Text generator ChatGPT is the fastest-growing consumer app ever, and it’s still growing rapidly.
But the dirty secret of AI is that humans are still needed to create, label and structure training data — and training data is very expensive. The dark side of this is that an exponential feedback loop is being created where AI is a surveillance technology. And so, managing the humans in the AI loop is crucial.
Some experts believe that when (potentially) robots take over the world, they’d better be controlled by decentralized networks. And humans must be incentivized to prepare the data sets. Blockchain and tokens can help… but can blockchain save humanity from AI?
ChatGPT is just regurgitated data
ChatGPT is a big deal according to famed AI researcher Ben Goertzel, given that “the ChatGPT thing caused the Google founders to show up at the office for the first time in years!” he laughs. Goertzel is the founder of blockchain-based AI marketplace SingularityNET and an outspoken proponent of artificial general intelligence (AGI) — computers thinking for themselves. That means he sees where ChatGPT falls short more clearly than most.
“What’s interesting about ChatGPT and other neuro models is that they achieve a certain amount of generality without having much ability to generalize. They achieve a general scope of ability relative to an individual human by having so much training data.”
Read also: How to prevent AI from ‘annihilating humanity’ using blockchain
In other words, ChatGPT is really one function achieved by the brute force of having so much data. “This is not the way humans achieve breadth by iterative acts of creative generalization,” he says, adding, “It’s a hack; it’s a beautiful hack; it’s very cool. I think it is a big leap forward.”
He’s not discounting where that hack can take us either. “I won’t be shocked if GPT-7 can do 80% of human jobs,” he says. “That’s big but it doesn’t mean they can be human-level thinking machines. But they can do a majority of human-level jobs.”
Logic predicated on experience remains harder for AI than scraping the internet. Predicate logic means that humans know how to open bottle caps, for example, but AIs need trillions of data to learn that simple task. And good large language models (LLMs) can still…
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