The OP tweet seems to be leaning pretty hard on the “AI bad” sentiment. If LLMs make academic knowledge more accessible to people that’s a good thing for the same reason what Aaron Swartz was doing was a good thing.
That would be good if they did that but that is not the intent of the org, the purpose of the tool, the expected or even available outcome.
It’s important to remember this data is not being scraped to make it available or presentable but to make a machine that echos human authography convincingly more convincingly.
On an extremely simplified level, it doesn’t want to answer 1+1=? with “2”, it wants to appear like a human confidently answering an arithmetic question, even if the exchange is “1+1=?” “yes, 2+3 does equal 9”
Obviously it can handle simple sums, this is an illustrative example
On the whole, maybe LLMs do make these subjects more accessible in a way that’s a net-positive, but there are a lot of monied interests that make positive, transparent design choices unlikely. The companies that create and tweak these generalized models want to make a return in the long run. Consequently, they have deliberately made their products speak in authoritative, neutral tones to make them seem more correct, unbiased and trustworthy to people.
The problem is that LLMs ‘hallucinate’ details as an unavoidable consequence of their design. People can tell untruths as well, but if a person lies or misspeaks about a scientific study, they can be called out on it. An LLM cannot be held accountable in the same way, as it’s essentially a complex statistical prediction algorithm. Non-savvy users can easily be fed misinfo straight from the tap, and bad actors can easily generate correct-sounding misinformation to deliberately try and sway others.
ChatGPT completely fabricating authors, titles, and even (fake) links to studies is a known problem. Far too often, unsuspecting users take its output at face value and believe it to be correct because it sounds correct. This is bad, and part of the issue is marketing these models as though they’re intelligent. They’re very good at generating plausible responses, but this should never be construed as them being good at generating correct ones.
The OP tweet seems to be leaning pretty hard on the “AI bad” sentiment. If LLMs make academic knowledge more accessible to people that’s a good thing for the same reason what Aaron Swartz was doing was a good thing.
Except it won’t. And AI we’ll be pay to play
That would be good if they did that but that is not the intent of the org, the purpose of the tool, the expected or even available outcome.
It’s important to remember this data is not being scraped to make it available or presentable but to make a machine that echos human authography convincingly more convincingly.
On an extremely simplified level, it doesn’t want to answer 1+1=? with “2”, it wants to appear like a human confidently answering an arithmetic question, even if the exchange is “1+1=?” “yes, 2+3 does equal 9”
Obviously it can handle simple sums, this is an illustrative example
On the whole, maybe LLMs do make these subjects more accessible in a way that’s a net-positive, but there are a lot of monied interests that make positive, transparent design choices unlikely. The companies that create and tweak these generalized models want to make a return in the long run. Consequently, they have deliberately made their products speak in authoritative, neutral tones to make them seem more correct, unbiased and trustworthy to people.
The problem is that LLMs ‘hallucinate’ details as an unavoidable consequence of their design. People can tell untruths as well, but if a person lies or misspeaks about a scientific study, they can be called out on it. An LLM cannot be held accountable in the same way, as it’s essentially a complex statistical prediction algorithm. Non-savvy users can easily be fed misinfo straight from the tap, and bad actors can easily generate correct-sounding misinformation to deliberately try and sway others.
ChatGPT completely fabricating authors, titles, and even (fake) links to studies is a known problem. Far too often, unsuspecting users take its output at face value and believe it to be correct because it sounds correct. This is bad, and part of the issue is marketing these models as though they’re intelligent. They’re very good at generating plausible responses, but this should never be construed as them being good at generating correct ones.