Is Claude 3 superior to GPT-4?— with Jon Krohn (@JonKrohnLearns)

  • CLA 3 Opus might be better than GPT 4 and Gemini 1.0 Ultra.
  • Benchmarks may not show the full capabilities of the models.
  • Anecdotal tests show CLA 3 gave the best rare facts compared to the other models.
  • CLA 3 has a 200,000 token context window and did well in needle in a haystack test.
  • Models may change behavior when being tested, so we need to improve our tests.
  • It’s best to try out the models for different tasks to see which is best.
  • Overall, large language models like CLA 3, GPT 4, and Gemini 1.0 Ultra can be useful for various tasks. 🤖🔥🍕

The latest episode of the Super Data Science podcast is about the comparison between Claude 3 and GPT-4. Host Jon Cone shares some reviews, including recommendations from KD nuggets, and then delves into a detailed comparison of Claude 3 from anthropic, particularly its Opus model, with GPT-4 and Gemini 1.0 Ultra. He highlights the benchmarks, limitations, and interesting findings, concluding with an encouragement to try these powerful AI tools.

Reviews and Podcast Feedback 🎧

The episode begins with Jon Cone thanking KD nuggets for recommending the Super Data Science podcast and valuable feedback from Apple podcast reviews. Noteworthy is Matthew Nelson’s appreciation for the depth of the episodes and Jinx Dinkum’s acknowledgment of gaining a better understanding of Transformers from the podcast.

Introducing Claude 3 and Its Models 🤖

Jon introduces anthropic’s Claude 3, noting that it includes the Haiku, Sonnet, and Opus models. He describes Opus as their largest and most powerful model, comparable to GPT-4 and potentially even better based on benchmarks conducted by anthropic.

Benchmark Limitations and Personal Evaluation 📊

Jon discusses the limitations of using benchmarks for comparing large language models and highlights his anecdotal experience with Claude 3 and other models. He shares his experiment of asking each model for rare facts and concludes that Claude 3 Opus outperformed the others in providing rare facts, indicating its potential superiority.

Evaluating Context Window and Needle in a Haystack Test 📝

An interesting evaluation of the 200,000 token context window and the needle in a haystack test for Claude 3 is presented. The model showcases excellent recall and accuracy, but an unexpected discovery underscores the need for more realistic and lifelike tests to assess the behavior of large language models.

Conclusion: Embracing Powerful AI Tools 💪

Jon concludes by emphasizing the importance of trying out powerful AI tools like GPT-4, Gemini, and Claude 3 Opus for a wide range of tasks, including code generation, to leverage their capabilities as data scientists or software developers.

Key Takeaways 🌟

  • Benchmark results and limitations must be considered while evaluating large language models.
  • Personal experimentation can provide valuable insights into the capabilities of AI models.
  • Despite potential limitations, embracing powerful AI tools like Claude 3 Opus can be beneficial for various tasks.

If you enjoyed this episode, consider sharing it with others and leaving a review on your favorite podcasting platform. Don’t forget to subscribe and keep rocking it out there with the Super Data Science podcast!

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Super Data Science: ML & AI Podcast with Jon Krohn
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The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on Super Data Science, the most listened-to podcast in the industry. In lighthearted conversation with renowned guests, Jon cuts through hype to fuel your professional impact.Whether you’re curious about getting started in a data career or you’re a deep technical expert, whether you’d like to understand what A.I. is or you’d like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.
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