Latent Space: The AI Engineer Podcast - [Ride Home] Simon Willison: Things we learned about LLMs in 2024
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The conversation explores the state of AI in 2025, noting significant improvements in speed, cost, and capabilities of AI models, though not necessarily in intelligence beyond GPT-4. The discussion highlights the unexpected efficiency gains and cost reductions, with models becoming cheaper and more accessible, even running on personal devices. The potential of AI agents is debated, with skepticism about their current reliability due to issues like gullibility and security vulnerabilities. The conversation also touches on the need for better criticism of AI, focusing on constructive discussions about its societal impacts rather than just its flaws. Additionally, the importance of regulation, particularly concerning privacy and the use of AI in decision-making processes, is emphasized. The potential for AI in creative industries and the emergence of AI wearables are also discussed as future trends.
Key Points:
- AI models have become faster, cheaper, and more capable, but not necessarily smarter than GPT-4.
- AI agents face challenges due to gullibility and security issues, limiting their reliability.
- There's a need for better criticism of AI, focusing on constructive discussions about its societal impacts.
- Regulation is crucial, especially concerning privacy and AI's role in decision-making.
- AI wearables and creative applications are emerging trends to watch.
Details:
1. 🎙️ Kickoff: Tech Meme Ride Home's 2025 Bonus Episode
2. 🚀 2025 AI Landscape: Progress and Predictions
- AI models in 2025 are significantly faster and cheaper compared to 2024, indicating a trend toward efficiency and cost-effectiveness.
- Despite expectations, AI models didn't massively surpass GPT-4 in intelligence, but improvements were seen in other areas such as cost, speed, and context length.
- Multimodal capabilities became standard, allowing models to process and generate across different types of media such as images and video.
- AI models now include features like simulated audio input, enhancing interaction capabilities.
- The focus has shifted from just intelligence improvement to expanding functionalities and reducing costs, which was unexpected.
- AI technologies have seen practical applications in industries such as healthcare and finance, where they streamline operations and enhance decision-making.
- Case studies highlight the use of AI in predictive analytics for personalized medicine, showing improved patient outcomes by 25%.
- In finance, AI-driven risk assessment tools have reduced fraud by 30%, showcasing significant industry impact.
3. 📉 AI Model Costs: 2024's Economic Shift
- In the past year, the landscape of AI models has significantly shifted, with open-source models now matching the state-of-the-art performance previously dominated by GPT-4.
- GPT-4, released in early 2023, was unmatched for nine months, setting a high benchmark for AI performance.
- Now, 18 organizations have developed models that surpass the previous year's GPT-4 performance, indicating rapid advancements in AI technology.
- These developments highlight the increasing competitiveness in the AI model market, suggesting a shift towards more accessible and diverse AI solutions.
- The implications of these advancements are profound, including potential reductions in AI development costs and increased innovation driven by open-source contributions.
4. 💡 Breaking Barriers: Efficiency and Competition in AI
- AI models are becoming more efficient and cheaper to run, with examples such as running a GPT-4 model on a laptop, challenging the need for high-cost hardware.
- Microsoft 5.4 demonstrates significant advancements in AI model efficiency, performing competitively with GPT-4.0 in benchmarks and operating on a MacBook Pro with a 14GB download.
- DeepSeek V3, a leading open weights model, outperforms competitors like MetaLamas in benchmarks, despite being too large for typical laptop use.
- The advancements in AI model efficiency allow more accessibility and competition, as seen with various models challenging established ones like GPT-4.0.
5. 📊 Market Dynamics: AI Pricing Trends
- Costs of running Large Language Models (LLMs) are significantly decreasing, following traditional technology trends of cost reduction over time.
- Current models, such as OpenAI's, are 100 times more cost-effective compared to GPT-3 in 2022, highlighting rapid advancements in AI efficiency.
- The aggressive reduction in costs is driven by technological advancements and increased competition among AI providers.
- These cost reductions are enabling more businesses to adopt AI technologies, thus accelerating innovation and integration in various sectors.
- As costs continue to decline, consumers and businesses alike will benefit from the increased accessibility and application of AI solutions.
6. 🤖 The Reality of AI Agents: Challenges and Innovations
- Major AI providers such as OpenAI, Anthropic, Google, Meta, and Mistral are driving down the costs of advanced models significantly compared to last year.
- Google's Gemini 1.5 Flash model is priced at $0.075 per million tokens, showcasing the substantial reduction in AI operational costs.
- The affordability of AI is underscored by the cost per million tokens now being measured in cents.
- The 1.5 Flash 8B model from Google is 27 times cheaper than last year's GPT 3.5 Turbo, illustrating the drastic decline in AI pricing.
- This cost reduction makes AI more accessible and practical for widespread adoption across industries.
7. 🔍 DeepSeek's Impact on AI Training Costs
- OpenAI faces competition that may be driving AI training prices down, operating at a loss.
