Quantum computing is unlikely to have significant impact on AI within the next two decades; the only medium-term application is simulation of quantum systems, and scaling, error correction, and efficiency remain highly uncertain.

Large language models appear intelligent in only narrow domains (mathematics, coding, law) where reasoning is substrate-based (symbol manipulation itself aids thought) and where answers can be self-corrected (code can be tested, proofs verified).

A 4-year-old child is trained on approximately 16,000 waking hours of sensory data (vision, touch, hearing), which through the optic nerve (2 million fibers, each carrying ~1 byte/second) results in about 10^14 bytes of raw data—the same volume as the largest LLMs trained on all publicly available text on the internet.

The Moravec paradox, articulated by roboticist Moravec in 1988, describes the counter-intuitive observation that complex intellectual tasks like proving theorems and playing chess are easy for computers, while simple sensorimotor tasks like manipulating objects remain fundamentally difficult despite decades of robotics research.

Current AI systems are fundamentally limited compared to animals and humans because they cannot perform basic real-world tasks like clearing a dinner table, cleaning a house, or learning to drive in a few hours despite being trained on millions of hours of human driving data.

Chomsky's theory of universal grammar and hardwired language modules has been disproven by the success of data-driven neural networks like LLMs, which can achieve good language understanding without explicit linguistic structures.

JEPA (joint embedding predictive architecture) is an architecture that predicts abstract representations of future states rather than reconstructing raw sensory details, eliminating the need to predict unpredictable information by focusing only on predictable, relevant information.

Double descent is an empirical phenomenon where test error first increases then decreases as model size increases beyond the interpolation threshold, contradicting classical statistical intuition that overfitting increases with model capacity.

Abstract representations are fundamental not just for building intelligent AI systems but for all of science: in physics, chemistry, and biology, scientists always identify abstractions (particles, atoms, molecules, organisms) that allow tractable modeling and prediction without simulating underlying details like quantum mechanics.

Babies and animals learn primarily through observation and self-supervised learning, acquiring basic concepts like object permanence at 9 months, intuitive physics (gravity, inertia) by age 10 months, and the difference between animate and inanimate objects without explicit instruction.

A world model is an AI system that predicts the consequences of actions before they are executed, enabling an AI system to accomplish new tasks without being explicitly trained on them by planning sequences of actions.

Generative models that attempt to predict every pixel or detail of future video or high-dimensional continuous data produce blurry, averaged predictions and fail because most details in sensory data are fundamentally unpredictable.

Training JEPA architectures faces the 'collapse' problem: if you simply minimize prediction error in representation space, the system can cheat by predicting constant representations, making the prediction trivial and useless.

Sigreg (Sketch anisotropic Gaussian regularization) prevents JEPA collapse by maximizing information content in encoder outputs through a regularization method based on projecting distributions along multiple axes and ensuring marginal distributions match isotropic Gaussians.

DINO (Self-Supervised Vision Transformers for Image Representation) is the state-of-the-art method for extracting generic features from images without labels, now at version DINO V3, and represents the best system globally for image representation learning.

V-JEPA can acquire a level of physical common sense by learning to predict at the representation level, as demonstrated by the system being 'surprised' (high prediction error) when shown impossible physical scenarios like objects floating or changing shape.

V-JEPA implicitly learns 3D geometric structure (depth) without being explicitly trained on 3D annotations, as evidenced by strong depth estimation performance when a simple supervised head is added to the learned representation.

LLMs fail on basic physical reasoning (e.g., whether to walk to a car wash 100m away) and exhibit no real understanding of physical intuition, instead performing retrieval-based responses fine-tuned on human feedback.

LeCun's 2023 claim that ChatGPT cannot understand basic physics (objects moving with tables) was later 'debunked' when ChatGPT was fine-tuned on this specific question after his podcast appeared, demonstrating fine-tuning on individual examples rather than learning understanding.

AI development should not be feared due to inherent dangers from AI itself, but society should exercise normal caution as with any powerful technology, regulating for safety and beneficial impact without being captured by asymmetric risk narratives.