What Are Some Important Limitations of Machine Learning?

Machine learning, an essential subset of artificial intelligence, has brought a revolutionary shift to the world in the past decade. With the exponential data surge and rapid technological advancements, large companies like Facebook and Google are now efficiently studying massive data volumes. This meteoric rise of machine learning, however, is not devoid of limitations. Let’s explore its potential and constraints in depth.

The Rise and Ubiquity of Machine Learning

The potential of machine learning is tremendous, making it a highly sought-after technology. The past few years witnessed a significant surge in AI consulting agencies, fueled by a 100% increase in AI-related jobs between 2015 and 2018. Businesses are increasingly adopting AI capabilities, with Forbes reporting a 47% incorporation rate as of December 2018. Deloitte predicts the penetration rate of enterprise software with built-in AI and cloud-based AI development services to reach 87% and 83%, respectively. This potential and growing adoption, however, also bring along critical challenges.

Understanding Machine Learning Limitations

Limitation 1 — Ethics

While machine learning has revolutionized data interpretation, it has also given rise to ethical questions. Trusting data and algorithms over human judgment could potentially replace jobs, raising ethical concerns. In case of an error, who’s to blame? The controversy surrounding self-driving cars is a prime example, raising issues about liability in fatal collisions.

Limitation 2 — Deterministic Problems

Machine learning may not be the ideal solution for deterministic problems that rely heavily on computational modeling. For instance, using a neural network for weather prediction is feasible, but it fails to grasp the physics of weather systems. The introduction of physical constraints to algorithms, however, could be a game-changer.

Limitation 3 — Data

Machine learning algorithms thrive on good quality data. Lack of data or poor data quality could hamper the performance of these algorithms. It becomes crucial to not just collect data but ensure its quality and relevance. The bias in training data, like in breast cancer prediction models, can lead to skewed results, underscoring the need for representative data.

Limitation 4 — Misapplication

The misapplication of machine learning to analyze deterministic or stochastic systems can lead to inaccurate results. The practice of ‘p-hacking’ or data dredging in large datasets can result in spurious correlations. Machine learning, being inherently exploratory, may not always be suitable for confirmatory analysis, which traditional statistical methods can handle better.

Limitation 5 — Interpretability

Interpretability is a significant concern in machine learning. An algorithm’s decision-making process must be interpretable to ensure trust and acceptance. Machine learning models must aim to achieve interpretability, especially when applied in practice.

Conclusion

Machine learning has dramatically transformed the way businesses and industries operate, contributing significantly to their growth. However, the limitations inherent to this AI subset must be addressed to ensure its sustainable and ethical use. By understanding these challenges, we can work towards leveraging machine learning’s potential while mitigating its limitations.