What happens when AI becomes vastly smarter than humans? It sounds like science fiction, but researchers are grappling with the very real challenge of ensuring Artificial General Intelligence (AGI) is safe for humanity. Join us for a deep dive into the cutting edge of AI safety research, unpacking the technical hurdles and potential solutions. We explore the core risks – from intentional misalignment and misuse to unintentional mistakes – and the crucial assumptions guiding current research, like the pace of AI progress and the "approximate continuity" of its development. Learn about the key strategies being developed, including safer design patterns, robust control measures, and the concept of "informed oversight," as we navigate the complex balance between harnessing AGI's immense potential benefits and mitigating its profound risks.

An Approach to Technical AGI Safety and

Security: https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/evaluating-potential-cybersecurity-threats-of-advanced-ai/An_Approach_to_Technical_AGI_Safety_Apr_2025.pdf

Google Deepmind AGI Safety Course: https://youtube.com/playlist?list=PLw9kjlF6lD5UqaZvMTbhJB8sV-yuXu5eW

Ever tried to understand how AI actually learns, only to get lost in a sea of equations and jargon? This episode is your fast track through the fundamentals of machine learning, breaking down complex concepts into understandable nuggets.

Drawing inspiration from Stanford course materials, we ditch the dense textbook approach and offer a clear, conversational deep dive into the core mechanics of AI learning. Join us as we explore:

• • Linear Predictors: The versatile workhorses of early ML, from classifying spam to predicting prices.

  • Feature Extraction: The art of turning raw data (like an email) into numbers the algorithm can understand.

  • Weights & Scores: How AI weighs different information (like ingredients in a recipe) to make a prediction using the dot product.

  • Loss Minimization & Margin: How do we measure when AI gets it wrong, and how does it use that feedback (like the concept of 'margin') to improve?

  • Optimization Powerhouses: Unpacking Gradient Descent and its faster cousin, Stochastic Gradient Descent (SGD) – the engines that drive the learning process.

Whether you're curious about AI or need a refresher on the basics, this episode provides a solid foundation, explaining how machines learn without needing an advanced degree. Get ready to understand the building blocks of artificial intelligence!

Stanford's Algorithms for Artificial Intelligence: https://web.stanford.edu/~mossr/pdf/alg4ai.pdf

It's over 50 years old, yet the C programming language remains a fundamental building block beneath countless technologies we use daily, from operating systems to embedded devices. Join us as we dive deep into the core principles of C, guided by the classic Kernighan & Ritchie text. We explore its history, key syntax, control flow, functions, and the low-level power that explains why C still matters profoundly today.

The C Programming Language (2nd Edition): http://cslabcms.nju.edu.cn/problem_solving/images/c/cc/The_C_Programming_Language_%282nd_Edition_Ritchie_Kernighan%29.pdf