What Human Learning Can Teach AI

Sep 28 2025

This essay examines how emotional weighting fundamentally distinguishes human from artificial learning, drawing on neuroscience research showing that amygdala-hippocampus interactions create a biological "highlighting" system for emotionally salient information—absent in current Large Language Models despite their sophisticated attention mechanisms. While AI systems excel at systematic processing through statistical optimization, they lack the subjective relevance judgments and persistent purposefulness that characterize human cognition, particularly the ability to maintain coherent goal-directed behavior across complex, multi-step tasks. We argue that effective human-AI collaboration will emerge from leveraging complementary cognitive architectures: AI's consistent attention mechanisms paired with human emotional weighting, subjective prioritization, and embodied purposefulness, with meta-learning skills like problem decomposition becoming crucial for humans to enhance their effectiveness as AI partners.

AI Philosophy

 The New Ontology of Code

Aug 30 2025

AI code generation isn't just changing how we write software—it's changing what code is. We're witnessing a fundamental inversion where code transforms from permanent artifact to regenerable output, where implementation becomes fluid while interfaces become the new bedrock. But this shift creates uncomfortable paradoxes: "easier" coding that doesn't eliminate complexity but displaces it, democratization that centralizes power, and the strange alienation of debugging code you commanded but didn't create. This essay explores what we're really trading in this transformation—intimate understanding for broad capability, craft for productivity, independence for efficiency. Neither utopian nor dystopian, it offers language for that unsettling feeling many developers can't quite name: working with systems we control but don't comprehend. For anyone grappling with AI's impact on software, this is about recognizing the trade-offs we're making before we've made them irreversible.

AI Philosophy

 Shoucheng Zhang: "Quantum Computing, AI and Blockchain: The Future of IT" | Talks at Google

Dec 11 2018

Prof. Shoucheng Zhang discusses three pillars of information technology: quantum computing, AI and blockchain. He presents the fundamentals of crypto-economic science and answers questions such as: What is the intrinsic value of a medium of exchange? What is the value of consensus and how does it emerge? How can math be used to create distributed self-organizing consensus networks to create a data-marketplace for AI and machine learning? Prof. Zhang is the JG Jackson and CJ Wood professor of physics at Stanford University. He is a member of the US National Academy of Science, the American Academy of Arts and Sciences and a foreign member of the Chinese Academy of Sciences. He discovered a new state of matter called topological insulator in which electrons can conduct along the edge without dissipation, enabling a new generation of electronic devices with much lower power consumption. For this groundbreaking work he received numerous international awards, including the Buckley Prize, the Dirac Medal and Prize, the Europhysics Prize, the Physics Frontiers Prize and the Benjamin Franklin Medal. He is also the founding chairman of the DHVC venture capital fund, which invests in AI, blockchain, mobile internet, big data, AR/VR, genomics and precision medicine, sharing economy and robotics.

AI Blockchain Quantum Computing Video