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What’s the difference between artificial intelligence (AI), machine learning, and deep learning?

The terms artificial intelligence (AI), machine learning, and deep learning are increasingly being used, but unfortunately often incorrectly or in a haphazard way.

Do you know what these terms really mean, and the differences between them? An article in TechRepublic provides a good overview:

  • Artificial Intelligence (AI) is the broadest way to think about advanced, computer intelligence. It can refer to anything from a computer program playing a game of chess to a voice-recognition system interpreting and responding to speech. There are three broad AI groups: Narrow AI, artificial general intelligence (AGI), and superintelligent AI. Examples of narrow AI include IBM Deep Blue, which beat chess grand master Garry Kasparov in 1996, and Google DeepMind AlphaGo, which beat Lee Sedol at Go in 2016.
  • Machine learning is one subfield of AI where machines take data and “learn” for themselves, unlike software programs that are hand-coded with specific instructions for task completion. Machine learning systems can quickly apply knowledge and training from large data sets to excel at a range of tasks including facial recognition, speech recognition, object recognition, and translation. Continuing with the Deep Blue and DeepMind examples, Deep Blue was rule-based and dependent on programming so not a form of machine learning, but DeepMind is because it trained itself on a large dataset of expert moves.
  • Deep learning is a subset of machine learning that solves real-world problems by tapping into neural networks that simulate human decision-making. Deep learning requires massive datasets to train itself on because there are a huge number of parameters that need to be understood by a learning algorithm, which can initially produce a lot of false-positives. For example, it would take a very massive dataset of images for a deep learning algorithm to understand the very minor details that distinguish a cat from a cheetah, panther, or fox. DeepMind is an example of deep learning.

The relationship between artificial intelligence, machine learning, and deep learning is illustrated in the image above from an NVIDIA blog article. As that article states, while deep learning and machine learning are progressively smaller subsets of artificial intelligence (AI), they have created larger disruptions:

Deep Learning has enabled many practical applications of Machine Learning and by extension the overall field of AI. Deep Learning breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. Driverless cars, better preventive healthcare, even better movie recommendations, are all here today or on the horizon. AI is the present and the future. With Deep Learning’s help, AI may even get to that science fiction state we’ve so long imagined.

Header image source: What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? (NVIDIA blog article).


Also published on Medium.

Bruce Boyes

Bruce Boyes (www.bruceboyes.info) is editor, lead writer, and a director of the award-winning RealKM Magazine (www.realkm.com), and a knowledge management (KM), environmental management, and project management consultant. He holds a Master of Environmental Management with Distinction, and his expertise and experience includes knowledge management (KM), environmental management, project management, stakeholder engagement, teaching and training, communications, research, and writing and editing. With a demonstrated ability to identify and implement innovative solutions to social and ecological complexity, Bruce's many career highlights include establishing RealKM Magazine as an award-winning resource, using agile and knowledge management approaches to oversee an award-winning $77.4 million western Sydney river recovery program, leading a knowledge strategy process for Australia's 56 natural resource management (NRM) regional organisations, pioneering collaborative learning and governance approaches to support the sustainable management of landscapes and catchments, and initiating and teaching two new knowledge management subjects at Shanxi University in China. With a demonstrated ability to identify and implement innovative solutions to social and ecological complexity, Bruce's many career highlights include establishing RealKM Magazine as an award-winning resource for knowledge managers, using agile and knowledge management approaches to oversee the implementation of an award-winning $77.4 million river recovery program in western Sydney on time and under budget, leading a knowledge strategy process for Australia's 56 natural resource management (NRM) regional organisations, pioneering collaborative learning and governance approaches to support communities to sustainably manage landscapes and catchments, and initiating and teaching two new knowledge management subjects at Shanxi University in China.

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