This episode of Beyond the Algorithm explores a novel method for improving AI performance using language feedback. The sources examine Language Feedback Models (LFMs), which leverage the world knowledge of large language models (LLMs) to identify and reward “desirable behaviour” in AI agents.

This episode of Beyond the Algorithm explores the unavoidable issue of hallucinations in Large Language Models (LLMs). Using mathematical and logical proofs, the sources argue that the very structure of LLMs makes hallucinations an inherent feature, not just occasional errors. From incomplete training data to the challenges of information retrieval and intent classification, every step in the LLM generation process carries a risk of producing false information. Tune in to understand why hallucinations are a reality we must live with and how professionals can navigate the limitations of these powerful AI tools.

This episode examines the limitations of Large Language Models (LLMs) when it comes to complex reasoning tasks. While LLMs excel at linguistic tasks, they often struggle with problems that require non-linear, multi-step deduction, a challenge highlighted in the article's newly introduced Non-Linear Reasoning (NLR) dataset. The episode explores a potential solution: neurosymbolic AI, a hybrid approach that combines the strengths of LLMs with the logical reasoning power of symbolic systems like Prolog.

Article: https://arxiv.org/abs/2407.11373

This episode of Beyond the Algorithm explores a new approach to working with large language models: thinking of them as "method actors." The sources present this mental model as a framework for prompt engineering and architecture, likening LLMs to actors, prompts to scripts, and LLM responses to performances. By embracing this analogy, the episode argues that we can design prompts that leverage the strengths of LLMs while accounting for their limitations.

Article: https://arxiv.org/html/2411.05778v1

This episode of Beyond the Algorithm explores diffusion models, a powerful type of AI model that is rapidly changing the world. Diffusion models learn to generate data by reversing a process of gradual noising, and they have been shown to outperform other generative models in many applications, including image generation, text-to-speech synthesis, and drug design. Join us as we discuss the basics of diffusion models, explore their wide range of applications, and consider their potential impact on the future of AI.

This episode of Beyond the Algorithm explores FISHNET, a new AI system designed to analyze complex financial data. Instead of relying on costly, fine-tuned language models, FISHNET uses a team of specialized AI agents, each expert in a different type of financial document, to gather insights from regulatory filings like N-PORT and 13F123. These "expert agents" work together like a swarm, collaborating to understand intricate data relationships and answer complex queries, ultimately offering a more flexible and scalable approach to financial analysis.

Article: https://arxiv.org/html/2410.19727v1

Can AI truly see and understand visual information like humans do? This episode of Beyond the Algorithm explores a groundbreaking new method called "Whiteboard-of-Thought" that's changing the way AI approaches visual reasoning. Unlike humans who naturally switch between words and images when thinking, large language models (LLMs) have struggled to apply their reasoning abilities to visual problems, even with advanced training. This is because they primarily process information as text, making it difficult to grasp spatial relationships and visual concepts. Whiteboard-of-Thought aims to solve this by giving AI a metaphorical whiteboard to sketch out its reasoning steps as images. By leveraging the AI's existing ability to write code using libraries like Matplotlib and Turtle, the model can generate simple visuals to help it solve tasks. Join us as we go Beyond the Algorithm and explore the fascinating intersection of visual reasoning and artificial intelligence, discussing the implications of this research for professionals across various fields.

This episode introduces the "Transformer," the new neural network architecture that challenged the traditional encoder-decoder structure used in sequence transduction models. Instead of recurrent or convolutional layers, the Transformer relies on "multi-head self-attention" to process sequential data, enabling it to process information from all positions in the sequence simultaneously. This parallel processing capability leads to faster training times, especially for long sequences. The episode explores the Transformer's impressive performance in machine translation. It also showcases the model's generalization ability, achieving strong results in English constituency parsing.

Article: https://arxiv.org/abs/1706.03762

This episode examines the potential for Large Language Models (LLMs) to revolutionize the way we generate research ideas. The discussion focuses on a novel framework called the "Chain-of-Ideas" (CoI) agent, which draws inspiration from the cognitive processes of human researchers. The episode explores how this AI-powered system organizes relevant research papers into a chain structure, mimicking the historical progression of ideas.

Article: https://arxiv.org/html/2410.13185v1

Episode 11 explores AGENTOCCAM, a surprisingly simple AI agent that redefines web navigation. This agent challenges the notion that complex strategies are needed for AI to effectively use the web. Instead of relying on intricate designs, AGENTOCCAM leverages the power of large language models (LLMs) by streamlining their interaction with web pages.