[HN Gopher] Microsoft Kosmos-1: A Multimodal Large Language Model
___________________________________________________________________
Microsoft Kosmos-1: A Multimodal Large Language Model
Author : solarist
Score : 197 points
Date : 2023-03-01 09:38 UTC (13 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| RcouF1uZ4gsC wrote:
| I don't trust any report of model performance from papers, unless
| there is a publicly accessible demo. It is way too easy to test
| things the model has trained on and for the model to then
| completely fall flat when used by people in the real world.
| nwoli wrote:
| The fb galactica model is a good example of this. Sounded
| really promising, impressive paper, lots of weights. But when
| you actually tried it it mostly produced garbage
| nl wrote:
| It's worth noting that this is a comparatively small model (1.6B
| params from memory).
|
| It'll be interesting what capabilities emerge as they grow that
| model capacity.
| kenjackson wrote:
| That'sa good point. There's a paper that talks about the non-
| linear nature of these models. That is at some very large size
| they seem to show a leap in ability.
| xfalcox wrote:
| Anyone know if this will be an openly available model?
| tomp wrote:
| Is there a better page to link to? I cannot even see "Kosmos" on
| this page!
|
| Edit: Ah, looks like this is the link to the paper:
| https://arxiv.org/abs/2302.14045
|
| It was discussed yesterday:
| https://news.ycombinator.com/item?id=34965326
| thenaturalist wrote:
| Better link would have been the tweet, it includes the paper &
| GH repo:
| https://twitter.com/alphasignalai/status/1630651280019292161
| PaulHoule wrote:
| I like this feature they are working on
|
| https://arxiv.org/abs/2212.10554
|
| as I'd say the most obvious limitation of today's transformers is
| the limited attention window. If you want ChatGPT to do a good
| job of summarizing a topic based on the literature the obvious
| thing is to feed a bunch of articles into it and ask it to
| summarize (how can you cite a paper you didn't read?) and that
| requires looking at maybe 400,000 - 4,000,000 tokens.
|
| Similarly there is a place for a word embedding, a sentence
| embedding, a paragraph embedding, a chapter embedding, a book
| embedding, etc. but these have to be scalable and obviously the
| book embedding is bigger but I ought to be able to turn a query
| into a sentence embedding and somehow match it against larger
| document embeddings.
| p1esk wrote:
| _feed a bunch of articles into it and ask it to summarize_
|
| A better way (that's how humans do it) is to first summarize
| each article, then feed the summaries to get an overview of the
| topic. This way there's no need to expand the attention window.
| PaulHoule wrote:
| I've thought about that one for a long time. A long time ago
| I was reading proceedings of TREC trying to understand why
| Google was so much better than the search engines I knew how
| to build. TREC is pretty depressing because you find that 95%
| of the things you might think would improve search rankings
| do not. Particularly before BM25 was developed people tried
| indexing sub documents and consolidating them and
| consistently struck out.
|
| Since BERT came out there is a considerable literature of
| people struggling mightily to combine transformer
| representations of document parts into a whole that convinces
| me that one could spend a few lifetimes pushing a bubble
| around underneath that rug.
|
| I think the best argument for your case is that people seem
| to get along just fine with a limited short term memory. I'd
| temper that with the observation that a person writing a
| summary is actually doing a multiple stage process in which
| their short term memory is attending to part of what they are
| writing, part of what they are reading, and they are building
| long term memory structures at the same time. So there is a
| lot going on.
|
| In the sense that abstracts work well for information
| retrieval and that many of them would fit in the GPT
| attention window or only be a little bigger you could make
| the case that a fixed-size structure could be highly useful
| for IR.
|
| On the other hand, many documents, such as scientific papers,
| are considerably bigger than the current attention window and
| direct summarization _of a single document_ via transformer
| will still need a bigger window, more like 40,000 tokens.
|
| A lot of things in the literature are complex, muddy,
| contradictory or all of the above. (Try a question like "What
| did Freud think about narcissism?" or "What is the clinical
| relevance of Bleuler's concept of ambivalence?" or "Tell me
| about cosmic inflation" or "What is the dark matter
| particle?")
