Deep Learning Architectures

Jul 30, 2026 · 20:24

First Steps Toward Automated AI Research — Richard Socher, CEO Recursive AI

Richard Socher proposes the concept of the 'Eureka machine,' a system designed to automate scientific discovery across all fields—from physics and biology to economics. Drawing parallels with evolution and Popper's philosophy of science, he argues that humanity is at an inflection point where Artificial Intelligence (AI) can achieve Recursive Self-Improvement (RSI). This process involves building systems that improve their own code and architecture over long time horizons, accelerating scientific progress far beyond current human capacity.

Key takeaways

1. The Eureka Machine Goal 15:07

   The ultimate goal is to build a machine that automates the entire process of scientific discovery. This requires integrating knowledge (scientific data), simulation, physical experimentation, and an agent swarm to manage all inputs.

2. Evolutionary Analogy for Progress

   Scientific progress is viewed as an open-ended evolutionary process. Just as biology evolved over billions of years, technology and AI are expected to undergo rapid, exponential shifts (S-curves) leading to massive human flourishing.

3. The Necessity of RSI

   AI progress is accelerating because modern AI can code. The next major step involves building a system with Recursive Self-Improvement (RSI)—an AI that has self-awareness of its shortcomings and autonomously updates its entire architecture, moving beyond manual processes.

• AI Engineer
• Artificial Intelligence
• Recursive Self-Improvement (RSI)
• Scientific Automation
• Evolutionary Computing
• Deep Learning Architectures
• CUDA Kernels
• Recursive AI
• you.com

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Jul 27, 2026 · 29:46

How to Lie with AI: Understanding Bias, Ethics, and the Hidden Risks in ML - Clarissa Rodrigues

The presentation explores how Machine Learning models can exhibit bias and 'lie' unintentionally due to flawed or non-representative training data. While AI is rapidly integrating into daily life (e.g., pricing, search, criminal justice), the speaker emphasizes that developers must maintain vigilance, prioritize explainable models, and ensure that model complexity aligns with problem complexity to mitigate ethical risks and unintended bias.

Key takeaways

1. ML Models are not inherently predictable like humans. 2:00

   Unlike traditional algorithms where input/output is predictable, ML models can produce varied outputs based on internal weights and parameters. This lack of inherent transparency requires developers to be critically aware of model decisions.

2. Bias originates from data, not the algorithm itself (Garbage In, Garbage Out). 8:30

   To build a robust model, it is crucial that the training data is representative and free from historical or aggregation biases. Simply having more data does not guarantee accuracy; representativeness is key.

3. The importance of Explainable AI (XAI). 3:25

   Developers must strive for transparency by using explainable models to understand what the system is doing behind the scenes, rather than relying solely on complex black-box architectures.

• NDC Conferences
• AI Ethics
• Machine Learning Bias
• Data Governance
• Deep Learning Architectures
• Explainable AI (XAI)
• COMPAS
• Uber AI Solutions

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