They are terribly monotonous tasks.
I can see why it's shutting down if its still the same thing.
Lllms could probably do everything there without rotting out minds for basically pennies.
I believe the issue is that this can no longer be a horizontal play. MTurk was mostly for unskilled tasks...the kind AI can do well enough that it isn't worth the cost differential to verify it or keep farmed to humans. The "trust but verify" AI output is now the kind that requires domain expertise. This is what most full stack AI companies are bringing to industries.
Curious if this kind of work will come around again one day or was just a moment in time. If it does I'm sure it will be specifically about generating training data.
"This robot is having trouble folding a tshirt help it out for 1$"
Unless they go the waymo route of highly trusted people but I think mass deployed robots are a bit safer than a car for this.
Mercor has a market value of $20B basically doing the same thing but desperately trying to find workers.
My favorite part of AMT is always going to be figuring out that if we only paid in $0.07 intervals, their commission algorithm would round down to the nearest whole cent when their 20% commission resulted in a fractional cent on the unit transaction level, not the monthly invoice level. Was ultimately worth it to have implemented https://git.generalresearch.com/panels/amt-jb/tree/jb/flow/a...
However, I'm not sure a single platform will be how it emerges
Amazon Mechanical Turk (MTurk) is a crowdsourcing marketplace that makes it easier for individuals and businesses to outsource their processes and jobs to a distributed workforce who can perform these tasks virtually. This could include anything from conducting simple data validation and research to more subjective tasks like survey participation, content moderation, and more. MTurk enables companies to harness the collective intelligence, skills, and insights from a global workforce to streamline business processes, augment data collection and analysis, and accelerate machine learning development.
While technology continues to improve, there are still many things that human beings can do much more effectively than computers, such as moderating content, performing data deduplication, or research. Traditionally, tasks like this have been accomplished by hiring a large temporary workforce, which is time consuming, expensive and difficult to scale, or have gone undone. Crowdsourcing is a good way to break down a manual, time-consuming project into smaller, more manageable tasks to be completed by distributed workers over the Internet (also known as ‘microtasks’).