Comparing Unlike Objects
The comparison between a person and an artificial network cannot be made on an equal basis. The former is a composition of many things that the later is not.
Unlike an artificial network sitting in computer memory on a laptop or server, a human being is an organism, from head to toe, living in the biosphere and interacting with other human beings from birth.
We have latent intelligence in the zygotes that met to form us and solidified as our genetic code during meiosis, but it is not yet trained. It cannot be until the brain grows from its first cells, directed by the genetic expressions of the brain's metabolic, sensory, cognitive, motor control, and immune structure and function. After nine months of growth, a newborn baby's intelligence is not yet exhibited in motion, language, or behavior other than to suck liquid food.
Our intelligence begins to emerge after initial basic behavioral training and does not reach the ability to pass a test indicating academic abilities until the corresponding stages of development in a family structure and components of education are complete. These are all observations well studied and documented by those in the field of developmental psychology.
Artificial Networks are Not Particularly Neural
An artificial network is a distant and distorted conceptual offspring of a now obsolete model of how neurons behave in networks. Even when the perceptron was first conceived, it was known that neurons reacted to activation from electrical pulses transmitted across synapses from other neurons arranged in complex micro-structures, not by performing an activation function to a vector-matrix product. The parameter matrix at the input of artificial neurons are summing attenuated signals, not electro-chemically reacting to pulses that may only be roughly aligned in time.
Since then, imaging and in vetro study of neurons are revealing the complexities of neuro-plasticity (genetically directed morphing of the network topology of neurons), the many varieties of cell types, the groupings of cells to form function geometrically, and the involvement of energy metabolism in the axon.
In the human brain, chemical pathways of dozens of compounds that regulate function and comprise global and regional states and the secretion, transmission, agonist and antagonist reception, interaction, and uptake of those components is under study. There is barely, if at all, an equivalent in the environment of the artificial networks deployed today, although nothing stops us from designing such regulation systems, and some of the most recent work has pushed the envelope in that direction.
Artificial networks are also not brains inside individuals produced by sexual reproduction, therefore potentially exhibiting in neurological capacity the best of two parents, or the worst. We do not yet spawn artificial networks from genetic algorithms, although that has been thought of and it is likely to be researched again.
Adjusting the Basis for Comparison
In short, the basis for comparison renders it meaningless, however, with some adjustment based on the above, another similar comparison can be considered that is meaningful and on a more equal basis.
What is the difference between a college student and an artificial network that has billions of artificial neurons, well configured and attached to five senses and motor control, integrated inside a humanoid robot that has been nurtured and educated like a member of a family and a community for eighteen years since its initial deployment?
We don't know. We can't even simulate such a robotic experience of eighteen years or properly project what might happen with scientific confidence. Many of the AI components of the above are not yet well developed. When they are — and there is no particularly compelling reason to think they cannot — then we will find out together.
Research that May Provide an Answer
From further cognitive science development, real time neuron level imaging, work on the genetic expressions out of which brains grow, artificial neuron designs will likely progress beyond perceptrons and the more temporally aware LSTM, B-LSTM, and GRU varieties and the topologies of neuron arrangements may break from their current Cartesian structural limitations.
The neurons in a brain are not arranged in orthogonal rows and columns. They form clusters that exhibit closed loop feedback at low structural levels. This can be simulated by a B-LSTM type artificial network cell, but any electrical engineer schooled in digital circuit design understands that simulation and realization are miles apart in efficiency. A signal processor can run thousands of times faster than its simulation.
From development of computer vision, hearing, tactile-motor coordination, olfactory sensing, materials science support, robotic packaging, and miniature power sources far beyond what lithium batteries can produce may come humanoid robots that can learn while interacting. At that time it would probably be easy to find a family that cannot have children that would adopt an artificial child.
Progress in these areas is necessary for such a comparison to be made on a scientific basis and for confidence in the comparison results to be published and pass peer review by serious researchers not interested in media hype, making the right career moves, or hiking their company's stock prices.