|
| Hard cases really do require matching up parts of document A
| with parts of document B and certainly having them in the
| same attention window would help an LLM do that in a natural
| way.
|
| It might be completely impractical, not just because of
| computational scalability but possibly more fundamental
| scalability limits. (I'm not sure a person with a 10x bigger
| short term memory would really be able to solve problems
| better than the average person... There are transformers with
| a 500,000 token attention window today and they suck.)
|
| There could be some procedure where you cut documents up into
| pieces in various ways, extract critical context from
| documents A and B and other literature and also put in the
| parts you want to critique against each other, or even match
| up different parts of the same document to do the same. Maybe
| a small attention window could still be used to decompose
| documents into knowledge graphs but it is by no means trivial
| to reason over a KG once you have it.
|
| What I do know today is that I have documents >4096 tokens
| that I want to retrieve, cluster and classify right now and
| transformers were not up to the task in Feb 2023, and I am
| hoping for some progress soon that will help.
| solarist wrote:
| Paper: https://arXiv.org/abs/2302.14045
|
| Examples:
| https://twitter.com/alphasignalai/status/1630651280019292161
| aegistudio wrote:
| Hmm... LLMs / MLLMs might be truly a unified input / output
| interface of a would-be AGI, I think.
| Tepix wrote:
| Yeah, check out the Lex Friedman podcast episode #333 around
| minute 52 where Andrew Karpathy talks about the OpenAI project
| "World of Bits" that did this.
|
| https://youtu.be/cdiD-9MMpb0?t=3013
| mkmk3 wrote:
| And the work he's talking about:
| https://paperswithcode.com/paper/world-of-bits-an-open-
| domai...
| ducktective wrote:
| It can even solve IQ tests...I mean, how much further are we
| moving the goal post?
|
| Is there a model that can solve differential equations
| symbolically and numerically? Most of modern engineering just
| boils down to diff.eqs whether ordinary or partial. It's our
| current best method to reason about stuff and control them.
| Jevon23 wrote:
| >how much further are we moving the goal post?
|
| My goalpost for AGI is when Microsoft can fire their entire
| engineering staff, replace them with AI, and not notice any
| decrease in productivity or quality of output.
|
| This test is empirically verifiable (in principle). No need to
| argue over whether the AI scoring X% on Y assessment task is
| "truly" impressive or not.
| gfodor wrote:
| You're confusing the goal - the goal here isn't about the
| finish line but the point where people all concede that the
| finish line is actually reachable without any major,
| presently unthinkable advances.
| Jevon23 wrote:
| Surely anyone familiar with software engineering knows that
| the finish line is reached when the finish line is reached.
| And no sooner.
|
| Physicists at the beginning of the 20th century also
| thought that the finish line of physics was in sight and
| all that remained was tightening a few constants. Look how
| that turned out.
| gfodor wrote:
| This perspective is too reductionist - we make
| predictions of success all the time based on first
| principles reasoning. It's perfectly sane to try to
| predict, and make good arguments, if AGI is possible to
| achieve without new breakthroughs.
| staticautomatic wrote:
| You mean that isn't the Teams origin story?
| akiselev wrote:
| Teams is the projection of a fourth dimension ancient
| Eldritch horror onto the mortal plane. AI had nothing to do
| with it.
| seydor wrote:
| I prefer not focusing on games and benchmarks. Hopefully we ll
| get to robotics soon
| moffkalast wrote:
| Roger roger.
| brookst wrote:
| Arnold was great in that documentary!
| mhh__ wrote:
| Writing down the equations is the task for the AI.
| bootsmann wrote:
| > It can even solve IQ tests...I mean, how much further are we
| moving the goal post?
|
| The problem with test like this is that when trained on the
| existing big datasets (commoncrawl etc.), chances are the test
| is already in the input so the validation is not proper. Its
| the same thing with all the "AI beats SAT" headlines. The
| exercises for those very tests exist all over the internet
| already.
