I used to think typos meant that the author (and/or editor) hadn’t checked what they wrote, so the article was likely poor quality and less trustworthy. Now I’m reassured that it’s a human behind it and not a glorified word-prediction algorithm.
I used to think typos meant that the author (and/or editor) hadn’t checked what they wrote, so the article was likely poor quality and less trustworthy. Now I’m reassured that it’s a human behind it and not a glorified word-prediction algorithm.
Photoshop is a general purpose image editting tool that is mostly harmless. That’s not the same for AI. The people who created them and allow other people to use them do so anyway without enough consideration to the risks they know is much much higher than something like Photoshop.
What you say applies to photoshop because the devs know what it can do and the possible damage it can cause from misuse is within reasons. The AI you are talking about are controlled by the companies that create them and use them to provide services. It follows it is their responsibility to make sure their products are not harmful to the extend they are, especially when the consequences are not fully known.
Your reasoning is the equivalent of saying it’s the kids fault for getting addicted to predatory mobile games and wasting excessive money on them. Except that it’s not entirely their fault and programs aren’t just a neutral tool but a tool that is customised to the wills of the owners (the companies that own them). So there is such a thing as an evil tool.
It’s all those companies, and the people involved, as well as law makers responsiblity to make the new technology safe with minimal negative impacts to society rather than chase after their own profits while ignoring the moral choices.
This is not true. You do not know all the options that exist, or how they really work. I do. I am only using open source offline AI. I do not use anything proprietary. All of the LLM’s are just a combination of a complex system of categories, with a complex network that calculates what word should come next. Everything else is external to the model. The model itself is not anything like an artificial general intelligence. It has no persistent memory. The only thing is actually does is predict what word should come next.
Do you always remember things as is? Or do you remember an abstraction of it?
You also don’t need to know everything about something to be able to interpret risks and possibilities, btw.