Artificial Intelligence vs Machine Learning: What’s the Difference?

If you read or hear about emerging technologies, you may encounter the terms Artificial intelligence (AI) and Machine Learning (ML). Are they the same thing? What exactly sets them apart?

In short, machine learning is a subset or approach within the broader field of AI. Here’s a quick breakdown:

Artificial Intelligence (AI)

– The ability of a computer or system to exhibit human-like intelligence and perform tasks like reasoning, learning, problem solving.

– Encompasses a wide range of techniques like machine learning, rules-based systems, computer vision, natural language processing, robotics, and more.

– Focused on developing systems that are intelligent, autonomous and can adapt and interact with their environment.

Machine Learning (ML)

– An application and approach to AI that enables systems to learn and improve from data without being explicitly programmed for every scenario.

– Focused on developing algorithms that can learn, identify patterns and make data-driven predictions or decisions.

– Relies on supplying the system with quality data to train on rather than coding complex rules.

– Involves techniques like supervised learning, unsupervised learning, deep learning, neural networks, etc.

So in essence, machine learning represents an approach to achieving artificial intelligence by developing algorithms that can learn and evolve based on data. It is one of the most widely used techniques for developing AI applications today. The end goal is still the creation of intelligent systems. But machine learning provides a powerful set of tools to get there.

Let’s now expand and recap 

Goals

– The overall goal of AI is to create intelligent machines that can perform human-like cognitive functions. This includes a wide range of capabilities like reasoning, planning, creativity, problem-solving, perception, social intelligence, and more.

– Machine learning has a more narrow focus on developing algorithms that can learn from data to make predictions and improve at tasks without explicit programming. But it is aimed at enabling broader AI capabilities.

Approaches

– In addition to machine learning, AI also incorporates rule-based systems, knowledge representation, search and optimization methods, natural language processing, robotics, computer vision, and more.

– Machine learning utilises statistical techniques and neural networks to train models on data. Main approaches are supervised learning, unsupervised learning, reinforcement learning, and deep learning.

– AI researchers also study how human intelligence works and aim to mimic cognitive processes like thinking, problem solving, intuition, creativity. This goes beyond data-driven techniques.

Applications

– AI has a wide range of applications from self-driving cars, to content recommendation systems, medical diagnosis, game-playing bots, language translation tools, and much more. Its use in the food and beverage industry, as well as traditional agriculture, is booming, for the control of all operations from manufacturing to logistics and transportation.

– Machine learning powers many today’s most prominent AI applications like ad targeting, predictive text, image recognition, search rankings, fraud detection, etc. But it has limitations in replicating generalised intelligence.

Conclusion

Machine learning (ML) provides a subfield of techniques and algorithms that can be applied towards the larger goals and applications of AI. ML focuses on data-driven approaches while AI incorporates a multifaceted set of theories, methods, and disciplines for generating all facets of intelligence.

Check my free Guide to Implementing AI in the Food and Beverage Industry where you can learn the main principles. To apply AI in your business, please contact me; it will be my pleasure to assist you in any way I can.

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Bob Mazzei
Bob Mazzei

AI Consultant, IT Engineer

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