DeepMind’s Vice President of Drastic Research and Gemini co-tech lead Oriol Vinyals describes how artificial intelligence is moving from narrowly focused systems toward autonomous agents, and what challenges remain ahead.

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According to Vinyals, AI is going through a fundamental transformation away from highly specialized systems and toward autonomous agents. Speaking on a company podcast, he explained that early AI systems like AlphaStar, which focused on playing StarCraft, were just the beginning of this development.

The technique, called “universal transformer memory,” uses special neural networks to optimize LLMs to keep bits of information that matter and discard redundant details from their context.

But it’s not clear how soon you’ll be able to get its help in finding them.

I have liked kdnuggets for a while now. I used it for information on Tensorflow. This is cool though:

Neural networks are the building blocks behind every advanced AI system nowadays: from computer vision solutions to generative AI solutions and language models, most real-world solutions that involve some degree of AI have intricate neural network architectures at their core. But, what are neural networks and how do they perform surprisingly well in intelligently solving challenging tasks? To satisfy your curiosity at no cost, this post lists five resources to help you understand the mechanisms behind neural networks.

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Here are five free resources in diverse formats and difficulty levels to acquaint with deep learning models at no cost.