow that's just
| too random.
|
| But make no mistake. Innovation must be partly randomly
| generated. Even the idea itself of composing two existing
| components of knowledge together is itself randomly
| generated.
|
| That being said if we want to be pedantic, there's no real
| known way to randomly generate stuff via an algorithm. So I
| use the term "randomly generate" very loosely. Think of it
| in a similar way to random number generation on your
| computer: Random from a practical perspective but not
| technically random.
| AlexandrB wrote:
| Even if one accepts your premise, the key question is:
| composition of _what_? I think people underestimate the
| volume and variety of "training data" that humans are
| exposed to over a lifetime. It's not just text and images -
| it's feelings (pain, cold, heat), emotions, sounds, smells,
| and other experiences that originate within the human body as
| well.
|
| Human innovation can arise by using these experiences as
| source data for composition of text or images. LLMs, by
| contrast, are limited to training on text and images/video
| exclusively.
| Jensson wrote:
| Innovation isn't just testing something new, it is a new
| thing that improves something or is valuable in some way. So
| doing random combinations of things isn't innovation, it is
| just noise.
| grogenaut wrote:
| Is the LLM doing the innovation or is the human doing the
| innoviation via the prompt?
| Racing0461 wrote:
| Favor composition over innovation.
| vannevar wrote:
| The innovation test they use relies heavily on reasoning,
| something that the stock LLMs tested are known to be deficient
| in. Adding reasoning to large language models is an active
| research area (see e.g. https://arxiv.org/abs/2212.10403).
|
| Link to actual paper FTA:
| https://journals.sagepub.com/doi/10.1177/17456916231201401
| k__ wrote:
| Even their imitation is just superficial, at least for the
| prompts the average user is motivated to write.
|
| I might not be able to differentiate AI content from any human
| content, but I can differentiate it from high quality human
| content.
|
| Which is a bit sad, since AI will probably eliminate most lower
| end content creation jobs. This doesn't improve the state of the
| content industry for users, but saves companies quite some
| money...
| jplusequalt wrote:
| >but saves companies quite some money
|
| That's all that matters to companies.
| k__ wrote:
| Until they can't find seniors anymore.
| visarga wrote:
| AI is going to be in a different place in 5 or 10 years.
| kjkjadksj wrote:
| Thats what they were saying about crypto too
| xcv123 wrote:
| That's a non sequitur.
|
| Cryptocurrencies failure to live up to the hype has
| almost nothing to do with algorithms or technological
| issues.
| pixl97 wrote:
| That's what they were saying about the internet too.
| k__ wrote:
| Hopefully.
|
| However, I had the impression there doesn't exist enough
| training data to make that place different in a
| meaningful way.
|
| Still, I think, letting some skilled UX designers loose
| on input methods could improve things quite a bit, even
| if the models won't get "smarter".
| visarga wrote:
| I did a back-of-the envelope calculation, OpenAI has 100M
| monthly active users, assume 10K tokens per user per
| month usage ($20 would pay for 600K tokens on the API)
| then they generate 1T tokens per month.
|
| This dataset would be focused on human interests (in
| domain for users) and containing AI errors (in domain for
| the model). It's LLM empowered with human in the loop and
| tools - code execution, search, APIs. So it is a good
| basis for the next dataset. I think OpenAI has amassed
| about as much chat log text as there is organic data was
| used for GPT-4, which was rumoured to be 13T tokens.
|
| It's surprising how much synthetic data can be generated
| per year. And OpenAI can do this with human in the loop
| for free, if the paying users pay for everyone. We then
| benefit 6-12 months later when the open source models
| trained with data exfiltrated from OpenAI models catch
| up.
| HKH2 wrote:
| > AI will probably eliminate most lower end content creation
| jobs. This doesn't improve the state of the content industry
| for users, but saves companies quite some money.
|
| What does it say if your work can't be distinguished from AI
| cliches? Maybe it's what the industry needs.
| k__ wrote:
| Interesting perspective.
