Self-driving cars, automated surveillance systems and personal bots. The scope of 机器学习 is immense and is growing every day. 机器学习 has become a huge part of our life and yet many people don’t understand how machine learning works. Sometimes, even tech-savvy individuals seem to be baffled by the concept of machine learning. While machine learning may seem daunting at the beginning, it is something of value to understand. Apart from the tech sector, scientific researchers have stated that understanding machine learning can improve our motor skills because it influences us to develop a systematic way of thinking.
What is Machine Learning?
To start off, it is important to understand what machine learning is. Tom Mitchell(C.S professor at C.M.U) categorizes machine learning as a computer program that is said to learn from experience “E” with respect to some class of tasks “T” and performance measure “P”, if its performance at tasks in “T”, as measured by “P”, improves with experience “E”. In simple words, the essence of machine learning is when a computer program can improve how it performs a certain task compared to its previous performances. If a computer program is able to consistently improve in a game each time it plays, then that computer program is using machine learning to improve its performance.
This arises a critical question as to how a computer program can learn by itself. For example, most programs behave in the way they are instructed to. For the vast majority of computer programs there are guidelines and restrictions on what a program can do and can’t do. Therefore, at first it seems odd that a program is able to learn from experience and is able to improve after each task. Nevertheless, that is the purpose and specialty of machine learning. Machine learning gives computer programs the ability to learn and improve. The two primary ways in which computer programs manifest machine learning is through supervised learning, and reinforced learning.
Supervised Learning vs Reinforced Learning
Supervised learning is when a machine is trained to do something using existing data. Machines are fed tons of data and the algorithm uses their previously analyzed data in order to learn and make decisions. For example, if a machine is fed tons of data about the real estate industry, soon it will learn to understand the housing market based on factors such as the economy, the stock market, the tax rate, and the population growth.
On the other hand, reinforced learning follows a different approach to machine learning. Reinforced learning feeds the machine random or sporadic data. After going through massive amounts of data, the machine is able to make patterns and judgements from which it can learn. Then these patterns are evaluated and corrected allowing for the machine to get a better understanding of the task at hand. For example, toddlers learn a language by listening to others around them. After hearing random words and phrases continuously, they start to craft a pattern that makes sense to them. This allows them to learn a language fairly quick and interact with others. The same concept is being applied to natural language processing 系统。
Scope of Machine Learning and Some Applications
-Predicting security breaches, finding malware and other anomalies in data
-Personalized recommendations (ex: Netflix, Amazon)
-Improving online search results based on preferences
-Natural language processing
-Smart cars and smart homes (IoT)
–Wearable technology, especially in 医疗保健
Latest Research on Machine Learning
Current developments in machine learning are primarily focused on revamping neural networks. Researchers believe that by streamlining neural networks it would be possible for machines to mimic human learning processes. These new learning frameworks can be extremely powerful tools and they have the ability to dramatically transform any industry. The field of machine learning is constantly achieving new breakthroughs everyday and it has the potential to completely revolutionize our future.
With the rapidly increasing scope of machine learning, businesses and researchers often need to consult machine learning experts for help with writing algorithms and developing effective AI/ML solutions. Will machine learning take over every industry, and will humanoid bots and smart cars become commonplace in households? We’ll have to wait and watch.