Agreed, machine learning (ML) has changed the game for several industries and businesses over the last few years, along with AI (artificial intelligence).
However, there continue to be challenges and issues in machine learning that are present even today.
Here’s taking a closer look at some of the biggest limitations of machine learning and even the limitations of deep learning as many call it. ML can be immensely practical for several projects, although it is not always proving to be a good solution for some projects too.
One of the biggest issues in machine learning is ethics.
Trusting and relying on algorithms for automation, analysis, and decision-making is good. Yet, there are a few challenges, since these algorithms may also be vulnerable towards biases at a developmental level, especially since they are trained by human beings. Tough moral or other decisions cannot be taken by ML all by itself.
Another one of the limitations of deep learning is at a deterministic level. ML can be used for forecasting the weather for example, and also for analyzing the atmosphere and climates of various regions.
Models can be deployed via sensors that analyze aspects like pressure, temperature, and humidity, in this regard. While you can program ML models for the simulation of weather and atmospheric emissions with a view towards forecasting pollution levels, this may require sizable time based on complexity factors.
Data may be used for forecasting, although neural networks may not always be able to understand varying dynamics of weather systems or the prevalent laws. Calculating a few variables may lead to outcomes that are beyond science altogether.
Neural networks can identify connections between output and input information, but cannot always explain the reasons for the same.
Neural networks will naturally require vast training information in order to give suitable results. With the growth of architecture, the requirements for data will also go up accordingly. Some may go for data reuse systems, although this may not always ensure proper results.
The absence of data in such scenarios thus becomes an issue, along with the absence of high-quality data as well. This also lowers accuracy for these models.
Yet another one of the limitations of machine learning pertains to interpretability. Justifying the labeling/classification of certain activities may require more responsiveness or accuracy, without validation of the solutions.
Interpretation is a must for AI-based models and this will require human intervention at least till now.
ML issues also include reproducibility. Sometimes new models are created for quick usage in various applications in the real world. While they sometimes consider latest upgrades, this may not always work in several real-life scenarios.
Reproducibility may enable various businesses execute similar models for finding solutions to diverse issues. The absence of this aspect may create issues where biases are found, and there are hurdles pertaining to reliability and safety alike.
Experts feel that ML application should not be carried out without labeled and classified information. This is vital for models of deep learning today.
Data labeling is a procedure where clean information/data is marked and organized for the machine learning algorithm to do its job. The absence of high-quality data creates a situation where ML should not be used by a company for any real-life applications.
These are some limitations of machine learning that should be addressed in the near future, although they still exist. Businesses and professionals should navigate these aspects carefully as a result.
