And that’s exactly where management fails! You can’t demand AI adoption, provide no training, no guidance, no quality standards, and then blame employees for poor results. If workers don’t know how to use AI effectively, that’s a leadership failure. Management wanted instant productivity without investing in skills.
Your comment above is being downvoted because this:
That’s because they do not know how to use AI correctly,
seems to imply that there is a correct way to use AI.
In the context of the current generation of labor theft algorithms and surveillance tools, the only correct way to interact with them is as a conscientious objector - which is to say, abject refusal on moral grounds.
Failure to abstain from the use of LLMs and their ilk indicates either:
Ignorance of the damage being done by these algorithms, their training, and the corporations that develop and operate them (have you somehow been living under a rock?)
-or-
Complete moral and good sense judgment failure of the individual.
“AI” is continually improving prediction models. There are excellent use cases for prediction models, like weather forecasting, or planning distribution of emergency resources ahead of impending disaster.
But expecting a desk jockey to expect any prediction model to accurately create anew thing based on a human description of what that thing should be like would require the prediction model to possess its own creativity. That’s just not a thing.
I can only assume that you didn’t fully read my comment, because it is specifically not talking about such prediction models. It is specifically talking about LLMs and the related corporations around which all of the current VC hype nonsense is centered.
seems to imply that there is a correct way to use AI.
LLMs are universally hugbots, telling you what they think you want to hear. That’s always going to be a bad way to get accurate solutions. I think we agree on that.
There are prediction models that aren’t LLMs which can be useful. Your comment was unclear on whether you considered LLMs to be the entirety of “AI” or not.
And that’s exactly where management fails! You can’t demand AI adoption, provide no training, no guidance, no quality standards, and then blame employees for poor results. If workers don’t know how to use AI effectively, that’s a leadership failure. Management wanted instant productivity without investing in skills.
Yeah exactly I agree. Weird my comment has a down vote 😬
Your comment above is being downvoted because this:
seems to imply that there is a correct way to use AI.
In the context of the current generation of labor theft algorithms and surveillance tools, the only correct way to interact with them is as a conscientious objector - which is to say, abject refusal on moral grounds.
Failure to abstain from the use of LLMs and their ilk indicates either:
-or-
“AI” is continually improving prediction models. There are excellent use cases for prediction models, like weather forecasting, or planning distribution of emergency resources ahead of impending disaster.
But expecting a desk jockey to expect any prediction model to accurately create anew thing based on a human description of what that thing should be like would require the prediction model to possess its own creativity. That’s just not a thing.
and llms are suited for none of those examples you gave.
I can only assume that you didn’t fully read my comment, because it is specifically not talking about such prediction models. It is specifically talking about LLMs and the related corporations around which all of the current VC hype nonsense is centered.
The early part of your comment said
LLMs are universally hugbots, telling you what they think you want to hear. That’s always going to be a bad way to get accurate solutions. I think we agree on that.
There are prediction models that aren’t LLMs which can be useful. Your comment was unclear on whether you considered LLMs to be the entirety of “AI” or not.
I suppose I’ll never understand why comments are upvoted or downvoted. Sometimes I get the impression that it’s more down to chance or mistakes.