11 Insane Machine Learning Myths Debunked for You!

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11 Insane Machine Learning Myths Debunked for You!

The world is becoming smart, smarter than ever before. There are homes that know how to turn on the lights by judging their intensity and there are cars that can drive themselves. Isn’t it something like living in a sci-fi world? Everything that was imagined is turning into reality.

Among all that we hear about the upcoming technology, machine learning (ML) is a common term being associated with almost all of them. The term has been more misinterpreted than understood and there has been a considerable measure of hype buzzing around it.

With more gadgets and technologies being launched every day, customers are keen to know what is it that is making them smarter? They are curious to discern the tech running behind the smartness and understand how it can benefit them in their personal as well as business ventures.

This inquisitiveness towards the “working” has lured people to read and question about the same, however, the responses have not been palatable. For instance, you may often see mobile companies using the terms artificial intelligence and machine learning interchangeably for their products, now this is how a misperception is shaped. The customers do not understand the difference between the two and start treating them as synonymous with each other.

The aim here is to make you understand the similarities and differences between “machine learning” and the terms it is confused with. this write-up shall provide you with a clear insight so that you can differentiate between the hype and the reality.

It is important because machine learning forms an integral part of almost all data-driven work. In the event that you intend to consolidate it into your business, you should discern what it may or may not be able to do for you. Having a clear perspective will ensure that you develop a strategy that fits into your business module and helps you accomplish the set objectives.

Removing the Misconception

You know how they say in school that if your basics are clear, you will understand each and every concept and if not then surely there will be trouble. This concept will hold true in your entire life and therefore if you recognize the simple notion of machine learning you’ll never be influenced by the related hysterias.  The figure below describes machine learning in its most naive form.

There is a lot of reality and there is a lot of hype pertaining to machine learning. But with the above-illustrated diagram, it should be clear that machine learning is, training a machine by giving it a large amount of data and then letting it perform based on that learning.

Exposing the Machine Learning Myths

Machine learning is currently going through a phase of inflated expectations. Along with ongoing machine learning developments around the globe , there are still a lot of organizations looking forward to conceptualizing and running ML projects without even exploring the power of basic analytics. How do you expect them to meet their goals when they do not know what ML can or cannot do? In such a scenario it becomes imperative to know the myths and truths related to the subject.

#1 Machine Learning and Artificial Intelligence Are Same

One of the most common  misconceptions is between artificial intelligence and machine learning. Both the terms are not only different in words but are two different fields belonging to a bigger pool of data science. In order to understand the difference consider this example – You wish that the camera of your phone should recognize a dog.

Now in order to do that you provide it with a huge amount of data that contains pictures of all the types of dogs present in the world. With the help of these images, the camera is able to create a pattern that resembles a dog. Now whenever you point the camera toward the dog, it matches the pattern and that is how you get a positive hit. On the other hand, pointing the camera toward a cat doesn’t identify it as anything.

This is a machine-learning process where the machine is trained to accomplish a particular task. Artificial Intelligence on the other hand is a broader concept, where the machines are trained in such a way that they can make their own decisions just like the human brain.

If you put a cat in front of a camera that works on an AI technology, it will use it as another input and further reuse it to train itself.  This training would help the AI-enabled phone to tell that isn’t a dog but it may be something else that can be explored.

#2 Hiring the Best ML Talent Is Sufficient to Resolve Business Issues

Business firms are spending a lot of money in gathering the best machine learning talent which can analyze their data and offer useful insights. What they forget in the process is that machine learning is just one part of an effective strategy, the basics are to have the right type and amount of data.

If there is no one who can fetch the data, what will the professionals work upon? Therefore, businesses do not need a staff good in one field but someone who knows how to work from the scratch. There are data science firms all over the globe that can help businesses develop a correct approach and provide the useful insights they have been looking for.

