[HN Gopher] Things I don't know about AI
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Things I don't know about AI
Author : todsacerdoti
Score : 155 points
Date : 2024-02-21 13:40 UTC (9 hours ago)
(HTM) web link (blog.eladgil.com)
(TXT) w3m dump (blog.eladgil.com)
| eladgil wrote:
| Would love to get any views / counterpoints on these topics.
|
| Eg which AI infra approach thrives relative to the big clouds?
|
| What new architectures are needed for agents vs transformers?
|
| Etc...
|
| Lots of stuff to figure out....
| texas2toss wrote:
| Do you think there is the possibility of consumer or end-user
| apps collecting enough specialized data to move downwards on
| your graph to infra and foundational models?
| eladgil wrote:
| I think this is already true for things based on diffusion
| models - e.g. Midjourney, Pika etc
|
| For LLMs, Character and ChatGPT are arguably two vertically
| integrated consumer apps (with some B2B applications for
| ChatGPT as well)
| danielmarkbruce wrote:
| There is a decent (<50%, >20%) chance that frontier foundation
| models are less oligopoly like than it seems. The reason is
| that there are so many levers to pull, so much low hanging
| fruit.
|
| For example: * Read the Bloomberg GPT paper - they create their
| own tokenizer. For specialized domains (finance, law, medicine,
| etc) the vocabulary is very different and there is likely a lot
| to do here, where individual tokens really need to map to
| specific concepts and having a concept capture in several
| tokens makes it too hard to learn on limited domain data. *
| Data - so many ways to do different data - more/less, cleaner,
| "better" on some dimension. * Read the recent papers on
| different decoding strategies - there seems to be a lot to do
| here. * Model architecture (SSM etc). If you speak to people
| who aren't even researchers, they have 10 ideas around
| architecture and some of them are decent sounding ideas - lots
| of low hanging fruit. * System architecture - ie likely to see
| more and more "models" served via API which are actually
| systems of several model calls, and there is a lot to do here.
| * Hardware, lower precision etc likely to make training much
| cheaper
|
| It's reasonably likely (again, guessing < 50% > 20%) that this
| large set of levers to pull become ways to see constant leap-
| frogging for years and years. Or, at least they become
| choices/trade-offs rather than strictly "better".
| eladgil wrote:
| I agree this is a potential outcome. One big question is
| generalizability versus niche models. For example, is the
| best legal model a frontier model + a giant context window +
| RAG? Or is it a niche model trained or fine tuned for law?
|
| Right now at least people seem to decouple some measures of
| how smart the model is from knowledge base, and at least for
| now the really big models seem smartest. So part of the
| question is well is how insightful / synthesis centric the
| model needs to be versus effectively doing regressions....
| danielmarkbruce wrote:
| Yup, it's unclear. The current ~consensus is "general
| purpose frontier model + very sophisticated RAG/system
| architecture" for legal as an example. I'm building
| something here using this idea and think its 50/50 (at
| best) I'm on the right path. It's quite easy to build very
| clever sounding but often wrong insights into various legal
| agreements (m&a docs for example). When looking at the
| tokenization, the training data, decode, architecture (lots
| of guesses) of the big models, there are a lot of things
| where the knobs seem turned slightly incorrectly for the
| domain.
|
| Some of the domains are so large that a specialized model
| might seem niche but the value prop is potentially
| astronomical.
| CuriouslyC wrote:
| Frontier model + rag is good when you need cross-discipline
| abilities and general knowledge, niche models are best when
| the domain is somewhat self contained (for instance, if you
| wanted a model that is amazing at role playing certain
| types of characters).
|
| The future is model graphs with networked mixtures of
| experts, where models know about other models and can call
| them as part of recursive prompts, with some sort of online
| training to tune the weights of the model graph.
| sfink wrote:
| > The future is model graphs with networked mixtures of
| experts, where models know about other models and can
| call them as part of recursive prompts, with some sort of
| online training to tune the weights of the model graph.
