10 Common Misconceptions About Machine Learning
Machine learning has been here for a long time. Lately, it has been grabbing a lot of attention, primarily because of the way social networks like Facebook are interacting with users. The news feed on Facebook has become more intelligent and is catering more to the wishes of users. Google too is mining data, to make ad placements more meaningful and relevant. They are using Machine Learning algorithms to change the face of digital interactions.
That said, you can still find people reading stuff like ‘What is Machine Learning?’, which shows that we have a huge knowledge gap to fill. Just like with everything, half-baked knowledge of Machine learning has led to certain misconceptions. Let’s go through some of them.
That it merely summarizes data: Absolutely not. It does summarize data, but based on that, it predicts the future too. For example, it can summarize the data on your movie-watching history, to predict the movie you will watch next.
That it merely correlates between a pair of events: What’s a correlation? Let’s understand this by an example. If we say that an increase in the Google searches for X product reflects its increased popularity, then it’s a correlation. Machine learning certainly does more than correlation. It provides a more enriched and actionable form of knowledge, like predicting about skin cancer by having the information of an irregular mole on the body. You can opt for a machine learning course in India, to understand it in detail.
That it cannot determine complex relationships among parameters: Machine learning is actually based on hit and trial, and analyzing different consequences that come out of it. It’s like how an e-commerce site keeps changing its layout and zero-in on the one which results in maximum customer engagement. Through constant exposure to different data sets, machine learning algorithms keep refining themselves, to understand complex relationships.
That it can’t predict events that have never happened before: It’s not true. Machine learning can predict the rarest of rare events. For example, if it’s a known fact that an event A leads to an event B, and the event B leads to an event C, the machine learning can also predict the possibility of the event A leading to the event C.
That it doesn’t cater to preexisting knowledge: A better future is built by learning from history. Same applies to Machine learning. It’s a myth that machine learning algorithms start with a blank slate. They, in fact, refine the established knowledge to come up with productive outcomes.
That simplicity of its model improves its accuracy: People generally prefer simple answers for they are easier to understand and remember. But when it comes to predicting outcomes, the simple hypothesis may not give accurate results. This is why most of the Machine learning models are rather complex in their structure.
That outcomes via Machine learning are flawless: The patterns established by Machine learning algorithms are always a work in progress. Algorithms can induce a different outcome, with of a slight change in data. So, don’t take the outcomes at their face value, as they might get bettered.
That Machine learning is at par with superhuman intelligence: With humanoids on the verge of imitating humans to perfection, there is an impression that Machine Learning will soon leave humans behind. That’s not happening anytime soon, as machine learning algorithms still don’t have the natural ability to reason, and doesn’t have a thing called common sense. Such algorithms are still dependent on data sets that are provided by humans only. The potential of AI has been extensively tapped, but the exact answer to ‘What is Machine Learning?’ is yet to be discovered.
That Machine learning Models are incomprehensible to humans: Can you trust something which you can’t understand? It’s certainly difficult. If ML algorithms are like a black box, it’s difficult to trust the results which they provide. There are a few Models like deep neural networks which are greatly successful but incomprehensible at the same time. But others are quite unambiguous. Take, for example, the Model for diagnosing Skin cancer on the basis of irregular moles.
That too much of input data lead to incorrect patterns: Suppose a security agency goes through thousands of phone records. There is a chance that an innocent may get flagged due to an accidental match with unscrupulous activities. This may happen because there is too much data to go through. However, in the case of Machine learning, chances of getting such false results are minimum, contrary to popular belief. In fact, mining too many entities having the same attributes actually decreases this risk.
In conclusion, we have tried to see both sides of the same coin. Some of us undermine the value of machine learning, and some of us exaggerate our dependency on it. We need to find a balanced approach and learn more about AI by taking up the Machine Learning course in India. We hope this write-up will help you take that balanced approach.