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John Paul Mueller
Luca Massaron
Stephanie Diamond
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Published:
November 4, 2024

Artificial Intelligence For Dummies

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Overview

Dive into the intelligence that powers artificial intelligence

Artificial intelligence is swiftly moving from a sci-fi future to a modern reality. This edition of Artificial Intelligence For Dummies keeps pace with the lighting-fast expansion of AI tools that are overhauling every corner of reality. This book demystifies how artificial intelligence systems operate, giving you a look at the inner workings of AI and explaining the important role of data in creating intelligence. You'll get a primer on using AI in everyday life, and you'll also get a glimpse into possible AI-driven futures. What's next for humanity in the age of AI? How will your job and your life change as AI continue to evolve? How can you take advantage of AI today to make your live easier? This jargon-free Dummies guide answers all your most pressing questions about the world of artificial intelligence.

  • Learn the basics of AI hardware and software, and how intelligence is created from code
  • Get up to date with the latest AI trends and disruptions across industries
  • Wrap your mind around what the AI revolution means for humanity, and for you
  • Discover tips on using generative AI ethically and effectively

Artificial Intelligence For Dummies is the ideal starting point for anyone seeking a deeper technological understanding of how artificial intelligence works and what promise it holds for the future.

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About The Author

John Paul Mueller was a long-time tech author whose credits include previous editions of this book along with Machine Learning For Dummies and Algorithms For Dummies.

Luca Massaron is a data scientist who specializes in organizing big data and transforming it into smart data. He is a Google Developer Expert (GDE) in AI and machine learning.

Stephanie ­Diamond is a marketing professional and author of or contributor to over two dozen books, including ­Digital Marketing All-in-One For Dummies and Writing AI Prompts For Dummies.

