OpenAI’s o1 model
The release of OpenAI’s o1 model has sparked some confusion. Unlike previous models that focused on increasing parameters and capabilities, this one takes a different approach. Let’s explore the technical distinctions first, share a real-world experience, and wrap up with some recommendations on when to use each model. Technical Differences The core difference is that o1 serves as an “agentic wrapper” around GPT-4 (or a similar model). This means it incorporates a layer of metacognition, or “thinking about thinking,” before addressing a query. Instead of immediately answering the question, o1 first evaluates the best strategy for tackling it by breaking it down into subtasks. Once this analysis is complete, o1 begins executing each subtask. Depending on the answers it receives, it may adjust its approach. This method resembles the “tree of thought” strategy, allowing users to see real-time explanations of the subtasks being addressed. For a deeper dive into agentic approaches, I highly recommend Andrew Ng’s insightful letters on the topic. However, this method comes with a cost—it’s about six times more expensive and approximately six times slower than traditional approaches. While this metacognitive process can enhance understanding, it doesn’t guarantee improved answers for straightforward factual queries or tasks like generating trivia questions, where simplicity may yield better results. Real-World Example To illustrate the practical implications, Tectonic began to deepen the understanding of variational autoencoders—a trend in multimodal LLMs. While we had a basic grasp of the concept, we had specific questions about their advantages over traditional autoencoders and the nuances of training them. This information isn’t easily accessible through a simple search; it’s more akin to seeking insight from a domain expert. To enhance our comprehension, we engaged with both GPT-4 and o1. We quickly noticed that o1’s responses were more thoughtful and facilitated a meaningful dialogue. In contrast, GPT-4 tended to recycle the same information, offering limited depth—much like how some people might respond in conversation. A particularly striking example occurred when we attempted to clarify our understanding. The difference was notable. o1 responded like a thoughtful colleague, addressing our specific points, while GPT-4 felt more like a know-it-all friend who rambled on, requiring me to sift through the information for valuable insights. Summary and Recommendations In essence, if we were to personify these models, GPT-4 would be the overzealous friend who dives into a stream of consciousness, while o1 would be the more attentive listener who takes a moment to reflect before delivering precise and relevant insights. Here are some scenarios where o1 may outperform GPT-4, justifying its higher cost: By leveraging these insights, you can better navigate the strengths of each model in your tasks and inquiries. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
