Now that we’ve learned about artificial intelligence and how its intention is to mimic the human mind, it’s time to take a deeper dive into this technology and learn about what it needs in order to function. For artificial intelligence to work without humans providing the rules, it requires a subfield called machine learning (ML), which itself has a subset named deep learning (DL). Separately, we have data science (DS), which is more of a static algorithm. Let’s have a look at how they all work in relation to each other.
Machine learning (ML) is the “mind” inside of artificial intelligence’s technology. ML creates the algorithms needed to learn and predict data. From datasets to facial, speech, and object recognition, and more. Any software that uses machine learning does not require encoded instructions for performing specific tasks. Even though this process is independent, the steps of the process need to be provided.
Deep learning (DL) is the newest addition to artificial intelligence. DL is a subset of machine learning, and comes into play when machine learning cannot fully deliver the desired outcome. Deep learning improves its accuracy by offering several different interpretations to the data it learns from. It is able to achieve this because of its use of neural networks. Neural networks are computing systems vaguely inspired by human biological neural networks.
These two technologies are closely related, but they do have their differences. Machine learning works with labeled and structured data, while deep learning uses neural networks and patterns to seek out conclusions. Deep learning also possesses a learning algorithm which can understand unstructured data. Machine learning needs human intervention when its presented output is not the desired one, while deep learning understands errors and learns from them.
Data science represents the entire process of functional value in data. It allows humans to develop insights that can help a business make better decisions. You might think some applications use AI, when in reality they are using data science. This is because data science also works with machine learning algorithms to build predictive models.
Depending on which type of data is to be analyzed, you can usually choose the correct method from the choices mentioned in this article. We now know that deep learning requires large datasets to enable its self-learning capabilities, think of it like reading several books to expand your vocabulary. On the other hand, machine learning works better with medium-sized structured data sets. This is because it knows what to do, and generally accomplishes tasks fairly quickly. Data science is an amalgamation of algorithms that allow humans to organize data and analyze it more easily. All of these technologies and applications help to accomplish one main goal – to help humans make better, faster knowledge-based decisions.