Today, devices are trained to replicate human intelligence. While the model of machines automating various tasks have gained acceptance and is evolving, it comes up with a number of facts and notions. Similar is the case with Machine Learning. Here we are with some of the verities about ML that I think everyone should be aware of.
1. Machine Learning is Different from AI or Data Mining
Artificial Intelligence (AI) is an umbrella term, which is given to computer systems that perform tasks, requiring human efforts. Very often, the terms AI, ML, and data mining are used interchangeably, but all of them are completely different. Let’s understand why.
AI is a machine programmed to think like a human. If we talk about human the brain, it could be described as one of the finest computing machines in existence. At any given time, it can capture tons of data. Using it's five senses, it saves, recalls, and processes what it's captured whenever needed to make informed decisions. It learns by recognizing patterns and this is one of the most effective examples of cognitive learning. But, as they say, even geniuses have a limit, and so does the human brain.
So, you can think of machine learning as an automated and continuous version of data mining. And these data sets can be of any size. ML can analyze dynamically changing big data sets, detect and extrapolate the patterns, derive information, and apply it to new solutions and actions.
2. Machine Learning means Data and Algorithms Together
Machine Learning is enabling the computers to learn, without being explicitly programmed for the same. The entire idea is based on cognitive learning, wherein the former actions are analyzed to process the recent input. This means, for learning to give an output, data is the key. The more data, the better the experience and accurate the result will be.
There’s a lot of excitement about advances in machine learning algorithms, and particularly about deep learning. But data is the key ingredient that makes machine learning possible. You can have machine learning without sophisticated algorithms, but not good data.
3. Machine Learning is Incomplete without Humans
Undoubtedly, machine learning is adding more power to how humans perform their day to day tasks. However, that should not be mistaken for the possibility that machines will eventually do away with the need for human intelligence.
Even though machines will learn and will be smart enough, they still require human operators to build ML models, provide context, set parameters of operation, and do the needful to augment the algorithm.
ML is the competency of machines to recognize the complex patterns that humans can’t. Nevertheless, machines are clueless about the fact as to why those patterns exist. It’s the human who decides how the machines should work.
4. Machine Learning is Vulnerable to Human Error
If the machine learning fails, it’s because of an algorithm failure. No matter how relevant or qualitative the data is, a human error in generating patterns and designing algorithms can lead to unbiased error, which ultimately ruins the system. Therefore, the best practice is to approach ML with a discipline and update it regularly for improved output and error detection.
5. Machine Learning Follows Garbage In Garbage Out
What you give is what you get is the universal law. While it defines that relevant data and algorithm is the key to ML performance, it also defines its limitation. ML is only capable of responding to the data and patterns that are predefined. For supervised machine learning, it is important to categorize and train that data well.
Certainly, Machine Learning brings in a number of advantages for every industry. We have seen how chatbots have been of huge importance in HealthIT, eCommerce, Sales, and many other segments. For those reasons, AI Application Development is having a huge demand in the business world.
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