|
| What's the difference between that and combining all of
| the models into a single model? Aren't you just
| introducing limitations in communication and training
| between different parts of that uber-model, limitations
| that may as well be encoded into the single model if
| they're useful? Are you just partitioning for training
| performance? Which is a big deal, of course, but it just
| seems like guessing the right partitioning and
| communication limitations is not going to be
| straightforward compared to the usual stupid "throw it
| all in one big pile and let it work itself out" approach.
| CuriouslyC wrote:
| The limitation is the amount of model you can fit on your
| hardware, and also sometimes information about one domain
| can incorrectly introduce biases in another which are
| very hard to fix, so training on one domain only will
| produce much better results.
| jchonphoenix wrote:
| Arguably one of the earliest consumer use cases that found
| footing was AI girlfriend/boyfriend. Large amounts of revenue
| spread across many small players are generated here but it's
| glossed over due to the category.
| notpachet wrote:
| I think one related area we'll start seeing more of in the
| future is "resurrected" companions. You have a terminally ill
| family member, so you train a model on a bunch of video
| recordings of them, then you can talk to "them" after they've
| shuffled off this mortal coil.
| swozey wrote:
| Licensing my soul as closed source now ..
| datadrivenangel wrote:
| DNR: Do Not Retrain
| babyshake wrote:
| Be right back.
| rockostrich wrote:
| Weird how close your user name is to Joaquin Phoenix, star of
| the film "Her" centered around an AI girlfriend.
| doctorpangloss wrote:
| I haven't listen to your great podcasts so hard to say what is
| not covered.
|
| Architectures matter a lot less than data. "Knowledge" and
| "reasoning" in LLMs is a manifestation of instruct-style data.
| It won't matter how much cheaper training gets if there is
| limited instruct data for use cases.
|
| How do you make 100k context window data for example? Still
| need thousands of people. Same with so-called niches.
|
| Maybe it turns out to be a complex coordination problem to
| share the data. That's bad for equity investors and giant
| companies. Anyway, all of this would cost less than the moon
| landing so it's practicable, you don't need cheaper, you just
| need risk-taking.
|
| The obviousness of the path from here to there means it's not
| about innovation. It's all about strategy.
|
| If Google could marshal $10b for Stadia it could spend $10b on
| generating 100k context window instruct style data and have the
| best model. It could also synthesize videos from Unity/Unreal
| for Sora-style generation. It would just be very hard in an org
| with 100,000+ people to spend $10b on 10 developers and 10,000
| writers compared to 400 developers and 3,600 product managers
| and other egos. At the end of the day you are revisiting the
| weaknesses that brought Google and other big companies to this
| mess in the first place.
|
| Anyway I personally think the biggest weakness with ChatGPT and
| the chat-style UX is that it feels like work. Netflix, TikTok,
| etc. don't feel like work. Nobody at Google (or OpenAI for that
| matter) knows how to make stuff that doesn't feel like work.
| And you can't measure "fun." So the biggest thing to figure out
| is how much technical stuff matters in a world where people can
| be poached here and there and walk out with the whole
| architecture in their heads, versus the non-technical stuff
| that takes decades of hard-worn personal experiences and
| strongly held opinions like answers to questions "How do you
| make AI fun?"
| mywittyname wrote:
| > "How do you make AI fun?"
|
| Bring back text-based adventure games.
| doctorpangloss wrote:
| People go for this level of obviousness and it doesn't
| work. I have no doubt that a meme text game will find some
| level of literal objective success. But it will still suck.
| Meme games are a terrible business, both in terms of
| profits and equity.
|
| This also speaks to why OpenAI, Google and the other
| developers will struggle to create anything that feels like
| fun: they will chase obvious stuff like this, they will
| think its similar to all problems. And in reality, you
| don't need any testing or data or whatever to know that
| people hate reading in video games, the best video game
| writing is worse than the average movie's screenplay, that
| most dialogue is extremely tedious, so why are you going to
| try to make it even worse by making it generated by an AI?
