Retool, a development platform for business software, recently published the results of its State of AI survey. Over 1,500 people took part, all from the tech industry:...
Over half of all tech industry workers view AI as overrated::undefined
AI doesn’t necessarily mean human-level intelligence, if that’s what you mean. The AI field has wrestled with this for decades. There can be “strong AI”, which is aiming for that human-level intelligence, but that’s probably a far off goal. The “weak AI” is about pushing the boundaries of what computers can do, and that stuff has been massively useful even before we talk about the more modern stuff.
Sounds like people here are expecting to see GPAI and singularity stuff, but all they see is a pitiful LLM or other even more narrow AI applications. Remember, even optical character recognition (OCR) used to be called AI until it became so common that it wasn’t exciting any more. What AI developers call AI today, is just basic automation and few decades later.
Given that AI isn’t purported to be AGI, how do you define AI such that multimodal transformers capable of developing abstract world models as linear representations and trained on unthinkable amounts of human content mirroring a wide array of capabilities which lead to the ability to do things thought to be impossible as recently as three years ago (such as explain jokes not in the training set or solve riddles not in the training set) isn’t “artificial intelligence”?
for many years AI referred to that type of technology. It is not infact AGI but AI historically in the technical field refers more towards decision trees, and classification/ linear regression models.
The main difference is that crypto was/is burning huge amounts of energy to run a distributed ponzi scheme. LLMs are at least using energy to create a useful tool (even if there is discussion over how useful they are).
There are significant differences between statistical models and AI.
I work for an analytics department at a fortune 100 company. We have a very clear delineation between what constitutes a model and what constitutes an AI.
That’s true. Statistical models are very carefully engineered and tested and current machine learning models are created by throwing a lot of training data at the software and hope for the best that the things that the model learns are not complete bullshit.
You really should listen rather than talk. This is not AI, it’s just a word prediction model. The media calls it AI because it sells and the companies calls it AI because it brings the stock value up.
Largely because we understand that what they’re calling “AI” isn’t AI.
AI doesn’t necessarily mean human-level intelligence, if that’s what you mean. The AI field has wrestled with this for decades. There can be “strong AI”, which is aiming for that human-level intelligence, but that’s probably a far off goal. The “weak AI” is about pushing the boundaries of what computers can do, and that stuff has been massively useful even before we talk about the more modern stuff.
Sounds like people here are expecting to see GPAI and singularity stuff, but all they see is a pitiful LLM or other even more narrow AI applications. Remember, even optical character recognition (OCR) used to be called AI until it became so common that it wasn’t exciting any more. What AI developers call AI today, is just basic automation and few decades later.
Yup. LLM RAG is just search 2.0 with a GPU.
For certain use cases it’s incredible, but those use cases shouldn’t be your first idea for a pipeline
Given that AI isn’t purported to be AGI, how do you define AI such that multimodal transformers capable of developing abstract world models as linear representations and trained on unthinkable amounts of human content mirroring a wide array of capabilities which lead to the ability to do things thought to be impossible as recently as three years ago (such as explain jokes not in the training set or solve riddles not in the training set) isn’t “artificial intelligence”?
THANK YOU! I’ve been saying this a long time, but have just kind of accepted that the definition of AI is no longer what it was.
It absolutely is AI. A lot of stuff is AI.
It’s just not that useful.
The decision tree my company uses to deny customer claims is not AI despite the business constantly referring to it as such.
There’s definitely a ton of “AI” that is nothing more than an If/Else statement.
for many years AI referred to that type of technology. It is not infact AGI but AI historically in the technical field refers more towards decision trees, and classification/ linear regression models.
That’s basically what video game AI is, and we’re happy enough to call it that
Well… it’s a video game. We also call them “CPU” which is also entirely inaccurate.
That’s called an expert system, and has been commonly called a form of AI for decades.
That is indeed what most of it is, my company was doing “sentiment analysis” and it was literally just checking it against a good and bad word list
When someone corporate says “AI” you should hear “extremely rudimentary machine learning” until given more details
It’s useful at sucking down all the compute we complained crypto used
The main difference is that crypto was/is burning huge amounts of energy to run a distributed ponzi scheme. LLMs are at least using energy to create a useful tool (even if there is discussion over how useful they are).
Yeah it’s funny how that little tidbit just went quietly into the bin not to talked about again.
There are significant differences between statistical models and AI.
I work for an analytics department at a fortune 100 company. We have a very clear delineation between what constitutes a model and what constitutes an AI.
That’s true. Statistical models are very carefully engineered and tested and current machine learning models are created by throwing a lot of training data at the software and hope for the best that the things that the model learns are not complete bullshit.
Yeah, an AI is a model you can’t explain.
Optimizing compilers came directly out of AI research. The entirety of modern computing is built on things the field produced.
You really should listen rather than talk. This is not AI, it’s just a word prediction model. The media calls it AI because it sells and the companies calls it AI because it brings the stock value up.
Yes, what you’re describing is also AI.
Then we may as well call the field of statistics AI now, but sure, it’s a crazy world. :)
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