AI in “Education”
Natural Language Models (NLMs) are all the range, with new models going into service across the globe literally every day. These models are based on ever increasing pools of data that the NLM can ‘view’ and learn to identify and understand. These data pools are huge and vary widely in terms of what they contain and their sources, but they all tend to have one thing in common, and that is the billions or trillions of pieces of data in these pools, must be identified and annotated, so the Ai has a point of reference. Such an SFT (Supervised Fine Tuning) system, known as RLHF (Reinforced Learning w. Human Feedback) places humans in the loop to identify data and images for the NLM so it might understand that another datapoint or image is similar.
The folks that do this work are not data scientists or programmers that get paid ~$0.03 per item, and with somewhere between 800 and 1,000 items the peak for experienced workers, they are not high on the global pay scale. The only thing in their favor is that NLMs are popular, and there is ‘competition’ between NLM producers (?) to keep enlarging the datasets that NLMs learn from. There are large open-source data sets that can be a basis for a NLM, but the more data you have to learn with the ‘smarter’ your NLM (or so they say).
At $0.03 per item, and billions or trillions of items, things can get expensive, so Google (GOOG) has come up with a system that replaces the RLAF model with an RLAIF, where the AIF stands for AI Feedback, rather than human feedback. By replacing the human component with an Ai system that will ‘identify’ items based on its own training and algorithms, the cost can be reduced, and Google says that users actually preferred the NLMs based on AI feedback over those using human feedback. Of course there are some serious ethical issues that arise when you remove humans from the feedback loop, but why worry about that when you can save money and come up with a better mousetrap. Ok, there is the possibility that something might not be identified correctly, or a rule, such as those that try to eliminate p[profanity or racism from NLMs might get missed because it is embedded in previously ‘learned’ data, and that would mean it could be passed on as ‘correct’ to NLMs and to other Ai systems without human oversight. It is easy to see how quickly something like this might get out of control, but don’t worry because, well, because we wouldn’t let that happen, right?
The folks that do this work are not data scientists or programmers that get paid ~$0.03 per item, and with somewhere between 800 and 1,000 items the peak for experienced workers, they are not high on the global pay scale. The only thing in their favor is that NLMs are popular, and there is ‘competition’ between NLM producers (?) to keep enlarging the datasets that NLMs learn from. There are large open-source data sets that can be a basis for a NLM, but the more data you have to learn with the ‘smarter’ your NLM (or so they say).
At $0.03 per item, and billions or trillions of items, things can get expensive, so Google (GOOG) has come up with a system that replaces the RLAF model with an RLAIF, where the AIF stands for AI Feedback, rather than human feedback. By replacing the human component with an Ai system that will ‘identify’ items based on its own training and algorithms, the cost can be reduced, and Google says that users actually preferred the NLMs based on AI feedback over those using human feedback. Of course there are some serious ethical issues that arise when you remove humans from the feedback loop, but why worry about that when you can save money and come up with a better mousetrap. Ok, there is the possibility that something might not be identified correctly, or a rule, such as those that try to eliminate p[profanity or racism from NLMs might get missed because it is embedded in previously ‘learned’ data, and that would mean it could be passed on as ‘correct’ to NLMs and to other Ai systems without human oversight. It is easy to see how quickly something like this might get out of control, but don’t worry because, well, because we wouldn’t let that happen, right?
RSS Feed