| xianshou wrote:
| From the article:
|
| "It is important to note that the scale of investments being made
| by these cloud providers is dwarfed by actual cloud revenue. For
| example, Azure from Microsoft generates $25B in revenue a
| quarter. The ~$10B OpenAI investment by Microsoft is roughly 6
| weeks of Azure revenue. This suggests the cloud business (at
| least for now) is more important than any one model set for
| Azure."
|
| The most interesting implication in the short term is that the
| impact of model availability on cloud provider choice is more
| important than the models themselves. Will organizations choose
| Azure because it offers GPT-4, or AWS because it offers Claude?
|
| This also explains reciprocal VC structures whereby e.g. Amazon
| invests billions in Anthropic while Anthropic in turn promises to
| spend billions on AWS cloud resources. Anthropic gets a higher
| valuation and broader distribution, while AWS gets more
| customers. It would be a fascinating outcome if the predicted
| oligopoly in fact ends up as several exclusive partnerships.
| infecto wrote:
| Doubtful, at the end of the day the models are just like any
| other API. No reason not to signup for all the big players
| (GCP/Azure/AWS) and have access to the various models. Should
| have no weight as to where you should build out your infra.
| simpletone wrote:
| > The ~$10B OpenAI investment by Microsoft is roughly 6 weeks
| of Azure revenue.
|
| Revenue isn't what is important. Profit is since profit is the
| money you have after costs to invest. But since cloud is highly
| profitable as it is, the point still stands I suppose.
|
| > It would be a fascinating outcome if the predicted oligopoly
| in fact ends up as several exclusive partnerships.
|
| I thought people were predicting independent silos? Where
| highly profitable and cash rich companies like apple,
| microsoft, google, facebook, etc own their entire stack or
| exist as their own ecosystem. Where the likes of apple does
| everything in-house - AI, cloud, OS, chips, etc. The whole she-
| bang.
| bbor wrote:
| Opened the comments for exactly this. This person seems very
| nice but also convinced that they're a business maven, which
| I think leads them to making "clear" observations like that.
| Microsoft hasn't invested a full _year_ of _revenue_ into
| OpenAI, so it's not that invested? Meh
| bane wrote:
| The confusion that everybody feels right now about "what do I do
| in this rapidly evolving space" is likely more an artifact of one
| of the most vertical lead-edges to a hype curve in a generation.
| There is so much piling on, at absolutely tremendous investment
| amounts, that it's not really even possible to pick a launch
| point to a business plan that would survive even months at this
| point.
|
| The second problem is more obvious, but not talked about in
| concrete terms yet. As these technologies mature, the legal,
| policy, and social constructs around them will also mature, and
| will shape the future of what is coming as much as any technical
| or business decision. Some examples:
|
| - Imagine if courts _really_ find that these models are in gross
| copyright violation and that model builders will need to work
| through a scheme similar to sampling in music in order to do
| their work into the future. Not only will it dramatically
| increase the cost of model development, but will drive some of
| the work to alternative sources of data if it 's cheaper. What
| effect will this have on these models?
|
| - Suppose these models are found to be reliable enough for
| certain mission critical work (healthcare, defense, etc.) and
| they lead a user down a path that results in harm or death? Who's
| at fault? Suppose it becomes a liability of the company offering
| the model? How does the insurance market respond?
|
| - What if these models become good enough, and the automation
| around them as well, that we start turning them into fully
| automated agents that make up a significant part of our economy?
| Do people still have jobs? Or will these things just become the
| equivalent of hiring more people into an economy and we work in a
| blended AI/Human Intelligence world? What happens when these
| things decide to tank capital markets or choke supply chains?