#3 ML Implementation Requires Humongous Infrastructure

Machine learning sounds scientific and complicated that many presume it is not meant for their business. After all, what will an ordinary business do with advanced technology? Not every SME hires AI experts, isn’t it? That’s where we are wrong

Years before it was said that if you wish to carry our ML operations on your premises, you’ll need to invest a large amount in infrastructure. The scenarios have changed now. Since data science and data analytics has become such an integral part of the business world, there are professionals who are teaming up to form organizations that work purely on data and offer all the insights you want.

Artificial intelligence and machine learning are used in countless ways, and not all of them need to be built from scratch. A simple way to explain this is-

Consider your smartphone. You haven’t made it, but you know how to use it. You use it for professional and personal work, right? ML models are the same. Experts build the models, and you use them in your business. They will help customize the software to meet the enterprise’s requirements. 

Isn’t that easier to just let the professionals handle all the work? This not only allows a business owner to explore the problem that needs to be solved but also saves time that he/she would have invested in conducting the ML operations on their grounds.

Why do you think the market is full of AI and ML offshore companies? They do the backend work so that you can directly implement the software in your business systems. At the most, you’ll have to train your employees, and ML consulting companies help with that too.

Know more about our AI and ML Solutions

#4 ML Is Only for Large Enterprises 

Machine learning is considered out of bounds by many SMEs and startups. They might think it’s majorly meant only for large enterprises. The truth, however, is far from this assumption. We don’t deny the costs involved. But at the same time, it is not necessary to make a huge investment in machine learning. 

Machine learning can be even used for something as simple as automating emails, reports, updating address books, sending reminders, and scheduling phone calls. ML doesn’t have to do the heavy lifting all the time. It can take care of the recurring tasks and save time, money, and effort for small enterprises. The simple reason is that instead of hiring additional employees for entry-level work, you can automate the process and ask an existing employee to oversee it.

In fact, adopting AI and ML during the earlier stages of the business will help you get used to advanced technology at work and maintain high working standards. 

#5 Data Science Means to Build Machine Learning Models

Artificial intelligence, machine learning, and data science are interrelated even though they are not the same. Experts say that machine learning is essential for data scientists to deliver ‘high-value predictions’ in real-time. However, data science is not limited to building machine learning models. 

Data science is a field where mathematics, statistics, and computer science are combined and used to derive actionable insights from raw data. The processes and algorithms are complex and can analyze vast amounts of data in a quick time. However, data science is not the ultimate solution to every business problem

Around 50-60% of a data scientist’s time is spent on data collection, data cleaning, and data preparation to feed it to the ML model.

For example, if you want to know why your customers are moving on to other brands, you’ll need to use data sets from the CRM systems. The purchase records, the pricing, customer service, competitors, and even the market conditions can influence a customer’s decision. Data scientists will get the data ready and feed it into the ML model to understand the reason for customers’ disloyalty towards the brand.

#6 Machine Learning and Deep Learning Are the Same

Deep learning is actually a subset of machine learning and a highly intricate neural network with multiple layers. The artificial neural networks (ANNs) try to mimic the human brain to understand data. In fact, neural networks are considered the backbone of deep learning algorithms. The deep learning algorithm should have at least three neural networks.

Contrary to the popular opinion in the market, deep learning is not a solution to machine learning problems, nor does it work the same way as ML models do.

When you build a deep learning model, you are creating a predictive system capable of generalizing and adapting to specific conditions of the business. While machine learning extracts actionable insights by processing data sets, deep learning predicts future scenarios based on past and real-time data. A highly advanced deep learning model is dynamic and can work in sync with the changes in the business.

Difference between artificial intelligence, machine learning and deep learning
Difference Between AI, ML and DL

#7 Data Cleaning Is Not Necessary in Implementing ML Models

In 2016, IBM estimated that businesses in the US lost $3.1 trillion per year due to poor data management competencies and bad data quality. Almost five years later, we can imagine the extent of losses enterprises suffer due to a lack of data quality. 

