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The Gradient Substack delves into advancements, challenges, and societal impacts of AI research, including deep learning, personal robotics, large language models, and generative AI. It explores technical innovations, regulatory frameworks, and ethical considerations shaping the field, while fostering understanding through essays, updates, and interviews.

AI Research Trends Deep Learning Personal Robotics Large Language Models Generative AI AI in Law and Art AI Misuse and Ethics Regulatory Frameworks for AI AI Safety and Alignment Technological Competition

Top posts of the year

And their main takeaways

Shape, Symmetries, and Structure: The Changing Role of Mathematics in Machine Learning Research

87 implied HN points • 16 Nov 24
🕹 Technology Machine Learning Mathematics Research AI
  1. Mathematics is playing a bigger role in machine learning by connecting with fields like topology and geometry. This helps researchers create better tools and methods.
  2. It's not just about scaling up current methods; there's a need for new approaches based on mathematical theories. This can lead to more innovative solutions in machine learning.
  3. Mathematicians should view advancements in machine learning as chances to explore and deepen their theoretical work, not as threats to their field. Embracing these changes can lead to new discoveries.

AGI is Not Multimodal

49 implied HN points • 04 Jun 25
🕹 Technology AI Machine Learning Robotics Cognitive Science Automation
  1. Recent AI models have shown impressive capabilities, but they don't represent true human-like intelligence. They succeed because of scaled hardware and not because they think like us.
  2. Trying to combine different AI models into a single system won't lead to real understanding or human-level AI. This approach is flawed and unlikely to work.
  3. Instead of mixing models, we should focus on how AI interacts with the world and learns from it. Understanding AI should be about its actions and experiences in the environment.