Researchers uncover algorithm which may solve human intelligence

If we have the algorithm, we also have the key to true artificial intelligence.
Written by Charlie Osborne, Contributing Writer
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The key element which separates today's artificial intelligence (AI) systems and what we consider to be human thought and learning processes could be boiled down to no more than an algorithm.

That's according to a recent paper published in the journal Frontiers in Systems Neuroscience, which suggests that despite the complexity of the human brain, an algorithm may be all it takes for our technological creations to mimic our way of thinking.

As reported by Business Insider, the idea that human thought can be whittled down to an algorithm lies in the "Theory of Connectivity," which proposes that human intelligence is rooted in "a power-of-two-based permutation logic (N = 2i-1)" algorithm, capable of producing perceptions, memories, generalized knowledge and flexible actions, according to the paper.

First proposed in 2015, the theory suggests that how we acquire and process knowledge can be explained by how different neurons interact and align in separate areas of the brain.

It may also be that our brain power is based on "a relatively simple mathematical logic," according to Dr. Joe Tsien, neuroscientist at the Medical College of Georgia at Augusta University and author of the paper.

The logic proposed, N = 2i-1, relates to how groups of similar neurons come together to handle tasks such as recognizing food, shelter, and threats. These cliques then cluster together to form functional connectivity motifs (FCMs), which handle additional ideas and conclusions.

The more complex the task, the larger the group of FCMs.

In order to test the theory and how many cliques are necessary to create an FCM, the researchers analyzed how the algorithm performed in seven different regions of the brain, all of which handled primal, basic responses such as food, shelter, and fear in lab mice and hamsters.

By offering different food combinations and monitoring brain responses, the team was able to document 15 unique combinations of neuron clusters.

Furthermore, these cliques "appear prewired," according to the researchers, as they appeared immediately when the food choices did.

"The fundamental mathematical rule even remained largely intact when the NMDA receptor, a master switch for learning and memory, was disabled after the brain matured," the scientists say.

Such research is an important step in improving our understanding of how the brain, and mind, works -- and therefore how this scientific understanding could hypothetically be implied to future AI projects. It may not give us the key to improving our own intelligence, but if the basic components of how the brain is wired could be applied to artificial intelligence models, then who knows how far future AI will advance.

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