The field of artificial intelligence (AI) has witnessed extraordinary advancements in recent years, ranging from natural language processing breakthroughs to the development of sophisticated robotics. Among these innovations, multi-agent systems (MAS) have emerged as a transformative approach for solving problems that single agents struggle to address. Multi-agent collaboration harnesses the power of interactions between autonomous entities, or “agents,” to achieve shared or individual objectives. In this article, we explore one specific and impactful technique within multi-agent collaboration: role-based collaboration enhanced by prompt engineering. This approach has proven particularly effective in practical applications, such as developing a software application.

Originally published on Towards AI.

One of the major challenges in using LLMs in business is that LLMs hallucinate. How can you entrust your clients to a chatbot that can go mad and tell them something inappropriate at any moment? Or how can you trust your corporate AI assistant if it makes things up randomly?

That’s a problem, especially given that an LLM can’t be fired or held accountable.

PlantRNA-FM, an AI model trained on RNA data from over 1,100 plants, decodes genetic patterns to advance plant science, improve crops, and tackle global agricultural challenges.

A groundbreaking Artificial Intelligence (AI) model designed to decode the sequences and structural patterns that form the genetic “language” of plants has been launched by a research collaboration.

Named Plant RNA-FM, this innovative model is the first of its kind and was developed by a partnership between plant researchers at the John Innes Centre and computer scientists at the University of Exeter.

Built to full scale, it mimics our anatomy in an incredibly precise way. But it is not yet fully functional.

A muscuskeletal designed for the home.

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.

Learn about the benefits of robotic surgery, how surgeons perform it and what surgeries you can get done with a da Vinci robot.

Futurism, transhumanism, bioethics, ethics, science, philosophy, artificial intelligence, personhood.

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.

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