Many computer scientists take it on faith that one day machines will become conscious. Led by futurist Ray Kurzweil, proponents of the so-called strong-AI school believe that a sufficient number of digitally simulated neurons, running at a high enough speed, can awaken into awareness. Once computing speed reaches 1016 operations per second — roughly by 2020 — the trick will be simply to come up with an algorithm for the mind. When we find it, machines will become self-aware, with unpredictable consequences. This event is known as the singularity.

These techno-utopians should pay closer attention to developments in neuroscience. Sure, artificial intelligence techniques like neural networks have led to better spam filters. But research suggests that the current approach to AI won't result in a conscious machine on anything like Kurzweil's timeline. The latest evidence shows that, when it comes to consciousness, the brain simply doesn't work the way computer scientists think it does. Almost nothing is known about how the brain produces awareness, and current models of brain function don't accord with the little that is known.

Singulatarians would respond by predicting that exponentially growing scientific progress will fill the gap. This notion sweeps under the rug a messy philosophical problem: An algorithm is only a set of instructions, and even the most sophisticated machine executing the most elaborate instructions is still an unconscious automaton. Philosophy aside, a constellation of recent scientific findings indicates that no matter how fast CPUs become in future decades, they'll be no more aware than a toaster. Building a conscious machine will likely require paradigm shifts in brain science — conceptual leaps that, by definition, won't come on a schedule. Here, then, are five reasons why the singularity is not near.

The mind is synchronized, but no one knows how. New York University neurologist E. Roy John has established that the hallmark of consciousness is a regular electrical oscillation, or gamma wave, readily detected by electrodes attached to the scalp. More recently, Wolf Singer and his colleagues at the Max Planck Institute for Brain Research in Frankfurt, Germany, confirmed that brain cells flicker in time with the gamma wave. This flickering takes place among widely dispersed neurons throughout the brain with no apparent spatial pattern. What keeps these ever-shifting, widely distributed groups of cells in sync? Neurochemical reactions take place too slowly to explain the phenomenon. This mystery alone seems to demand a wholesale rethinking of AI's underpinnings.

Current brain maps are of little use in explaining awareness. For more than a century, the brain cell, or neuron, has been seen as a tiny switching station with multiple signals coming in through many input wires, known as dendrites, but only one signal going out through a single output wire, or axon. AI is based on this circuitry model. When it comes to consciousness, though, the model has its wires crossed. Singer has discovered that gamma waves — the indicators of consciousness — issue from the neuron's supposed inputs, not its output. Confusing matters further, researchers, including Takaichi Fukuda and Toshio Kosaka of Japan's Kyushu University, have revealed that many inputs interconnect, forming an altogether different set of networks. In other words, the vast strides made by neuroscientists in their attempt to map the brain may reveal little about consciousness.

The brain is faster than singularity theorists think. AI assumes that the neuron is analogous to a single computer bit. But it turns out that each neuron is supported by a supercomputer's worth of additional circuitry. MIT bioengineer Andreas Mershin and UCLA psychologist Nancy Woolf have independently confirmed the importance of microtubules, the scaffolding that undergirds each neuron, in animal memory and learning. At the University of Alberta, physicist Jack Tuszynski has developed computational models suggesting that these supposedly dumb structures could be smarter than previously recognized. Stuart Hameroff at the University of Arizona argues that trillions of computations per second take place in the microtubules of each neuron. If he's right, the brain's speed is 1028 operations per second — a trillion times faster than is generally thought — which pushes the vaunted singularity back by decades.

The on/off switch isn't where it's supposed to be. As it happens, doctors have a handy way to flick the switch of consciousness: anesthesia. When you're under, awareness is disabled, but everything else in the brain operates normally. So how does anesthesia work? Hameroff has come up with a simple model in which anesthetic drugs interact almost exclusively with microtubules; the rest of the neuron plays only a marginal role. This model is the closest anyone has come to a unified theory of anesthesia — yet it flatly contradicts the notion that consciousness arises from firing neurons.

Understanding consciousness may require new physics. In his 1989 book, The Emperor's New Mind, Oxford physicist Roger Penrose proposed that the classical physics ruling neurobiology can't explain consciousness. The mind, he declared, relies on the baffling mechanics of quantum physics. Although his point remains controversial, evidence in its favor is accumulating. Most recently, physicist Efstratios Manousakis at Florida State University showed that certain confounding quirks of visual perception are most easily explained by quantum mechanics. If consciousness is indeed a quantum phenomenon, then AI becomes an entirely new game. The singularity will have to wait for engineers to catch up.