This presentation outlines the speaker's vision for a new "science of intelligence," a multidisciplinary field combining physics, math, neuroscience, psychology, and computer science to understand both biological and artificial intelligence. The core argument is that AI engineering has outstripped our scientific understanding, leading to powerful but opaque and inefficient systems. The talk addresses five critical areas where AI needs improvement: data efficiency, energy efficiency, going beyond evolution, explainability, and mind-machine melding.
**Data Efficiency:** AI models require vastly more data than humans to learn effectively. The speaker contrasts the trillion words used to train language models with the relatively small amount of information encoded in human DNA (700 megabytes) and learning experiences (100 million words by adulthood). This points to the inherent efficiency of human learning. The solution lies in moving beyond brute-force training and creating "non-redundant" datasets. The speaker's research demonstrates that by strategically selecting data points to maximize new information, scaling laws governing error reduction can be significantly improved, requiring far less data. Moreover, machine learning needs to evolve into a "science of machine teaching," drawing inspiration from how humans, particularly children, are taught, emphasizing algorithmic understanding rather than mere exposure to vast datasets.
**Energy Efficiency:** The human brain consumes a mere 20 watts, compared to the millions of watts required to train large AI models. This discrepancy stems from digital computation's reliance on fast, reliable bit flips, which are thermodynamically expensive. Biology, conversely, uses slow, unreliable intermediate steps and matches computation to the native physics of the universe. For example, neurons directly add voltage inputs based on Maxwell's laws, whereas computers use complex transistor circuits for addition. Achieving energy efficiency in AI necessitates rethinking the technology stack, from the underlying electronics to the algorithms. The presentation mentions research exploring fundamental limits on computational speed and accuracy given an energy budget, and the discovery of chemical computers (similar to G-protein coupled receptors in neurons) that approach these limits. Furthermore, neuroscience reveals predictive energy allocation in the brain, where ATP levels rise in anticipation of neural activity, suggesting an optimized energy delivery system.
**Going Beyond Evolution:** The limitations of evolution can be bypassed by implementing neural algorithms, discovered by evolution, on quantum hardware. Replacing neurons with atoms and synapses with photons allows for the construction of quantum associative memories and quantum optimizers. These quantum systems, leveraging the unique properties of quantum mechanics, offer enhanced memory capacity, robustness, recall, and novel optimization approaches, forming the basis of "quantum neuromorphic computing."
**Explainability:** While AI can create highly accurate models of the brain, these models are often complex and difficult to understand. The goal is to move beyond mere replication and achieve a conceptual understanding of brain function. The speaker illustrates this with an example of explainable AI applied to a detailed model of the retina. The model accurately reproduces various experimental results, including the detection of violations of Newton's first law. By developing methods to identify the essential sub-circuits responsible for specific neural responses, researchers can understand how the model, and by extension the biological retina, processes information. This approach can accelerate neuroscience discovery by enabling the construction of digital twins of brain regions followed by explainable AI-driven analysis.
**Melding Minds and Machines:** Digital twins of the brain can facilitate bidirectional communication between brains and machines. Control theory can be used to learn neural activity patterns that control the digital twin, and those patterns can then be used to stimulate the real brain. In mice, AI was used to decode visual percepts from brain activity and, conversely, to write specific neural activity patterns to induce hallucinations. By controlling a mere 20 neurons, researchers could reliably control the mouse's perception. The potential of bidirectional brain-machine communication is immense for understanding, treating, and augmenting the brain.
The speaker concludes by emphasizing that this unified science of intelligence must be pursued openly and with a long-term vision. Academia, free from short-term financial pressures and corporate censorship, is ideally suited for this endeavor. To that end, a new center for the science of intelligence is being established at Stanford, aiming to drive fundamental advances and share knowledge globally. The speaker positions this effort as a shift from exploring the external universe to delving into the inner workings of intelligence, both biological and artificial.