IN THIS CHAPTER
Discovering how you can use AI Recognizing the key technologies that make AI possible Looking under the hood to see how it works While some have traced the history of artificial intelligence back to Greek mythology and philosophers, fast-forward with me to the twentieth century when serious work on AI was directed to practical applications.
The term artificial intelligence was first used in a 1955 proposal for the Dartmouth Summer Research Project on Artificial Intelligence, in which American computer scientist John McCarthy and others wrote:
Over the following decades, AI progress waxed and waned as development overcame one obstacle only to encounter another.
In this chapter, you get an idea of what, why, and how:
- What the fuss is all about, what AI can do for you, and what it can’t.
- Why now and not 20 years ago, and why AI is suddenly all the rage and wherever you look you see news about everything from self-driving cars to AI-powered showerheads.
- How it works, and how all the moving parts fit together to solve interesting and challenging problems.
Before I go any further, let me get a few definitions out of the way right up front so you’ll know what I mean when I use a term.
Algorithm: A set of rules and calculations used for problem-solving. Some compare an algorithm to the process you follow when you make dinner. The problem to be solved is getting a fully prepared meal on the table, and the algorithm consists of the recipes you use to turn ingredients into the dishes you will serve. An algorithm is not a magic formula; it’s just a regular kind of formula, or rather a set of formulas and instructions.
Machine learning: A collection of algorithms that discover relationships in data with an associated level of confidence based on the likelihood, or probability, that it is a true relationship. Note that I didn’t say ML teaches the machine to think or make decisions the same way humans do. It’s just math. Some pretty fancy math, but still math.
Artificial intelligence: A collection of machine-learning and related technologies used to accomplish tasks that normally require human intelligence, such as to recognize and categorize speech, identify an object or person in a photo or video, or summarize the content of a social media post.
It comes down to pattern recognition. You can think of the human brain as a massively parallel pattern-recognition engine. AI enlists the processing power of computers to automate pattern recognition.
In some ways, AI is more powerful than the human brain, especially in how fast it can match certain patterns. JP Morgan Chase developed a machine-learning system that processed loans that took lawyers and loan officers a total of 360,000 hours to complete; it did this in less than a minute and with fewer mistakes.
In other ways, the human brain is more powerful than current AI implementations. Humans can use all the pattern matching processes that they have learned before to contextualize new pattern matching processes. This ability allows them to be far more adaptable than AI, for now. For example, if you take a photo of a chihuahua from a certain angle, it can look surprisingly like a blueberry muffin. A human can quickly identify which photos are chihuahuas and which are muffins. AI, not so much.
Understanding the Demand for AI
If there is a universal constant in commerce throughout the ages, it is competition. Always changing, always expanding, always looking for a foothold, an advantage — whether from reducing costs, increasing revenue, or unlocking new, innovative business models.
Similarly, while much discussion has taken place in the last few decades about the challenges posed by a global economy, international trade is not a recent phenomenon. It dates back at least to the Assyrians.
Four millennia later, the goal is the same for the modern enterprise: establish a competitive advantage. However, the specific challenges to tackle are new.
Converting big data into actionable information
As data increased in volume, variety, and velocity (known as the three Vs of data), data processing departments experienced an increasing challenge in turning that data into information.
Enter big-data analytics, which is a collection of analytical methods that provide increasing levels of understanding and value.
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Descriptive analytics = information
- Diagnostic analytics = hindsight
- Predictive analytics = insight
- Prescriptive analytics = foresight
Descriptive analytics
Descriptive analytics reveal what happened. Sometimes called business intelligence, this tool turns historical data into information in the form of simple reports, visualizations, and decision trees to show what occurred at a point in time or over a period of time. In the larger landscape of big-data analytics, it performs a basic but essential function useful for improving performance.
Diagnostic analytics
Diagnostic analytics reveal why something happened. More advanced than descriptive reporting tools, they allow a deep dive into the historical data, apply big-data modelling, and determine the root causes for a given situation.
Predictive analytics
Predictive analytics present what will likely happen next. Based on the same historical data used by descriptive and diagnostic analytics, this tool uses data, analytical algorithms, and machine-learning techniques to identify patterns and trends within the data that suggest how machines, parts, and people will behave in the future.
Prescriptive analytics
Prescriptive analytics recommend what to do next. This tool builds on the predictive function to show the implications of each course of action and identify the optimum alternative in real time.
AI-powered analytics
AI-powered analytics expose the context in vast amounts of structured and unstructured data to reveal underlying patterns and relationships. Sometimes called cognitive computing, this tool combines advanced analytics capabilities with comprehensive AI techniques such as deep learning, machine learning, and natural-language recognition.
Figure 1-1 shows the relationship between business value and difficulty of an analytic method.
All these tools combine to bring a fourth “V” to the table: visualization.
