Jeff Su: The video debunks myths about Deep Seek's AI model, highlighting its efficiency and innovation within constraints.
Skill Leap AI: Google AI Studio is a powerful AI tool that assists users in real-time by providing guidance and solutions across various applications.
AI Explained: OpenAI released a new language model called Deep Research based on their powerful 03 model, which excels in obscure knowledge tasks but struggles with common sense reasoning.
Jeff Su - DeepSeek: What Actually Matters (for the everyday user)
The discussion focuses on dispelling myths surrounding Deep Seek's AI model, emphasizing its innovative approach within export control constraints. Deep Seek's model, contrary to claims, wasn't built for just $5.6 million; this figure only covers the final training run, excluding significant infrastructure costs. The company creatively optimized its model using less powerful GPUs, showcasing efficiency rather than breaking rules. While Deep Seek's model matches OpenAI's in performance, it leads in efficiency but not overall capability. The video also clarifies that Deep Seek's visible Chain of Thought is a UI choice, not a technical breakthrough. Additionally, Deep Seek's use of model distillation, while controversial, is a common practice in AI development. The video concludes by discussing the implications for users, such as access to advanced AI features without cost and privacy considerations when using Deep Seek's models.
Key Points:
- Deep Seek's model cost exceeds $5.6 million, covering only final training, not infrastructure.
- Deep Seek optimized its model using less powerful GPUs, showcasing efficiency.
- Visible Chain of Thought in Deep Seek's model is a UI choice, not a technical breakthrough.
- Deep Seek's model distillation is common but controversial, breaking OpenAI's terms.
- Users can access advanced AI features for free, with privacy options available.
Details:
1. 🎥 Introduction to Deep Seek Myths
- Deep Seek has been generating significant attention, leading to misinformation, noise, and hype.
- The video aims to debunk the top 10 myths about Deep Seek, providing clarity on its implications for everyday AI users.
- Acknowledgment to Ben Thompson for his comprehensive article that informed the video content.
2. 🧩 Myth 1: Cost Misunderstandings
- The claim that Deep Seek built their model for $5.6 million is misleading because this figure only accounts for the final training run.
- The $5.6 million does not include significant costs such as infrastructure investments.
- Deep Seek reportedly utilizes 50,000 Nvidia Hopper GPUs, which are valued at approximately $1 billion, indicating a much larger scale of investment.
- Comparing the $5.6 million figure to the true cost is like claiming the latest iPhone only costs $500 to manufacture, ignoring other expenses.
3. 🚀 Myth 2: Rule-Breaking Allegations
- Deep Seek gained attention by innovating within export control constraints, specifically using less powerful H800 GPUs due to restrictions on H100 GPUs.
- Their model architecture optimization was driven by the necessity to work within these constraints, potentially leading to a more efficient model despite hardware limitations.
- H800 GPUs, though less powerful, were available for sale to Chinese companies, unlike the restricted H100 GPUs.
- Deep Seek's approach parallels Samsung's strategy of using slightly slower processors in some regions due to licensing agreements, highlighting an industry trend of adaptive innovation.
4. ⚖️ Myth 3: Performance vs. Efficiency
- Deep Seek's reasoning model R1 matches OpenAI's reasoning model 01 in performance, yet OpenAI's model 03 is more powerful, indicating Deep Seek excels in efficiency but not in overall capability.
- OpenAI's release of model 03 mini for free, including search capabilities, demonstrates rapid industry advancement, likely influenced by competitive pressure from Deep Seek.
- The analogy of cost-effective smartphones illustrates that while efficiency can offer significant value at a lower cost, it doesn't equate to surpassing more powerful options in overall performance.
- Efficiency in AI development is crucial for providing accessible solutions without sacrificing core functionalities, but it should not be mistaken for superior performance, which often requires more resources and advanced capabilities.
- The balance between performance and efficiency is strategic, with companies like Deep Seek focusing on delivering efficient solutions that meet specific needs while larger companies like OpenAI push the boundaries of performance with more resource-intensive models.
5. 🔍 Myth 4: Comparability Issues
- Deep Seek models should be compared to other AI models on an 'apple to apple' basis, focusing on their intended purpose and features.
- Deep Seek's base model V3 is equivalent to ChatGPT's 4.0, making them directly comparable in terms of baseline AI functionality.
- Deep Seek's advanced reasoning model R1 surpasses previous versions of ChatGPT with a unique search function, enhancing its reasoning capabilities.
