Last week, Google Research held an online workshop on the conceptual understanding of deep learning.
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Theres little doubt that all of this happens with spikes, neurons, and synapses.
This is a huge question, Papadimitriou said.
A lot of studies focus on activities at the level of single neurons.
Until a few decades ago, scientists thought that single neurons corresponded to single thoughts.
Award-winning neuroscientist Gyorgy Buzsaki describes assemblies as the alphabet of the brain.
There is recursion within each area, which means the neurons interact with each other.
And each of these areas has connections to several other areas.
These inter-area connections can be excited or inhibited.
This model provides randomness, plasticity, and inhibition.
Randomness means the neurons in each brain area are randomly connected.
Also, different areas have random connections between them.
Plasticity enables the connections between the neurons and areas to adjust through experience and training.
And inhibition means that at any moment, a limited number of neurons are excited.
Papadimitriou describes this as a very simple mathematical model that is based on the three main forces of life.
The operations are not just pulled out of thin air.
I believe these operations are real, Papadimitriou said.
Much of cognition could fit that, Papadimitriou said in his talk at the Google deep learning conference.
The model receives a sequence of words and produces a syntax tree.
The AI model is still very rudimentary and is missing many important parts of language, Papadimitriou acknowledges.
The researchers are working on plans to fill the linguistic gaps that exist.
Can this be the neural basis of language?
But Papadimitriou believes that the assembly model brings us closer to understanding these functions and answering the remaining questions.
Language parsing is just one way to test the assembly calculus theory.
The hypothesis is that the assembly calculusor something like itfills the bill for access logic, Papadimitriou said.
In other words, it is a useful abstraction of the way our brain does computation.
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