| nayroclade wrote:
| It's well documented that these models can solve variations
| of questions that are not found anywhere in their training
| set, and even entirely novel problems invented by prompters.
| Not with 100% success, but they can do it far with a rate far
| better than chance, so the idea that they're pulling
| responses from their training data is simply not correct.
| bootsmann wrote:
| Ok maybe I should rephrase but think of it like this: Would
| your IQ test score be accurate if you had a week beforehand
| to train solving IQ tests?
|
| You don't get exactly the same test in the end, similar
| with SAT, but the constraints we put on these tests (they
| have to be comparable) produce patterns in the questions
| you can train for. This is the same logic why people can
| train to improve their SAT scores, if they were a measure
| of true innate intelligence their training would have no
| impact on their score.
| adamsmith143 wrote:
| > This is the same logic why people can train to improve
| their SAT scores
|
| So isn't this literally moving the goalpost? "So what an
| AI can beat the SAT, so can humans"
| siva7 wrote:
| I mean, let's be fair. That's also how many humans learn
| and do IQ tests.
| prng2021 wrote:
| Yes but the critical difference is that AI couldn't pass
| these tests if they weren't trained on a very similar set
| of questions and answers.
|
| Not every person takes a SAT prep class to improve their
| test score. There are lots of people who are truly above
| average in terms of intelligence and can score very high
| on the first try.
| naasking wrote:
| > Yes but the critical difference is that AI couldn't
| pass these tests if they weren't trained on a very
| similar set of questions and answers.
|
| First, I don't think that's strictly true. Obviously they
| wouldn't do as well but they still do better than chance.
|
| Second, there's evidence that this is a big part of the
| Flynn effect, which means humans are susceptible to a
| similar phenomenon.
| prng2021 wrote:
| " Obviously they wouldn't do as well but they still do
| better than chance" is a huge understatement. If a model
| wasn't trained with any SAT questions and answers but was
| trained with the same verbal and mathematical knowledge a
| high school student would have, the AI would do extremely
| poorly in an actual test. In contrast, the vast majority
| of human test takers would score leagues above picking
| answers by chance.
|
| Your original reply insinuated that the AI is learning
| very similar to how humans do and that's just not true.
| Yes, humans do pattern matching based on prior
| experiences/knowledge like AI does when you train a
| model, but human intelligence goes way beyond that.
| naasking wrote:
| > Yes, humans do pattern matching based on prior
| experiences/knowledge like AI does when you train a
| model, but human intelligence goes way beyond that.
|
| Humans are trained on orders of magnitude more multimodal
| data over their lifetimes. Also, humans are not borne as
| an unbiased model, billions of years of evolution have
| crafted many implicit biases into our cognition (like a
| propensity to language, facial recognition, etc.). All
| machine learning models are true blank slates, so it
| takes a lot more data just to build up to the same
| starting point as a newborn human.
|
| All that's to say that you have no basis upon which to
| claim that AI learning is NOT similar to how humans do
| it, or that human intelligence "goes way beyond hat",
| it's just that humans have a head start and a lot more
| data to work with.
| prng2021 wrote:
| "Humans are trained on orders of magnitude more
| multimodal data over their lifetimes. Also, humans are
| not borne as an unbiased model, billions of years of
| evolution have crafted many implicit biases into our
| cognition (like a propensity to language, facial
| recognition, etc.)."
|
| What in the world are you talking about? I must be
| talking to chatgpt and am done with this thread. We were
| originally discussing the differences in methodology
| between AI and humans for passing standardized exams.
| Those involve tasks like applying well-defined
| mathematical concepts to a brand new problem, not
| "multimodal" data or facial recognition.
| naasking wrote:
| If you don't understand what I'm talking about then you
| don't understand how how these transformer AIs learn and
| solve problems, so maybe you shouldn't opine about how AI
| couldn't pass these tests. Your claimed differences
| between how humans and AIs work is conjecture that can be
| explained by what I described rather than fundamental
| differences in how these systems work.