Data cleaning is the first step in implementing the machine learning model. Data is collected from several sources and stored in a central repository such as a data warehouse or a data lake. This data is then cleaned to remove redundancy. It is structured and formatted into a uniform database. Though data cleaning can be expensive, skipping this step can lead to-

  • Increased costs of analytics 
  • Inaccurate insights 
  • Bad customer service 
  • Decrease in manufacturing efficiency and employee productivity 
  • A mismatch between market demand and supply 
  • Decrease in customer loyalty 
  • Reduced brand authority and credibility 
  • Unsuccessful lead generation and missed opportunities 
  • Wastage of resources in processing low-quality data 
Machine Learning Development Lifecycle
Machine Learning Development Lifecycle

#8 Machine Learning and Data Mining Are Similar and Do the Same Thing

When we hear so many terms and definitions that sound similar, it’s easier to assume they mean the same thing. Machine Learning and data mining are being considered the same by many people.

Though they are not entirely different and commonly deal with huge data sets, data mining is different from machine learning. The major difference is that ML is a technology and data mining is a technique. The approaches to processing data are also different.  

Data mining is where data sets are analyzed to identify previously unknown patterns and properties. Machine learning is where existing patterns are used to provide a solution to a business problem. 

Data mining is used to identify anomalies in data sets and correlate various elements in a huge volume of data. It uses a combination of techniques, including machine learning, statistics, etc. This process is essentially used to convert raw data into valuable information and for predictive analytics. The best example of data mining is the anti-fraud systems used by banks to identify users who are likely to conduct illegal transactions. 

#9 ML Doesn’t Need Human Intervention

The main reason to use machine learning is for automation and minimizing human intervention, isn’t it? Then how is ML dependent on humans? Here, we are talking about developing machine learning algorithms and models. 

Without a programmer writing code, executing, and debugging it, how can the ML algorithm be deployed? Who will feed data into the system for the algorithm to learn? That’s where humans are necessary, and the demand for ML engineers has increased. 

A study estimates that machine learning jobs across the globe will be worth $31 billion by 2024. Machine learning jobs are mid-level jobs that require skills, talent, and expertise. That’s one of the reasons why SMEs and large-scale enterprises hire ML consulting companies to develop and deploy machine learning models in their business. 

Solve Complex Business Problem with Machine Learning

A most important factor to remember is that a machine learning model is only as good as the data fed into it. If you enter wrong or poor-quality data, the results will be the same.

#10 Machine Learning Is All About Algorithm 

The machine learning algorithm is pretty much a catchphrase that led to many people assuming that ML is only about algorithms. Yes, algorithms play a vital role, but some elements are even more crucial for the ML models to function. 

  • Training Data 
  • Handling Data
  • Humans in the loop 
  • Domain Expertise

These are the three aspects that form the ML framework. Training data is the collection of data sets we first feed the ML algorithm. It learns patterns from this data and applies them to other datasets for analysis. As we mentioned earlier, feeding wrong data at this stage will lead to substandard and defective algorithms. 

If the data or the humans in the loop have prejudices and biases, the ML algorithm is going to reflect the same. Also, the term algorithm doesn’t relate to the human brain. An algorithm is a set of rules and procedures the computer should follow to analyze data and deliver insights.

#11 ML Is Not Accurate Enough to Predict Black Swans

A black swan is an unforeseen event that can majorly affect a business. While some people believe that ML is 100% accurate, others think ML cannot predict everything, especially black swans. How can a machine predict the most unexpected incidents or events? 

Well, it is possible. Machine learning models can actually predict the unpredictable with great accuracy. They are capable of uncovering patterns and trends which humans and ordinary calculations cannot. However, it also depends on the team and the management, who might ignore the prediction, thinking it’s a system lapse or a missed assessment. 

Even if machine learning is not yet 100% accurate and needs further development, we cannot deny that it has the superpower to see things we cannot otherwise see. The results depend on how an enterprise uses AI and ML in the business.

Insights for you

After exploring the myths and truths about machine learning, it is time to incorporate it into your business. The machine learning experts at DataToBiz are well-versed with the recent trends in data science and excel in machine learning techniques. Let them identify whether it is actually smart insights that your business needs or how can they work upon your data so as to give you what your business needs or you need to design a data strategy for your business. They can help you devise a smooth and comprehensive data strategy to boost your business.

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