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Deep learning vs. machine learning

Deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is actually a sub-field of machine learning.

Deep learning is actually comprised of neural networks. “Deep” in deep learning refers to a neural network comprised of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm. This is generally represented using the diagram below.

Recent developments within artificial intelligence (AI) have demonstrated the scale and power of this technology on business and society. However, businesses need to determine how to structure and govern these systems responsibly to avoid bias and errors as the scalability of AI technology can have costly effects to both business and society.

As your organization uses different datasets to apply machine learning and automation to workflows, it’s important to have the right guardrails in place to ensure data quality, compliance, and transparency within your AI systems.

Our researchers are developing the next generation of advances in AI software and hardware to bring frictionless, cloud-native development and use of foundation models to enterprise AI.