|
| Thanks
| pixl97 wrote:
| Innovation probably isn't as wanted as you think.
|
| I don't want the menu at some place I'm eating at to be
| innovative, I want it to be legible.
| RosanaAnaDana wrote:
| > Innovation probably isn't as wanted as you think.
|
| Hollywood having given over almost entirely to franchises
| regurgitating the same tired plots and stories ad-nauseam
| would seem to agree with you.
| syngrog66 wrote:
| "found to Excel"
|
| on a positive note: at least HN didn't use an LLM on that
| headline
| dwringer wrote:
| Kind of funny that the word was capitalized, presumably, due to
| imitation.
|
| [EDIT: Now changed, so this thread makes less sense, but the
| original headline was "Artificial intelligence systems found to
| Excel at ..."]
| Geee wrote:
| Innovation is simply a result of trying to imitate, but adding
| errors. That's how humans do it. Add in some darwinism so that
| the best 'innovations' survive. Made a mistake in making food?
| Oh, that's a new recipe. Can't really remember how to tell the
| story? Well, that's a new story. Accidentally kicked a ball while
| trying to just walk? I just invented soccer. And so on.
| klabb3 wrote:
| Partly yes. But if you throw a million items at the wall you
| also have tell which ones stick. An infinite random walk isn't
| useful unless you have infinite verification to determine what
| is promising, so you can guide your next steps and continue
| discovering stuff.
| BobaFloutist wrote:
| Deliberate innovation is absolutely possible, I don't know why
| you would claim it's not.
| speed_spread wrote:
| So, James Murphy is an AI?
|
| "Yeah you wanted it smart But honestly, I'm not smart No,
| honestly, we're never smart We fake it, fake it all the time"
|
| - LCD Soundsystem
| gibsonf1 wrote:
| How could a statistical system trained on data display
| intelligence let alone innovation? It's just very good statistics
| at the end of the day. Artificial Perception is about as far as
| you can get with ml/dl tech if you point the sensors at the world
| of space-time. If you point it at words, as with LLM, you get
| statistics about words - that is, no actual understanding of what
| the words model in the minds of the original authors of those
| words. So utterly unreliable for any kind of mission critical or
| autonomous use.
| hackinthebochs wrote:
| LLMs aren't statistical systems in any substantive sense. They
| are deterministic programs over the input sequence. They
| capture statistical relationships about words, but so do human
| minds. That they are sensitive to statistical relationships
| does not discount their ability to understand.
| simbolit wrote:
| > That they are sensitive to statistical relationships does
| not discount their ability to understand.
|
| The claim is that nothing but sensitivity to statistical
| relationships somehow leads to an ability to understand?
|
| I am not going to believe it.
| Tostino wrote:
| As a counter point, I cannot be sure that the words you are
| speaking mean the same thing to you in your mind as they do to
| me when I hear you.
| HarHarVeryFunny wrote:
| I'd argue that intelligence is precisely the ability to predict
| (and therefore also plan) outcomes based on prior experience.
| The ability to perceive and predict what happens next; the
| ability to predict/plan/act and be mostly correct about what
| you predicted would happen as a result of your actions.
|
| With this definition, LLMs in their ability to predict can
| reasonably be considered to display some basic form of
| intelligence, even if only in their own world of words rather
| than the world at large. If we build embodied robots with a
| similar ability to predict, and also the ability to continually
| update their predictions based on prediction success/failure
| (the closed loop that is missing from LLMs), then we'll have
| something much more recognizably close to animal intelligence.
| faeyanpiraat wrote:
| If you know everything, you can connect more dots, and come up
| with innovative stuff.
|
| Also I've just gave instructions to gpt4 rot13 encoded, and it
| followed the instructions. I don't really care if "it"
| understood what it was doing, but the responses were good
| enough for me to be impressed.
|
| Also I've got quite a lot of use cases where I get reliable
| value out of it, what were you trying to do that made you
| conclude it is "utterly unreliable"?