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CHEAT SHEET

Artificial intelligence (AI) is a technology that has grabbed a lot of attention in movies, books, products, and a slew of other places. When it works, AI performs amazing feats, but they are often of a mundane nature. Vendors frequently equate AI with “smart” products (products that are connected to the internet to provide added value).
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Artificial intelligence (AI) and humans differ and humans have absolutely nothing to worry about in the job market. Yes, some jobs will go away, but the use of AI will actually create a wealth of new jobs — most of them a lot more interesting than working on an assembly line. The new jobs that humans will have rely on the areas of intelligence that an AI simply can’t master.
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A technology like artificial intelligence (AI) is useful only as long as it makes some sort of substantial contribution to society. Moreover, the contribution must come with a strong financial incentive, or investors won’t contribute to it. Although the government may contribute to a technology that it sees as useful for military or other purposes for a short time, long-term technological health relies on investor support.
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Artificial Intelligence (AI) hasn’t just failed to meet expectations set by overly enthusiastic proponents; it has failed to meet specific needs and basic requirements. This list is about the failures that will keep AI from excelling and performing the tasks we need it to do. AI is currently an evolving technology that is partially successful at best.
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An algorithm is a kind of container. It provides a box for storing a method to solve a particular kind of a problem. Algorithms process data through a series of well-defined states. The states need not be deterministic, but the states are defined nonetheless. The goal is to create an output that solves a problem.
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Alan Turing’s Bombe machine wasn’t any form of artificial intelligence (AI). In fact, it isn’t even a real computer. It broke Enigma cryptographic messages, and that’s it. However, it did provide food for thought for Turing, which eventually led to a paper titled “Computing Machinery and Intelligence” that he published in the 1950s that describes The Imitation Game.
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Artificial intelligence (AI) is a technology that has grabbed a lot of attention in movies, books, products, and a slew of other places. When it works, AI performs amazing feats, but they are often of a mundane nature. Vendors frequently equate AI with “smart” products (products that are connected to the internet to provide added value).
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Polls often show what people think they want, rather than what they do want, but they’re still useful. When polled to see what kind of life recent college graduates wanted, not one of them said boredom. In fact, you could possibly poll just about any group and not come up with a single boring response. Most humans (saying all would likely result in an avalanche of email with examples) don’t want to be bored.
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One of the most often stated roles for AI, besides automating tasks, is keeping humans safe in various ways. Articles such as this one describe an environment in which AI acts as an intermediary, taking the hit that humans would normally take when a safety issue occurs. Safety takes all sorts of forms. Yes, AI will make working in various environments safer, but it’ll also help create a healthier environment and reduce risks associated with common tasks, including surfing the Internet.
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At one time, losing a limb or having another special need meant years of doctor visits, reduced capability, and a shorter and less happy life. However, better prosthetics and other devices, many of them AI-enabled, have made this scenario a thing of the past for many people. For example, check out this couple dancing.
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Algorithms and AI changed the data game. The human race is now at an incredible intersection of unprecedented volumes of data, generated by increasingly smaller and powerful hardware. The data is also increasingly processed and analyzed by the same computers that the process helped spread and develop. This statement may seem obvious, but data has become so ubiquitous that its value no longer resides only in the information it contains (such as the case of data stored in a firm’s database that allows its daily operations), but rather in its use as a means to create new values; such data is described as the “new oil.
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Humans constantly correct everything. It isn’t a matter of everything being wrong. Rather, it’s a matter of making everything slightly better (or at least trying to make it better). Even when humans manage to achieve just the right level of rightness at a particular moment, a new experience brings that level of rightness into question because now the person has additional data by which to judge the whole question of what constitutes right in a particular situation.
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Two types of confusion arise regarding the use of artificial intelligence (AI) in an actual product. The first type relates to the smart device, which merely provides connectivity to a backend application and appears to use an AI. For example, a smart thermometer might provide connectivity to your smartphone, but it doesn't rely on an AI to do anything.
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When collecting data for artificial intelligence algorithms, you must consider data misalignments and how to correct them. Data might exist for each of the data records in a dataset, but it might not align with other data in other datasets you own. For example, the numeric data in a field in one dataset might be a floating-point type (with decimal point), but an integer type in another dataset.
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Bayes’ theorem can help you deduce how likely something is to happen in a certain context, based on the general probabilities of the fact itself and the evidence you examine, and combined with the probability of the evidence given the fact. Seldom will a single piece of evidence diminish doubts and provide enough certainty in a prediction to ensure that it will happen.
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You can hardly avoid hearing about artificial intelligence (AI) today. You see AI in the movies, in the news, in books, and online. It's been in the news a lot lately, with all of the frenzy surrounding ChatGPT (see more about that below). ©Blue Planet Studio / Adobe StockAI is part of robots, self-driving (SD) cars, drones, medical systems, online shopping sites, and all sorts of other technologies that affect your daily life in so many ways.
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Many of the current techniques for extending the healthy range of human life (the segment of life that contains no significant sickness), rather than just increasing the number of years of life depends on making humans more capable of improving their own health in various ways.You can find any number of articles that tell you 30, 40, or even 50 ways to extend this healthy range, but often it comes down to a combination of eating right, exercising enough and in the right way, and sleeping well.
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There are a number of different ways in which to view the question of application friendliness addressed by artificial intelligence (AI). At its most basic level, an AI can provide anticipation of user input. For example, when the user has typed just a few letters of a particular word, the AI guesses the remaining characters.
