Exploring the Future of AI: From Neural Networks to AI Agents with Andreu

Produced by Daniel Aharonoff & Mogul Media AI -

From Neural Networks to AI Agents: A Deep Dive into AI Development Trends

In the early stages of neural network development, Stanford computer science professor Andreu was a trailblazer. He was instrumental in the development of neural networks with GPUs, a cornerstone of deep learning technology today. As the founder of popular online learning platforms like Coursera and deeplearning.ai, as well as Google Brain, Andreu's influence in the field of artificial intelligence (AI) is undeniable. However, the focus of our discussion today is not on Andreu's past achievements, but rather on the future trend he has highlighted - AI agents.

The Evolution of AI: From Non-Agentic to Agentic Workflows

Traditionally, we have been using language models (Lish models) in a non-agentic workflow. This is akin to asking someone to write an essay on a topic, sit at the keyboard, and type out the essay from start to finish without ever using the backspace key. This is a daunting task, yet these models have been performing remarkably well.

However, the future lies in agentic workflows, which propose a more iterative process. For instance, an AI might be instructed to write an essay outline, conduct web research if necessary, draft the essay, then review and revise it. This process can be repeated as many times as necessary for refinement.

Case Study: The Human Eval Benchmark

To illustrate the effectiveness of agentic workflows, Andreu's team analyzed data using the Human Eval Benchmark, a coding benchmark released by OpenAI. The task involved coding problems like summing elements at even positions in a list of integers.

Traditionally, we would use zero-shot prompting, instructing the AI to write the code and run it immediately. This method yielded a success rate of 48% with GPT-3.5 and 67% with GPT-4.

However, when they wrapped an agentic workflow around GPT-3.5, the success rate exceeded even that of GPT-4. This indicates that an iterative, thoughtful process can yield better results, even with a "less advanced" AI model.

The Future of AI: The Rise of AI Agents

The term 'agents' is thrown around frequently, primarily in consultant reports discussing the future of AI. Andreu has identified four broad design patterns in the messy, chaotic space of AI agents:

  • Agents Reflection
  • Use
  • Planning
  • Multi-agent collaboration

Among these, Agents Reflection and Use are seen as robust technologies that typically yield good results. On the other hand, Planning and Multi-agent collaboration are emerging trends that might not be as reliable, though they have the potential to be mind-blowing when they do work well.

The Final Thought

The evolution from non-agentic to agentic workflows and the rise of AI agents are transformative trends in AI development. While we can appreciate the impressive capabilities of current AI models, there is significant potential for even more remarkable results with these new approaches. The future of AI is bright and filled with exciting possibilities, and we all have a part to play in shaping it.

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