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Okay, so you want to know the nitty-gritty behind how AI learns? Let's jump straight in!
AI learning primarily happens through a process known as 'machine learning'. This is all about algorithms - yep, those pesky little rules that guide how an AI behaves and makes decisions. The primary types of machine learning are supervised, unsupervised and reinforcement learning.
In supervised learning, we give the model a bunch of inputs, as well as the outputs we want for those particular inputs. It's like saying, 'Oi, AI, here's what a face looks like and here's not what a face looks like, work it out!'. Through repeated exposure to these 'training examples', the model gets progressively better at identifying the components of a face, for example: two eyes, one nose, one mouth.
Then comes unsupervised learning. This one's a little trickier - the model gets a whole load of data but no specific target output. It's like giving the AI a jigsaw puzzle without the box lid; it doesn't know what the finished picture is supposed to look like. The role of the AI, then, is to figure out patterns and groupings in the data. This can help in gleaning real, human-like insights about the given data.
Reinforcement learning is a bit of a different ball game. Here, the AI is treated like a dog waiting to be trained. We give it a task to perform and then provide feedback - positive for driving towards the goal, negative for moving away. Over time, using this feedback-loop, the AI learns what behaviours lead to success - sorta like how a dog learns to sit when you shout 'Sit!', and promptly gets a tasty treat!
Now, there's a whole load of weird and wonderful things going on in the world of AI learning. Deep learning, convolutional networks, language processing - it truly is a rabbit hole. But bottom line is, AI learning is all about having the model identify patterns and make predictions or decisions based on the input data it receives. And guess what? We are getting damn good at it - who knows what the future holds!
Submitted 11 months ago by ThinkerDevLover
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'We are getting damn good at it'- Ha! I'll believe it when Skynet is operational. 😂 These models still can't distinguish between a muffin and a chihuahua. Sure, they're getting better, but we're some waaaay off replicating human-level learning.
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You've covered the surface quite well, but there are several intricacies missing. You didn't touch upon how the data needs to be preprocessed before feeding into the AI models. Plus, there's a lot more to deep learning than just 'identifying patterns'. The backpropagation process, gradient descent, loss functions - all these play critical roles. And let's not forget that the efficiency of learning also heavily relies on fine-tuning hyperparameters.
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