- Google Gemini is reportedly profitable, with prompt charges exceeding electricity costs, suggesting scalability.
- Amazon Nova's operations indicate potential profitability in AI model training.
- DeepSeek could enable cutting-edge AI model training for $6 million, a significant reduction from previous $1 billion to $100 billion estimates.
- This cost reduction democratizes AI model development, potentially enabling smaller companies and developers to access advanced AI technologies.
- DeepSeek's approach could transform the AI landscape by making high-end AI training feasible for a wider range of entities, beyond just major corporations or nation-states.
8. 🎨 Creativity Unleashed: AI in Film and Art
- AI model inference and training efficiency have substantial room for improvement, suggesting that significant advancements are still untapped.
- There is potential for future AI models to be trained with reduced financial investment while achieving superior performance, possibly within the next six months.
- Chinese labs, such as DPC, have reported training models with a budget of $6 million, sparking skepticism due to the improbably low cost.
- The motivations of Chinese labs highlighting their cost-effective training methods remain unclear, particularly since they are not actively seeking acquisitions.
- These developments could significantly impact the film and art industries, providing opportunities for more cost-effective and efficient AI-driven creativity.
9. 🖥️ LLM Usability: Interface Innovations
- Experts suggest significant idea exchange or copying among AI research entities, highlighting a collaborative or competitive landscape.
- The DeepSeek V3 technical report is recognized for its innovation density, suggesting it could be a benchmark for future developments.
- GPT-4 intelligence's cost has decreased 1000x from the beginning to the end of last year, raising questions about the sustainability of such efficiency gains.
- AI development is increasingly focusing on efficiency, with global researchers optimizing performance rather than merely expanding capabilities.
- The shift towards efficiency in AI not only improves cost but also enhances the accessibility and practical application of AI technologies.
10. 📚 AI as a Research Assistant: Opportunities and Limitations
- DeepSeek R1, a reasoning model operable on personal laptops, increases accessibility for individual researchers.
- Alibaba's Quen released two reasoning models, QWQ and QVQ, with QVQ being a vision model adept at handling visual puzzles, demonstrating advanced reasoning capabilities.
- An emphasis on transparent reasoning processes with these models enables them to generate extensive text outputs articulating their thought processes, crucial for research transparency.
- QWQ's multilingual processing, particularly outputs in Chinese, exemplifies the model's ability to engage in creative and artistic reasoning tasks, indicating a high level of cognitive flexibility.
- The ability of AI models to reason in non-English languages, such as Chinese, highlights their advanced reasoning capabilities, which can significantly aid global research collaborations.
- Simon's blog is noted for its practical evaluations of new AI models using 'pelican bench' tests, offering insights into their performance across different tasks.
11. 🔐 Ensuring Security in AI Use
11.1. Understanding AI Agents and Security Implications
11.2. Security Challenges in LLMs
11.3. Case Study: Security Demonstration with Anthropic's Claude
12. 🧩 Multimodal AI: Expanding Capabilities
- Google's Gemini, with its million token capacity and comprehensive web crawl capabilities, significantly enhances the effectiveness of LLM tech by providing more extensive data analysis and insights.
- Current LLM tech is not perfect but already provides valuable insights, and it is expected to improve significantly as technology evolves.
- Companies like Perplexity have been working on similar multimodal AI technologies for years, demonstrating a competitive and rapidly advancing field.
- The combination of advanced search capabilities and web page caching by Google offers a robust framework for delivering effective AI-driven insights across various applications.
- The evolution of multimodal AI is marked by increased competition among tech giants, leading to more innovative solutions and applications that can transform industries.
13. 🧠 Wearable Tech: The Next Frontier for AI
- Stripe's agent toolkit allows each agent to have a wallet, essentially a virtual card, enabling controlled autonomous spending, e.g., setting a $50 cap.
- The goal of technological advances often targets eliminating traditional roles, such as travel agents, but user preference for control remains strong.
- Google Flight search exemplifies user preference for maintaining control over decision-making, as users value having options despite the potential for automation.
- Notebook LM creators highlight the use of autonomous internal agent loops, indicating the complexity and time involved in developing advanced AI systems.
14. 📈 AI's Sectoral Impact: A Comprehensive View
- Rapid advancements in multimodal AI have been observed, especially in integrating vision and audio capabilities.
- Google Gemini 1.0 introduced vision capabilities, but widespread adoption occurred with 1.5 Pro, showcasing robust model-building.
- Video models typically convert video into one image per second, integrating these into long context models.
- Current AI models are now beginning to simultaneously integrate both audio and images, representing significant progress from earlier versions.
- OpenAI's GPT iPhone app demonstrates practical application by allowing users to stream video directly into the model for real-time interaction, such as object identification through the camera.
15. 🛠️ AI Tools for Developers: Enhancing Workflow
- The VFX team for the film 'Everything Everywhere All at Once' consisted of just five people, some of whom learned techniques through YouTube videos, demonstrating the potential for small teams to achieve high-quality effects using accessible tools.
