Machine learning powers many of today’s most innovative technologies, from the predictive analytics engines that generate shopping recommendations on Amazon to the artificial intelligence technology used in countless security and antivirus applications worldwide. But like any form of technology, it’s not entirely perfect. So, let’s examine the pros and cons of machine learning and how they may impact you and your company’s goals.
Pro: Trends and Patterns Are Identified With Ease
Machine learning is adept at reviewing large volumes of data and identifying patterns and trends that might not be apparent to a human. For instance, a machine learning program may successfully pinpoint a causal relationship between two events. This makes the technology highly effective at data mining, particularly on a continual, ongoing basis, as would be required for an algorithm.
Con: There’s a High Level of Error Susceptibility
An error can cause havoc within a machine learning interface, as all events subsequent to the error may be flawed, skewed or just plain undesirable. Errors do occur and it’s a susceptibility that developers have thus far been unable to premeditate and negate consistently. These errors can take many forms, which vary according to the way in which you’re using machine learning technology. For instance, you might have a faulty sensor that generates a flawed data set. The inaccurate data may then be fed into the machine learning program, which uses it as the basis of an algorithm update. This would cause skewed results in the algorithm’s output. In real life, the result could be a situation where related product recommendations are not actually related or similar. So, you might have dog bowls, beach towels and footwear included in the same batch of “related” product recommendations. A computer lacks the ability to understand that these items are not in any way related; this is where human intelligence is required.
Errors are problematic with machine learning due to the autonomous, independent nature of this technology. You run a machine learning program because you don’t want a human to babysit the project. However, this means an error may not be discovered immediately. Then, when the problem is identified, it can take a fair amount of time and effort to root out the source of the issue. And finally, you must implement measures to correct the error and remedy any damages that arose from the situation.
Machine learning proponents argue that even with the sometimes time-consuming diagnosis and correction process, this technology is far better than the alternatives when it comes to productivity and efficiency. This stance can be proven in many situations by simply reviewing historical data.
On a related note, machine learning deals in theoretical and statistical truths, which can sometimes differ from literal, real-life truths. It is essential that you account for this fact when using machine learning.
Pro: Machine Learning Improves Over Time
Machine learning technology typically improves efficiency and accuracy over time thanks to the ever-increasing amounts of data that are processed. This gives the algorithm or program more “experience,” which can, in turn, be used to make better decisions or predictions.
A great example of this improvement over time involves weather prediction models. Predictions are made by looking at past weather patterns and events; this data is then used to determine what’s most likely to occur in a particular scenario. The more data you have in your data set, the greater the accuracy of a given forecast. The same basic concept is also true for algorithms that are used to make decisions or recommendations.
Con: It May Take Time (and Resources) for Machine Learning to Bring Results
Since machine learning occurs over time, as a result of exposure to massive data sets, there may be a period when the algorithm or interface just isn’t developed enough for your needs.
In other words, machine learning takes time, especially if you have limited computing power. Handling tremendous volumes of data and running computer models sucks up a lot of computing power, which can potentially be quite costly. So, before turning to machine learning, it’s important to consider whether you can invest the amount of time and/or money required to develop the technology to a point where it will be useful. The precise amount of time involved will vary dramatically depending on the data source, the nature of the data and how it’s being utilized. Therefore, it’s wise to consult with an expert in data mining and machine learning concerning your project.
You should also consider whether you’ll need to wait for new data to be generated. For instance, you could have all the computing power on the planet and you will ultimately reach a point where this computing power will do nothing to speed the development of a weather prediction algorithm because there is only so much historical data. You’ll simply need to wait as new data is generated—something that can take days, weeks, months or even years.
In a way, this process is similar to the training period required for a new employee. Fortunately, however, a machine learning engine can’t walk into your office and put in its two-weeks notice.
Pro: Machine Learning Lets You Adapt Without Human Intervention
Machine learning allows for instantaneous adaptation, without the need for human intervention.
An excellent example of this can be found in security and anti-virus software programs, which leverage machine learning and AI technology to implement filters and other safeguards in response to new threats.
These systems use machine learning to identify new threats and trends. Then, the AI technology is used to implement the appropriate measures for neutralizing or protecting against that threat. Machine learning has eliminated the gap between the time when a new threat is identified and the time when a response is issued. This near-immediate response is critical in a niche where bots, viruses, worms, hackers and other cyber threats can impact thousands or even millions of people in minutes.
Pro and Con: Automation
Machine learning is a key component in technologies such as predictive analytics and artificial intelligence. The automated nature of machine learning means it can save time and money, as developers and analysts are freed up to perform high-level tasks that a computer simply cannot handle.
On the flip side, you have a computer running the show and that’s something that is certain to make any developer squirm with discomfort. For now, technology is imperfect. Still, there are workarounds. For instance, if you’re employing machine learning technology in order to develop an algorithm, you might program the machine learning interface so it just suggests improvements or changes that must be implemented by a human.
This workaround adds a human gatekeeper to the equation, thereby eliminating the potential for problems that can arise when a computer is in charge. After all, an algorithm update that looks good on paper may not work effectively when it’s put into practice. If your system is configured to automatically implement improvements that are suggested by the machine learning interface, operations could run off the rails until a human intervenes, identifies the problem and takes corrective measures.
Like many technologies, machine learning isn’t right for every company or every application, and the impact of these pros and cons will vary according to your unique objectives. But there are many businesses that would enjoy a tremendous benefit from machine learning and related technologies like AI and predictive analytics. If you fall into the latter group, SevenTablets can help. Our world-class team of developers specialize in many of the newest technologies, including machine learning, blockchain, predictive analytics, augmented reality, virtual reality, artificial intelligence and natural language processing.
Based in Dallas, SevenTablets maintains regional offices in Austin and Houston. Our clients are spread across the country, though, so no matter where you’re located, we encourage you to reach out to our team today to discuss your project.
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Lacey earned a B.A. from Baylor University. Sic'em!