'neural networks' 검색 분석 결과

Visualization of what is inside of AI models. This represents the layers of interconnected neural networks. And yes patterns do develop and they can form a signature of how they think. The pattern can be seen as the thought process.

Yann LeCun, Chief AI Scientist at Meta, called LLMs (like ChatGPT) merely creating the illusion of intelligence through fluent language manipulation, but lacking the depth of human understanding or reasoning. He asserted that this is a recurring pattern in the history of AI since the 1950s: each generation of AI technology (including the perceptrons of the 1960s and the neural networks of the 1980s) has been hyped as a step towards AGI, but has consistently failed to deliver on its promises.

Want to know why neural networks will never replace real programmers? The answer is in the picture below: because of optimization. AI has no imagination, and it doesn't seek elegant solutions. It just rushes ahead and cuts corners where it shouldn't. The problem is that today even real programmers don't know how to optimize the code (or just don't want to): you need no optimization if you can buy more powerful hardware.

The single most undervalued fact of mathematics: mathematical expressions are graphs, and graphs are matrices. Viewing neural networks as graphs is the idea that led to their success.

Hate to break it to you, but your brain is a neural network. It’s only a matter of time.

When you're learning about AI & machine learning it's helpful to build things from scratch. And that's what you'll do in this course. You'll learn Neural Networks by manually adjusting the network parameters while building a self-driving car playground. https://www.freecodecamp.org/news/understand-ai-and-neural-networks-by-manually-adjusting-paramaters/

NeurIPS 2025 just gave Best Paper to something that sounds obvious in hindsight. They scaled neural networks in reinforcement learning to 1024 layers. Got 2x to 50x better performance. Meanwhile, most RL papers use 2-5 layers. Have we been doing this wrong for years?

In 2030, neural networks will have approximately the same number of connections as the human brain. This does not necessarily mean that something new will be achieved as a result, but I always keep in mind the dialectical relationship between quantity and quality.

Dear algo, please connect me with computational neuroscience researchers at NeurIPS currently.

Neural networks consistently converge regardless of their initial weights even if you encode an image in a model’s parameters, training still finds a solution and the hidden pattern remains undetectable.