Types of Artificial Intelligence
These six types of artificial intelligence (AI) aren’t all created equal. A lot depends on how well experts define and measure the term, as well as which type of AI you’re looking to use. What follows is a list of some of the main trends that make up each type. This post will give an introduction to three major groups of artificial intelligence (AI), including examples from machine learning, natural language processing (NLP), and computer vision.
Types of Artificial Intelligence
Each group offers different types of AI systems—some are more powerful and complex than others. Let’s look at two important ones: machine learning and natural language processing (NLP).
- Machine Learning — The most recent form of AI that is attracting much attention, machine learning uses algorithms to provide machines with increasingly accurate predictions about data. Machine learning has been around for decades, yet it recently got a huge boost in development: Google’s Deepmind is credited with being the first company to develop neural networks and the programming languages necessary to use them.
- Natural Language Processing (NLP)— NLP uses computers to understand speech or written text in a way that can be understood by humans. With this ability, people who don’t speak the same language can receive services using voice recognition technology. It’s not just talking; NLP also refers to using chatbots to help people communicate more effectively. Such machines do this via conversation, which allows human professionals to communicate over the internet and other mediums as easily as email.
- Computer Vision — Computer Vision uses light patterns to find objects like apples, dogs, trees, cars, etc. Researchers have found several applications for this sort of technology, including autonomous driving and face identification. This type of AI can be used to recognize faces or handwriting and match it against images stored in facial databases. It can also find fingerprints or scars on X-ray scans of patients’ hearts. Computer vision can identify what objects in pictures contain one of its primary features (for example, eyes, ears, nose).
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Machine Learning vs. Natural Language Processing
Both types of AI use similar algorithms and techniques. For instance, both offer machine learning, but they use a very different set of algorithms. The difference between these terms can only be used to describe the basic concepts involved in the technologies. However, there are also instances when they aren’t interchangeable. As we take a closer look at the differences that separate them, we’ll find out why we need to work so closely to make sure our models are truly unique.
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Machine Learning versus NLP
NLP and the application of machine learning share many similarities. Both technologies are algorithms that work to automatically learn and predict the outcomes of inputs. In AI, the predictions are based on data that the algorithm learns from, like images, text, speech, etc. Although this general description may sound vague, NLP and machine learning are quite different. When speaking to machine learning, we talk about a model that uses patterns learned from large sets of data and then applies that pattern to new data; NLP uses models that learn how to analyze new data, often without being explicitly programmed and using existing frameworks. While there are plenty of practical examples to help illustrate the differences, let’s take a brief look at some key distinctions between these topics.
Machine Learning and Image Classification
Machine learning is a type of AI that focuses on finding patterns in data and building predictive models on them. Unlike image classification software that automatically classifies images, these types of tools classify data from a training dataset and apply it to new data. The process can be repeated endlessly as more and more data is fed to the system. In addition to using visual analysis to help us understand images, machine learning algorithms can also detect associations between images and categorical features such as age or gender. They can even discover areas where images have no meaningful relationship with any known categories, like skin lesions of animals or insects. This type of machine-learning approach isn’t just useful for computers; it can be used on smartphones as well. Mobile phones can use neural nets for image classification (or face identification), for example. By detecting which categories are recognized through machine learning, smartphones can display content that users might want to see and hear.
Natural Language Processing vs Text Recognition
Natural language processing allows us to read and understand spoken and written text, but it doesn’t require specific rules or grammar to create sentences such as asking or thanking someone, telling or requesting things, or replying to a question or making a request. Instead of relying on a set of predefined phrases, we can easily interpret ambiguous speech and write code that contains keywords and verbs. Rather than giving away information, we can simply enter text into a document and tell the computer to identify the meaning of phrases and keywords that can represent that text.
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Computer Vision vs Face Recognition
Computer vision and face recognition are other areas that are growing quickly. Just as with ML, computer vision is the use of algorithms to find objects via various imaging methods. Here again, there are still plenty of ways to think about the distinction between AI and VR, but it makes sense when you think of these fields. If someone spots an apple, say, with their hands, an algorithm can use those observations to predict more accurately. But unlike ML, this model relies on an already existing framework for recognizing and predicting things like apples and dogs. Computer vision models for face recognition rely heavily on algorithms, including deep learning models.
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Conclusion
Each of these two types of AI is a bit different, but each serves a distinct purpose. Each type serves a particular goal while allowing researchers to explore the full range of possibilities. We’ve seen several successful implementations of both of these technologies, but we haven’t yet fully explored the potential of each one. To date, each area has been covered in depth by a wide array of experts, including academic researchers and engineers like Nicholas Thompson and Yoshua Bengio.
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