Artificial Intelligence (AI)

Artificial Intelligence (AI) is a discipline that uses computers and data to simulate the capabilities of the human mind, but much more quickly, at a scale and a speed that no human could ever achieve.

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Artificial Intelligence (AI) is a discipline that uses computers and data to simulate the capabilities of the human mind, but much more quickly, at a scale and a speed that no human could ever achieve. It includes a complex set of approaches, methodologies and technologies that work together. 

There are two categories of artificial intelligence, Weak AI and Strong AI. Weak AI, also referred to as Narrow AI, is focused on performing a specific set of tasks. Weak AI is the most common form of AI today. Strong AI, on the other hand, is a form of artificial intelligence that closely resembles the general intelligence of the human mind, including reasoning and original thought. While researchers study Strong AI, it’s a theoretical form of AI with no known examples yet. 

Two important fields within artificial intelligence are machine learning and deep learning. 

Machine learning couples large datasets with computer processing to understand and learn from patterns in that data. Unlike traditional computer programs that use an explicit set of instructions specified in a programming language, machine learning adapts based on the recognition of patterns in its underlying datasets. 

Deep learning is a subset of machine learning that operates in a more scalable manner. Deep learning utilizes neural networks, an AI methodology that’s modeled after the workings of neurons in animal brains. Deep learning is considered more scalable because it doesn’t rely on humans to structure and label data, as machine learning requires. Deep learning can process raw, unstructured data (such as images and text-based files) and automatically understand the hierarchy of features inherent in that data.

Examples of AI in everyday life

Human beings are surrounded by artificial intelligence in everyday life. The smartphones we carry in our pockets are dependent on AI. The feature to unlock one’s phone using facial recognition is based on an AI technology called computer vision. 

The digital assistants on phones (such as Apple’s Siri) also use AI technologies. Speech recognition is used to parse the spoken word, and natural language processing (NLP) is used to understand the meaning of those words. Artificial intelligence is used to determine the answer to a question or to take action based on a spoken command. 

Social media platforms (such as Twitter, Facebook, TikTok, Instagram, etc.) use AI to select, prioritize, and show posts in users’ feeds for personalization. Rather than display all posts in a chronological fashion, social media platforms use AI to predict which posts users most likely want to see. 

Examples of AI in software systems

Chatbots are a common feature of websites and apps. The text-based chat interactions of a chatbot are used to assist users and customers, without a human being in the middle. Chatbots use NLP to recognize words and phrases, then generate responses to users’ questions or comments. Companies deploying chatbots construct a playbook or flowchart that guides the chatbot on how to respond to specific comments or requests.

Search engines are heavy users of AI. When a user enters a search query, the search engines use NLP to understand the query. Google takes things a step further, using a machine learning technique called BERT (Bidirectional Encoder Representations from Transformers) to understand complete sentences, as well as the context of searches. 

Search engine results pages (SERP) rely on AI algorithms to determine which pages to show and the order to show them in. The search engines use a combination of machine learning and deep learning approaches that rank pages based on a complex assortment of factors.

CRM (customer relationship management) software and email systems use AI to prompt users to respond to messages they deem the most important. Machine learning and NLP are used to auto-suggest a reply to a particular email or message.

Customer data platforms (CDP) bring together a number of machine learning algorithms to make data actionable at scale. CDPs examine past customer behaviors and use machine learning to predict actions or offers with a high likelihood of succeeding. 

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