including personalized marketing and fraud detection.
Deep learning
Deep learning techniques mimic the brain’s neuron activities, which is why they are also referred to as neural networks. Some common applications include natural-language processing, image recognition, and realistic photo and video generation. Table 1-4 shows the relationship among artificial intelligence, machine learning, and deep learning.
TABLE 1-4 Artificial Intelligence, Machine Learning, and Deep Learning
| Technique | Description | Example |
| Artificial Intelligence | Computing systems capable of performing tasks that humans are very good at | Recognize objects, recognize and make sense of speech, self-driving cars |
| Machine Learning | Field of AI that learns from historical data toward an end goal or outcome | Predict customers likely to churn |
| Deep Learning | Powerful set of machine-learning techniques that mimic the brain’s neuron activities | Computer vision, colorize photos, deep fakes, mastering a game |
Sentiment analysis
Sentiment analysis uses text mining, NLP, and other AI techniques to detect the opinions and emotions of a person based on written or spoken content, such as social media posts, reviews, videos, and podcasts. It identifies the person expressing the opinion, what the person is talking about, and whether the opinion is positive or negative.
Also called opinion mining, sentiment analysis is often used to process reviews or survey results to discern the voice of the customer and adjust a policy, product, or response accordingly.
In the early days of Twitter, many corporate social media teams would auto-retweet any content that tagged the brand. The resulting retweets of complaints of bad service were a great source of amusement to the general populace but did little to build the value of the brand. Sentiment analysis not only avoids such embarrassing moments, but also creates an opportunity to be proactive in engaging customers with knowledge and empathy.
Chapter 2
Looking at Uses for Practical AI
IN THIS CHAPTER
Mention artificial intelligence, and you’ll get all kinds of reactions, from cartoon fantasies of Rosie, the Jetson’s robot maid, to the dystopian cityscape of Ridley Scott’s Blade Runner, James Cameron’s The Terminator, or Michael Crichton’s Westworld.
These representations of AI are examples of artificial general intelligence, also called pure AI. Even from the beginning at the Dartmouth workshop, the pioneers of AI aimed for the stars, asserting that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it,” but by the time it filtered down to the masses, it presented itself in humbler forms.
These days, AI is pervasive, but it slips past unnoticed because it is not a manifestation of the romantic vision of writers. Instead, it is eminently practical. Pragmatic. Useful.
Recognizing AI When You See It
Like good design, good AI is invisible. When done right, both remove some of the friction from daily life. In fact, you have probably been using artificial intelligence for longer than you realize.
ELIZA
In the mid-1960s, Joseph Weizenbaum at the Massachusetts Institute of Technology Artificial Intelligence Laboratory developed ELIZA, a natural-language processing program that converses in the style of a psychologist asking questions based on previous responses. With the advent of the personal computer, ELIZA escaped the MIT lab and ventured into people’s homes. You can still find implementations online.
Grammar check
Spell check has been around for a long time, but that’s a simple application that doesn’t require artificial intelligence, just a fuzzy search that reacts to a not-found condition by returning items that are similar but not identical to the search term. By contrast, grammar check uses natural-language processing (NLP) and supervised machine learning (ML) to learn language rules and usage.
In 1981, Aspen Software released Grammatik, an add-on diction and style checker for personal computers. In 1992, Microsoft Office embedded a grammar checker in Microsoft Word. In 2007, Grammarly launched a cloud-based grammar checker.
Virtual assistants
Back in the day, a virtual assistant was really virtual, as in digital, not a term to describe a remote clerical worker.
As the personal computer gained popularity, it migrated into the homes of users, who had widely varying degrees of computer literacy and aptitude. Software providers rushed in to fill the void between computer capabilities and consumer competence.
Anybody using computers in 1997 will remember Clippy, Microsoft’s ill-fated virtual assistant. Officially named Clippit, it was a stylized paper clip with googly eyes and Groucho eyebrows standing on a sheet of yellow legal-pad paper like Aladdin on a magic carpet. Watching from a corner of the window, Clippy would monitor what you typed and jump in when he thought you might need help. For example, if you opened a new document and typed “Dear” followed by a space, Clippy would jump to the middle of the screen and say, “It looks like you’re typing a letter. Would you like help?”
In 2010, three years after Clippy officially died, Apple launched Siri, followed in the next six years by Google Now, Alexa, Cortana, and Google Assistant. This new wave of virtual assistants uses voice recognition and expands the scope beyond help with a specific computer application to help with almost every aspect of life. Like chatbots, virtual assistants can be deployed in the enterprise to enhance internal or external customer service.
Clippy provided assistance based on Bayesian algorithms, a family of probabilistic classifiers. Modern virtual assistants