|
| - What about the long-term use of these models by individuals
| with severe mental impairments or psychological issues? What
| happens when those models go down, or produce a response that's
| not liked, or there's a loss of historical conversational data?
| What about otherwise normal people who end up down a path of
| social isolation?
|
| - There's an entire legal market about to be created for remote-
| work fraud where somebody just replaces themself with one of
| these models for 90% of their work. It's sci-fi utopia, but
| fraudulent if you misrepresent who's doing the work in many
| cases.
|
| - Some company, somewhere, will crack the nut on figuring out how
| to improve the speed and performance of these models, and build
| custom hardware that offloads the entire effort onto rapidly
| commoditizing systems. When multi-modal LLMs and diffusion models
| come preloaded on disposable $.10 SOCs that are put on cereal
| boxes and in Birthday cards, we'll be well beyond every one of
| these questions. But we'll be firmly in a different future.
|
| We have yet to encounter most of these things to the point that
| as a society we have to build responses, and that itself points
| to the immaturity of both these technologies and the use cases
| for which they will be adopted.
|
| Which cloud to pick and why aren't consumers spending thousands
| of dollars getting the complex systems in place to use this stuff
| aren't even the problems that are going to become interesting in
| the next 10 years.
| xamuel wrote:
| >Suppose these models are found to be reliable enough for
| certain mission critical work (healthcare, defense, etc.) and
| they lead a user down a path that results in harm or death?
| Who's at fault? Suppose it becomes a liability of the company
| offering the model? How does the insurance market respond?
|
| So much this. I think a lot of people overlook this aspect so
| much. I wonder if we'll see a corporate push to make people
| think of individual instantiations of LLMs as being their own
| entities, for scapegoating purposes. It's completely ludicrous,
| but you know they'd just love to point a finger at Machine
| #73583 and make that take all the blame.
| chasd00 wrote:
| > corporate push to make people think of individual
| instantiations of LLMs as being their own entities, for
| scapegoating purposes
|
| an airline already tried this.
|
| "Air Canada argues it cannot be held liable for information
| provided by one of its agents, servants, or representatives -
| including a chatbot. It does not explain why it believes that
| is the case. In effect, Air Canada suggests the chatbot is a
| separate legal entity that is responsible for its own
| actions."
|
| https://www.forbes.com/sites/marisagarcia/2024/02/19/what-
| ai...
| shermantanktop wrote:
| What happens when AI-generated content organically becomes a
| large percentage of the user-generated corpus that is used for
| training, with no way to differentiate it from human-generated
| content?
|
| We may look back on 2022/3 as the last time we had a training
| set that was clean. The fact that it was already polluted with
| SEO garbage will be a quaint problem.
| bane wrote:
| The current thinking is that it becomes a negative feedback
| loop, a kind of lossy "compression" of our cultural output.
| Combined with normal bitrot we'll eventually start losing
| components of our shared culture until civilization ends.
| somewhereoutth wrote:
| - What happens if everyone realises that the areas where LLMs
| can be usefully applied are few and not particularly lucrative?
| bane wrote:
| That's the far right of the hype curve.
| cs702 wrote:
| I would add to the list a few questions about the evolution of
| cost dynamics going forward, given the advent of new sequence
| modeling architectures.
|
| As we all know, Transformers are _very_ expensive to train and
| run because their compute cost is _quadratic_ in context length:
| O(N2).
|
| If newer model architectures like RWKV, Mamba, and various
| others, which incur cost that is _linear_ in context length,
| O(N), prove as successful as Transformers, the demand for compute
| for a query of N tokens would decline from O(N2) to O(N).
|
| For a sequence with N = 100,000 tokens, it would mean _cost
| dropping by a factor of 100,000x_. That 's not peanuts!
|
| The implications for all market participants would be
| significant.
| nyrikki wrote:
| In the general case that would be the same as hierarchy
| collapse of PH, which would prove P=NP
| cs702 wrote:
| Well, it would depend on how many tokens from the vocabulary
| are required, in the worst case, to solve NP problems.