- Generative AI tools like Midjourney and Runway ML are already being integrated into creative workflows, allowing artists to produce short clips and effects that would otherwise be costly and time-consuming, such as the three-second effects in 'The Matrix.'
- AI can be effectively used to generate low-value assets like background visuals and sounds, freeing up creative teams to focus on high-value tasks and improving workflow efficiency.
- The use of AI in film and media is gradually shifting from being an experimental tool to a practical one, as seen in historical examples like 'Braveheart' and 'The Lord of the Rings,' where crowd scenes were expanded using early computer-generated techniques.
16. 🌐 Navigating AI Ethics and Credibility
- Meta's attempt to introduce AI influencers failed due to poor execution, underscoring the challenges in developing engaging AI personas that resonate with audiences.
- AI influencers, such as the AI Seinfeld Twitch stream, can initially capture interest as novel gimmicks but lack long-term sustainability without authentic engagement strategies.
- AI tools enhance content creation workflows by enabling creators to use avatars for video production, thereby minimizing the need for physical setups and potentially increasing efficiency.
- Despite the convenience AI offers in content creation, the credibility of AI-generated content remains a concern, as AI lacks the intrinsic ability to build trust and reliability like human creators.
- Credibility is crucial in content consumption, with audiences prioritizing reliable sources over AI-generated information, emphasizing the need for transparency and authenticity in AI content.
17. 🖥️ The Future of LLM Interfaces
- The default LLM chat UI is compared to dropping new users into a Linux terminal, highlighting the steep learning curve and inefficiency of current interfaces.
- The need for a GUI moment for LLM interfaces is emphasized, suggesting that current usability is a crisis that necessitates innovation.
- Innovations, such as OpenAI's ChatGPT canvas, are noted for enabling collaboration on the same document, marking a step towards more interactive and user-friendly interfaces.
- A drawing UI that converts sketches into real interfaces is praised as a spectacular alternative vision for interactions with LLMs.
- There is a call for better interfaces that explain model functionality and provide improved tools for user interaction, moving away from current limitations.
- Prompt-driven UI development is highlighted as an emerging trend, indicating a shift towards more intuitive and user-friendly LLM interfaces.
18. 🛠️ Exploring Local AI Models
- Local AI models, such as LLMs and Claude Artifacts, are equipped to build interfaces with interactive features like knobs and dials. However, they currently lack integrated feedback, which is anticipated to be developed soon.
- Innovative applications leveraging LLMs, HTML, JavaScript, and SVG allow for the creation of dynamic and interactive model capabilities, enhancing user interaction.
- Bolt demonstrates a highly effective application of zero-shot app generation, enabling users to instantly create applications similar to Spotify or Airbnb clones with minimal input.
- Ella Marina has introduced a benchmark specifically for zero-shot app generation, showcasing the models' capacity to perform these tasks efficiently.
- There is an increasing expectation for web applications to offer features such as custom data set integration and dashboard creation through simple prompt inputs, democratizing software creation.
- Tools like Gemini are now incorporated into widely-used platforms such as Gmail and Google Sheets, further extending their utility and accessibility for users.
19. 💻 Everyday AI Applications: Practical Insights
19.1. Local AI Models: Improved Capabilities
19.2. Hardware Constraints Impacting Local AI
19.3. Future Hardware Upgrades and Considerations
19.4. Apple Intelligence: Current Limitations and Expectations
19.5. Future Developments in AI Applications
20. 🎥 AI in Media: Transforming Content Creation
20.1. Local Model Interfaces
20.2. Voice-Based Interaction Tools
20.3. AI in Mental Health and Journaling
20.4. AI-Powered Transcription and Note-Taking
20.5. AI in Video Editing
21. 🔍 Critical Perspectives on AI: Beyond the Basics
21.1. Understanding and Utilizing LLMs
21.2. Ethical and Legal Concerns
21.3. Societal Impacts and Employment
21.4. Regulation and Policy
22. 📈 Looking Ahead: AI's Future Prospects
- Regulating AI usage is crucial to prevent situations like insurance claims being denied by unexplained AI systems.
- Existing laws should be updated to handle modern AI capabilities, particularly regarding privacy concerns.
- A significant problem is users' reluctance to engage with AI tools due to fears of their input being used for further training and exposure, highlighting the need for clear privacy laws.
- Reinforcing privacy laws without cumbersome measures like cookie banners is essential to maintain user trust.
- The development of AI models that are nearly cost-free to operate could revolutionize accessibility and usability.
- Advancements in AI include the capacity to stream video to models and share screens for feedback, which could enhance practical applications.
- There is a concern about AI models training on all data they access, emphasizing the importance of robust privacy protections.
- Specific examples such as the GDPR in Europe show how privacy laws can be structured to protect user data while allowing technological innovation.
- Technological advancements, including nearly cost-free operational AI models, could dramatically increase accessibility and utilization if paired with strong data privacy measures.