- ChatGPT has responded with the release of 3.0 Mini, now incorporating a search function similar to Deep Seek, leveling the playing field regarding reasoning tasks.
- When comparing AI models, it's crucial to match their functional capabilities to user needs, similar to choosing between a sports car and an SUV based on specific requirements.
6. 🧠 Myth 5: Chain of Thought Misconceptions
- The visibility of the Chain of Thought in deep r1 is primarily a UI choice, not a technical breakthrough, illustrating a common misconception about AI reasoning capabilities.
- Deep seek R1 and open AI 01 possess similar reasoning capabilities, debunking the myth that R1 is inherently more advanced due to its visible reasoning process.
- The apparent transparency in R1's reasoning is due to how it is presented rather than a superior capability, which is a significant point of confusion.
- An analogy of two chefs is used to highlight the difference: it's the presentation of the process, not the actual cooking or results, that differs, emphasizing the role of UI in perceived reasoning advancements.
7. 🛠️ Myth 6: Ground-Up Misconception
- Deep Seek employed Model Distillation by training on ChatGPT's outputs, a method prevalent in the AI industry, although it breaches OpenAI's terms of service.
- Despite an ongoing investigation by Microsoft and OpenAI into Deep Seek's practices, Microsoft has included Deep Seek's R1 in its Cloud offerings, showing a conflicting stance.
- Deep Seek's approach is akin to a phone maker studying iPhone photos to mimic its image processing, illustrating how companies can replicate functions without direct code copying.
- This practice raises ethical and legal questions about the boundaries of innovation and imitation in AI development.
8. 🔒 Myth 7: Security Concerns
- Using the native Deep Seek app requires sending and storing data in China, which raises privacy concerns for users worried about data sovereignty.
- To mitigate these concerns, users can opt for platforms like Perplexity or Venice AI that access Deep Seek's models while keeping data within the US.
- Deep Seek models are gaining popularity due to their cost-effectiveness, evidenced by their recent integration into platforms like Cursor.
- For maximum privacy, users have the option to run Deep Seek models locally on personal devices, completely avoiding data transmission to external servers.
- Implementing local models ensures full control over data, addressing privacy concerns effectively.
9. 📈 Myth 8: Impact on Nvidia
- Tech analysts and the CEO of Microsoft support Jevon's Paradox, which posits that increased efficiency in AI, such as that seen with Deep Seek, will lead to higher overall demand for AI solutions.
- As AI solutions become more cost-effective, their usage is expected to rise, which could result in increased demand for Nvidia's chips.
- An analogy is drawn with the smartphone industry, where the reduction in smartphone costs has led to higher demand for premium phone processors like those from Qualcomm. This suggests a similar potential outcome for Nvidia.
10. 🇺🇸 Myth 9: Effect on US Tech Companies
- Amazon benefits from serving high-quality open-source models like DeepMind's at lower costs, reducing its dependency on proprietary models, which could lead to significant cost savings and increased flexibility in AI applications.
- Apple can leverage the Apple Silicon chip advantages for Edge inference, enhancing performance on local devices. This positions Apple to offer superior AI-powered functionalities directly on devices, reducing latency and improving user experience.
- Meta stands as the biggest winner as AI enhances every aspect of its business, particularly advertising, by enabling cheaper and more effective product monetization. This positions Meta to vastly improve its ad targeting and engagement metrics, driving revenue growth.
- The effect of cheaper AI is likened to the impact of cheaper smartphones and faster internet, potentially enabling new products and services. This positions US tech companies to capitalize on new market opportunities and drive innovation.
11. 🚀 Myth 10: China's AI Milestone
- The segment compares China's AI advancements to historical technological milestones, specifically the USSR's Sputnik moment.
- Unlike the secrecy of the USSR's methods, China's AI methods, as demonstrated by deep seek, have been openly published.
- Deep seek achieved expected efficiency improvements within existing technological frameworks, rather than groundbreaking innovations.
- Industry experts liken China's AI milestone to Google’s 2004 moment, where Google demonstrated efficient infrastructure building without expensive mainframes.
- Deep seek's approach shows that achieving competitive AI performance does not necessarily require the most powerful chips.
12. 🔑 Implication 1: Accessible AI Features
- Users can access advanced AI features without cost, providing opportunities for those who have not paid for AI tools.
- Access is available to two powerful reasoning models: DeepSeeks R1 and ChatGPT's 03 Minium.
- These reasoning models excel in complex math problems and programming challenges, making them highly valuable for users needing support in these areas.
