Herein lies the problem with the media spouting AI to cover everything. There are many forms that the term AI covers. From LLMs (the token prediction machines), machine learning, behavioural AI (used in gaming and simulations), computer vision, generative, evolutionary, natural language processing, etc Each with its own strengths, weaknesses and application.
Why are people so smart on technology so stupid on communication?
Context is king in communication. Consider the several different meanings of the word “bank”. How do you distinguish between them in “I will bank my plane over the river’s bank on my way to the bank”?
Context.
In current conversational context, when people talk about AI, they’re not talking about:
perceptrons (later rebranded to neural networks because that gives you that sweet grant money)
generalized portrait methods (now called support vector machines because USSR terminology is icky)
mechanized significance (now called pattern recognition)
heuristic classification systems (now called symbolic AI or expert systems)
augmented transition networks (now called NLP)
genetic algorithms (now called evolutionary computation)
or a cast of thousands
They call these various things “image recognition software” or “translation software” or other such terms because both the term “AI” has come in and out of fashion over the decades since the '40s, and because the technology has found its niches where it is useful and calling it “AI” at that point is just confusing to anybody but a practitioner of the field.
No, today, when people are talking about AI, they’re talking specifically about (de)generative AI software, LLMbeciles in particular, and people who try to bring up all these other technologies that were once called AI come across as pretty disingenuous to most of the population.
YES! Exactly. For a simile, imagine going to a string theory conference and telling everyone “satellites and astronomers have relied on the principles of general relativity for decades, you can’t argue with the success of all these theories, including string theory.”
It’s linguistic laundering, plain and simple. Two truths in service of a lie.
The example I like to use is “computer”. I can, with a perfectly straight face, talk about hiring junior computers, reporting to senior computers, and talk salaries, benefits, working hours, etc. And I’d be “right” … except that in modern contexts that’s not what anybody means when they say “computer”. They mean what used to be called “electronic computers” specifically to distinguish them from human ones.
Anybody insisting that human “computers” are meaningfully included in modern non-practitioner conversations about computers is being just as disingenuous as the people talking about how “AI” is used in tomography when non-practitioners converse about AI. That’s not what people are talking about, and the person bringing it up knows full well this is the case; they’re being disingenuous and deflecting, not smart.
Yeah and one of my points was that these other technologies can also benefit greatly from new advancements in neural-side of AI technologies.
Personally I am interested in Neuro-Symbolic AI systems where most if not all reasoning, planning, execution, validation etc happens deterministically in the symbolic side while neural models are mostly used to perceive the world or intrepet client demands.
Herein lies the problem with the media spouting AI to cover everything. There are many forms that the term AI covers. From LLMs (the token prediction machines), machine learning, behavioural AI (used in gaming and simulations), computer vision, generative, evolutionary, natural language processing, etc Each with its own strengths, weaknesses and application.
*sigh*
Why are people so smart on technology so stupid on communication?
Context is king in communication. Consider the several different meanings of the word “bank”. How do you distinguish between them in “I will bank my plane over the river’s bank on my way to the bank”?
Context.
In current conversational context, when people talk about AI, they’re not talking about:
They call these various things “image recognition software” or “translation software” or other such terms because both the term “AI” has come in and out of fashion over the decades since the '40s, and because the technology has found its niches where it is useful and calling it “AI” at that point is just confusing to anybody but a practitioner of the field.
No, today, when people are talking about AI, they’re talking specifically about (de)generative AI software, LLMbeciles in particular, and people who try to bring up all these other technologies that were once called AI come across as pretty disingenuous to most of the population.
YES! Exactly. For a simile, imagine going to a string theory conference and telling everyone “satellites and astronomers have relied on the principles of general relativity for decades, you can’t argue with the success of all these theories, including string theory.”
It’s linguistic laundering, plain and simple. Two truths in service of a lie.
The example I like to use is “computer”. I can, with a perfectly straight face, talk about hiring junior computers, reporting to senior computers, and talk salaries, benefits, working hours, etc. And I’d be “right” … except that in modern contexts that’s not what anybody means when they say “computer”. They mean what used to be called “electronic computers” specifically to distinguish them from human ones.
Anybody insisting that human “computers” are meaningfully included in modern non-practitioner conversations about computers is being just as disingenuous as the people talking about how “AI” is used in tomography when non-practitioners converse about AI. That’s not what people are talking about, and the person bringing it up knows full well this is the case; they’re being disingenuous and deflecting, not smart.
Thank you for this.
I wish I could claim credit for it, but I saw someone else using it and thought “perfect!”.
Yeah and one of my points was that these other technologies can also benefit greatly from new advancements in neural-side of AI technologies.
Personally I am interested in Neuro-Symbolic AI systems where most if not all reasoning, planning, execution, validation etc happens deterministically in the symbolic side while neural models are mostly used to perceive the world or intrepet client demands.