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If AI seems smarter, it's thanks to smarter human trainers
- GaggiX 2y agoLet's not ignore better architectures, training techniques and computing power.
- kaycebasques 2y agoSuppose you are competing to create the "best" frontier model and have finite R&D budget to allocate into the 4 buckets (plus a catchall in case we're missing something): * Data * Architecture * Training techniques * Compute * Other What allocation gives you the best chance for success? And how are you defining "best"?
- NitpickLawyer 2y agoRight now I'd prioritise compute over anything, because it allows for more experiments, and some of those experiments might turn out to be the key to better models (either specific applications or overall generalist models). Meta did this with L3. They used L2 to pre-filter the training data, filtering out a lot of junk. They also used it to classify datasets. Then after pre-training (involving lots of compute) they also used almost exclusively synthetic data for fine-tuning (forgoing RLHF when it was surpassed). So yet more compute. The results are pretty good, L3, 3.1 and 3.2 are pretty high up there in terms of open access SotA. oAI did this with their o1 models. They used lots of compute to have the models go over the space of generating tokens, analysing, correcting, and so on. They RLd the "reasoning traces" in a way. Lots of compute. The results seem to be pretty good, with impressive showings on "reasoning" tasks, math, code, and so on. The thing is, they weren't the first ones to propose these techniques! What differentiates them is the available compute. WizardML tried and were really successful with their RLAIF implementation (tho never released code afaik) about a year ago. And while they were connected to MS research, they probably didn't have as much compute available as Meta. But the WizardML fine-tunes on open models like Mistral and Mixtral were pretty much SotA when released, scoring way higher than the creator's own fine-tunes. In the same vein, but at lower scales is the team behind DeepSeek. They used RL on math problems, in their DeepSeekMath-7bRL model, and that model was SotA at the time of release as well. It took a team of multiple really talented folks to fine-tune a better model (in the AIMO kaggle competition) and everyone except the 1st place used the RL model. The 1st place used the base model, with different fine-tuning. So again, the methods were tried, just at much lower scales. Yeah, I think compute would be my bet in the short run.
- JCharante 2y agousing human feedback for reinforcement learning is a training technique
- ben_w 2y agoIt's both. I recently saw a comparison of various models on two IQ tests, one of which was public and the other of which was carefully curated to be not directly learnable from the likely training sets. On public tests, LLMs vary between "just below average human" and "genius". On the hopefully-private test (it's difficult to be sure*), the best was o1, which was "merely" just below an average human, Claude-3 Opus which was stupid, and all the rest were "would need a full time caretaker". In both cases, the improvements to the models came with higher scores; but there's still a lot you can do by learning for the test — and one thing that LLMs are definitely superhuman at is that. https://www.maximumtruth.org/p/massive-breakthrough-in-ai-intelligence https://www.maximumtruth.org/p/massive-breakthrough-in-ai-in... * I could have said the same last year about last year's models, so I'm emphatically not saying o1 really is as smart as this test claims; I'm only saying this demonstrates these IQ tests are a learnable skill up to at least this magnitude of difference.
- CamperBob2 2y agoWhich is fine. If all AI does is represent human knowledge in a way that makes it explainable and transformable rather than merely searchable, then the hype is justified... along with Google's howling, terrified panic. The role played by humans on the training side is of little interest when considering the technology from a user's perspective.
- iwontberude 2y agoI think the most interesting aspect of it is the human training. Human blindsides, dogma, ignorance, etc. All on demand and faster than you can validate its accuracy or utility. This is good.
- CamperBob2 2y agoShrug... I don't know what anyone expected, once humans got involved. Like all of us (and all of our tools), AI is vulnerable to human flaws.
- ddulaney 2y agoI think that’s really important to reinforce! You probably know better, but lots of the less technical people I talk to don’t think that way. It’s not at all obvious to an observer who doesn’t know how this stuff works that a computer could be racist or misogynist.
- CamperBob2 2y agoYeah, I do think that's going to be a problem. Years ago, my GF asked me why we bother with judges and juries, given all the uneven sentencing practices and other issues with the current legal system. "Why can't the courts run on computers?" This was back in the pre-Alpha Go era, so when I answered her, I focused on technical reasons why Computers Can't Do That... reasons that are all basically obsolete now, or soon will be. The real answer lies in the original premise of her question: because Humans Also Can't Do That with the degree of accuracy and accountability that she was asking for. Our laws simply aren't compatible with perfect mechanized jurisprudence and enforcement. Code may be law, but law isn't code. That problem exists in a lot of areas where people will be looking to AI to save us from our own faults. Again, this has little to do with how training is conducted, or how humans participate in it. Just getting the racism and misogyny out of the training data isn't going to be enough.
- Stem0037 2y agoAI, at least in its current form, is not so much replacing human expertise as it is augmenting and redistributing it.