| d0mine wrote:
| Here's simple example: _(x-x+c)=c_ gpt struggles with
| such examples if `x` is a large number e.g.,
| x=123_000_000_456 and `c` some specific number, but it is
| easy for humans.
| naasking wrote:
| It's easy for humans once they're taught what variables
| mean and after 10 or so years of exposure to a real world
| multimodal training set that's orders of magnitude more
| data than GPT has seen. Also, algebra is not so easy for
| people with IQs lower than 90, so not exactly all humans
| right? What exactly am I supposed infer about how GPT or
| other AIs and human brains operate from this apples to
| cars comparison?
|
| You don't have to point out failure modes of GPT, I know
| what they are. The question we're discussing here is what
| this indicates, if anything, about how these systems
| operate as compared to human brains, and whether the
| differences come down to training data or the fundamental
| architecture.
| codethief wrote:
| But hasn't that always been a problem inherent to IQ
| tests (and SAT tests, of course)?
| beepbooptheory wrote:
| It is definitely one of the problems you add to a pile
| marked "IQ tests are meaningless and cater to the worst
| conceptions of human intelligence."
| amoss wrote:
| If we flip the question around it seems more reasonable:
| would your IQ test score be accurate if you did not train
| beforehand on practice IQ tests?
|
| I would presume not - most tests are timed, and if you
| are spending time on first-time only tasks in
| understanding the problem then the result is inaccurate.
| If you train out those first-time tasks so that you are
| repeatably using the time budget in the test to solve
| problems then you should reach some kind of steady state
| and produce repeatable and more accurate test scores.
|
| My take is that the repeatable scores measuring your
| steady state in the task would be more accurate than the
| untrained scores with an unknown amount of initialization
| time within each problem. I would make a similar claim to
| naasking below that this could account for some of the
| Flynn effect.
| 331c8c71 wrote:
| Well, my experience was that ChatGPT produced utter
| nonsense including literally "1/19+1/19=2/19=1" when asked
| a simple probability question.
| nayroclade wrote:
| Transcript of a conversation I just had with ChatGPT:
|
| Prompt:
|
| I'm talking with a user named "331c8c71" on Hacker News.
| According to statistics I found by Googling, the number
| of Hacker News readers living in the USA is 36%. Assuming
| this is correct, what is the probability that 331c8c71
| does not reside in the USA? Please explain your
| reasoning.
|
| Response:
|
| If we assume that the statistic you found is accurate,
| then we know that 36% of Hacker News readers live in the
| USA. Therefore, the probability that any given Hacker
| News reader does not live in the USA is 100% - 36% = 64%.
|
| However, we do not know if 331c8c71 is a typical Hacker
| News reader. They could be part of a demographic that is
| more or less likely to live in the USA. Without more
| information, we cannot say for certain what the
| probability is that 331c8c71 does not reside in the USA.
|
| So, the answer is 64% assuming that 331c8c71 is a typical
| Hacker News reader, but it may not be an accurate
| estimate depending on additional information about the
| user.
|
| ---
|
| Like I said, these models are far from 100% accurate, and
| there is lots they get wrong, but they clearly _are_
| capable of some kind of reasoning that goes beyond simple
| text substitution of training data.
| kenjackson wrote:
| The fact that just a few years ago even parsing the
| question would be an achievement. I feel like people are
| just glossing over how incredible this answer is.
| Workaccount2 wrote:
| I feel like there is some contingent of people who are
| really bent on downplaying the achievements of AI as of
| late. Its objectively insane, yet somehow every
| discussion is still sprinkled with some form of "It told
| me 8x8=60 so I closed the window and never used it again"
| 331c8c71 wrote:
| I am impressed with LLMs but I think their inability to
| produce an honest "I don't know" instead of hallucinating
| is an issue.
| d0mine wrote:
| Google search was incredibly valuable immediately even if
| most links could have been rubbish. I can't say the same
| with the current LLMs
|
| It is an incredible achievement that LLMs produce human-
| like output (e.g., wouldn't know if a gpt bot answers me
| unless we are discussing a topic where precision/accuracy
| are important) but they hallucinate (they are confident
| BS-generators).
