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 machine learning, 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.

Machine learning 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 machine learning 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 machine learning 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 machine learning. 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 machine learning and artificial intelligence 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 machine learning 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 machine learning 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.

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While many industries are struggling amid the coronavirus pandemic, both the IT industry and the broader trend of transition to remote work have revealed many areas where traditional approaches to managing businesses create unnecessary waste. Still, data science and its subdivision – machine learning – reveal that such expansion is nearly limitless.

Machine learning uses powerful algorithms to discover insights based on real-world data that can then be used to make predictions about future outcomes. As new data comes available, machine learning programs can automatically adapt and produce updated predictions. As with any tool, machine learning is not a silver bullet. However, there are many situations in which the technology can outperform linear and statistical algorithms.

Here are five of the most common use cases where machine learning can make a big difference:

When engineers can’t code rules for certain problems

Many human-oriented tasks (such as recognising whether an email is spam) aren’t solvable using simple (deterministic), rule-based solutions. Because so many factors may influence an answer, engineers would have to write and frequently update billions of lines of code. In addition, when rules depend on too many factors, and when those rules overlap or need fine-tuning, it becomes difficult for humans to code precise rules. Fortunately, machine learning programs don’t require users to encode actual patterns. These programs only need proper algorithms to extract patterns automatically.

When you need to scale a solution to millions of cases

You might be able to manually categorise a few hundred payments as either fraudulent or not. However, this becomes tedious or impossible when dealing with millions of transactions. As user bases grow, it’s no longer feasible for organisations to process payments by hand – end-users today want answers about their money in milliseconds, not minutes or hours. Machine learning solutions are effective at handling these types of large-scale problems with little or no human intervention.

When you can do it manually, but it’s not cost-efficient

There are situations in which in-house experts could process many requests quickly and accurately but at a high cost. For instance, imagine you assess DMV forms for in-state and cross-state car purchases to determine their validity before passing them on. In this situation, the business processes are well-defined, optimised, and serialised. It may take only a few minutes to check each form thoroughly. But allocating so much manual labor to this work is likely not the best use for your budget. Machine learning, on the other hand, offers predictable, pay-as-you-go pricing for fully scaled operations.

When you have a massive dataset without obvious patterns

Consider this – you’ve successfully prepared a well-curated dataset and know the underlying problem. However, you don’t see any explicit patterns in the data, preventing you from encoding those validations. Plus, there are many typos, missing fields, and other human-caused errors with no validation in place. You may even know the data is poor quality and can manually determine every affected row. But you can’t see any actual connections between valid and invalid records. Machine Learning algorithms can solve this problem. They can find hidden connections between data points that aren’t clear to humans. Tools like Interpreting Tracers can even describe how machine learning models arrive at their conclusion.

When you live in an ever-changing universe (adaptive)

The world, and its problems, are always changing. A problem you solved yesterday can easily mutate into something else entirely, rendering your previous solution inefficient or even useless. For example, if your organisation processed medical appointment recordings to extract diagnoses, procedure information, and billing codes, your rules might have to evolve constantly. However, you can’t make updates in real-time 24/7. Meanwhile, incorrectly labelled items could lead to insurance rejections, huge fines, and legal penalties. One major advantage of machine learning methods is that they can learn from data across the entire lifecycle of your application – from the first line of code written to the moment when the model is finally shut down. Moreover, it’s important for production-grade systems to have feedback loops so that you can catch the moment when your model no longer solves problems correctly.

It’s important to remember that machine learning is a tool – it’s not magic. Machine learning models are essentially advanced math-based algorithms, which identify patterns in data and learn from them. However, when properly applied to the right use cases, machine learning can reduce the amount of time spent error-prone manual IT operations, adding significant business value and greatly reducing IT costs.

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