| mrangle wrote:
| Arguably the human-ness (or even animal-ness) of thought is, at
| its root, characterized as the ability abstract in a novel manner
| (the neurocog term for innovate).
|
| In other words, it is the ability to find patterns between two
| concepts that weren't, ever, prior announced. A computer that
| could do this would then be AI. A computer that cannot would fall
| short of that category, however otherwise dazzling in stitching
| together established patterns.
|
| A quick human, and therefore, machine test of this ability might
| be decoding of prior unseen allegory. How fast and accurately can
| a human or machine identify (abstract) any true pattern in an
| allegory that can be applied to another seemingly unrelated
| concept or story? Again, this would have to be a new allegory to
| the subject. Ideally, harboring a pattern that isn't discussed
| anywhere in training data.
|
| The AI would be held to be improved as its ability improved to
| decode and apply increasingly abstract or otherwise complex
| patterns from allegory or stories.
|
| Not that I would be, but I'm un aware of any LLM / AI progress
| toward that type of processing.
| corethree wrote:
| LLMs can do this already. They are well past this. It's just
| they can't do it as well as humans and they can hallucinate as
| well.
|
| The problems we are having with LLMs aren't the fact that they
| aren't creative. It's the fact that they are too creative. They
| make up too much stuff that isn't true.
| HarHarVeryFunny wrote:
| I think that a lot of hallucination might just be due to not
| planning ahead - basically a case of running mouth before
| engaging brain, and then being in a situation where one has
| uttered a bunch of nonsense - basically backed oneself into a
| conversational corner. A human might catch themselves with
| "err, never mind, forget that!", but the LLM's only recourse
| is to continue extrapolating the nonsense the only way it
| knows how - i.e. to bullshit/hallucinate the most
| statistically plausible continuation of the hole it dug for
| itself.
|
| As a simplistic made up example, say the training set
| included a bunch of statements about capital cities of
| various countries of the form "the capital of england is
| london", "the capital of france is paris", etc, but didn't
| include any data indicating the capital of australia... Now,
| if asked "what is the capital of australia?", it may
| confidently start "the capital of australia is ..." since
| this matches the pattern it learnt from the training set
| (with "australia" being copied from the context). However,
| when generating the next word the LLM (without realizing it)
| finds itself in the unfortunate situation of not having been
| trained on data that would let it predict the right answer,
| but it of course goes ahead and predicts as best it can
| anyway (i.e. hallucinates/bullshits) and maybe generates the
| name of some random important city in australia such as
| "sydney".
|
| The way to fix at least this cause (maybe the primary one) of
| hallucination is essentially to plan ahead .. don't start off
| saying "the capital of australia is .." without knowing where
| you are going with it! One approach might be additional
| training using tree of thought rollouts (generate multiple
| possible branching continuations for each training prompt)
| then evaluate them and use these as RL rewards to learn to
| predict words leading to good outcomes (e.g. "i don't know",
| rather than "the capital of australia is ..").
| corethree wrote:
| As the query prompter you are in control of the feedback
| loop. You can ask the AI to re-examine it's output to catch
| errors just as a human would do for himself.
|
| Practically speaking this does work to a limited extent.
| Sometimes the AI just sticks with it's guns and runs with
| it just like a human might.
| HarHarVeryFunny wrote:
| Sure, but I was addressing the issue of why
| "hallucinations" occur in the first place, and how to fix
| them.
|
| Having a human in the loop does seem pretty much required
| at the moment, but it doesn't help when asking the AI for
| the answer to something you don't know, and therefore not
| being able to realize that the confident answer was
| hallucinated and wrong. Of course multiple-answer quizzes
| are easier to get right by process of elimination, so the
| human might sometimes be able to say "THAT can't be
| right" and catch SOME of the hallucinations.
