I've been working on formalizing knowledge theory. One of the issues I've ran into is AI learning systems. This involves trying to distinguish between cognitive learning and AI learning. However, I haven't been able to find an AI system that can learn. Either I'm just not looking hard enough or the AI definition is very different from what I consider learning to be.
You haven’t looked hard enough. Scientists have been working a long time with neural nets that by definition learn in a general way. It may not be cognition, but it is learning.
You should start by working out why a neural net program is NOT cognition. Unless we understand how your concept of learning is different from an advanced neural net program, we have no way to understand your 'theory'.
http://uhaweb.hartford.edu/compsci/neural-networks-history.html
‘The earliest work in neural computing goes back to the 1940's when McCulloch and Pitts introduced the first neural network computing model. In the 1950's, Rosenblatt's work resulted in a two-layer network, the perceptron, which was capable of learning certain classifications by adjusting connection weights. Although the perceptron was successful in classifying certain patterns, it had a number of limitations. The perceptron was not able to solve the classic XOR (exclusive or) problem. Such limitations led to the decline of the field of neural networks. However, the perceptron had laid foundations for later work in neural computing.’
https://www.coursera.org/course/neuralnets
‘Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains.’
Here is an AI program that learned chess.
https://www.technologyreview.com/s/...ss-in-72-hours-plays-at-international-master/
‘It’s been almost 20 years since IBM’s Deep Blue supercomputer beat the reigning world chess champion, Gary Kasparov, for the first time under standard tournament rules. *Since then, chess-playing computers have become significantly stronger, leaving the best humans little chance even against a modern chess engine running on a smartphone.
…
Computers have never been good at this, but today that changes thanks to the work of Matthew Lai at Imperial College London. Lai has created an artificial intelligence machine called Giraffe that has taught itself to play chess by evaluating positions much more like humans and in an entirely different way to conventional chess engines.’
https://en.wikipedia.org/wiki/Deep_learning
‘Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations.[1][2][3][4][5][6]
Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition[7]) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction.’
So there are computer programs that in a commonly accepted sense ‘learn’. I agree that they don’t think precisely like human beings. They couldn’t pass a Turing test, for example. However, there are animals that are thought to be conscious that can’t pass a Turing test.
You didn’t make it clear if there are any nonhuman animals that learn but do not cognate. Does a dolphin or gorilla learn by cognition? A shark? A cockroach? And about those nematodes that learn. Are you absolutely sure with no doubt at all that the nematodes don’t have a very weak form of cognition?!
Examples have just been provided of AI programs that learn in a general way. You have also been provided examples of animals that learn in a general way. What is lacking is a reproducible means of testing whether a particular case of ‘learning’ is different from ‘cognating’.
Your mission, should you chose to accept it, is to quantify how these AI learning programs ARE NOT ‘cognition’.
Do you cognate this
, I mean coffee on the ground.
