AI and Neuroscience: A Remarkable Relationship
Neuroscience and artificial intelligence are more linked than you might expect.
Recent progress in artificial intelligence has been exceptional. Current artificial systems and machines can outperform humans at tasks like solving Rubik’s cubes and can even produce handwriting and speech identical to ours!
These advances in AI are a result of several factors, two of them being the improvement of computer processing power and new methods of application of AI into several fields.
However, one overlooked application of AI is in the study of experimental and theoretical neuroscience. Neuroscience and psychology have profoundly contributed to the history of AI. The human brain has been a huge source of inspiration for building human-like AI. There are two ways, though, that neuroscience inspires scientists and engineers to design AI systems.
The first one is imitating human intelligence and the second one is constructing neural networks that help AI systems emulate the brain. People like Donald Hebb and Marvin Minsky were roused to understand how the human brain functions, so in the late 20th century, they made breakthroughs on neural networks.
What is Neuroscience?
Before going into detail about how neuroscience and AI are closely connected, it is important to understand what neuroscience is.
Neuroscience is the study of the “structure and function of the human brain and nervous system.” Neuroscientists use biology, anatomy, physiology, human behavior, and cognition to map out the brain. The brain is the most complex part of our body, made up of nerve cells, soft tissue, gray and white matter, and blood vessels. Furthermore, it allows us to learn, teach, react, imagine, and perceive. Important concepts like biology and psychology are used in conjunction to address difficult questions, including the cause of neurological disorders like Parkinson’s disease and Alzheimer’s disease.
If neuroscience wasn’t an important field of study, we would be living in a different world. It’s because of neuroscience that scientists can understand how the brain works, thus enabling us to diagnose and treat various brain diseases.
Reinforcement Learning and AI
It’s a well-known fact that humans and animals learn through rewards. We are motivated by various internal and external factors to learn more.
For example, many of our day-to-day choices, tasks, and behaviors are driven by how the end result will make us feel, regardless of the outcome being positive or negative. Scientists have been fixated to learn about how organisms learn from experience and reward.
One famous psychological experiment called ‘Pavlov’s Dog’ explains the concept of reinforcement learning. In the experiment, Pavlov trained dogs to expect food after a buzzer went off. Soon enough, the dogs would start to drool as the buzzer went off, knowing they would be fed shortly after. This was a clear sign that they understood they would be fed when they heard the buzzer. Recently, computer engineers are trying to replicate reinforcement learning in machines. Many computer scientists utilize AI programs that adjust their actions to get a reward. Reinforcement learning in AI could also help the general public by carrying out simple tasks in exchange for money.
Neural Networks and the Human Brain
Neurological diseases are popping up worldwide, making it difficult to have a better understanding of computation in the brain. Luckily, the ability to create artificial neural networks to solve problems may unlock priceless secrets about malfunctions in the human brain.
A neural network is a computer program that can simulate and replicate the tasks performed by the neurons in the human brain. These tasks are our thoughts and behaviors that are generated by ‘computations’ that take place in our brains. To cure neurological diseases, we have to understand how these computations can go wrong.
Artificial neural networks have proved useful for studying the brain. If such a system can produce a pattern of neural activity that mirrors the pattern that is recorded in the brain, scientists can examine how the system generates its output and then make inferences about how the brain does the same thing. This method can be applied to any type of cognitive task of interest, including brain computation.
A Look Into the Future
As AI is used more and more in medical discovery and advancement, in the future, treatments for incurable ailments that differ from person to person may be easier to design and manufacture. Customized healthcare has become more accessible for patients through wearable technology, and AI can accelerate its growth tenfold.
Rather than handing over immediate answers of the brain’s mysteries to neuroscientists, artificial intelligence will probably give partial answers that neuroscientists can confirm through experiments. Nevertheless, AI has already made a profound impact on neuroscience. According to DeepMind,
With so much at stake, the need for the field of neuroscience and AI to come together is now more urgent than ever before.
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