As software developers, we understand the detailed workings of the different components of our computer systems. And–probably due to how computers were presented since their appearance as “digital brains” in the 1940s–we sometimes believe we can transpose that knowledge to how our biological brains work, be it as learners or as problem solvers. This article aims at making the reader understand several mechanisms related to how learning and problem solving actually work in our brains. It focuses on helping expert developers convey knowledge to new learners, as well as learners who need to get up to speed and “start coding.”...
Gunnar Wolf - Nice grey life - page 3
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So let’s assume you already know and understand that artificial intelligence’s main building blocks are perceptrons, that is, mathematical models of neurons. And you know that, while a single perceptron is too limited to get “interesting” information from, very interesting structures–neural networks–can be built with them. You also understand that neural networks can be “trained” with large datasets, and you can get them to become quite efficient and accurate classifiers for data comparable to your dataset. Finally, you are interested in applying this knowledge to defensive network security, particularly in choosing the right recurrent neural network (RNN) framework to create...



While readers of Computing Reviews are more aware than the general population when it comes to whether artificial intelligence (AI) is a magical panacea or the probability of a general intelligence that will develop thinking capabilities and make decisions on its own, we are actually aware of AI’s greatest strengths: finding patterns, probably hidden to the naked eye, and arriving at inferences based on said patterns. That ability has made AI-based systems a tool of choice to make statistical predictions and estimations, learning from enormous datasets, about human behavior. AI tools are often applied to risk assessment for financial operations....


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