|
| The hype is that LLMs can solve any problem and replace
| humans (jobs). It is not so.
|
| It may depend on what you do but I find it is
| easier/faster to do the work myself then to spot and fix
| [a possibly subtle] error in AI output. Though some of
| the specific things will improve in time and you can find
| tasks where AI is useful even today.
|
| I don't see how the models can improve for general tasks
| (AGI) without being existential threat to humans (not
| just jobs).
| magicalhippo wrote:
| Also it doesn't seem like such a leap to couple these
| language models with dedicated computation systems and
| similar. Think training a model to feed prompts to
| Wolfram Alpha to actually compute the results, then
| reporting back.
| shaunsingh0207 wrote:
| IQ test, the sat, and exact mathematics are very
| different fields though. ChatGPT etc. are very good at
| "emulating" by sheer force and size logic, exact math
| doesn't work with "emulation"
| lumost wrote:
| ChatGPT does a great job on symbolic manipulation. You have to
| prompt it to show derivations however vs. discussing the topic
| at a high level.
| RivieraKid wrote:
| What goalpost specifically?
| ducktective wrote:
| Solving IQ tests which measure quantitative reasoning.
| RivieraKid wrote:
| There was no movement of this goalpost.
| espadrine wrote:
| > _I mean, how much further are we moving the goal post?_
|
| Look at it this way: humans don't have BPE-encoded text as
| input to their brain. It is ALL visual input. For AGI, you
| would at least need to add audio input as well. And be driven
| by action and reward.
|
| The learning capabilities of the brain are currently beyond the
| processing capabilities of current architectures. Just the
| notion of a model receiving only pixel data that contains a
| question and being able to output voice data that produces a
| correct answer, using no partial model trained on another
| corpus, is probably not tractable without significant
| improvements.
|
| But the models can be very useful without being AGI!
| sillysaurusx wrote:
| AGI is closer to tokenization than you might think. I
| realized this recently when trying to do audio prediction.
|
| There was recently a project called riffusion which generates
| spectrograms, then recovers audio from the spectrograms.
|
| You might be tempted to apply this to predict speech. But
| speech isn't like music. We're communicating in language,
| using a sequence of tones. It's why most speech codecs use
| linear predictive coding. Predicting the waveforms won't get
| you anywhere; no semantic understanding of language.
|
| So the next step up is to divide speech into a series of
| tones, and try to predict those sounds rather than raw
| waveforms.
|
| Except... that's literally tokenization. And there's some
| evidence that this is precisely what our brains are doing.
| bravura wrote:
| Actually there's a whole new subfield called textless NLP
| doing just that: Learning language models from raw audio.
| https://ai.facebook.com/blog/textless-nlp-generating-
| express...
| espadrine wrote:
| There is definitely something symbol-adjacent that needs to
| happen inside of the model; this is what I assume happens
| in the brain. But it is not purely symbolic.
|
| For instance, consider voicing the end of a letter: "I will
| definitely not be stabbed in the bac..." (where the word
| "back" quickly devolves into a line that crosses through
| the rest of the letter). It goes from symbolic to
| contextual, implying that the author was stabbed midway
| through writing it, so the voicing must end with a yell of
| playful agony.
|
| The same goes for calligraphic art, such as the Al Jazeera
| logo, for instance, which is intended to be understood as
| both a sequence of Arabic letters, and a depiction of a
| fire. A model seeing this image for the first time, needs
| to see it both ways at the same time.
|
| But it's true that we can't just throw a transformer at the
| problem, train it from scratch with video inputs and audio
| outputs, coupled with a sporadic reward, and suddenly have
| it be able to solve scans of civil engineering exams. The
| brain can do it, but not silicon (yet). It is easier to
| combine models that were trained on simpler losses
| (tokenized cross-entropy) on simpler problems (next-token
| prediction), and combine them. Not true AGI learning, but
| eventually it will fool people into believing it is.
