EP78: One Week Later: o1-mini o1-preview & Can We Now Build Agents?
Sep 20, 2024
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The hosts discuss their experiences with the O1 Mini and O1 Preview, sharing insights on AI coding models and their practical applications. They explore what it takes to create effective AI agents and the balance required between automation and human oversight. The conversation also touches on the impressive capabilities of the Pixtral model and its strengths in image processing. Humorous moments abound, especially while contemplating the philosophical implications of AI free will and sentience. Tune in for a lively mix of tech and lighthearted banter!
The initial challenges faced with O1 preview and mini models highlighted the importance of refining prompts for effective AI problem-solving.
An experiment with AI models autonomously constructing a CRM application demonstrated the potential for AI to adapt and improve based on real-time user feedback.
The conversation about evolving AI interactions suggests a shift towards complex multi-agent collaborations, promising a deeper integration in professional workflows.
Deep dives
Reflecting on O1 Preview and O1 Mini Experiences
The discussion centers around personal experiences with the O1 preview and O1 mini models, highlighting the initial challenges faced while adapting to O1 preview for programming tasks, particularly during late-night coding sessions. One speaker experienced frustration with both models when attempting complex coding issues but later found that O1 mini was more effective for isolated problems. The consistency in receiving insufficient help from the models raised doubts about the advancements in AI's problem-solving capabilities, leading to a reconsideration of expectations. However, as familiarity with O1 mini grew, it became clear that it was adept at generating code solutions quickly, contrasting the earlier disappointments.
Working Patterns with AI Models
The participants compare their working methodologies when utilizing different AI models such as O1 mini and Sonnet. While the speaker appreciated O1 mini's ability to process verbose responses, the traditional workflows often felt interrupted due to the lengthy outputs. The experience of using these models highlighted a transaction-based approach where posing clear problems led to satisfactory solutions, thus reinforcing trust in O1 mini's capabilities. There's a realization that fine-tuning prompts led to increased efficiency, resulting in the models progressing further towards desired outcomes with fewer iterations.
Experimenting with Iterative AI Applications
An intriguing project involved running both O1 models to autonomously build a CRM application, emphasizing their ability to self-prompt and improve based on real user feedback. The outcomes showcased the models' capacity to respond dynamically to prompts and generate test cases that efficiently refined the application. Iteration led to the eventual emergence of a functioning product that not only performed well but was also visually appealing, resembling major enterprise solutions. This experiment sparked broader conversations about the potential for AI agents to create and adapt tools within business environments over time.
The Future of AI Interaction and Agency
A significant topic raised was the potential evolution of AI interactions from simple queries to complex, multi-agent collaborations. The conversation explored the idea of AI systems training themselves through past experiences while effectively isolating relevant information to address tasks effectively. The hypothesis presented suggested that future iterations of AI could operate more like a team of employees, addressing multiple tasks simultaneously without extensive human intervention. This shift from a traditional user-agent relationship offers the possibility for a more streamlined approach and a deeper integration into work processes.
Emerging Trends in AI and Model Development
The speakers reflect on broader trends within the AI landscape, including the anticipated integration of vision capabilities and improvements in model responsiveness. They speculate about OpenAI's internal focus on enhancing reasoning capabilities across models, aiming to leverage multi-agent collaboration to bolster output quality. Encouragement is given toward building robust tools that enhance task completions, while acknowledging the limitations of current models. There’s an overarching sense of anticipation for future developments that could significantly reshape how individuals interact with AI in various professional settings.
Sign up to Simtheory: https://simtheory.ai Community: https://thisdayinai.com ----- CHAPTERS: 00:00 - Introduction 01:56 - o1-mini & o1-preview 1 week later: our thoughts and experiments including Klarna CRM, Chess, & DOOM 20:45 - What is Missing to Make AI Agents Now? 40:12 - Are OpenAI laser focused now of the best models/AGI? 48:42 - OpenAI's o1 is getting great reviews from experts, 53:52 - Pixtral 12B: about the model and thoughts 59:24 - LOLs: Is Google Gemini Live Sentient?
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