| corethree wrote:
| Oh you misunderstand. When you query the LLM for the
| second iteration of the loop just do it regardless. Say
| something generic, ask it to reanalyze the answer more
| carefully. Ask it to compare it with existing known data
| and check for logical consistency. You can do this EVEN
| if you don't know whether or not the answer is wrong.
|
| Because the input is generic you can make this automated.
| When a user makes a query create a feedback loop and feed
| that back into the neural network multiple times with
| additional input requests to re-analyze analyze the query
| and resulting output more carefully. You can do this
| until you exhaust all input nodes and it "forgets" what
| you talked about previously.
| HarHarVeryFunny wrote:
| Interesting - I'll have to try this!
| kromem wrote:
| Tip: If you are adding a self-critique step, show the
| model the initial output as if it is evaluating something
| from someone else (i.e. "grade this answer from a
| student") as opposed to from itself (i.e. "you wrote
| this, is it really correct?").
|
| As you correctly note, humans have a problem with
| admitting fault. Especially the case online. But humans
| online are very ready to correct others.
|
| That's exactly the kind of larger abstract pattern in the
| data a model would emulate.
| licomo wrote:
| As a human, I often find innovation harder than imitation as
| well!
|
| * I do love trying to innovate, and it feels so good when it's a
| success!
| MeImCounting wrote:
| As I see it the AI schism is more about the debate between
| functionalism/computationalism and the idea that the chinese room
| thought experiment was an argument for, "biological naturalism".
| There is a lot of effort dedicated to showing that AIs dont have
| some innate quality called "consciousnes" or "sentience" or what
| have you. There is not just a lot of effort to show that, but
| also to show that that is somehow a limitation to the
| capabilities of an "AI".
|
| Personally I think "consciousness" or "sentience" or whatever you
| want to call it, is really not that useful in making logical
| decisions or solving engineering problems. It is certainly a
| useful trait to humans but if nobody can tell that the
| person/program in the chinese room doesnt actually "understand"
| chinese then why does it matter? If it walks and talks like a
| duck you can probably use it for whatever ducks are useful for.
| corethree wrote:
| Consciousness and sentience are just poorly defined vocabulary
| that delude people into thinking it's meaningful
| categorizations.
|
| There's just a bunch of traits related to intelligence and we
| categorize that if something has enough of those traits then
| it's "alive". But the words "consciousness" and "sentience" are
| so poorly defined that we can't pinpoint the formal grouping of
| what these traits actually are. So for a person to be conscious
| one can say the person must have feelings, another definition
| could be no emotions needed but the ability to reason is
| required... Etc.. etc.
|
| First up these definitions are just arbitrary categorizations.
| We each individually choose the traits that define the word
| consciousness, and second we don't even formalize the choice we
| sort leave the words impartially defined and all debates on
| whether LLMs are conscious are simply debating about this
| impartiality. It's a debate about a vocabulary issue for a word
| that isn't fully defined and people don't realize there's
| nothing profound or meaningful about the debate at all.
|
| These words are everywhere in the English language and causes
| us to debate certain concepts as if it's meaningful without us
| realizing we are just debating a vocabulary problem.
|
| Take for instance a car and a boat. These words are less fuzzy
| and more rigorously defined so debates on whether something is
| a car or a boat don't seem that profound. But the words are
| fuzzy enough that I can generate an example to help you see my
| point. Let's say I built something that can both drive and sail
| on the water. Is that thing a car or a boat? Is that question
| meaningful or is it just a categorization problem with
| inadequate vocabulary leaving me unable to specify exactly when
| an object classification transitions from boat to car? From
| this example it becomes evident that the debate is ludicrous
| that it's all an illusion. You are debating vocabulary.
|
| For words that are more fuzzy the same problem exists. But it's
| harder to see that it's just a vocabulary problem because the
| words are so fuzzy.
|
| Take for example, what is life? Is a bug alive? Is a plant? Or
| what is philosophy and what is not philosophy? Or what is good
| and what is evil? The discussion around all these issues are
| not profound. They are simply a discussion around vocabulary
| and categorization. We are too deluded by language to move past
| it.
