Key insights

• 🧠 🧠 OpenAI's Deep Research is built on GPT-3, showing promising capabilities, especially in obscure knowledge exploration.
• 💰 💰 Access to Deep Research costs $200 monthly, with potential VPN needs for European users, raising questions about accessibility.
• 🤔 🤔 Despite impressive benchmarks, OpenAI's models still show performance gaps compared to human intelligence, particularly in common sense tasks.
• 🏆 🏆 The Humanity's Last Exam benchmark reveals Deep Research's strengths in obscure queries but highlights its limitations in everyday tasks.
• 🔄 🔄 Testing different AI models can reveal significant discrepancies in performance and user experience, influencing resource preference.
• 📉 📉 Inaccurate reporting of AI model performances indicates a need for clearer benchmarks and more reliable evaluation methods.
• 👁️‍🗨️ 👁️‍🗨️ The speaker's prototype for synthesizing research insights has been overshadowed by Deep Research's enhanced capabilities.
• ⚠️ ⚠️ AI systems still face challenges in accuracy, particularly in sourcing historical data, often leading to misinformation and hallucination.

Q&A

• What implications does AI advancement have on jobs? 🤖

  The potential for job redundancies due to AI advancements is a concern among many. While AI is becoming increasingly capable, leading to more efficient processes, there is an ongoing debate about how this technology might impact employment in various sectors.

• What are the concerns regarding AI's accuracy in sourcing information? 🤖

  There are notable concerns about AI systems misrepresenting historical pricing data and citing inaccurate sources, such as camelcamelcamel. These inaccuracies highlight the issues of hallucination within AI outputs, prompting discussions about the reliability of AI in research and content creation.

• Why did GPT 40 score lower than previous models? 🧠

  Despite having direct access to source material, GPT 40 scored 82%, which is lower than an earlier model that achieved 88%. This indicates that even with improved access, context retention and performance can vary significantly across different models.

• What challenges do AI models face in performance evaluation? 🔍

  Evaluating AI models can be tricky as reported performances may be inaccurately represented. For example, some models are incorrectly stated to fall in the bottom 20% of coders, and there are inconsistencies in how human evaluators assess AI's hallucinations and accuracy.

• What are the advantages of Deep Research over other AI models? 🤖

  Deep Research has been noted for its speed and effectiveness in finding specific posts or information, such as those in the Beehive newsletter. Despite its high cost, many users prefer its accuracy over alternatives like deep seek R1, which can be slower and more error-prone.

• How does Deep Research compare to human performance? 🤖

  While Deep Research shows improvements in benchmarks, achieving scores of 72-73% compared to 92% for humans, it still struggles with common sense reasoning and real-world scenarios, often requiring clarification instead of delivering direct answers.

• What is Deep Research? 🤖

  Deep Research is a new system developed by OpenAI, leveraging their most advanced language model, GPT-3. It focuses on retrieving obscure knowledge and is currently available for $200 a month, though users in certain regions like Europe may need a VPN to access it.

• 00:00 OpenAI recently launched Deep Research, a new system powered by their most advanced language model, GPT-3. While initial tests show promising results, especially in obscure knowledge retrieval, there are caveats regarding accessibility and effectiveness for economically valuable tasks. 🤖
• 03:11 OpenAI's recent models show significant improvement in benchmarks but still trail behind human performance. Despite advancements, issues remain in common sense reasoning and response behavior. 🤖
• 06:06 The speaker compares deep seek R1 and deep research from Google in finding specific content from the Beehive newsletter, concluding that deep research is generally more effective but tends to hallucinate. 🤖
• 09:12 The speaker discusses their experience testing various AI models against benchmarks, revealing inaccuracies in reported performances and highlights the challenges of evaluating these models. 🔍
• 12:07 Despite giving GPT 40 direct access to source material, it scored lower than a previous model. The author shares their experience with a prototype they developed that synthesized research insights, which has now been rendered obsolete by newer deep research capabilities. 🧠
• 15:17 The speaker discusses the shortcomings of AI in accurately sourcing and presenting historical pricing data, illustrating issues of hallucination and misinformation, while acknowledging rapid advancements in AI technology. 🤖