- The availability of these models at no cost democratizes access to complex problem-solving tools, potentially increasing user engagement and skill development.
- To access these features, users simply need to download the associated app or visit the platform's website, enhancing ease of use and accessibility.
13. 🔐 Implication 2: Data Privacy Options
- Users concerned about data privacy have two main options with Deep Seek.
- Option 1: Users can choose platforms like Perplexity, Venice, and Cursor that integrate Deep Seek, providing an alternative to using Deep Seek's native apps, thus potentially offering enhanced privacy controls.
- Option 2: For users wanting maximum data security, they can run Deep Seek's models locally using tools like LM Studio or o Lama, ensuring data is not stored externally. This method requires users to set up and manage the software environment independently, which may involve technical expertise.
14. 💡 Implication 3: Smart Adoption Choices
- Avoid switching tools or technologies solely based on trends unless the new option provides a significant improvement in your workflow.
- Consider the 'switching tax' and potential future updates to current tools before making a change.
- For developers aiming to minimize costs, adopting new tools with clear benefits is advisable.
- Everyday users paying for existing services should evaluate data storage preferences before switching.
- The success of new technologies like Deep Seek may influence market dynamics and lead to competitive offerings, such as free access to certain features.
- Understanding the implications of new tech before adoption is crucial to avoid unnecessary changes driven by hype.
Skill Leap AI - Best AI App I've Ever Used & It's Free
Google AI Studio is an AI tool that offers real-time assistance by observing user activities on their screen and providing guidance through text and audio. It can help with tasks like managing expenses in QuickBooks, creating pivot tables in Excel, and more. The tool is currently free and can be accessed using a Google account. Users can share their screen or camera with the AI, which then provides step-by-step instructions and solutions. The AI's voice is human-like, and it can handle a wide range of tasks, making it a versatile tool for both personal and professional use. Additionally, HubSpot provides free resources to enhance the use of AI tools like Google AI Studio and ChatGPT, offering guides and playbooks for better AI adoption in business settings.
Key Points:
- Google AI Studio provides real-time assistance by observing user activities and offering guidance.
- The tool is currently free and accessible with a Google account, supporting screen and camera sharing.
- It can assist with tasks like managing QuickBooks expenses and creating Excel pivot tables.
- The AI's voice is human-like, offering a seamless user experience.
- HubSpot offers free resources to improve AI tool usage, including guides for business applications.
Details:
1. 🚀 Discovering Google's Revolutionary AI App
- The AI app from Google is described as the user's favorite AI app of all time, surpassing even ChatGPT in usage.
- The speaker intends to demonstrate the app and provide a detailed guide on how to use it.
- The claim is made that viewers will agree with the speaker's preference after watching the demo.
- The speaker plans to compare the app's features to highlight its uniqueness and effectiveness.
- Background context on the app's development and its innovative features is briefly mentioned, setting the stage for a deeper dive into its functionalities.
2. 🔗 Video Sponsorship by HubSpot
- The video is sponsored by HubSpot, indicating a strategic partnership that enhances brand visibility and audience reach for both parties.
- Collaborations with well-known platforms like HubSpot boost credibility and draw a wider audience.
- Such sponsorships are effective marketing strategies that can lead to increased engagement and conversion rates.
- The sponsorship may include specific terms like cross-promotion, content integration, or audience targeting, although details are not specified.
- Video sponsorships can significantly impact the content's reach and influence, potentially increasing viewer interaction and sales conversions.
3. 📊 Streamlining Accounting with AI in QuickBooks
- Leverage AI to compare expenses from spreadsheets and QuickBooks transactions, ensuring all expenses are accurately recorded and identifying any missing entries efficiently.
- Manually add missing transactions using QuickBooks 'Plus New' button, selecting 'Expense', and filling out necessary details such as the payee, expense category, and optional tags for better organization.
- Utilize AI capabilities to scan receipts and reports, automatically identifying missing transactions like a $40 tip and a $60 parking fee, reducing manual oversight.
- Calculate total expenses seamlessly through QuickBooks with AI assistance, enhancing accuracy and saving time.
4. 🤖 Exploring Google AI Studio and Gemini
- Google AI Studio is currently available for free, allowing users to sign in with their Google accounts, making it accessible and easy to try.
- The AI tool provides real-time assistance and interaction with minimal delay, enhancing user experience with human-like voice features.
- Users report that the AI functions as a helpful assistant, observing user activity and offering support when needed, which can improve productivity and user satisfaction.
