4 key AI concepts you need to understand

Every artificial intelligence solution is built on these four foundations; here’s your quick guide

4 key AI concepts you need to understand
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Artificial intelligence (AI) is taking the world by storm, with innovative use cases being applied across all industry segments. We are decades away from replacing a doctor with an AI robot, as seen in the movies, but AI is helping experts across all industries diagnose and solve problems faster, enabling consumers like myself to do amazing things, like find songs with a voice command.

Most people focus on the results of AI. For those of us who like to look under the hood, there are four foundational elements to understand: categorization, classification, machine learning, and collaborative filtering. These four pillars also represent steps in an analytical process.

Categorization involves creating metrics that are specific to the problem domain (e.g. finance, networking). Classification involves determining which data is most relevant to solving the problem. Machine learning involves anomaly detection, clustering, deep learning, and linear regression. Collaborative filtering involves looking for patterns across large data sets.

Categorization

AI requires a lot of data that is relevant to the problem being solved. The first step to building an AI solution is creating what I call “design intent metrics,” which are used to categorize the problem. Whether users are trying to build a system that can play Jeopardy, help a doctor diagnose cancer, or help an IT administrator diagnose wireless problems, users need to define metrics that allow the problem to be broken into smaller pieces. In wireless networking, for example, key metrics are user connection time, throughput, coverage, and roaming. In cancer diagnosis, key metrics are white cell count, ethnic background, and X-ray scans.

Classification

Once users have the problem categorized into different areas, the next step is to have classifiers for each category that will point users in the direction of a meaningful conclusion. For example, when training an AI system to play Jeopardy, users must first classify a question as being literal in nature or a play on words, and then classify by time, person, thing, or place. In wireless networking, once users know the category of a problem (e.g. a pre- or post-connection problem), users need to start classifying what is causing the problem: association, authentication, dynamic host configuration protocol (DHCP), or other wireless, wired, and device factors.

Machine learning

Now that the problem is divided into domain-specific chunks of metadata, users are ready to feed this information into the magical and powerful world of machine learning. There are many machine learning algorithms and techniques, with supervised machine learning using neural networks (i.e. deep learning) now becoming one of the most popular approaches. The concept of neural networks has been around since 1949, and I built my first neural network in the 1980s. But with the latest increases in compute and storage capabilities, neural networks are now being trained to solve a variety of real-world problems, from image recognition and natural language processing to predicting network performance. Other applications include anomaly feature discovery, time series anomaly detection, and event correlation for root cause analysis.

Collaborative filtering

Most people experience collaborative filtering when they pick a movie on Netflix or buy something from Amazon and receive recommendations for other movies or items they might like. Beyond recommenders, collaborative filtering is also used to sort through large sets of data and put a face on an AI solution. This is where all the data collection and analysis is turned into meaningful insight or action. Whether used in a game show, or by a doctor, or by a network administrator, collaborative filtering is the means to providing answers with a high degree of confidence. It is like a virtual assistant that helps solve complex problems.

AI is still very much an emerging space, but its impact is profound and will be felt even more keenly as it becomes an ever larger part of our daily lives. When choosing an AI solution, like when buying a car, we’ll need to understand what is under the hood to make sure we are buying the best product for our needs. 

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