those aren’t examples they’re hearsay. “oh everybody knows this to be true”
You are ignoring ALL of the of the positive applications of AI from several decades of development, and only focusing on the negative aspects of generative AI.
generative AI is the only “AI”. everything that came before that was a thought experiment based on the human perception of a neural network. it’d be like calling a first draft a finished book.
if you consider the Turing Test AI then it blurs the line between a neural net and nested if/else logic.
Here is a non-exhaustive list of some applications:
In healthcare as a tool for earlier detection and prevention of certain diseases
great, give an example of this being used to save lives from a peer reviewed source that won’t be biased by product development or hospital marketing.
For anomaly detection in intrusion detection system, protecting web servers
let’s be real here, this is still a golden turd and is more ML than AI. I know because it’s my job to know.
Disaster relief for identifying the affected areas and aiding in planning the rescue effort
hearsay, give a creditable source of when this was used to save lives. I doubt that AI could ever be used in this way because it’s basic disaster triage, which would open ANY company up to litigation should their algorithm kill someone.
Fall detection in e.g. phones and smartwatches that can alert medical services, especially useful for the elderly.
this dumb. AI isn’t even used in this and you know it. algorithms are not AI. falls are detected when a sudden gyroscopic speed/ direction is identified based on a set number of variables. everyone falls the same when your phone is in your pocket. dropping your phone will show differently due to a change in mass and spin. again, algorithmic not AI.
Various forecasting applications that can help plan e.g. production to reduce waste.
Etc…
forecasting is an algorithm not AI. ML would determine the percentage of an algorithm is accurate based on what it knows. algorithms and ML is not AI.
There have even been a lot of good applications of generative AI, e.g. in production, especially for construction, where a generative AI can the functionally same product but with less material, while still maintaining the strength. This reduces cost of manufacturing, and also the environmental impact due to the reduced material usage.
this reads just like the marketing bullshit companies promote to show how “altruistic” they are.
Does AI have its problems? Sure. Is generative AI being misused and abused? Definitely. But just because some applications are useless it doesn’t mean that the whole field is.
I won’t deny there is potential there, but we’re a loooong way from meaningful impact.
A hammer can be used to murder someone, that does not mean that all hammers are murder weapons.
just because a hammer is a hammer doesn’t mean it can’t be used to commit murder. dumbest argument ever, right up there with “only way to stop a bad guy with a gun is a good guy with a gun.”
generative AI is the only “AI”. everything that came before that was a thought experiment based on the human perception of a neural network. it’d be like calling a first draft a finished book.
You clearly don’t know much about the field. Generative AI is the new thing that people are going crazy over, and yes it is pretty cool. But it’s built on research into other types of AI-- classifiers being a big one-- that still exist in their own distinct form and are not simply a draft of ChatGPT. In fact, I believe classification is one of the most immediately useful tasks that you can train an AI for. You were given several examples of this in an earlier comment.
Fundamentally, AI is a way to process fuzzy data. It’s an alternative to traditional algorithms, where you need a hard answer with a fairly high confidence but have no concrete rules for determining the answer. It analyzes patterns and predicts what the answer will be. For patterns that have fuzzy inputs but answers that are relatively unambiguous, this allows us to tackle an entire class of computational problems which were previously impossible. To summarize, and at risk of sounding buzzwordy, it lets computers think more like humans. And no, for the record, it has nothing to do with crypto.
Nobody here will give you peer-reviewed articles because it’s clear that your position is overconfident for your subject knowledge, so the likelihood a valid response will change your mind is very small, so it’s not worth the effort. That includes me, sorry. I can explain in more detail how non-generative AI works if you’d like to know more.
those aren’t examples they’re hearsay. “oh everybody knows this to be true”
generative AI is the only “AI”. everything that came before that was a thought experiment based on the human perception of a neural network. it’d be like calling a first draft a finished book.
if you consider the Turing Test AI then it blurs the line between a neural net and nested if/else logic.
great, give an example of this being used to save lives from a peer reviewed source that won’t be biased by product development or hospital marketing.
let’s be real here, this is still a golden turd and is more ML than AI. I know because it’s my job to know.
hearsay, give a creditable source of when this was used to save lives. I doubt that AI could ever be used in this way because it’s basic disaster triage, which would open ANY company up to litigation should their algorithm kill someone.
this dumb. AI isn’t even used in this and you know it. algorithms are not AI. falls are detected when a sudden gyroscopic speed/ direction is identified based on a set number of variables. everyone falls the same when your phone is in your pocket. dropping your phone will show differently due to a change in mass and spin. again, algorithmic not AI.
forecasting is an algorithm not AI. ML would determine the percentage of an algorithm is accurate based on what it knows. algorithms and ML is not AI.
this reads just like the marketing bullshit companies promote to show how “altruistic” they are.
I won’t deny there is potential there, but we’re a loooong way from meaningful impact.
just because a hammer is a hammer doesn’t mean it can’t be used to commit murder. dumbest argument ever, right up there with “only way to stop a bad guy with a gun is a good guy with a gun.”
You clearly don’t know much about the field. Generative AI is the new thing that people are going crazy over, and yes it is pretty cool. But it’s built on research into other types of AI-- classifiers being a big one-- that still exist in their own distinct form and are not simply a draft of ChatGPT. In fact, I believe classification is one of the most immediately useful tasks that you can train an AI for. You were given several examples of this in an earlier comment.
Fundamentally, AI is a way to process fuzzy data. It’s an alternative to traditional algorithms, where you need a hard answer with a fairly high confidence but have no concrete rules for determining the answer. It analyzes patterns and predicts what the answer will be. For patterns that have fuzzy inputs but answers that are relatively unambiguous, this allows us to tackle an entire class of computational problems which were previously impossible. To summarize, and at risk of sounding buzzwordy, it lets computers think more like humans. And no, for the record, it has nothing to do with crypto.
Nobody here will give you peer-reviewed articles because it’s clear that your position is overconfident for your subject knowledge, so the likelihood a valid response will change your mind is very small, so it’s not worth the effort. That includes me, sorry. I can explain in more detail how non-generative AI works if you’d like to know more.