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Artificial intelligence is a less clear topic for many, as it is difficult to understand how computers can learn and make intelligent decisions. However, the principles of AI are not that complex.

Machine learning and deep learning are the binary main notions that make AI possible. The terms are often equated but describe two fundamentally different methods with different application areas.

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Both machine learning and deep learning remain two subcategories of artificial intelligence, and both approaches show that computers can make intelligent decisions. On the other hand, deep learning is a form of machine learning based on unsupervised learning.

In any case, intelligence is limited to individual use cases. We are talking about a weak artificial intelligence versus a robust artificial intelligence which, like humans, could make intelligent decisions in many areas and under many circumstances.

Both technologies rely on massive sums of data for systems to learn from, but that’s where the similarities end.

Deep Learning vs Machine Learning: What’s the Difference?

Machine learning is historically the oldest and simplest technology. It uses an algorithm that the system adjusts only after receiving human feedback. A prerequisite for the use of technology is the existence of structured data. The system is first fed with structured and categorized data and then knows how to classify the new data by type. Based on the classification, the system executes the scheduled tasks. For example, you can recognize a dog or a cat in a photo and move files to the appropriate folders.

After an initial application phase, the algorithm is optimized by human feedback, which tells the system wrong ratings and correct categorizations.

Structured data is not necessary for deep learning. The system works on multi-layer neural networks that combine different algorithms and are modelled on the human brain, allowing the system to process unstructured data.

The approach is particularly suitable for complex tasks where not all aspects of the objects can be categorized in advance. With deep learning, it is the system itself that identifies the appropriate differentiators. At each level, input for a different characteristic is validated, and the system uses this process to decide how to categorize that data.

Important: With deep learning, the system identifies appropriate differentiators in the data without needing external categorization. No developer training is required, and the system checks whether the classifications change due to new entries or new ones.

While machine learning already works with a controllable database, deep understanding requires significantly more data, and the system must have more than 100 million data points to provide reliable results.

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