|
| Think of the tokens as symbols, and of the vocabulary as a
| finite alphabet of symbols, in a formal system.
|
| In other words, no, it would NOT necessarily prove P = NP.
| nyrikki wrote:
| The Singleton case is NP-complete
|
| If you consider NP second-order existential logic, meaning
| that given x, you will get a y, While there may be many
| true values that aren't y, it will help
|
| Attention, which requires feed forward networks, which
| approximate a DAG, you can think of attention as run time
| reweighing to change what y value is returned.
|
| PH is a query expressible in second-order logic, which
| would allow for 'for any' at the second level.
|
| The reduction in the general case to just logarithmic time:
| O(n log n), would cause issue, there will not be any
| movement from quadratic time to linear time without massive
| new discoveries.
|
| It is inherent from the restrictions of ANNs having binary
| output.
|
| Considering FP and FNP vs P and NP may help as they make
| that property clearer than the concept of second-order
| existential logic does in some contexts.
| cs702 wrote:
| Thank you. Right now, my puny little brain is searching
| through what I would describe as "those distant and
| slightly vague memories" that make up my poor man's
| knowledge of computational complexity theory.
|
| _> Attention, which requires feed forward networks,
| which approximate a DAG, you can think of attention as
| run time reweighing to change what y value is returned._
|
| Isn't that's true for linear RNN mechanisms too? Note
| that linear RNNs like RWKV and Mamba are linear in the
| number of tokens in the sequence, but not in the number
| of features per token.
| nyrikki wrote:
| You don't typically get to the Sigma_2^P level unless are
| a sadist or forced.
|
| Attention works differently in RNNs, and it may be able
| to find different tractable forms, and may solve
| different problems, but increasing generalization is a
| reduction of compression in the general case.
|
| A many to one reduction to a recursively enumerable set
| gets you to finite time, a reduction to NP gets you to
| quadratic time.
|
| Obviously those are upper limits and specific instances
| or classes of instances may do better. P being inside NP
| is a good example.
|
| All ANNs are binary output, which helps with that. SNN or
| spikey neutral networks, which better model cortical
| neurons and have continuous output have problems with
| being un-computable without much complexity as an
| example.
|
| That can be viewed as the problems with computable
| numbers in place of the reals. But there are lots of
| interesting and practical things to solve. It is just
| those solutions will be more domain specific IMHO.
| dadadad100 wrote:
| As in the case of constraint solvers there are tons of
| heuristics that have been applied over the years to make
| performance look like it scales somewhat linearly in many or
| most problems. This doesn't mean p==np
| nyrikki wrote:
| Most of those have had practical deciders functions.
|
| If you consider NP-complete as the intersection of NP,
| which is a decision problem, and NP-hard which a decider
| function isn't possible; look at the success of heuristics
| between those two sets.
|
| Existential quantifiers on the second order terms in NP is
| the same thing.
|
| Over Parameterization can be thought as less lossy
| compression and attention can be thought of as improved
| retrieval or as additional expressiveness by not missing
| out on as many outputs using binary output.
|
| You can consider how LLMs tend to append, resulting in
| telescope reduction as one of those approximation
| reductions.
|
| But this is due to the combinatorial optimization, which is
| far more difficult.
|
| Another way to think about it is that PH is equal to the
| set of boolean queries on a concurent random acess machine
| using exponentially many processors and constant time.
|
| If your datasets have the Markovian property and are close
| to ergotic there are options, but you probably wouldn't
| need to resort to attention and over parameterization in
| that case.
|
| The word sense disambiguation problem is probably another
| lens. That is harder yet but may be a way to think about
| what I am trying to explain.
| dadadad100 wrote:
| Thanks for this reply. HN paid for itself again today
| doctorpangloss wrote:
| > The implications [of a bunch of different architectures] for
| all market participants would be significant.
