Can neuroscience open doors for AI?

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Artificial Intelligence (AI) always had the competence to perform almost every chore that a human could perform. Right from creating documents to winning games like chess, AI has successfully done all. AI-based machines can write exactly like humans, analyze data, and perform language translations.

But still the developments in AI are moving at a snail’s pace and if I am guessing it right, the reason is the ability to judge a situation, take decisions based on its judgment, and then react. It is easy said than done to design algorithms that will help an AI-based machine to do this.

These future developments and the ones that AI has achieved link to neuroscience, which is quite similar to AI. Neuroscience is a rich reservoir of ideas for new algorithms isolated from data science and neural networks. It can also prove to be the coin tester for an algorithm. Let’s say, an algorithm is already used in the human brain then it becomes easy to use that algorithm in AI implementation.

But before we take a deeper look into what neuroscience and AI can teach each other, let’s first understand what neuroscience is. Shall we?

What is neuroscience?

Neuroscience in principle is the scientific research and exploration of the functioning and structure of the nervous system. It merges physiology, molecular biology, mathematical modeling, psychology, etc to understand the working of neurons and the neural networks of our brain.  Over time, the scope of the study of neuroscience has expanded to incorporate various approaches at different levels to examine the nervous system.

It is a known fact that decision making, judging and analyzing qualities of a human are linked to one focal point – The Brain. Studying the structure and working of the brain has not been easy but with passing time, we could study the structure and working of the brain in a better way. However, in the 20^th century, the field of neuroscience has expanded at a rapid pace.

Boundaries of AI

Machine learning algorithms of AI are created with the limited mathematical framework. Through billions of scenarios, the artificial neural networks (ANN) learn to strengthen their connections until they can accomplish a task with the highest accuracy. But, if a new task is added in an algorithm, the existing task’s connections are erased most of the times because that algorithm is designed for the task in the bag.

The competence to develop knowledge and create synopsis based on interaction with the world is a tough nut to crack and AI enthusiasts are still working on this. To solve the riddles of daily life, humans use their intuition and common sense which are very hard to formulate in a machine. Much more challenging is programming the imagination with which humans have marveled.

How neuroscience can contribute to AI

Even if an algorithm mimicking a brain function isn’t the correct approach for an AI system; it does give important insight and help discover something thus, neuroscience can help in validating an AI system. It also is a source for diverse and complicated new algorithms and systems when an artificial brain is designed.

AI implementation is dominated by logical and mathematical calculation methods but neuroscience can help in identifying the biological computing classes; this is a quite critical part for intellectual functions.

Transfer learning is another major roadblock which can be overcome with neuroscience. In order to handle unusual situations, AI systems need to refer to existing information to take a decision. This information can be obtained from the existing findings of neuroscience to a possible extent.

Another side of the coin is that neuroscience can also progress from AI. Filtering intelligence to into algorithms and cross-checking it against a human brain can throw light on some baffling mystery of our brain.

Take Away

With an investment, AI can progress with neuroscience and learn from it. Enthusiasts can explore neuroscience to develop AI and thrust ahead new discoveries. It is also possible for neuroscience to unravel some of the prime mysteries of a human brain.

But putting these learning into a machine designed on AI is a challenge. Only the future will tell that to what level the intelligence of a machine can reach.