Artificial Intelligence (AI) and Machine Learning (ML) are often considered synonyms. Instead, ML is an application of AI, and it’s important to understand its key elements and the potential they offer for business value.
Artificial Intelligence vs. Machine Learning: the key differences
ML is an application or a subfield of AI that is based on the concept that a machine can learn on its own from data sets. In reality, however, ML systems are limited in their ability to understand natural language since there are no tools to refine the algorithm for content analysis. In other words, it’s a black box. Your only option is to feed more examples to the algorithm, but this doesn’t guarantee greater accuracy because you need to provide specific training documents to cover all the use cases.
Machine learning is most applicable for use cases where you have a large, relevant sample of documents. But what if you don’t have large data sets? What if your use case is very difficult and ambiguous? The more complex your use case, the more difficult it will be to identify correct samples for each situation. Instead, ML works well when you have a large number of sample documents and low complexity.
The reality is that most business scenarios do not fall into this category. In that case, other AI approaches, such as Natural Language Understanding based on a knowledge graph, offer concrete benefits for a range of cognitive tasks and can also be used in scenarios that involve a small, distributed set of sample documents with an average level of complexity. Thanks to the deep and wide representation of knowledge, AI solutions that leverage a knowledge graph understand and process natural language and any kind of unstructured texts faster and more accurately than a ML approach.
Combining the two worlds, knowledge graph and machine learning
Instead, the combination of ML (and deep learning) backed by a knowledge graph can provide high quality data to optimize the implementation of AI applications.