Will Machine Learning Outperform Humans in Learning from Experiences?
Machine learning (ML) algorithms have been developed to learn from their experiences, much like how we humans have evolved to learn from our past. However, there is a notable difference in how these two entities process and apply their experiences in decision-making. This article explores whether machine learning has the potential to outperform human decision-making when it comes to learning from experiences, experiences being a critical factor in both fields.
How Machine Learning Learns from Experiences
In the realm of machine learning, a specific subdomain known as reinforcement learning plays a crucial role. This domain focuses on making algorithms smarter by learning from their own mistakes. The algorithm receives rewards when it takes the correct decision and penalties when it makes an incorrect one. Over many iterations, the algorithm learns to maximize rewards and achieve its objectives.
The architecture of machine learning algorithms is designed to analyze situations, find feasible solutions, and match them with past experiences. If a current situation is similar to a previous one, the algorithm will apply its learned experience to make a decision. If the situation is entirely new, the algorithm will learn a new encounter and adjust its experience accordingly.
Contrary to human decision-making, machine learning is devoid of emotional interference. This means that machine learning algorithms do not attach sentiments to decisions, and their learning process is purely rational and based on experience.
The Role of Human Emotions in Decision-Making
Humans, on the other hand, often ignore specific details in their experiences and remember only the gist or conclusions. This tendency can stem from the fact that our decision-making process is heavily influenced by emotional thinking. We tend to ignore our experiences when they are associated with negative emotions, leading to repeated mistakes despite past lessons.
A prime example of this is the movie 'Slumdog Millionaire.' In the film, Jamal Malik intuitively answers questions that align with his life experiences, not just the obvious details. Despite facing life-threatening situations, Jamal remains observant and continues to learn from his surroundings. This is a stark contrast to how most people would become blind due to fear and desperation.
Why Humans Often Overlook Subtle Cues
Humans frequently overlook a lot of details in the experiences they encounter in daily life. We tend to remember the general idea or conclusion rather than the specific facts and details. These details often enter our subconscious mind and are not easily accessible for decision-making unless we focus intently on the decision, which is rarely the case.
Similar to how people who read fast focus on significant words in a sentence and skip small words, humans often ignore subtle cues in their experiences. This is because we are more concerned with surviving the immediate moment rather than learning from every detail of our environment.
However, in critical situations, certain individuals like Jamal Malik, are able to remember and apply even the smallest details. This highlights the intrinsic learning mechanism that life provides us with, but we often fail to utilize it fully.
Conclusion: Can Machines Outwit Humans in Learning from Experiences?
In conclusion, while humans have the capability to learn from experiences, they often ignore these lessons due to emotional interference. Machine learning, on the other hand, is designed to learn from experiences and apply that learning without emotional bias. If humans continue to ignore their experiences without rational thought, machines have the potential to outperform them in decision-making based on experience.
However, it is important to recognize that while machines excel in learning from experiences, they do not possess the emotional intelligence and empathy that humans do. Therefore, a balanced approach that incorporates both rational and emotional thinking is the key to effective decision-making in complex situations.