|
| I don't know, who cares. How much good training data is there
| that correctly exercises 100k context windows?
|
| All the costs and complexity are tied up in authoring good
| training data, not compute. 1 person can invent an alternative
| easier to train architecture. 1 person cannot author 100k
| context window instruct examples with the same quality as the
| 4k ones. It will still take thousands of people for 100k just
| like it took 1000s of people for 4k windows, image labeling,
| etc.
| og_kalu wrote:
| >All the costs and complexity are tied up in authoring good
| training data, not compute.
|
| No it's compute. Post-Training by Human reinforcement is not
| necessary. Anthropic employs RLAIF and it works just fine.
| Some don't bother with reinforcement learning at all and just
| leave it at fine-tuning on Instruction-response pairs.
|
| The work being done to pre or post-training data is
| insignificant in comparison.
|
| You don't need 100k instruct-tuning examples. The vast
| majority of instruct-tuning data is nowhere maxing even a 4k
| context.
|
| 100k _pre-training_ runs would probably be very helpful but
| the thing stopping that from happening even in domains that
| regularly match or exceed that context (fiction, law, code,
| etc) is the ridiculous compute it would require to train with
| that much context.
| doctorpangloss wrote:
| > The vast majority of instruct-tuning data is nowhere
| maxing even a 4k context
|
| This should indicate for you how much the problem really is
| the training data.
| og_kalu wrote:
| It shows you don't need a instruction-tuning dataset that
| wide to create a model that follows instructions for
| large contexts.
|
| I don't know how else to tell you that you're completely
| wrong on what costs construe the majority of LLM
| development/training.
| araes wrote:
| With the Big O notation, are there also possibilities for
| either the whole process to go to O(nlogn) or possibly
| subportions to the O(logn) range? There's also a lot of math
| operations over the years that have gone to O(Na) where 1 < a <
| 2.
| jxy wrote:
| > architectures like RWKV, Mamba, and various others, which
| incur cost that is linear in context length, O(N)
|
| These are actually O(N*M) where M is independent of inference
| time context length, but it controls the actual amount of
| information that gets retained during inference. Ignoring that
| factor of M is misleading.
| hansvm wrote:
| All models are wrong. Some are useful.
|
| The "linear" models I'm aware of have a cost of O(N * M),
| where M is the amount of information retaind _per token_
| (with some exceptions that store less state but still have
| intermediate computations for some M about the same size as
| the others, and with less state you necessarily have
| forgetting issues and can't scale to unbounded sequences
| anyway).
|
| Contrast that with a transformer, which has a cost of O(N^2 *
| M), where M is still the amount of information retained _per
| token_. Dividing the two you still find a multiplicative
| speedup of O(N).
|
| For accurate cost estimates or any number of other use cases,
| yes, the M is critical. When comparing architectures (or when
| talking about a single architecture with the current implicit
| conversation happening that whatever you say is meant to be
| taken with respect to other architectures) it's conventional
| to note that the M term always appears as a multiplicative
| factor beyond the rest of the costs. You drop it because it's
| obviously there but it's just visual noise that doesn't help
| you understand where the models sit with respect to each
| other. You only include it if it's relevant, like if you
| found a way to get similar performance with a smaller M.
|
| It's kind of like how we always drop the log term on a hash
| table (equivalently, we work in the "word RAM" model of
| computation), but bignum algorithm papers carefully consider
| every log(log(log(N))) that crops up. It's relevant in some
| contexts and not in others. You can't ever include enough
| context to fully represent your thoughts to another person,
| so you truncate somewhere, hopefully capturing the essence of
| what matters.
| bbor wrote:
| The Google CEO said that this (unexpected!) invention is more
| important than fire or electricity. In that light, I think it's
| arrogant to try to soothsay like this. Which is tough advice
| cause this is otherwise great analysis, and acknowledging the
| great unknown feels scary and of dubious value. But I think it's
| the only honest way forward
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