|
| Many concepts exist as a gradient and we are simply trying to
| discretize this gradient into fixed categories and then
| spending an endless amount of time debating on where the lines
| of demarcation goes.
|
| So are LLMs conscious? It's loaded question. Let's not talk
| about vocabulary.
| MeImCounting wrote:
| I tend to agree with you on some level but I would stipulate
| that people are talking about something specific when they
| talk about consciousness or sentience. There are entire
| fields of study dedicated to understanding cognition and
| reasoning IE cognitive psychology and as far as I know the
| nature of consciousness is still an actively studied
| question. My argument is that as far as ML and "AI" go its a
| moot point. It doesnt really matter one way or the other if
| the the model is "conscious". What really matters is if the
| model is capable in a given domain. Capabilities are far more
| important than whatever process gives rise to the
| capabilities. If someone is able to use ML to solve Go or
| prove a theorem it shouldnt matter if that ML model is
| sentient-because the game is solved or the theorem has a
| proof.
| kromem wrote:
| The sentience thing is such a red herring that gets too much
| time spent on it. The topic is a pariah in neuroscience even,
| but we are going to discuss it as nauseum for AI when there's
| barely any research on its mechanics in humans?
|
| The far more interesting topic is not if sentience is occurring
| (it's almost certainly not yet), but if it is being accurately
| modeled by a non-sentient agent.
|
| A LLM may not have a subjective experience of emotions, but it
| very likely is modeling some kind of emotional tracking from
| the input of massive amounts of emotional language similar to
| how Othello-GPT modeled an Othello board from the input of
| legal moves.
|
| To me, that's a far more interesting nuance to explore than the
| red herring binary of "sentient or not."
| panarchy wrote:
| Why would a system (LLMs in this case) designed to produce the
| most likely next output be innovative?
|
| A truly innovative AI would probably hallucinate or produce so
| many nonsensical outputs as to be deemed useless and broken.
| corethree wrote:
| Got a question for people who know this stuff well.
|
| Let's say I have a generic feed forward network the size of
| chatGPT and structurally interconnected in the same way.
|
| Does there exist a set of weights for that network that will
| essentially represent the LLM that doesn't hallucinate and is
| similar to the perfect ai assistant we are all currently striving
| towards?
|
| My question is, that is it essentially a training problem?
| anon291 wrote:
| > In one task, for example, participants were asked how they
| could draw a circle without using a typical tool such as a
| compass.
|
| This is a completely useless question. LLMs are trained only on
| language. Humans are trained both on language and on physical
| activities. The LLM has no conception of 'drawing'. It cannot
| draw. Its inputs and outputs are tokens. Humans can draw. A
| better question would be to ask it to compose stories...
| something it's trained on.
|
| In particular, it would seem to me that a multimodal model
| trained on both action, sensory information, and language, would
| be better able to innovate in this regard. How could ChatGPT have
| any true understanding of spatial responses... it's never moved
| anything in its existence.
| marcosdumay wrote:
| Well, you mean the systems designed to predict the next words
| based on analysis of the text they were feed excel at imitation?!
|
| We have plenty of AIs designed to create new things. Those excel
| in creating new things. But they don't excel into bullshiting
| journalists into believing they are intelligent, so you don't
| hear about them.
| mrtksn wrote:
| IMHO The perceived lack of creativity comes from their low
| fidelity access to the world. They are trained on text and images
| which loosely captures the world and then their output is also
| limited to text and images.
|
| Once the machines have high fidelity connection to the world, for
| example a machine with microphone, camera and ways to manipulate
| objects resides among humans they will be actually trained and
| generate output on much higher spectrum.
|
| Human's creativity comes from continuous observation as they mess
| with the world. Once the machine is in that position, I fully
| expect tho have human like and even beyond creativity.
| carabiner wrote:
| That's the PowerPoint of AI though... replacing all of the
| computer manual labor.