| pmontra wrote:
| > It is ALL visual input.
|
| And sound, taste, smell, touch.
| konfusinomicon wrote:
| don't forget humor
| KineticLensman wrote:
| phlegmatic, choleric, sanguine or melancholic?
| Tepix wrote:
| It wasn't very good at the IQ test. But yes, it is promising.
|
| _" Although there is still a large performance gap between the
| current model and the average level of adults, KOSMOS-1
| demonstrates the potential of MLLMs to perform zero-shot
| nonverbal reasoning by aligning perception with language
| models."_
| scotty79 wrote:
| Yeah, that could be good. I think LLMs will start to be really
| useful when they start to do math at human level. When this
| happens, the sky is the limit.
| p1esk wrote:
| What is human level for math? Terence Tao? Average American?
| scotty79 wrote:
| Definitely not an average American. Someone who learned to
| do math (proofs, application) and got fairly good at it.
| Y_Y wrote:
| It's pretty big by any standards, but you may find the work of
| Gradshteyn and Ryzhik solves this problem nicely.
| didntreadarticl wrote:
| Its crap at the visual Raven IQ test though, it scores 22% vs
| an algorithm that takes random guesses scoring 17%.
| nmarinov wrote:
| Semi related, there's a (pretty good) course at OMSCS where
| the main project is building an agent to solve RPM problems:
| https://lucylabs.gatech.edu/kbai/spring-2023/project-
| overvie...
|
| And quite a lot of papers about that: https://scholar.google.
| com/scholar?q=%22raven%27s+progressiv...
| kalium-xyz wrote:
| Bet you 5 bucks I can train one that gets 100%. Just gotta
| train it on the ravens answer key.
| thenaturalist wrote:
| I'd be cautiuous with such general statements given the rapid
| pace of development in this area.
|
| Benchmark shelf lives aren't that long.
|
| You ommitted the fact that tuning bumped it to 26% vs random.
|
| Sure, questionable what effort is involved in that step, but
| at the same time, that hints to me that tuning will be the
| new baseline within the next 12-24 months.
| didntreadarticl wrote:
| Sure I would expect it to improve. But it was a bit fishy
| how 'it took an IQ test!' is in all the highlights but then
| they mumble quietly about the score that it actually got
| and hope no-one is listening to that bit.
|
| Its notable that it was able to attempt it at all I
| suppose.
| naasking wrote:
| Another one that looks even more compelling:
|
| Multimodal Chain-of-Thought Reasoning in Language Models,
| https://arxiv.org/abs/2302.00923
|
| By building in chain of thought and multimodal learning, this 1B
| parameter model beats GPT-3.5's 170B parameter model.
| drKarl wrote:
| At Microsoft:
|
| Hey why don't we call our new LLM Cosmos? That's taken by the
| Azure Cosmos DB guys Damn it... how about Kosmos-1 ?
| 5- wrote:
| https://en.wikipedia.org/wiki/Kosmos_1
| Bajeezus wrote:
| "Fun" fact: There is another common internal service at
| Microsoft called Cosmos, and it is also a database.
|
| So now there is Cosmos, Cosmos DB, and Kosmos.
| outside1234 wrote:
| Isn't there also a batch processing system named... you
| guessed it... Cosmos?
| josalhor wrote:
| The examples in the paper are pretty impressive. There is an
| example of a windows 11 dialog image. The computer can figure out
| which button to press given the desired outcome of the user. If
| one where to take this model and scale it, I can see an advanced
| bot in <5 years navigating the web and doing work based on a text
| input of a human purely by visual means. Interesting times.
| ren_engineer wrote:
| this seems like the future to me, a huge chunk of work will be
| able to be done by just talking to your computer and then
| automating the task. Society is really going to need to adapt,
| knowledge workers being replaced will be as big a change as the
| industrial revolution replacing many manual laborers
|
| what's interesting is how will these systems be maintained when
| all the junior tier engineering work is replaced by AI?
