A core debate in AI is whether LLMs, which are text prediction engines, can achieve true intelligence. Critics argue they cannot because they lack a model of the real world. This prevents them from making meaningful, context-aware predictions about future events—a limitation that more data alone may not solve.

Human cognition is a full-body experience, not just a brain function. Current AIs are 'disembodied brains,' fundamentally limited by their lack of physical interaction with the world. Integrating AI into robotics is the necessary next step toward more holistic intelligence.

The leading theory of consciousness, Global Workspace Theory, posits a central "stage" where different siloed information processors converge. Today's AI models generally lack this specific architecture, making them unlikely to be conscious under this prominent scientific framework.

AI intelligence shouldn't be measured with a single metric like IQ. AIs exhibit "jagged intelligence," being superhuman in specific domains (e.g., mastering 200 languages) while simultaneously lacking basic capabilities like long-term planning, making them fundamentally unlike human minds.

Dr. Richard Wallace argues that chatbots' perceived intelligence reflects human predictability, not machine consciousness. Their ability to converse works because most human speech repeats things we've said or heard. If humans were truly original in every utterance, predictive models would fail, showing we are more 'robotic' than we assume.

The debate over AI consciousness isn't just because models mimic human conversation. Researchers are uncertain because the way LLMs process information is structurally similar enough to the human brain that it raises plausible scientific questions about shared properties like subjective experience.

Alistair Frost suggests we treat AI like a stage magician's trick. We are impressed and want to believe it's real intelligence, but we know it's a clever illusion. This mindset helps us use AI critically, recognizing it's pattern-matching at scale, not genuine thought, preventing over-reliance on its outputs.

A critical weakness of current AI models is their inefficient learning process. They require exponentially more experience—sometimes 100,000 times more data than a human encounters in a lifetime—to acquire their skills. This highlights a key difference from human cognition and a major hurdle for developing more advanced, human-like AI.

Current AI progress isn't true, scalable intelligence but a 'brute force' effort. Amjad Masad contends models improve via massive, manual data labeling and contrived RL environments for specific tasks, a method he calls 'functional AGI,' not a fundamental crack in understanding intelligence.