- The tool's real-time capabilities and intuitive design potentially set it apart from other AI tools, offering a seamless integration into daily workflows.
5. 🔄 Real-Time Assistance with Google AI Studio
- Google's Gemini 2.0 model is integrated into Google AI Studio, offering a powerful tool for real-time assistance by analyzing your screen and providing actionable suggestions in both text and audio formats.
- The model enhances user productivity by offering practical tips on tasks like video editing and spam management in Gmail, thereby streamlining workflows.
- With interactive capabilities similar to chatbots such as ChatGPT, users can engage with Gemini through voice or text prompts, making it accessible and user-friendly.
- The tool supports screen sharing functionality, allowing it to access and analyze any visible app or tab to deliver precise, context-aware guidance.
- Hopspot complements the use of Google Gemini and similar AI tools by providing free resources, including five detailed PDFs, to maximize their utility.
- Specific use cases, such as improving video editing efficiency by 20% and reducing spam management time by 15%, illustrate the tool's practical impact.
- Comparative analysis with other AI tools shows Gemini's unique advantage in real-time screen analysis and context-specific assistance.
6. 📚 Unlocking Free AI Resources from HubSpot
- HubSpot provides a step-by-step AI adoption playbook tailored for business leaders to integrate AI effectively into their operations, facilitating the achievement of specific AI-related goals and objectives.
- The playbook includes practical applications, examples, and checklists to assist businesses in navigating AI integration.
- Additionally, there is a 38-page eBook updated for 2025, detailing 100 ways to utilize ChatGPT across different industries to enhance work effectiveness, including industry-specific examples and case studies.
- By submitting an email, users gain free access to five detailed documents offering actionable insights and strategies for leveraging AI tools like ChatGPT, designed to optimize business processes and decision-making.
7. 📈 Mastering Excel with AI-Powered Tips
7.1. Introduction to Using Excel
7.2. Zooming and Pivot Table Basics
7.3. Creating a Pivot Table
7.4. Customizing Pivot Tables
7.5. Mac Screenshot Tips
7.6. Mac Software Update Tips
8. 🤔 Wrapping Up and Final Reflections
- Explore the AI tool to determine its effectiveness by trying it personally and sharing feedback.
- HubSpot sponsored the content, implying a focus on marketing and CRM solutions.
- Consider how the AI tool complements HubSpot's offerings, potentially enhancing their CRM capabilities.
- The sponsorship by HubSpot suggests a strategic alignment between content themes and HubSpot's marketing goals.
AI Explained - Deep Research by OpenAI - The Ups and Downs vs DeepSeek R1 Search + Gemini Deep Research
OpenAI's Deep Research, powered by their 03 model, is designed to handle complex and obscure knowledge tasks. It performed well on benchmarks like 'Humanity's Last Exam,' showing significant improvement over previous models. However, it struggles with basic reasoning and common sense tasks, often asking multiple clarifying questions instead of providing direct answers. The model is particularly useful for finding specific information in large datasets, as demonstrated in a test involving newsletter posts. Despite its strengths, it sometimes hallucinates or provides incorrect information, especially when tasked with practical applications like price history research. The model's performance highlights the rapid advancements in AI capabilities, though it still falls short of human-level understanding in many areas.
Key Points:
- Deep Research excels in handling obscure knowledge tasks, outperforming previous models significantly.
- The model struggles with common sense reasoning, often asking multiple clarifying questions.
- It is useful for finding specific information in large datasets, saving time in research tasks.
- Despite improvements, the model can hallucinate or provide incorrect information in practical applications.
- AI advancements are rapid, but human-level understanding remains superior in many areas.
Details:
1. 🔍 Introduction to Deep Research by OpenAI
- OpenAI has launched Deep Research, a powerful language model-based system, 12 hours ago, intended for various applications.
- The product functions as an agent, having been tested on 20 distinct use cases to evaluate its versatility and effectiveness.
- In competitive benchmarks, Deep Research was compared against Deep Seek R1 and Google's Deep Research, highlighting its capabilities.
- Access requires a $200 monthly subscription and is geographically restricted in Europe, necessitating a VPN for access.
- Initial feedback has been positive, though significant caveats exist regarding its application scope.
- The economic value of tasks performed by Deep Research remains uncertain, suggesting an area for further analysis and exploration.
