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Artificial Intelligence Vs Machine Learning: Explainer & Learning Tips

Artificial intelligence (AI) and machine learning (ML) are high on the hype cycle and increasingly transforming the world around us, from powering new technologies like self-driving cars to improving processes like medical diagnostics—but what’s the difference between the two?

Keep reading for a primer on these two rising technologies, where they fit into jobs and skills professionals use across industries today, and steps you can take to dive deeper and learn more.

How is Machine Learning Different From AI?

Machine learning is a subset of AI; machine learning is AI, but not all AI uses machine learning. Picture a Russian nesting doll. AI is the largest, all-encompassing doll with machine learning, neural networks, and deep learning as smaller and smaller subsets of the technology.

AI offers broad strokes for machines that mimic human intelligence, while machine learning is the practical application of human-like information processing. As the broadest and most general classifier, AI without machine learning behind it can be a one-trick pony, even if it performs its singular task with superhuman capability. For example, early AIs demonstrated the power of the technology by defeating world champions at games like checkers and chess, while today a simple AI may be employed for facial, speech, or image recognition, including translation.

More advanced AIs begin to incorporate more human components, such as chatbots like Siri and Alexa learning to interpret human tone and emotion. Machine learning, however, is how Siri, Alexa, and the rest acquire more diverse functionalities. Driven by machine learning, AI can go beyond the singular task to crunch raw data into patterns (for example, classifying images for Pinterest or Yelp) and make predictions (such as recommending shows on Netflix or music on Spotify).

What is AI?

Artificial intelligence is a broad phrase describing software and processes that mimic human intelligence and a range of areas of study—machine learning, computer vision, natural language processing, robotics, and other autonomous systems, such as self-driving cars. Using AI, machines learn, problem solve, and identify patterns, providing insights for humans in research or business.

layer "It's difficult to overstate AI's likely impact and reach in the next decade and beyond."

"It's difficult to overstate AI's likely impact and reach in the next decade and beyond," said Antonio Cangiano, AI evangelist, software developer at the IBM Digital Business Group, and instructor for courses in IBM’s Applied AI Professional Certificate program. "The best way of conceptualizing it is to imagine the same question being asked about electricity back in the late 1800s. We are all witness to the, unimaginable at the time, degree of transformation electricity enabled for well over a century. I'm willing to bet AI will impact today's world to an even greater extent. Virtually every industry is about to be radically transformed by the emergence of AI."

"Intelligence" is the computational aspect of these systems. Today, we don't define machine cognition outside of the relationship to the human brain and behavior, so artificial intelligence and human intelligence are inextricably linked.

What Is Machine Learning?

Machine learning is a subcategory of artificial intelligence. Where AI is the bigger picture of creating human-like machines, ML teaches machines to learn from data without explicit help from humans. Machine learning uses algorithms designed to ingest datasets and learn over time via set parameters and reward systems, getting better at specific tasks.

Machine learning vs. deep learning

Deep learning is a subset of machine learning that layers more than three structures of algorithms into an artificial neural network. The depth of these layers (the "deep" in deep learning) makes deep learning less dependent than classical machine learning on human intervention to learn.

Should I Learn AI or ML first?

Beginners can feel overwhelmed trying to learn AI because there are so many paths. Choosing between the bigger picture of creating artificial human-like intelligence or applying machine learning algorithms to learn from data will depend on your ultimate goals.

If you’re passionate about robotics or computer vision, for example, it might serve you better to jump into artificial intelligence. However, if you’re exploring data science as a general career, machine learning offers a more focused learning track. This specific skill set will provide a stepping stone to larger, more complex artificial intelligence projects.

Studying AI is mathematically rigorous, involving theoretical and computational mathematics designed to quantify a series of human intelligence functions. Machine learning is also a rigorous course of study, but requires fewer prerequisites for computer science and mathematics, which can make it a more accessible starting point for learners who are new to the field.

AI and Machine Learning Skills and Career Opportunities

Learning artificial intelligence and machine learning can open doors to a variety of careers in fields like data science, but also marketing, sales, customer service, finance, and research and development, according to a 2020 Gartner study.

Artificial intelligence skills:

  • Mathematics: Statistics, probability, logic, calculus, Bayesian algorithms
  • Science: Cognitive theories, physics, mechanics
  • Computer science: data structures, programming, computer logic and efficiency
  • Data science: Modeling and hypothesis testing
    A range of learning techniques, including reinforcement and transfer learning
  • Domain level knowledge: For jobs in research, domain-specific knowledge (biochemistry for healthcare research or mechanics for robotics, for example)

Machine learning skills:

  • Software engineering: algorithms and data structures (stacks, queues, decision trees, etc.)
  • Programming languages: Python, SQL, Java, R
  • Mathematics: Probability and statistics
  • Data science: Algorithms for modeling and hypothesis testing
  • Neural networks
  • Reinforcement learning
  • Natural language processing

Some essential AI programs:

  • GoogleAI
  • TensorFlow
  • Microsoft Azure
  • Infosys Nia
  • IBM Watson
  • NVIDIA Deep Learning AI
  • Wipro HOLMES

Some essential ML programs:

  • TensorFlow
  • Apache Spark or Hadoop
  • MATLAB
  • PyTorch
  • Google Cloud ML Engine

 

Cangiano advises: “The best job opportunities will be available to those who can apply AI to their existing domain-specific knowledge of a particular field.”

“For example,” Cangiano said, “if you specialize in cybersecurity, injecting AI into the mix (e.g., for anomaly detection) will allow you to distinguish yourself in the job marketplace.”

layer "The best job opportunities will be available to those who can apply AI to their existing domain-specific knowledge of a particular field.”

David Joyner—Ph.D., executive director of online education for the College of Computing at the Georgia Institute of Technology, and instructor for multiple computer science courses and programs offered through edX—predicts: “One of the big hot topics will be the application of machine learning to the things that go beyond the traditional areas of advertising and scientific research.” Joyner gave the example of vertical farming. Learning how to optimize nutrient and water delivery for maximum nutrition over a certain growth time is a job for AI and ML, Joyner said, and not one that most people would immediately think of, but one that is needed.

Get Started in AI, ML, and Data Science

At their heart, AI and ML are problem-solving tools. So what problems do you want to solve, and how can AI or ML help you do that? Explore edX courses and programs in AI, ML, and data science to learn more about how these skills can advance your career or learning journey.

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