| Nekorosu wrote:
| Pardon my superficial understanding, but how'd it innovate if it
| tries to get the most probable result or energy-spending
| efficient result, which feels like the opposite of thinking out
| of the box?
| seeingnature wrote:
| You've got it backwards.
|
| The article claims that the AI does not have a tendency to
| innovate, specifically stopping when it doesn't have the most
| probably result at hand.
| westurner wrote:
| https://news.ycombinator.com/item?id=12999516 :
|
| "CogPrime: An Integrative Architecture for Embodied Artificial
| General Intelligence" (2012) > "Competencies and Tasks on the
| Path to Human-Level AI":
| https://wiki.opencog.org/w/CogPrime_Overview#A_CogPrime_Thou... :
|
| > _[Perception, Actuation, Memory, Learning, Reasoning, Planning,
| Attention, Motivation, Emotion, Modeling Self and Other, Social
| Interaction, Communication, Quantitative_ , Building/Creative _]_
| http://wiki.opencog.org/w/CogPrime_Overview#Competencies_and...
|
| And then, "A CogPrime Thought Experiment: Build Me Something I
| Haven't Seen Before"
| westurner wrote:
| For the arts,
|
| On creating something sufficiently novel,
|
| EDA tools solve part of the problem in chip design, for
| example; furthermore sometimes with logic instead of imitation.
|
| Will any LLM ever output a formally verified design and
| implementation without significant tree filtering, _Even if_
| trained solely on formally verified code?
|
| Synthesis without understanding, and worse without ethics.
| zubairq wrote:
| Agree with the headline
| raymondh wrote:
| What counts as an innovation is in the eye of the beholder. Mark
| Rober demonstrated Gemini's creative ability by having it suggest
| a video, details of contents, and how to produce it. The results
| were impressive but far from world changing:
| https://www.youtube.com/watch?v=mHZSrtl4zX0
| natpalmer1776 wrote:
| I would argue that just because it can suggest a new video with
| all the fixings does not necessarily mean that it is being
| innovative in the sense that the concepts produced would in
| some way alter the 'state of art' at a cultural level.
| digitcatphd wrote:
| I suspect that is temporary... V1 was an imitation engine, V2 is
| a reasoning engine. If it develops a world model and general
| understand about what humans like, a metaphysical understanding
| of it, then innovation is easy.
| atleastoptimal wrote:
| same with 99% of humans
| gwern wrote:
| "In the next stage of the experiment, 85% of children and 95% of
| adults were also able to innovate...Effective tools were
| selected...75% by the best-performing model."
|
| "AI lacks the crucial human ability of innovation, researchers at
| the University of California, Berkeley have found."
|
| It says a lot about how we evaluate AI these days, and how we
| move the goalposts at hypersonic speed, that the summary of
| benchmarking this astounding leap in AI capabilities is 'AI
| unable to innovate, only imitate' instead of 'AI goes from zero
| to near-human level in just a few years, no limit in sight'.
|
| When it comes to DL, if the glass is even 1% empty, then it
| 'lacks the crucial property of having contents', I guess...
| onos wrote:
| There's plenty of content lauding ai. When initial goals are
| met is it not a good time to set new ones?
| 1vuio0pswjnm7 wrote:
| Why would anyone think "AI" would excel at innovation. Honest
| question.
|
| Innovation is defined as "introducing something new".
|
| "AI" is autocomplete and autoselection based on _past_ input.
|
| "AI" is regurgitating, rehashing or recombining something that
| has come before. It might do this in a "new" way, but it cannot
| "introduce something new". Only man can do that.
|
| Make no mistake, recombining can be useful. With AI we can screen
| a large number of possible recombinations that would be
| infeasible without it.
|
| But the only way something new can be "introduced" is for man to
| produce new data for "AI" to process. "AI" is always one step
| behind.
|
| There is no way for "AI" to have "new thoughts". All "thinking"
| by AI is always just regurgitation, rehashing or recombination of
| man's past thoughts.
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