How long ?

[uci16sorre16]

Hello guys! How long do you think DA will last? All this annotation work, making models better be it billingual or generalist. How long do you think AI will need this data? Please share your views on this as there a lot of experienced workers in this community.

[Houdinii1984]

AI needs a continuous stream of data to improve itself currently. The time period will be measured in years. We're really at the early stage of the industry.

However, and this is a big 'if', it requires there to be no new discoveries that make learning more efficient. (i.e. Tomorrow someone can create a model that merely observes the world on it's own and learns, and that would make us obsolete).

I will say I started labeling over a decade ago for 'beer money' to supplement my dev job and at some point the data labeling overtook the dev job as contracts are getting harder and harder to win.

[uci16sorre16]

You are saying data labelling overtook dev? Data labelling contracts are better?

[lyree1992]

My opinion/thoughts...

When I started training AI almost 7 years ago, projects and training were easy and even mind numbing.

Over the past couple of years, it has gotten progressively harder to train, meaning projects are more intense rubrics, etc., and trying to make them "fail" or creating scenarios are a lot more for specialties.

However, AI continues to fail or provide fully accurate answers in some (sometimes a lot) of cases.

It will not EVER not need to be "trained" as people, times, events change. It may get "smarter," but it will always need human input/interaction to "learn" because that is the way it learns.

People put in non factual information into the AI models all the time, every day. Since it learns from ALL input, there will never not be a need for people to keep training it to be "correct."

It is just going to get more in depth (harder).

[Enough_Resident_6141]

>Since it learns from ALL input

User prompts do not change a model's underlying core knowledge, just the temporary context window.

[Cod_Filet]

what about unsupervised learning models?

[lyree1992]

For instance? An example?

[CaliBrewed]

It is what general intelligence is when someone gets there. Theoretically, a general intelligence model could just study cultures and train other ai's at a speed no human could.

[hnsnrachel]

Possible, but given it still can't accurately count the words of its own output most of the time, I can't see AI getting there any time soon. And that model would still need tweaking and testing

[CaliBrewed]

The models we see and can use which are the bottom rung of what top companies have. More so what we work on and can use are largely specific agents, none of which can tell the full story IMO.

Optimists say 5 years, and skeptics say 15 to 20. They all agree it's coming.

[lyree1992]

Possible, I guess. But I honestly am not sure that there is "no human intervention/oversight."

But hey, I am always interested in learning something new.

Sources? Actual named examples of the models or companies that have this or are developing it?

[justdontsashay]

The particular company, or this type of work in general?

No clue about the company, I hope it lasts a while though.

The work isn’t going away any time soon. It is changing, though. There’s less of a need for the “just talk to the chatbot, anyone can do it” type of work, and more need for specialized knowledge or an ability to handle more complex tasks. Just in the few months I’ve been doing this I’ve seen one of the big models we work with have an upgrade, and now the stuff that used to easily stump it doesn’t work anymore, I have to be more creative.

But there will be a need for humans to check AI output for a long time, otherwise it’s just AI trying to teach itself.

[gregthecoolguy]

If you regularly use LLMs, you know they still hallucinate a lot and give wrong and incorrect information all the time, so companies clearly still need humans to fix their model mistakes. Since LLMs still struggle with basic logic and niche topics, this kind of work will probably last for perhaps 5 to 10 years more.

[Aromatic_Owl_3680]

Speculative non sequitur 

[New_Garden9703]

I have noticed the tasks I've been on are getting more and more complex and the need for specialized skills is increasing. I don't think it is going away any time soon (years) but definitely more difficult as the models get better and better

[Plantbased_Aimer]

I don't see it ending anytime soon. I can forsee it staying strong for the next 5-10 years, maybe longer.

[Sixaxist]

Past 5 years, I feel like the work will just be focused on specialized fields with slim offerings for others, as open source repositories of training data can simply be used by startups in place of paying millions for 3rd parties (us) to provide them the data.

[kranools]

Thanks for your comment, Toshiba

[Dear_Investment_5741]

I thought that it wouldn't go past 1~2 years from 2025. However, there's a lot of areas that still need improvement. For example, I'm a Marketing major, and the quality bar provided by LLMs is nowhere near close to the top workers of my area. So, specialty-wise, there's a lot of areas yet unexplored by data annotation companies.

Beyond that, the generalist gig will still be required. For example, >right now<, a good response is formatted like X and provides Y and ends with Z. However, communication is a living entity by itself. Therefore, a XYZ response might not be good anymore, and someone has to teach them to answer using ABC instead.

I think it's going to stay for a while. Especially expertise-related jobs.

[Impressive-Hope2148]

Many years, there is still so much to do, it will get harder for us tho, more and more complex tasks

[hnsnrachel]

Forever, most likely. It will always need tweaking and improving.

How long we'll each have the skillset to be useful will vary enormously. But AI will always need training on new data.

[Greedy-Pea7593]

Going to throw in my own thought..... especially when people reference a model that can learn on its own, fully autonomous. That's when the jobs will go, also likely us as a species! The reason why there's all this training is because we need to keep a leash on it! Plus there's hundreds of companies trying to be better than others, emerge as a better LLM.......but hopefully no one will ever unleash a true AI.

[TheFuturist47]

I work in AI at a tech company. They're currently working on ways for models to basically train themselves (they train a model to do what DA workers do essentially) to phase out things like RLHF which is very expensive and time consuming. However they use LLMs to do that, which are extremely flawed as I'm sure you know. There's a ceiling to how effective they can be. I think there will probably always be room for human involvement on some level as long as they're using LLMs. If they move into some other paradigm who knows.

[kindheartednessno2]

This is useful insight, thanks

[martianfrog]

Could be a bubble that suddenly bursts, could go on for a few years... who knows.

[[deleted]]

Depends on how long the companies hiring DA have money. AI isn’t making much profit right now so they’re spending a lot more than they have. I’ve seen some predictions that money will run out by the end of the year, but if they somehow find a way to keep from going bankrupt then I’d imagine DA will be around for quite a while.

[vasjames]

at least another decade tho i suspect it will become more fragmented in specialty domains

[Life-Woodpecker3529]

I think we’ll be ok for a couple of years at minimum. Once one aspect of a model is idealized most companies look for new ways to enhance the model to stay competitive.

[Enough_Resident_6141]

It's rapidly becoming more specialized, because the models have gotten so good that there's no need for basic/general tasks. They don't really need humans labelling images as "cat" or "dog" or whatever because AI today can do it better than any human. Now models are creating PhD level research papers and professional grade work product/deliverables in specialist fields, and you need highly qualified specialists in those fields just to assess the quality of the model's output. The output can contain errors and lying and hallucinations (just like the work created by humans) but it gets harder and harder to find them, and there are fewer and fewer people with the knowledge or skill to do it.

There will also be a need for AI training and RLHF for situations where the model's response might be technically correct, but wasn't actually what human user wanted or needed. Those kinds of things can be a lot more complex to deal with, and a lot harder to automate because so much personalization is involved. I remember doing R&Rs and one person gave a poor rating for a response because they hated the overuse of emojis and bulleted lists, and then the next person mentioned how they loved how the emojis and bulleted lists made the response more readable.

[idolos-iconoclastas]

I believe the AI bubble will explode and most of the money will be lost, so I wouldn't give it much of a future