| Companies don't like hiring junior engineers now, will be an
| even bigger gap before a junior engineer becomes net productive
| now. Plus people building stuff using AI without understanding
| how things work under the hood. Seems ripe for some 40K tier
| situation where we have tech priests running systems that
| nobody knows how to build from scratch anymore
| novaRom wrote:
| Talking to your computer? This will not last too long until
| another disruptive change will happen. How about computers
| will overtake those decisions you think you freely able to do
| right now. It is all running fast, accelerating actually.
| reset-password wrote:
| I've been following tech long enough to know that as soon as
| the computer can figure out which button to press it's only
| going to click on ads, I guarantee it.
| joshspankit wrote:
| and then that's going to be met with MS making it
| "impossible" for bots to automate clicking on ads which will
| have the unintended consequence of making it harder to use
| for power users.
| sebzim4500 wrote:
| Isn't it trivial to make a computer click on ads though? Just
| run selenium, apply the filtering rules from adblock and then
| click on a random element which would be blocked.
| 13of40 wrote:
| I think their point is the opposite - it's *not* trivial to
| make the computer click the correct "Download Now!" button
| to get Minecraft versus the other 4 that lead to malware.
| sebzim4500 wrote:
| Isn't it? Just run it on a machine with ublock origin.
| IanCal wrote:
| You might be interested in this:
| https://www.adept.ai/blog/act-1
| freakynit wrote:
| Holy tckin' cow!!! Is this real?
|
| Captchas be damned now. Beating AI with AI. What a time to be
| alive.
| moritonal wrote:
| We can't stop it, but giving an AI unbridled access to the
| Internet is a terrible idea. Whether it's a misphrased
| question or an clever prompt hack; entire sites will be
| crushed by the sheer superhuman performance of it.
|
| Hackernews will be just robots chatting to each other nudging
| towards the latest product-hunt.
| flangola7 wrote:
| https://www.youtube.com/watch?v=efPrtcLdcdM
|
| The internet will be saturated with fake people soon.
| Someone already did a PoC of this on 4chan as a joke with
| just a small GPT2 model. In a few years you won't be able
| to tell if you're talking to a human unless they're
| physically in front of you.
| [deleted]
| comboy wrote:
| We will develop web of trust [1]. I assign weighted(!)
| trust to my friends, they assign to their friends. Small
| world [2]. This also fixes fake reviews, dependencies
| security, politics and whole lot of other things.
|
| The idea is too good not to happen. I repeat it from time
| to time on HN. I'm currently not in the position to
| implement it (not sure if I ever will be, it is hard). I
| just hope it's created in some decentralized form before
| some corpo does it. When controlled by single entity it is
| useless.
|
| 1. http://comboy.pl/wot.html (not really worth your time,
| long time ago too verbose, but feel free)
|
| 2. https://en.wikipedia.org/wiki/Six_degrees_of_separation
| sangnoir wrote:
| > This also fixes fake reviews, dependencies security,
| politics and whole lot of other things.
|
| All you have to do is lookup some batshit-crazy things
| people in your social circle already share on Facebook
| (or LinkedIn) to know this won't solve most of those
| problems.
|
| I may trust my friend to thoroughly vet information on
| Disc Golf, but they may be out of their element when it
| comes to "Revolutionary cold fusion breakthrough", which
| I may get through them if there's a single generic weight
| for trust.
| comboy wrote:
| Good observation. I thought at first that it should be
| multidimensional, but categories are hard. Very hard.
|
| I think general trust could work. People I trust won't
| give strong opinions about thing they have no idea about.
| You choose these people and you assign weights. It's not
| a random family circle.
|
| I have some vague ideas about wallet of personalities
| which would serve the same purpose as categories of
| trust.
| furyofantares wrote:
| I've idly and very-casually thought about this for a long
| time, ever since decentralized filesharing really. If you
| ever do feel that you're in a position to take a stab at
| it, I'd be interested as well.
| Karellen wrote:
| Did anyone else initially read that as `Kosmos~1`, and wonder
| what the full name of the project was?
___________________________________________________________________
(page generated 2023-03-01 23:02 UTC)