- alephnerd 2y agoYep. And that's the real value add that is happening right now. HN concentrates on the hype but ignores the massive growth in startups that are applying commoditized foundational models to specific domains and applications. Early Stage investments are made with a 5-7 year timeline in mind (either for later stage funding if successful or acquisition if less successful). People also seem to ignore the fact that foundational models are on the verge of being commoditized over the next 5-7 years, which decreases the overall power of foundational ML companies, as applications become the key differentiator, and domain experience is hard to build (look at how it took Google 15 years to finally get on track in the cloud computing world)
- MostlyStable 2y agoI notice that a lot of people seem to only focus on the things that AI can't do or the cases where it breaks, and seem unwilling or incapable of focusing on things it can do. The reality is that both things are important. It is necessary to know the limitations of AI (and keep up with them as they change), to avoid getting yourself in trouble, but if you ignore the things that AI can do (which are many, and constantly increasing), you are leaving a ton of value on the table.
- ysofunny 2y agoI feel weird being stubborn against free tier google gemini I feel as though it 'extracts' some sort of "smartness" out of me (if any) and then whatever intelligence from me becomes part of google gemini this is why I would never want to pay for using these tools, anything good that comes from me in the chat becomes google's by AI training, which is ok so long as it's free to use i.e. I won't pay to make their stuff better through my own work
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- buzzerbetrayed 2y agoI totally sympathize with the sentiment. But how long until people who are taking a moral stand against AI are simply obsoleted by the people who don’t? Today it’s easy to code effectively without relying on AI. But in 10 years will you simply be too slow? Same argument can be made with nearly any industry.
- pixl97 2y agoPretty much like the people that don't care about privacy. You still get captured and tagged in their information and uploaded to the web. As an individual it's difficult to do much about it.
- croes 2y agoThat's same logic as for frameworks like react. With react you are more productive, my web experience is worse than without a those frameworks. And LLMs get worse if they are trained on AI generated text. At the current speed I don't know if in 10 years AI is still worse the high costs.
- joshstrange 2y ago> With react you are more productive, my web experience is worse than without a those frameworks. You cannot begin to know that for sure and really makes little to no sense if you think about it. As with the anti-electron crowd the options are not: * Electron app or * Bespoke, hand-crafted, made with love, native app The options are normally “electron app” or “nothing”. Same deal here. Taking away React/Angular/Vue won’t magically make people write more performant websites. I’m sure people bitched about (and continue to) PHP for making it easy for people to create websites that aren’t performant or Wordpress for all its flaws. It’s the same story that’s repeated over and over in tech circles and I find it both silly and incredibly short-sighted. Actually I find it tiring because you can always go one level deeper to one-up these absurd statements. It’s No True Scotsman all the way down.
- JCharante 2y ago> AI models now require trainers with advanced degrees Companies that create data for FM (foundational model) companies have been hiring people with degrees for years > Invisible Tech employs 5,000 specialized trainers globally Some of those companies have almost a million freelancers on their platforms, so 5k is honestly kinda medium sized. > It takes smart humans to avoid hallucinations in AI Many smart humans fail at critical thinking. I've seen people with masters fail at spotting hallucinations in elementary level word problems.
- aleph_minus_one 2y ago> Many smart humans fail at critical thinking. I've seen people with masters fail at spotting hallucinations in elementary level word problems. This is like lamenting that a person who has a doctoral degree, say, in mathematics or physics often don't have a more than basic knowledge about, for example, medicine or pharmacy.
- JCharante 2y agoIt depends on your definition of smart. I think that holding a degree != smart.
- aniviacat 2y ago> I think that holding a degree != smart. Does this mean that these two sentences are completely unrelated and only happen to exist within the same paragraph? > Many smart humans fail at critical thinking. I've seen people with masters [...] I've understood you to use "people with masters" as an example of "smart humans".
- JCharante 2y agoWell the article began by talking about how before these data training companies would just hire generalists for $2/hr, but now they're hiring degree holders. And it mentions that smart people will be necessary. I'm just saying that degree holding != smart and it's a trap that those data training companies have to avoid.
- SamGyamfi 2y agoThere is a cost-quality tradeoff companies are willing to make for AI model training using synthetic data. It shows up fairly often with AI research labs and their papers. There are also upcoming tools that remove the noise that would trip up some advanced models during annotation. Knowing this, I don't think the "human-labeled data is better" argument will last that long.
- yawnxyz 2y ago"raw dogging" non-RLHF'd language models (and getting good and unique output) is going to be a rare and sought-after skill soon. It's going to be a new art form someone should write a story about that!
- zmgsabst 2y agoI’m personally waiting on AI psychology to take off. Eg, why does ChatGPT like the concept of harmony so much and use it as a principle for its political analysis?
