10 Things You Should Know About Machine Learning - Blog - Sevaa Group

Machine learning (ML) is a tool that can be very advantageous for businesses. It’s used to solve problems and predict outcomes much more efficiently than any number of people could. For example, this online game of Rock Paper Scissors demonstrates how a machine picks up patterns over time.

However, in order to use ML correctly, you must first understand what it does and how it works. Here are 10 facts about ML to help you understand the concept.

 

1. Machine learning and artificial intelligence (AI) are two separate ideas

While there are things that AI and ML definitely have in common, they are not the same thing. Though AI does have components of natural language processing, it does not learn patterns or make predictions on any user input or data sets.

According to Google, AI is “the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” In a nutshell, it’s the idea that machines complete the tasks that humans would normally complete. When it comes to AI, machines are programmed to accomplish these tasks.

In ML, the machines are programmed to learn how to complete these tasks themselves based on the data sets they are given. Although AI and ML are both “smart machines,” they both have individual aspects that make them different.

 

2. Machine learning and deep learning are two separate ideas

When talking about AI, “machine learning” and “deep learning” are often used interchangeably. However, it’s important to note their differences. ML takes a set of data into account to perform a function. ML involves a lot of code to progressively get better at completing a specific task.

Deep learning, although technically a part of machine learning, offers different capabilities. If an ML algorithm returns an inaccurate prediction, an engineer will make adjustments. If the ML system employed a deep learning model, the algorithm would be able to determine if their prediction was right or wrong independently. Essentially, using a deep learning model takes ML to the next level by using an “artificial neural network.” With this new advancement, the machine will have the ability to correct its own mistakes without the help of an engineer.

 

3. Transfer learning makes machine learning simple

Thanks to transfer learning, it’s no longer necessary to require large data sets for teaching and applying the machine. Transfer learning is the application of what a machine has learned from one task to a second task, making ML much simpler. Whether the data set is large or small, a machine can pick up on the patterns and apply them to another data set.

 

 4. Not all data is helpful

ML is often used for a specific task. So, when picking the data set to train the machine, it’s important to make sure that the data being put into this set is relevant to the task at hand. It’s crucial to remember that the machine will pick up on any patterns in the data, whether those patterns were accidental or not. This unnecessary information, now stored in the machine, can affect the data results and create issues when transfer learning occurs. Not all data is relevant to accomplish a specific task, but it is important that the data being used is representative of the goal.

 

5. Building a machine learning system can be quite complex

While there are plenty of tutorials, resources, and frameworks on how to build an ML system, there is much more to it than that.

A machine learning system requires you to:

  • Prepare data for testing
  • Choose the best algorithm and logistics/heuristics for your data
  • Monitor the system
  • Check the model consistently to make sure it still fits your problem

Building an ML system not only requires advanced techniques to make the machine but also consistent maintenance to ensure that everything is still accurate.

 

6. Not every pattern is a useful one

While ML systems are extremely smart, they can’t determine whether or not patterns or data are relevant. ML picks up every pattern shown in the training data set, whether it is useful or not. The system then applies it to the second data set. While there may be substantial data, it could be completely insignificant. Another possibility is that the system might pick up a valid pattern that has no explanation and consider it inconsequential.

 

7. Reinforcement learning is still in the works

Like ML, reinforcement learning is a fairly new topic in the tech world. This is a fairly common concept: learning through trial and error and being rewarded for the right behavior. You might’ve used reinforcement learning when training your dog: when the dog performs a trick, you feed it a treat, and it will be motivated to do the trick for more treats in the future. It reinforces the positive behavior.

Reinforcement learning is now being expanded to technology, specifically ML. While it’s currently being tested on multiple video games, robots, and security software, reinforcement learning is not yet ready to be the main basis of ML. This may be because of the nature of immediate feedback: the computer may execute too many actions before an outcome occurs, and then none of the actions can be credited for any positive outcomes.

 

8. Machine learning can be biased

Many people think that ML is unbiased simply because it’s a machine. How can a computer be biased? However, an ML system picks up whatever bias is in the training data set and applies it to the second data set.

For example, let’s say that image data set X is used to train image recognition to an ML system. If X happens to show only women having long hair and wearing pink and only men having short hair and wearing blue, the machine will adopt that pattern. It will then use this pattern to recognize women as wearing pink and having long hair and men as wearing blue and having short hair. This bias gets carried through the system and into the following data sets.

 

9. Machine learning isn’t always used for good

While ML certainly has positive benefits, it can also be used for bad. Hackers can use an ML system in phishing attacks to see how successful an attack is. The hacker uses this information to improve the replicated website based on the data collected from the machine. They can then gain access to sensitive user information.

 

10. Machine learning won’t replace people

Machine learning will make some human jobs unnecessary, but it will also add jobs to the market. With the work machine learning has already accomplished and is expected to accomplish, it will free up employees’ time to use their skills and creativity in other areas of business. Additionally, these systems are only possible if humans build and maintain them.

Machine learning systems aim to increase efficiency for your business and your clients. Reach out to Sevaa about integrating your website or application with machine learning to support your brand.

 


Sources:

A Practical Guide to Machine Learning in Business, CIO

9 Machine Learning Myths, CIO

What is the Difference Between Artificial Intelligence and Machine Learning, Forbes

 

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