How are our addictions and Artificial Intelligence, related?
The relationships of Dopamine hits with reward system in the process of Reinforcement Learning.
Okay, before we deep dive into the relationships of human brain functioning, memory formation in humans, machine learning or reinforcement learning, let us first understand how our brain functions while forming a certain habit, addiction or performing a favorite task.
First of all, addiction has been described as a global humanitarian crisis, although we are in no way going into the facts about how it is destroying lives and proving to be one of the major cause of fatalities across the globe. We will primarily focus on finding connections between the working understanding of our brain developing an addiction and how the similar architecture is being followed in one of 21st century’s cutting edge state of art technology — Reinforcement Learning!
Let us understand what happens neurologically when we actually become addicted to something.
Scientists first began to seriously study addictive behaviors back in the 1930s. To understand the ways and factors responsible for a certain addiction, we need to learn about “rewards”. Deep in the brain, sits the reward pathway — a neuronal pathway that connects clusters of neurons from different areas of the brain in a highly organized way — also known as the “mesolimbic pathway”. The primary function of this reward pathway is to reinforce sets of behaviors.
If we think back in the evolutionary times, it was helpful to have a mechanism that rewards us for behaviors useful for our survival. Things like finding food in times of famine, lighting fire, or escaping from a source of danger — its even more helpful to have a way to remember how we managed to stay alive so that we can repeat it the next time we are in a similar situation. The reward pathway achieves all this primarily through the use of a particular neurotransmitter — “DOPAMINE”.
Following an appropriate action, a small burst of dopamine is released by the reward pathway — and this causes us to feel a small jolt of satisfaction, which acts as a reward for keeping us alive thus encouraging us to repeat the same set of behavior in future. Dopamine signals also act on areas of the brain which involve in memory and movement which help us build up memory of what is good for survival and makes it easier to do it again.
Rewarding experiences such as winning in a game, sport, being complimented at work, sends signals to release bursts of dopamine. Unfortunately if we keep on taking or engaging in these behaviors and flooding our reward system, overtime the brain attempts to adapt to these and chronically elevates the levels of dopamine.
Now, how does this relate with the concept of Reinforcement Learning in ML?
To reach to the understanding, firstly, let us understand what is Reinforcement Learning in AI means —
Reinforcement learning is one of the oldest and most powerful ideas linking neuroscience and AI. In the late 1980s, computer science researchers were trying to develop algorithms that could learn how to perform complex behaviors on their own, using only rewards and punishments as a teaching signal. These rewards would serve to reinforce whatever behaviors led to their acquisition. To solve a given problem, it’s necessary to understand how current actions result in future rewards. For example, a student might learn by reinforcement that studying for an exam leads to better scores on tests. In order to predict the total future reward that will result from an action, it’s often necessary to reason many steps into the future.
Reinforcement learning is the training of machine learning models to make a sequence of decisions.
The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward.
Although the designer sets the reward policy–that is, the rules of the game–he gives the model no hints or suggestions for how to solve the game. It’s up to the model to figure out how to perform the task to maximize the reward, starting from totally random trials and finishing with sophisticated tactics and superhuman skills. By leveraging the power of search and many trials, reinforcement learning is currently the most effective way to hint machine’s creativity.
So, in short, reinforcement learning is the type of learning methodology where we give rewards of feedback to the algorithm to learn from and improve future results.
This type of learning is on the many research fields on a global scale, as it is a big help to technologies like AI.
Advantages of Reinforcement Learning
- It can solve higher-order and complex problems. Also, the solutions obtained will be very accurate.
- The reason for its perfection is that it is very similar to the human learning technique.
- This model will undergo a rigorous training process that can take time. This can help to correct any errors.
- Due to it’s learning ability, it can be used with neural networks. This can be termed as deep reinforcement learning.
- Since the model learns constantly, a mistake made earlier would be unlikely to occur in the future.
- Various problem-solving models are possible to build using reinforcement learning.
- When it comes to creating simulators, object detection in automatic cars, robots, etc., reinforcement learning plays a great role in the models.
- The best part is that even when there is no training data, it will learn through the experience it has from processing the training data.
- For various problems, which might seem complex to us, it provides the perfect models to tackle them.
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