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Open Source "Thinking" Models Are Catching Up To OpenAI o1 Already... - Video Insight

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The video explores advancements in AI reasoning models, highlighting competition and the importance of structured reasoning for improved performance.

In this video, the speaker discusses the rapid advancements and competition in AI reasoning models, particularly focusing on the developments surrounding OpenAI's and other emerging models like Deep Seek R1 and Quill. The narrative highlights the transformative shift from traditional AI models to those utilizing test time compute (TTC) for improved reasoning capabilities, with comparisons of performance among various AI models and the necessity for structured reasoning processes. The discussion dives into the specifics of how different AI models handle reasoning tasks, emphasizing the importance of explicit chains of thought, and the benefits that come from this new approach, especially for complex queries and problems.

Content rate: B

The content provides valuable insights into the evolving landscape of AI reasoning, backed by comparative analysis and research claims, although some opinions lean on speculation about future model capabilities.

AI Reasoning Models OpenAI DeepSeek Research

Claims:

Claim: Deep Seek R1 has a better track record than OpenAI's models in reasoning tasks.

Evidence: In a test comparing basic reasoning tasks, OpenAI's model only succeeded in answering correctly 1 out of 5 times, while Deep Seek R1 performed better, showcasing its capabilities.

Counter evidence: Despite the test results, the model R1 is still considered lightweight and in its early stages, suggesting that performance may improve over time, while OpenAI models have been rigorously tested and developed over a longer period.

Claim rating: 5 / 10

Claim: Test time compute (TTC) can lead to better reasoning performance in AI models.

Evidence: Multiple studies indicate that models which employ TTC, through explicit generation and evaluation of reasoning steps, may outperform those relying solely on memorized patterns and intuitive reasoning.

Counter evidence: However, the effectiveness of TTC hasn’t been universally validated across all AI contextual applications, suggesting varying results depending on data distribution and complexity of tasks.

Claim rating: 7 / 10

Claim: The performance of reasoning models can greatly improve with structured methods like the Chain of Thought.

Evidence: The video highlights research proposing that AI models employing structured reasoning methods outperformed their non-structured counterparts by nearly 9%, indicating the importance of structure in reasoning tasks.

Counter evidence: Yet, the performance gains are observed under controlled conditions, and real-world application may present different challenges that aren’t accounted for in theoretical models.

Claim rating: 8 / 10

Model version: 0.25 ,chatGPT:gpt-4o-mini-2024-07-18

# BS Evaluation **BS Score: 8/10** ## Reasoning and Explanation: 1. **Jargon Overload**: The transcript is filled with technical jargon and acronyms, such as "TTC," "MCTS," "implicit reasoning," and "distillation." While these terms may be relevant to a specialized audience, they can create a barrier for understanding and make the content seem unnecessarily complicated, which may suggest an attempt to impress rather than inform. 2. **Vague Claims**: The transcript makes numerous bold claims, such as one reasoning model being the "smartest model in the world" without providing substantial evidence to back these claims. Statements like "reasoning is the next Frontier of AI" sound grandiose and speculative without clarifying what this means or citing relevant research. 3. **Qualitative Assessments**: The presenter provides subjective comparisons between different models, asserting that one is better than the other based on personal experience. This subjectivity can introduce bias and lacks rigorous testing or standardized metrics, which diminishes the credibility of these comparisons. 4. **Repetition of Ideas**: Many ideas, such as the description of reasoning processes and scaling laws, are repeated throughout the transcript in slightly different ways. This repetition can dilute the main points and create a sense of filler material rather than delivering concise, informative content. 5. **Promotion/Sponsorship**: The mention of the sponsor, "Matt.m.ai", signals a potential conflict of interest. While sponsorships are common in content creation, their presence can introduce bias, as there may be implicit pressure to speak favorably about the sponsor's products. 6. **Conspiracy-like Thinking**: There are hints of conspiracy-like rhetoric when discussing OpenAI's efforts to 'delay' other AI Labs, which can shift the tone from a technical discussion to one filled with suspicion without clear justification. 7. **Hyperbolic Conclusions**: Claims such as "the effectiveness of step-by-step reasoning has been proven time and time again" come off as hyperbolic unless substantial evidence from credible studies is provided. This type of sweeping statement serves to enhance the narrative without adding rigorous proof. 8. **Confusion and Ambiguity**: The presenter expresses confusion about model performance but fails to clarify the reasoning behind different outcomes adequately. This creates ambiguity, making it hard for viewers to form a clear understanding or actionable insights. In conclusion, while the transcript does contain some potentially valuable insights and references to emerging AI technologies, the abundance of technical jargon, repetitive elements, and vague, grandiose claims contribute significantly to a high BS score.

### Key Facts and Information: 1. **Days OpenAI's Four Horsemen Killed: 76 Days** - OpenAI has delayed other AI labs from producing models that can match their reasoning capabilities, preventing them from coming together for 76 days. 2. **New AI Models Emerged** - Several Chinese reasoning models have appeared, with one, Deep Seek R1 Light Preview, being the first to publish weights for local running. 3. **Inference Time Compute (TTC) Shift** - Scaling AI now emphasizes inference time compute rather than just increasing model layers. This allows models to generate more text tokens during reasoning. 4. **Deep Seek R1 Performance** - Deep Seek R1, a lightweight model, performed well initially but struggled with basic reasoning tasks compared to OpenAI’s flagship model. 5. **Reasoning Models**: - **OpenAI Model (O1)**: Achieves 83% accuracy on complex math problems. - **GPT-4**: Uses implicit reasoning and performs well on familiar questions, but has limitations with out-of-distribution data. 6. **Importance of Explicit Reasoning** - The explicit display of intermediate reasoning steps can enhance AI models’ accuracy, especially in complex tasks. 7. **New Tools in AI**: - **Matt.ai Sponsorship**: An all-in-one platform providing access to multiple AI models for $10/month, offering cost savings compared to paying for individual services. - **Deep Seek Features**: Can input PDFs for summarization and has a strict context window limitation. 8. **Focus on Emerging AI Models**: - **Quill Model** - Outperformed larger models in benchmarks while being a 32 billion parameter model. - **Lava Model** - A vision-language model that aims to improve systematic reasoning, using structured reasoning stages. 9. **Research Development** - New training paradigms like "The Journey Learning" aim to replicate OpenAI, integrating search and learning to improve reasoning processes. 10. **Chatbot Performance** - Chatbots improve significantly when provided with context, highlighting the need for high-quality data and structured outputs. 11. **Paper Publications and Community Engagement** - Researchers are actively publishing papers and findings, encouraging community members to analyze and discuss advancements in AI reasoning. 12. **Newsletter and Content Creation** - Ongoing content creation and the dissemination of research papers and AI news through newsletters and video updates keeps the community informed. This summary highlights the current landscape of AI reasoning models, their capabilities, and ongoing developments in the field.