In this special 2024 AI Year in Review, Ron is joined by AI experts ZZ Si (Co-Founder & Distinguished Engineer), Emma Pirchalski (AI Strategist), and Michael Wharton (VP of Engineering) to reflect on the most important AI moments of 2024. They come together to discuss the defining stories, key breakthroughs, and major challenges that shaped AI in 2024. Ron leads the conversation, drawing out their perspectives on the year's most impactful developments, unfiltered reflections, bold insights, and forward-looking predictions for the future of AI.
2024 AI Reading Guide
1. AI Governance and Regulation
- Report on Assessing AI Governance Tools (Read the Report)
- Purpose: Explore methodologies for assessing AI governance tools and their effectiveness.
- Key Takeaways:
- Frameworks for AI governance assessment.
- Risk mitigation strategies for AI deployments.
- Practical tools for compliance and accountability.
- Why It Matters: Understand best practices for ensuring responsible AI usage within organizations.
- FTC Enforcement on Rite Aid Case (Read the Press Release)
- Purpose: Analyze how regulatory enforcement is shaping AI deployment in retail.
- Key Takeaways:
- FTC’s stance on unauthorized facial recognition usage.
- Legal consequences for companies misusing AI technology.
- Implications for businesses using AI-driven surveillance tools.
- Why It Matters: Highlights the importance of compliance with AI privacy regulations and consumer protection laws.
- EU AI Act (Explore the AI Act)
- Purpose: Understand the most comprehensive regulatory framework for AI.
- Key Takeaways:
- Structure of the EU’s risk-based approach to AI governance.
- How "high-risk" AI applications are defined and regulated.
- Implications for companies developing or deploying AI systems in the EU.
- Why It Matters: Businesses seeking to operate in Europe must align with these regulatory frameworks.
2. Enterprise AI and Industry Trends
- State of Generative AI in the Enterprise 2024 (Read the Report)
- Purpose: Review the current and future state of generative AI within enterprises.
- Key Takeaways:
- Market trends and major players in generative AI.
- Use cases driving adoption in large enterprises.
- Challenges and opportunities for AI adoption.
- Why It Matters: Provides strategic insights for companies looking to adopt generative AI.
- Llama 3 Release (Read the Blog)
- Purpose: Learn about the latest advancements in open-source large language models (LLMs).
- Key Takeaways:
- New features and capabilities of Llama 3.
- Comparisons with other LLMs in terms of efficiency and performance.
- Potential use cases for enterprise AI adoption.
- Why It Matters: Highlights advances in open-source LLMs, offering alternatives to proprietary models like GPT.
- Open Source LLMs and Vision-Language Models
- Open Source LLMs Report (Read the Report)
- Key Takeaways:
- Analysis of open-source models’ progress relative to proprietary frontier models.
- Competitive landscape for open vs. closed AI models.
- Why It Matters: Provides insight into the open-source movement in AI, useful for businesses evaluating vendor lock-in risks.
- Vision Language Model Leaderboard (View the Leaderboard)
- Key Takeaways:
- Performance comparisons of vision-language models.
- Why It Matters: Helps companies select the right VLMs for AI-driven image recognition and search.
- Video Generation Model Battle Arena (View the Leaderboard)
- Key Takeaways:
- Ranking of video generation models on key metrics.
- Why It Matters: Informs decisions about video content generation tools for media and entertainment.
3. Emerging AI Technologies and Models
- Early Prototypes of World Models
- DeepMind’s Genie-2 (Read the Blog)
- WorldLabs AI (Explore the Blog)
- NWM Overview (Read More)
- Purpose: Track early-stage development of world models.
- Key Takeaways:
- New methodologies for training AI on world model frameworks.
- Potential applications of world models for robotics and simulation.
- Why It Matters: Helps track bleeding-edge AI concepts that could shape future technology development.
- "Textbooks are All You Need" Paper (Read the Paper)
- Purpose: Understand how static knowledge sources can train large models.
- Key Takeaways:
- Argument for using textbooks as data for model training.
- Potential to reduce the need for large-scale data collection.
- Why It Matters: Could make AI development more efficient and less data-intensive.
4. AI Risks and Failures
- Are We Hitting a Wall? (Read the Analysis)
- Purpose: Examine whether AI development is plateauing.
- Key Takeaways:
- Reflections on the stagnation of model performance improvements.
- Arguments for and against the "AI plateau" hypothesis.
- Why It Matters: Guides strategic decision-making on AI R&D investments.
- AI Failure Case: Finance Deepfake (Read the Report)
- Purpose: Examine a real-world failure case involving AI-generated deepfakes.
- Key Takeaways:
- Description of how fraudsters used a deepfake to impersonate a CFO.
- Insights on the need for stronger security measures.
- Why It Matters: Demonstrates the risks of generative AI being used for fraud.
- Gemini "What the Quack" Faked Video (Read the Report)
- Purpose: Review the controversy surrounding Google’s Gemini AI.
- Key Takeaways:
- Details on the AI model’s misrepresentation in marketing.
- Potential trust and reputational consequences for AI vendors.
- Why It Matters: Shows the risks of overhyping AI product capabilities.
5. Legal and Ethical Issues
- NH Predict/UHC Care Denials Lawsuit (Read the Report)
- Purpose: Review ethical issues tied to healthcare AI decisions.
- Key Takeaways:
- Allegations of wrongful denials for elderly care via AI.
- Why It Matters: Raises awareness of AI’s role in healthcare and potential for bias.
6. Scientific Milestones
- Nobel Prize in Physics 2024 (View the Press Release)
- Purpose: Highlight scientific milestones linked to AI or physics.
- Key Takeaways:
- Overview of key discoveries.
- Why It Matters: Provides broader context on the future of AI-related physics advancements.
