Machine learning has been a hot topic in the tech world for quite some time now. We are at a time when machine learning is really progressing at a fast pace, and it has already started affecting our lives. We are already seeing the influence of large language models (LLMs) like Chat GPT, and the influence of machine learning is only going to increase in the next few years. In this blog post, we’ll take a non-technical look at machine learning and explore an interesting perspective on its usefulness.
So first of all, what is machine learning? Arthur Samuel in 1959 defined machine learning as the field of study that gives computers the ability to learn without being explicitly programmed. Tom M. Mitchell in 1997 gave a more formal definition – A computer program is said to learn from experience E with respect to some class of tasks T and a performance measure P if its performance in tasks T, as measured by P, improves with experience E. In simpler terms, machine learning is a technique that enables machines to learn from data and improve their performance over time.
To understand the usefulness of machine learning, we need to take into account the width of its applications, as well as its depth, i.e., how complex problems it can solve. The width implies how wide the range of applications of machine learning can be in the real world. Machine learning can be utilized for literally a lot of things around us. We can use machine learning for anything as long as we can convert it into numbers and program it to find patterns. Literally, it could be anything – any input or output from the universe.
For instance, a self-driving car is using machine learning because images seen through its camera can be converted into numbers. Voice assistants can use machine learning because audio can be converted into numbers. This shows how machine learning can be utilized for literally a lot of things around us.
One interesting perspective is that the usefulness of machine learning is not just limited to the width of its applications, but also to its depth, i.e., how complex problems it can solve. Machine learning can be used to solve problems that are too complex for humans to solve on their own. For example, machine learning can be used in medical research to predict the risk of developing diseases. By analyzing large amounts of data, machine learning algorithms can identify patterns and predict outcomes that are beyond the capacity of human doctors.
Machine learning can also be used to solve complex problems in business, such as predicting customer behavior, detecting fraud, and optimizing supply chains. Machine learning can analyze vast amounts of data from various sources and provide insights that can help businesses make better decisions.
However, aside from many benefits, we face some dilemmas regarding it too. As the use of machine learning continues to expand, it raises important ethical questions around issues such as privacy, bias, and accountability. For example, the use of facial recognition technology raises concerns about privacy and the potential for misuse. Machine learning algorithms are only as good as the data they are trained on, and if the data is biased, the algorithm will also be biased.
In conclusion, machine learning is a powerful tool that has already started to affect our lives, and its influence is only going to increase in the next few years. Its width of applications and depth of problem-solving capabilities make it an indispensable tool in many industries. As we continue to unlock its potential, we can expect machine learning to make a significant impact on our world.