- yawnxyz 2y agoI thought it's b/c of RLHF? I think the earliest GPT-3 wasn't too keen on harmony, but I might be mis-remembering
- theptip 2y agoI feel this is one of the major ways that most pundits failed with their “the data is going to run out” predictions. First and foremost a chatbot generates plenty of new data (plus feedback!), but you can also commission new high-quality content. Karpathy recently commented that GPT-3 needs so many parameters because most of the training set is garbage, and that he expects eventually a GPT-2 sized model could reach GPT-3 level, if trained exclusively on high-quality textbooks. This is one of the ways you get textbooks to push the frontier capabilities.
- from-nibly 2y agoAt a good cost though? Last time I checked generating good data costs a tiny bit more than an http request to somebody elses website.
- theptip 2y agoIf the cost is not “good enough”, why are the big guys buying a lot of it?
- llm_trw 2y agoI've not done pre-training for LLMs, but years ago I generated a completely synthetic dataset for table recognition using an off the shelf document segmentation model, raw TeX, a random table generator, a discriminator and an evolutionary algorithm to generate different styles of tables. The project got killed due to management but I still got results on that dataset better than state of the art in 2023 with no human annotation. The Venn diagram of people who know TeX well enough to write a modular script for table generation with metadata and people who know how to train LLMs has an intersection of a dozen people I imagine.
- theptip 2y agoThis is a great example. Areas where an expert can write a synthetic data generator (eg code, physics simulators, etc) are the dream scenario here. It seems to me there is a huge amount of unharvested low-hanging fruit here, for example IIUC GPT is not trained on synthetic code in languages other than Python (and maybe JS, I don’t recall).
- recursive 2y agoIt kind of seems like it got dumber to me. Maybe because my first exposure to it was so magical. But now, I just notice all the ways it's wrong.
- joe_the_user 2y agoI think any given model is going to decay over time. The data used in them becomes out-dated and the models cost money to run and various cost-saving short-cuts are thus made to reduce accuracy. Also, having your old model seem clunky can make your new model seem great. Obviously, there are real ways new model get better too. But if we have diminishing returns, as many speculate, it will take a while for it to be entirely obvious.
- bdjsiqoocwk 2y agoSubmarine article placed by Cohere. Wtf is cohere.
- wlindley 2y agoa/k/a It is all a clever scam. True, or true?
- throwaway_2968 2y agoThrowaway account here. I recently spent a few months as a trainer for a major AI company's project. The well-paid gig mainly involved crafting specialized, reasoning-heavy questions that were supposed to stump the current top models. Most of the trainers had PhDs, and the company's idea was to use our questions to benchmark future AI systems. It was a real challenge. I managed to come up with a handful of questions that tripped up the models, but it was clear they stumbled for pretty mundane reasons—outdated info or faulty string parsing due to tokenization. A common gripe among the trainers was the project's insistence on questions with clear-cut right/wrong answers. Many of us worked in fields where good research tends to be more nuanced and open to interpretation. I saw plenty of questions from other trainers that only had definitive answers if you bought into specific (and often contentious) theoretical frameworks in psychology, sociology, linguistics, history, and so on. The AI company people running the projects seemed a bit out of their depth, too. Their detailed guidelines for us actually contained some fundamental contradictions that they had missed. (Ironically, when I ran those guidelines by Claude, ChatGPT, and Gemini, they all spotted the issues straight away.) After finishing the project, I came away even more impressed by how smart the current models can be.
- layer8 2y agoI wouldn’t look for questions with yes/no answers, but for questions where the answers can have correct/incorrect reasoning. Of course, you can’t turn those into automated benchmarks, but that’s maybe kinda the point.
- jwrallie 2y agoI wonder how much prompt skill is actually influencing the quality of the response. After using LLMs daily for some time, I have developed a feeling on how to phrase my requests as to get better quality answers. For example, ensure that it can process the information linearly, like asking to classify items in a list and adding the label directly after the item so that the order remains, instead of allowing it to create multiple lists as the output (which it tends to do by default). So, at least for me, the prompts are getting smarter.
- layer8 2y agoSmarter, but also more tedious. It would be great to have technology that automates this tedious work. ;)
- machiaweliczny 2y agoYeah, I feel like you get much better answers when you pull enough context with leading context (but without insinuating answers). I wonder if someone tried something like this: "Given user question generate prompt that tells LLM that he is a master in this subject and then ask the question" - something in this allows LLM to precompute what the focus of question will be so it can answer much better and it should be automated for one-off questions.
- tiku 2y agoI was watching the video of someone using openai voice chat, asking for a joke. Then I've tried it myself, asking for a joke and I got the exact same one. (Why don't skeletons fight eachother?). Seems like if then else haha.
- fsndz 2y ago> "In the early years, getting AI models like ChatGPT or its rival Cohere to spit out human-like responses required vast teams of low-cost workers helping models distinguish basic facts such as if an image was of a car or a carrot." Starting the article by comparing what is necessary for ChatGPT to work and image labelling is a bit weird
- butz 2y agoI bet that every "top" performing GenAI has dozens upon dozens of "if" statements to make them seem smart.