There is no code for language processing, it’s just math approximating results from weights. The whole weight set-up is what’s called ‘artificial intelligence’, because nobody wrote
if prompt like 'python'return ['large snake', 'programming language', 'australian car company']
the model ‘learned’ how to mimic human speech using training, not by 1000s of software engineers adding more branches to the code.
That technique is part of ‘artificial intelligence’, when computers solve problems they were not programmed to do. The neural network learns its knowledge by the code, but the code has no idea what is going on.
I am now properly confused as to what are you arguing for.
So let me go to the basics.
Computers follow instructions to the letter. Take input, process it, produce output.
There are specific instructions that computer can carry out, we can build on top of them to make them more complex. We write code to do that.
True/false gates can become numbers, which can become text, audio, video.
But everything ‘programmed’ or ‘digitally created’ is using the same instructions and only ever does what we tell the computer to do.
Cutting video will require video input, and then user has to do specific actions to produce a specific result.
Almost everything in existence is built like that - someone wrote specific code for technology to behave.
Now, this is very primitive way of solving tasks, specifically for real-world parameters. Computers have gigabytes (10^9) of memory, but just the earth has 10^50 atoms, so we can’t put eveything into a computers (which is why we can’t 100% predict the weather), and checking for every input parameter is not only futile, but also meaningless.
Enter ‘artificial intelligence’, approximated way of solving problems. Suddenly we don’t code the tasks themselves, we only specify the neural network - weights and connections between them, and code the ‘learning’ algorhitm that adjusts the weights based on inputs during ‘training’. Training is the expensive part, where we put huge amounts of input into the network, and if the answer we get is incorrect, we adjust the weights and try again with another sample.
It’s very expensive in every way, but the code involved doesn’t care about anything other than adjusting those weights. The network can be fed images and determining whether it’s a dog or a cat. It can be fed audio samples and expect to write down the lyrics. The code doesn’t know or care, apart from distinguishing between correct and not correct answers and adjusting those weights.
After those weights are set to our satisfaction, we can release them for others to use. We expect the network to have ‘reliable’ outputs for our inputs, so we just calculate the neuron activations based on those weights for every input, nothing else is necessary.
Therefore you do have code in the machine that learns, but only during training, and you have code that actually ‘runs’ the algorhitm for calculating output. But the actual solution to the problem is not inside the code, it can’t even be coded by humans in any way. The neural network is a statistical model generated by the training set and according to our learning algo. The bigger the network, the bigger the training set, the better should those outputs be (in theory).
To take the cutting video example further, you can train network to cut trailers from movies.
Or you can let editors do that.
They both will use computers, but one is using deteministically coded software that just follows specific orders one by one, and the other just computes the neuron activations based on the inputs and produces an output based on what it had available in the training data with some probability.
So yes, machines can learn, and it’s a subset of the ‘Artificial Intelligence’ field.
Well, it’s not AI, for starters.
As much as I hate to do this, it is AI, as ML is a part of Artificial Intelligence.
It isn’t AGI, some might say it may be, but they are wrong. But the model is learning.
An LLM is not capable of learning. It won’t hallucinate less with additional training input.
Just the notion of a computer having hallucinations should suggest that it’s doing more than just basic code.
It’s not ‘intelligent’, but it has ‘learned’ enough beyond standard CPU instructions.
That’s why it’s not a General AI, but it’s still an AI.
I also talk about gremlins inside CPUs, but that doesn’t mean I think there are magical critters turning a crank inside them.
It’s called a metaphor, brother.
Regardless, it’s all code that’s eventually run on a CPU, so there isn’t any step where magic is injected.
Sigh.
There is no code for language processing, it’s just math approximating results from weights. The whole weight set-up is what’s called ‘artificial intelligence’, because nobody wrote
if prompt like 'python' return ['large snake', 'programming language', 'australian car company']the model ‘learned’ how to mimic human speech using training, not by 1000s of software engineers adding more branches to the code.
That technique is part of ‘artificial intelligence’, when computers solve problems they were not programmed to do. The neural network learns its knowledge by the code, but the code has no idea what is going on.
How do you think math is implemented on a computer?
I am now properly confused as to what are you arguing for.
So let me go to the basics.
Computers follow instructions to the letter. Take input, process it, produce output.
There are specific instructions that computer can carry out, we can build on top of them to make them more complex. We write code to do that.
True/false gates can become numbers, which can become text, audio, video.
But everything ‘programmed’ or ‘digitally created’ is using the same instructions and only ever does what we tell the computer to do.
Cutting video will require video input, and then user has to do specific actions to produce a specific result.
Almost everything in existence is built like that - someone wrote specific code for technology to behave.
Now, this is very primitive way of solving tasks, specifically for real-world parameters. Computers have gigabytes (10^9) of memory, but just the earth has 10^50 atoms, so we can’t put eveything into a computers (which is why we can’t 100% predict the weather), and checking for every input parameter is not only futile, but also meaningless.
Enter ‘artificial intelligence’, approximated way of solving problems. Suddenly we don’t code the tasks themselves, we only specify the neural network - weights and connections between them, and code the ‘learning’ algorhitm that adjusts the weights based on inputs during ‘training’. Training is the expensive part, where we put huge amounts of input into the network, and if the answer we get is incorrect, we adjust the weights and try again with another sample.
It’s very expensive in every way, but the code involved doesn’t care about anything other than adjusting those weights. The network can be fed images and determining whether it’s a dog or a cat. It can be fed audio samples and expect to write down the lyrics. The code doesn’t know or care, apart from distinguishing between correct and not correct answers and adjusting those weights.
After those weights are set to our satisfaction, we can release them for others to use. We expect the network to have ‘reliable’ outputs for our inputs, so we just calculate the neuron activations based on those weights for every input, nothing else is necessary.
Therefore you do have code in the machine that learns, but only during training, and you have code that actually ‘runs’ the algorhitm for calculating output. But the actual solution to the problem is not inside the code, it can’t even be coded by humans in any way. The neural network is a statistical model generated by the training set and according to our learning algo. The bigger the network, the bigger the training set, the better should those outputs be (in theory).
To take the cutting video example further, you can train network to cut trailers from movies.
Or you can let editors do that.
They both will use computers, but one is using deteministically coded software that just follows specific orders one by one, and the other just computes the neuron activations based on the inputs and produces an output based on what it had available in the training data with some probability.
So yes, machines can learn, and it’s a subset of the ‘Artificial Intelligence’ field.
It won’t hallucinate less with additional training input.
An LLM is good at making sentences that seem convincing, but has no ability to reason.
Keep going…