Addressing the setbacks of machine learning and providing value-added solutions

You've probably heard of machine learning a million times before. It might have been mentioned in a casual meeting, a random LinkedIn post sharing a miraculous artificial intelligence resource, a blog post, etc. You may have come across this phrase, but to what extent do you understand the meaning of machine learning?

If you're in the field of information technology or data science, you're quite obviously well-versed with this new technological addition. However, for those who have no background, the term has to be appropriately explained. Because of many unclear explanations about ML, the buzz created numerous myths that confused people. 

What Is Machine Learning?

Let's put this out of the way. To dumb it down, machine learning involves learning from data. In simple terms, it helps process the data you've collected to provide better results. New businesses, big and small, have been popping out left and right. Likewise, each company collects information that piles up through time. Because of the vast collection, it isn't easy to sift through them manually.

ML can help you solve day-to-day problems by organizing your data and analyzing it for you. The term machine learning is part of artificial intelligence, but you can use both terms interchangeably—depending on how it's used and the requirements. Imagine how much time you can save with the right algorithms.

History Of Machine Learning

The first few whispers of ML were introduced in 1949 by Donald Hebb when he wrote about the model of brain cell interaction in his book entitled The Organization of Behavior. However, it wasn't fully explained by then. It was only in the 1950s when a breakthrough happened.

In the 1950s, a computer program of a game of checkers was created by Arthur Samuel from IBM. The program only required small storage, and he made a scoring system based on the position of the pieces on the board. This scoring function can calculate the chances of each side winning.

Over time, developments were made to improve machine learning. Today, people now enjoy speech and face recognition and camera filters. You can even make your machine learning infrastructure when you navigate to this site.

Common Pain Points And How To Strategize Against It

Just like any other program or project, there will always be issues that continue to recur. Here are a few common pain points from ML you can take note of:

1. Do You Need To Automate?

Because of so many articles released about machine learning, it's getting quite difficult to differentiate whether or not the information is real. There are many programs and software that involve the use of ML. The choices are endless. But before choosing which software to utilize, first see what kind of problem you're going to solve to find the right remedy.

There are common business problems that easy automation can solve, but some require a more in-depth study before going into automation that involves machine learning.

Remember this: machine learning can help your automation, but not all automation requires machine learning.

2. Quality Data

Machine learning only works when data is available. A lot of businesses depend on ML and AI to make work easier for them. This includes finding the best solutions to problems in the workplace. Thus, when working with machine learning and programs related to it, the data provided should be clean, well-prepared, and complete to produce more accurate results.

3. Infrastructure Systems

Since machine learning works so fast, it requires a massive amount of data-churning capabilities. The amount of work it needs to get done also requires advanced hardware. Thus, before you go into ML and explore what it can offer, make sure you have updated tech and hardware so that there's no limit to what you can do. 

Having the latest technology and purchasing it might be costly, but it'll pay off once you successfully make use of it. If you can't afford to buy the hottest drops in the market, try to upgrade a few hardware in your current system and expand your storage capacity. You'll notice an immediate change in speed.

4. Implementation

Machine learning is quite complicated, and when a company chooses to delve into that area, there needs to be proper guidance from experts. Shifting to different types of programs can cause confusion and takes a lot of time for adjustment. Other things need to be covered, including security. Thus, a company should seek help from an implementation partner who can guide them through the process.

Implementation partners are IT experts who are well-versed with the matter at hand. They can help you decide what's best for your company regarding machine learning and other programs. Likewise, they can detect anomalies, perform predictive analysis, and even model your needs more comfortably.

5. Number Of Skilled Resources

Machine learning and artificial intelligence are relatively new to the industry. This means only a handful of individuals are considered experts in this field. Thus, there's a lack of human resources that can support all the companies that need help with machine learning. Because of the limited number of individuals who can provide the best support, the cost to outsource is expensive, especially if you want someone who can offer you the best work quality.

Will Machine Learning Destroy Humanity?

There are many funny stories surrounding machine learning, and one of them says it may destroy humanity. People are afraid that AI and machine learning might be too smart and can develop better knowledge than humans. Thus, they believe machine learning is a force to be reckoned with—something that will invalidate the very existence of human beings.

People find machine learning dangerous because of how it is portrayed in movies where robots are harming humans and taking over the world. This has to stop. While artificial intelligence has managed to slowly understand the brain system through artificial neural connections, there is no real possibility of machines dominating the world.

Conclusion

Machine learning is beneficial. While there are still portions of ML that need to be reviewed and studied, there's no denying that it has made many people's lives better. While the concept of machine learning is difficult to understand, in time, experts can relay information in a simpler way. It's still in the development phase, and it might take years before experts discover the extent of what it can offer. Hopefully, this article helped a bit.