Most social media uses it. Video and music streaming services. SatNav. Speech recognition. OCR. Grammar checks. Translations. Banks. Hospitals. Large chunks of internet infrastructure.
Automated mail sorting has been using AI to read post codes from envelopes for deacades, only back then - pre hype - it was just called Neural Networks.
At the time I learned this at Uni (back in the early 90s) it was already NNs, not algorithms.
(This was maybe a decade before OCR became widespread)
In fact a coursework project I did there was recognition of handwritten numbers with a neural network. The thing was amazingly good (our implementation actually had a bug and the thing still managed to be almost 90% correct on a test data set, so it somehow mostly worked its way around the bug) and it was a small NN with no need for massive training sets (which is the main difference with Large Language Models versus the more run-off-the-mill neural networks), this at a time when algorithmic number and character recognition were considered a very difficult problem.
Back then Neural Networks (and other stuff like Genetic Algorithms) were all pretty new and using it in automated mail sorting was recent and not yet widespread.
Nowadays you have it doing stuff like face recognition, built-in on phones for phone unlocking…
I have to disagree with you in the machine learning definition. Sure, the machine doesn’t think in those circumstances, but it’s definitely learning, if we go by what you describe what they do.
Learning is a broad concept, sure. But say, if a kid is learning to draw apples, then is successful to draw apples without help in the future, we could way that the kid achieved “that ideal set of values.”
Thanks. I understood all that. But my point is that machine learning is still learning, just like machine walking is still walking. Can a human being be much better at walking than a machine? Sure. But that doesn’t mean that the machine isn’t walking.
Regardless, I appreciate your comment. Interesting discussion.
Or computers decades before that.
Many of these advances are incredibly recent.
And also many of the things we use in our day to day are ai powered without people even realising.
AI powered? Like what?
https://fusionchat.ai/news/10-everyday-ai-applications-you-didnt-realize-you-use
Some good examples here.
Most social media uses it. Video and music streaming services. SatNav. Speech recognition. OCR. Grammar checks. Translations. Banks. Hospitals. Large chunks of internet infrastructure.
The list goes on.
Got it. Thanks.
Automated mail sorting has been using AI to read post codes from envelopes for deacades, only back then - pre hype - it was just called Neural Networks.
That tech is almost 3 decades old.
But was it using neural networks or was it using OCR algorithms?
At the time I learned this at Uni (back in the early 90s) it was already NNs, not algorithms.
(This was maybe a decade before OCR became widespread)
In fact a coursework project I did there was recognition of handwritten numbers with a neural network. The thing was amazingly good (our implementation actually had a bug and the thing still managed to be almost 90% correct on a test data set, so it somehow mostly worked its way around the bug) and it was a small NN with no need for massive training sets (which is the main difference with Large Language Models versus the more run-off-the-mill neural networks), this at a time when algorithmic number and character recognition were considered a very difficult problem.
Back then Neural Networks (and other stuff like Genetic Algorithms) were all pretty new and using it in automated mail sorting was recent and not yet widespread.
Nowadays you have it doing stuff like face recognition, built-in on phones for phone unlocking…
Very interesting. Thanks for sharing!
I love people who talk about AI that don’t know the difference between an LLM and a bunch of if statements
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Oh but those pattern recognition examples are about machine learning, right? Which I guess it’s a form of AI.
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I have to disagree with you in the machine learning definition. Sure, the machine doesn’t think in those circumstances, but it’s definitely learning, if we go by what you describe what they do.
Learning is a broad concept, sure. But say, if a kid is learning to draw apples, then is successful to draw apples without help in the future, we could way that the kid achieved “that ideal set of values.”
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Thanks. I understood all that. But my point is that machine learning is still learning, just like machine walking is still walking. Can a human being be much better at walking than a machine? Sure. But that doesn’t mean that the machine isn’t walking.
Regardless, I appreciate your comment. Interesting discussion.