Many developers have not yet had much exposure with machine learning, and that’s why most of them get confused and think they are crazy robots. First, let’s clear up some of the confusion surrounding machine learning. Then we will go into science and mathematics (yes, there will be mathematics) behind the concepts.
Finally, we will immerse ourselves in some common applications of machine learning not only to change the way companies are doing business, but also to permeate the everyday life of ordinary people.
Definition of machine learning
Machine learning is not Artificial Intelligence (AI) in itself and is much more than automating a lot of simple tasks. It is a specific branch dedicated to helping computers learn from humans and how to interact with us in a manner similar to that of humans.
Sounds simple right? If it were, scientists would not devote so much effort to make this happen. Great progress has been made during the last century to ensure that machines accurately interpret human beings’ requests and provide us with what we need to respond.
Those requests are not happening naturally. Anyway, a lot of work is required for Alexa (smart speaker developed by Amazon) to retrieve your favorite playlist on demand.
How machine learning works
AI, which drives most modern applications, is due to rigorously designed algorithms created by developers and computer engineers. Tons of data sets are built and rebuilt until they are ready to go. Then, machines use them to help anticipate different aspects of human behavior.
Done correctly, the calculations drive AI to evaluate what you are asked to do and use those same algorithms to find out where to get the information needed to achieve your goal.
Every day brings hundreds of new algorithms of AI enthusiasts, so there is no way to calculate how many are out there. All algorithms contain a combination of the following concepts:
- Representation: the language used by the computer to understand us
- Evaluation: how the computer interprets our requests
- Optimization: how the computer arrives at the correct route to respond to what is requested
It doesn’t matter if you choose to code your AI application in the R, Python or any other programming language . The important thing is to provide the correct data sets to anticipate the behavior of humans.
Challenges of machine learning
Most algorithms follow an established pattern. Humans do not usually do this, which is where scientists still struggle to develop a truly autonomous AI.
Think about it. The patterns of human speech may vary depending on the part of the planet in which you were born. Each region has its own jargon and dialect that are easy to understand for those familiar with it.
Tasks and problems that involve a lot of ambiguity are not what AI does best for now.
Prerequisites for working with machine learning
Companies that want to take advantage of machine learning capabilities have some options:
- Hire a top-notch developer or engineer to design the company’s applications
- Hire an external provider that offers the tools and capabilities you are looking for
- Train a member of your team about machine learning
In most cases, companies that do not have the internal resources to dedicate themselves to such complex development projects, choose to hire an external provider to provide them with the capabilities they need.
This is the most practical option, since these providers are likely to have a solid team of data scientists, developers, engineers and other technology geniuses who are dedicated to developing machine learning applications and tools every day, That means they bring a great experience to the table.
It is important to constantly refine your data sets for responses to new information that your machine may not know. Life is not static, so the algorithms that your processes use should not continue to be that way.
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The most commonly used programming languages for machine learning are R and Python , but a good understanding of linear algebra will also be useful. The most important thing is to establish good habits to define the problem you are trying to solve, create appropriate data sets and evaluate them thoroughly.
You also need to have a good understanding of the following:
- Regression (linear and polynomial)
- Decision trees
- Markov chains
- Support vector groups
Applications for machine learning and AI
Problems with a clearly definable result are those that machine learning handles best. The recognition of images, the search for patterns in missing data and the perception of clear and unambiguous language are things that AI can do well.
It is also frequently used to find discrepancies in financial transactions, make predictions based on past data patterns (think of the stock market) and recognize when someone sends you some type of spam or fraudulent email and marks it as such. .
The hopes are that deep learning — an aspect of machine learning that models itself after the neural networks of a functioning brain — will help close the gap that exists by making machines respond to unknown data or input.
That should provide more opportunities for companies to find innovative ways to apply AI not only to their business processes, but also in advanced ways that help consumers in their daily lives.
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