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Having plentiful data available isn’t enough to create a successful AI. Presently, an AI algorithm can’t extract information directly from raw data. Most algorithms rely on external collection and manipulation prior to analysis. When an algorithm collects useful information, it may not represent the right information.
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To answer a given question correctly, you must have all the facts. You can guess the answer to a question without all the facts, but then the answer is just as likely to be wrong as correct. Often, someone who makes a decision, essentially answering a question, without all the facts is said to jump to a conclusion.
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The CPU still works well for business systems or in applications in which the need for general flexibility in programming outweighs pure processing power. However, GPUs are now the standard for various kinds of data science, machine learning, AI, and deep-learning needs. Of course, everyone is constantly looking for the next big thing in the development environment.
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A suggestion is different from a command. Even though some humans seem to miss the point entirely, a suggestion is simply an idea put forth as a potential solution to a problem. Making a suggestion implies that other solutions could exist and that accepting a suggestion doesn’t mean automatically implementing it.
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An AI that is self-contained and never interacts with the environment is useless. Of course, that interaction takes the form of inputs and outputs. The traditional method of providing inputs and outputs is directly through data streams that the computer can understand, such as datasets, text queries, and the like.
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Robots and artificial intelligence (AI) routinely participate in surgical procedures today. In fact, some surgeries would be nearly impossible without the use of robots and AI. However, the history of using this technology isn’t very lengthy. The first surgical robot, Arthrobot, made its appearance in 1983. Even so, the use of these life-saving technologies has reduced errors, improved results, decreased healing time, and generally made surgery less expensive over the long run.
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Artificial intelligence (AI) is great at automation, which can make it ideal for tasks in health care. It never deviates from the procedure, never gets tired, and never makes mistakes as long as the initial procedure is correct.Unlike humans, AI never needs a vacation or a break or even an eight-hour day (not that many in the medical profession have that, either).
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A medical professional isn’t always able to tell what is happening with a patient’s health simply by listening to their heart, checking vitals, or performing a blood test. The body doesn’t always send out useful signals that let a medical professional learn anything at all. In addition, some body functions, such as blood sugar, change over time, so constant monitoring becomes necessary.
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The ability to create a modular system does have significant benefits, especially in business. The ability to remove and replace individual components keeps costs low while allowing incremental improvements in both speed and efficiency. However, as with most things, there is no free lunch. The modularity provided by the Von Neumann architecture comes with some serious deficiencies: Von Neumann bottleneck: Of all the deficiencies, the Von Neumann bottleneck is the most serious when considering the requirements of disciplines such as AI, machine learning, and even data science.
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Bayesians, symbolists, and connectionists represent the present and future frontier of learning from data because any progress toward a human-like artificial intelligence (AI) derives from them, at least until a new breakthrough with new and more incredible and powerful learning algorithms occurs. The machine learning scenery is certainly much larger than these three algorithms, but the focus here is on these three tribes because of their current role in AI.
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Humans are used to seeing data for what it is in many cases: an opinion. In fact, in some cases, people skew data to the point where it becomes useless, a mistruth. A computer or AI application can’t tell the difference between truthful and untruthful data — all it sees is data. One of the issues that make it hard, if not impossible, to create an AI that actually thinks like a human is that humans can work with mistruths and computers can’t.
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Humans demonstrate seven kinds of intelligence. These forms of intelligence help distinguish humans from other species as well as from artificial intelligence (AI). Also, an awareness of these kinds of intelligence helps you see how humans will always excel over an AI. Many people fear that AIs will take over the world and eventually replace people.
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The Von Neumann bottleneck is a natural result of using a bus to transfer data between the processor, memory, long-term storage, and peripheral devices. No matter how fast the bus performs its task, overwhelming it — that is, forming a bottleneck that reduces speed — is always possible. Over time, processor speeds continue to increase while memory and other device improvements focus on density — the capability to store more in less space.
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Not all industries are using artificial intelligence (AI). Some industries have a wait-and-see attitude when it comes to AI because AI still hasn’t completely proven its worth, and the owners of these industries remember the AI winters of the past. In addition, AI research focuses on specific industries because of how AI actually works.
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Listing all the vendors who have something to do with artificial intelligence (AI) would be impossible. The number of vendors is vast, and the smaller vendors often go out of business quickly. (Research is expensive.) Here is a list of the top AI vendors that you should keep your eyes on. Amazon Apple Bai
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Saying that AI is an artificial intelligence doesn’t really tell you anything meaningful, which is why there are so many discussions and disagreements over this term. Yes, you can argue that what occurs is artificial, not having come from a natural source. However, the intelligence part is, at best, ambiguous.
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The first concept that’s important to understand is that artificial intelligence (AI) doesn’t really have anything to do with human intelligence. Yes, some AI is modeled to simulate human intelligence, but that’s what it is: a simulation.When asking "what is artificial intelligence?" notice an interplay between goal seeking, data processing used to achieve that goal, and data acquisition used to better understand the goal.
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In 1965, Gordon Moore, cofounder of Intel and Fairchild Semiconductor, wrote in an article titled “Cramming More Components Onto Integrated Circuits” that the number of components found in integrated circuits would double every year for the next decade. At that time, transistors dominated electronics. Being able to stuff more transistors into an Integrated Circuit (IC) meant being able to make electronic devices more capable and useful.
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