Explaining intelligence
itself.

Human intelligence is biological, embodied, energy-efficient (~20 watts), and learns continuously across a lifetime through episodic and semantic memory. Artificial intelligence — today dominated by large language models — runs on GPU clusters, learns once from massive datasets, and predicts the next token in a sequence. Humans are stronger at causal reasoning, social cognition, and long-horizon planning; AI is stronger at recall, pattern matching, and parallel language and code generation.

NeuroAI is the research frontier that uses neuroscience to build better AI and uses AI to better understand the brain. It includes biologically-plausible neural networks, predictive coding, neuromorphic computing, and AI models of cortical circuits. NeuroAI matters because the brain remains the only known example of general intelligence — and it does so on a fraction of the energy of any current AI system.

There is no scientific consensus. Surveyed AI researchers' median estimates have shifted from the 2060s to the 2030s–2040s as scaling and agentic systems have advanced, while critics argue that current architectures lack causal reasoning, persistent world models, and grounded perception required for general intelligence. AGI remains an open scientific problem with no working system in existence.

A BCI records electrical activity from neurons — either invasively, via implanted electrode arrays (Neuralink, Synchron, Blackrock), or non-invasively, via EEG and fNIRS. Machine-learning decoders translate those neural signals into intended actions: cursor movement, speech, robotic control. Modern clinical BCIs have restored communication to people with paralysis and ALS, and remain the most direct technology connecting the brain to digital systems.

The brain economy is the emerging global economic sector built on cognitive capital — neurotechnology, AI, brain health, education, mental health, and the productivity of knowledge workers. The OECD and WEF have begun tracking brain capital as a leading indicator of national competitiveness, treating cognition itself as critical infrastructure for the 21st century.

Cognitive enhancement covers any intervention that measurably improves attention, memory, or executive function — from sleep, aerobic exercise, and meditation (strong evidence) to nootropics, transcranial stimulation, and pharmaceuticals (mixed evidence). The most robust gains in healthy adults come from cardiovascular exercise, deep sleep, and deliberate practice; pharmacological gains tend to be small, narrow, or restricted to people with diagnosed deficits.

The peer-reviewed consensus from NIH, NIA, and Lancet Commission reports converges on a small set of high-leverage habits: regular aerobic and resistance exercise, 7–9 hours of consistent sleep, a Mediterranean-style diet, blood pressure and metabolic control, lifelong learning, social connection, and hearing-loss management. These reduce dementia risk and preserve cognitive reserve more reliably than any current supplement.

Human–AI collaboration is the practice and study of pairing human judgment with AI capability — copilots for writing and code, AI-augmented diagnosis, agentic workflows, and decision support. The best evidence shows the largest gains when humans handle goal-setting, context, and verification while AI handles synthesis, recall, and generation; the worst outcomes occur when humans defer entirely to systems they cannot evaluate.