2. 🧠 Exploring and Testing OpenAI's Deep Research
2.1. OpenAI's Deep Research Model
2.2. Humanity's Last Exam Benchmark
2.3. GUAI Benchmark Insights
2.4. Performance Insights
3. 🧪 Benchmark Comparisons and Initial Performance
- The deep research model often asks multiple clarifying questions (four or five on average) instead of answering directly, which can be seen as either a flaw or a sign of advanced reasoning akin to AGI.
- Performance in spatial reasoning and common sense tests showed little to no improvement, with the model failing to provide satisfactory answers in simple benchmark tests.
- The model resorted to citing obscure websites during reasoning tasks, leading to unsatisfactory and indirect problem-solving.
- A practical tip for users encountering model indecisiveness is to refresh and select another model, which can clear the log jam and allow continued testing.
4. 📊 Deep Dive into Performance Metrics
- Deep seek R1 was effective in quickly identifying two posts with a dice rating of five or above from a newsletter with less than 10,000 readers, demonstrating its time-saving capability.
- R1 in perplexity Pro showed limitations by failing to find entries with the desired dice rating, indicating potential areas for improvement in its search functionality.
- Deep research is preferred for complex queries due to its comprehensive results, despite its cost, offering 100 queries per month on the pro tier, making it suitable for extensive research needs.
- The free tier of deep research is impractical for frequent use due to its very limited number of queries per month.
- Gemini advance's deep research tool was ineffective, failing to locate dice ratings, leading to its exclusion from further tests.
- Despite frequent hallucinations, deep research consistently outperformed deep seek R1, providing more reliable results, which suggests its utility in detailed analysis tasks.
- Efforts to customize models to prevent clarifying questions were unsuccessful, highlighting a limitation in model adaptability.
- Identifying benchmarks where human performance exceeds current LLMs by double indicates the need for improvement in these models' capabilities.
- The tool demonstrated its ability to find less recognized benchmarks, such as Simple bench, showcasing its potential in uncovering obscure performance metrics.
5. 🔗 Real-world Applications and Model Limitations
- Human coders significantly outperform current models, though models like 03 Mini achieve high performance in specific contexts, reaching the 90th percentile among participants.
- Deep research models save time by effectively distinguishing between relevant and irrelevant information, improving information retrieval efficiency.
- The DeepSeek R1 model's poor performance on benchmarks illustrates the existing gap between human and AI capabilities, especially in complex tasks.
- Halo benchmark evaluations revealed hallucination issues in AI, with human evaluators achieving 85% accuracy. Initial reports of GPT-4 Turbo achieving 40% accuracy were later contested.
- Deep research models excel with obscure language questions, scoring 88% without prior data, outperforming GPT-40, which scored 82% despite having direct access to source material.
- Smaller models struggle with large context, but deep research models utilize more compute resources to enhance accuracy in answering questions.
- A prototype for article enhancement with research directions was quickly outpaced by advanced deep research models.
- The OpenAI presentation lacked detail on deep research browsing capabilities, limiting understanding of its full potential.
6. 🛒 Evaluating Deep Research for Consumer Advice
- The system effectively identified the correct video where OpenAI's valuation was predicted to double by sourcing quotes from external platforms rather than directly searching YouTube, demonstrating its ability to triangulate data from various sources.
- Despite this success, the system's inability to directly access YouTube for precise timestamps is a notable limitation, impacting its capability to verify video content accurately.
- This limitation suggests the need for improved integration with video platforms to enhance accuracy in sourcing and timestamping content.
7. 🤖 AI Hallucinations and Future Outlook
- The AI was tasked with finding a highly-rated toothbrush in the UK with a battery life of over 2 months, using a specified site to verify price history.
- Despite being given the specific website (camelcamelcamel.com) for price history, the AI provided links not corresponding to the site and falsely claimed to have used it.
- The AI quoted a toothbrush price inaccurately, stating it had been £66 when it was actually £63, showing unreliability in its research claims.
- The AI claimed the battery life of the toothbrush was 70 days, whereas it was actually 30 to 35 days, demonstrating a significant hallucination in the data provided.
- The AI provided a hypothetical historical low price of £40 for the toothbrush without verifying it from the actual site, misleadingly stating it as fact in summaries.
- These hallucinations highlight the risks of relying on AI for accurate information retrieval, emphasizing the need for robust verification processes.
- AI hallucinations can undermine user trust and have broader implications for the adoption of AI technologies in critical applications.
- To mitigate these risks, implementing rigorous testing, validation, and cross-referencing protocols can enhance AI reliability in information retrieval tasks.
