7 ms·
Obtaining high quality training data is the biggest moat right now.
by weberer 2y ago
Obtaining high quality training data is the biggest moat right now.
- segasaturn 2y agoWhere are they going to get that data? Everything on the open web after 2023 is polluted with lowquality AI slop that poisons the data sets. My prediction: Aggressive dragnet surveillance of users. As in, Google recording your phone calls on Android, Windows sending screen recordings from Recall to OpenAI, Meta training off Whatsapp messages... It sounds dystopian, but the Line Must Go Up.
- jazzyjackson 2y agoI'm really curious if Microsoft will ever give in to the urge to train on private business data - since transitioning office to o365, they hold the world's and even governments word documents and emails. I'm pretty sure they've promised never to touch it but they can certainly read it so... Information wants to be free.
- jhickok 2y agoMicrosoft "trains" on business data already, but typically for things like fine-tuning security automation and recognizing malicious signals. It sure would be a big step to reading chats and email and feeding them in to a model.
- ENGNR 2y agoSlack tried it but the backlash got them, this time anyway.
- bossyTeacher 2y agoElaborate? never heard of this
- ENGNR 2y agoEl Reg got the story: https://www.theregister.com/2024/05/20/slack_ts_and_cs_update/ https://www.theregister.com/2024/05/20/slack_ts_and_cs_updat... They blamed it on a 'misunderstanding' in the end
- wkat4242 2y agoTbh it would also make the model much more accurate for corporate uses. It's not a bad idea for that reason. But security and DLP teams will never accept it.
- crazygringo 2y ago> Everything on the open web after 2023 is polluted with lowquality AI slop that poisons the data sets. Not even close to everything. E.g. training on the NY Times and Wikipedia has zero meaningful AI. Training on books from reputable publishers similarly has zero meaningful AI. Any LLM usage was to polish prose or assist with research or whatever, but shouldn't affect the factual quality in any significant way. The web hasn't been polluted with AI any more than e-mail has been polluted with spam. Which is to say it's there, but it's also entirely viable to separate. Nobody's worried that the group email chain with friends is being overrun with spam or with AI.
- dgfitz 2y ago[flagged]
- mplewis 2y agoThere is no heuristic that reliably separates human-written text from GPT text. It is impossible to avoid model collapse in such an environment.
- ValentinA23 2y agoUnless you're OpenAI and provided like 98% of ai generated text that was posted to the internet.
- Der_Einzige 2y agoAnti-slop sampler and bigram/trigram stylometric analysis absolutely can give evidence that someone used a particular LLM with particular settings. But in general it’s hard to separate human from AI text.
- crazygringo 2y agoYou're ignoring the information about the source. It's not about a heuristic on text of unknown provenance -- it's about publishers that exert a certain level of editorial control and quality verification. Or social reputation mechanisms that achieve the same. That's what is preventing your "model collapse". Reputations of provenance. Not pure-text heuristics.
- lfmunoz4 2y agoWould think most quality data is books and news articles and scientific journals. Not crap people are texting each other. These companies will never admit it but AI is built on the back of piracy archives, easiest way and cheapest way to getting massive amounts of quality data.
- mcmcmc 2y agoThat entirely depends on what quality you’re going for. If the goal is to simulate passably human conversation, texts and dms are probably more desirable.
- mswen 2y agoA friend and I built a proof-of-concept of using a variation of Latent Semantic Analysis to automatically build up conceptual maps and loadings of individual words against the latent conceptual vectors back in 2000. In exploring what it would take to scale I concluded, like you, that we should use professionally written and edited content like books, news articles and scientific journals as the corpus against which to build up the core knowledge graph. Twenty-four years later I still regret not being able to raise money to enable us to keep working on that nascent startup. In most ways it was still too early. Google was still burning through VC money at that point and the midwestern investors we had some access to didn't get it. And, honestly they were probably correct. Compute power was still too expensive and quality data sources like published text were mostly locked up and generally not available to harvest.
- staticautomatic 2y agoI’m in this space and no it isn’t.
- sumedh 2y agoWhat is the moat then?
- staticautomatic 2y agoIdk but it’s not lack of training data.
- sumedh 2y agoYou work in this space and you dont know what the moat is?
- staticautomatic 2y agoThe market is two-sided
- sangnoir 2y agoAll of the people working in CS don't know if P=NP: working in a field doesn't mean you have all the answers.
- mplewis 2y agoTells you a lot about the state of the industry.
- tim333 2y agoThere doesn't seem to be much of a moat. OpenAI, Gemini, Meta, X.Ai and Anthropic all seem to be able to much the same stuff. o1 is novel at the moment but I bet it'll be copied soon.
- fastball 2y agoIf you don't know what it is, how do you know it's not training data?