Marketing with AI For Dummies (eBook)

Shiv Singh (Autor)

eBook Download: EPUB

2024
537 Seiten
For Dummies (Verlag)
978-1-394-23720-3 (ISBN)

Infos

Stay ahead in the marketing game by harnessing the power of artificial intelligence

Marketing with AI For Dummies is your introduction to the revolution that's occurring in the marketing industry, thanks to artificial intelligence tools that can create text, images, audio, video, websites, and beyond. This book captures the insight of leading marketing executive Shiv Singh on how AI will change marketing, helping new and experienced marketers tackle AI marketing plans, content, creative assets, and localized campaigns. You'll also learn to manage SEO and customer personalization with powerful new technologies.

• Peek at the inner workings of AI marketing tools to see how you can best leverage their capabilities
• Identify customers, create content, customize outreach, and personalize customer experience with AI
• Consider how your team, department, or organization can be retooled to thrive in an AI-enabled world
• Learn from valuable case studies that show how large organizations are using AI in their campaigns

This easy-to-understand Dummies guide is perfect for marketers at all levels, as well as those who only wear a marketing hat occasionally. Whatever your professional background, Marketing with AI For Dummies will usher you into the future of marketing.

Shiv Singh is a future-focused busi­­ness executive who has developed and executed cutting-edge marketing strategies, tools, and techniques for some of the world's largest brands. He is also the trailblazing author of Social Media Marketing For Dummies and Savvy, Navigating Fake Companies, Leaders & News. Along the way, he has served as VP and Global Social Media Lead for Razorfish, Head of Digital for PepsiCo Beverages, SVP Innovation Go-to-Market for Visa, and most recently, as the Chief Marketing and Customer Experience Officer for LendingTree, where he managed a media budget of $650 million and led a team of 150 marketers.

Textauszug

Chapter 1

A Brief History of AI

IN THIS CHAPTER

Tracking AI from conception to fruition

Watching machines fool people and beat the experts

Seeing advanced AI capabilities in everyday life

To fully grasp the role of artificial intelligence (AI) in business, I begin by helping you trace its fascinating history. This background exploration not only illuminates AI’s vast advancements, but also highlights its utility in business and marketing.

The earliest conceptions of artificial intelligence date back to Greek mythology, where Talos — an 8-foot-tall giant constructed of bronze — stood guard over the island of Crete to protect it from pirates and other invaders. Talos would throw boulders at ships and patrol the island each day. As the legend goes, Talos was eventually defeated when a plug near his foot was removed, allowing the ichor (blood of the gods) to flow out from the single vein in his body.

From that point forward, tales of automated entities flourished in mythology, captivating the minds of scientists, mathematicians, and inventors. Modern science and technology have realized some of these mythological concepts through recent advancements. In this chapter, I introduce you to those advancements, including the Turing test, machine learning, expert systems, and generative AI.

Early Technological Advances

Scientists trace the dawn of automation back to the 17th century and the invention of the pascaline, a mechanical calculator. Constructed by French inventor Blaise Pascal between 1642 and 1644, this groundbreaking device featured a controlled carry mechanism that facilitated the arithmetic operations of addition and subtraction by effectively carrying the 1 to the next column. This calculator worked especially efficiently when dealing with large numbers. Following in Pascal’s footsteps, Wilhelm Leibniz, a German mathematician, invented a calculator in 1694 that expanded upon the concept of the pascaline by enabling all four basic arithmetic operations — addition, subtraction, multiplication, and division (not just addition and subtraction). These devices first offered a glimpse into the potential for mechanical reasoning.

Fast-forward to the early 1800s, and you encounter the Jacquard system, developed by Joseph-Marie Jacquard of France, which used interchangeable punched cards to dictate the weaving of cloth and the design of intricate patterns. These punched cards laid the groundwork for future developments in computing. Near the mid-1800s, British inventor Charles Babbage unveiled the first computational device known as the analytical engine. Employing punch cards, this machine could perform a variety of calculations involving multiple variables, and it featured a reset function when it completed its task. Importantly, it also incorporated temporary data storage for more advanced computations — a feature crucial for any artificial intelligence (AI) system.

By the late 1880s, the development of the tabulating machine — designed by American inventor Herman Hollerith specifically to process data for the 1890 U.S. Census — helped the development of AI reach another milestone. This electro-mechanical device utilized punched cards to store and aggregate data, effectively enhancing the analytical engine’s storage capabilities through the inclusion of an accumulator. Remarkably, modified iterations of the tabulating machine remained operational until as recently as the 1980s.

Alan Turing and Machine Intelligence

Many people regard Alan Turing, a British mathematician, logician, and computer scientist, as the founding father of theoretical computer science, and he paved the way for further AI breakthroughs. During World War II, he served at Bletchley Park, the United Kingdom’s codebreaking establishment; and he played a pivotal role in decrypting messages encoded by the German Enigma machine (a code-generating device). Scholars and historians credit his work at Bletchley Park with both shortening the war and saving millions of lives.

Turing’s key innovation at Bletchley was the development of the Bombe, a machine that significantly accelerated the code-breaking process used to decode messages from the Enigma machine. The Enigma used a series of rotating disks to transform plain text messages into encrypted cipher text. The complexity of this encryption device and the coded messages it generated came in part from the fact that Enigma users changed the machine’s settings daily. The United Kingdom and all the Allies found cracking the code within the 24-hour window — before the settings were altered again — exceedingly difficult. The Bombe automated the process of identifying Enigma settings, sorting through various potential combinations far more rapidly than any human could. This automation enabled the British to regularly decode German communications.

Although the details of this code-breaking device remained classified for many years, the Bombe stands as one of the earliest examples of technology outperforming humans in tasks that traditionally required human intelligence, executing them more efficiently and accurately.

The Turing Test in 1950

Soon after World War II, in a paper published in 1950 titled “Computing Machinery and Intelligence,” Turing introduced the idea of defining a standard by which we can call a machine intelligent. He designed the experiment (now called the Turing test) to answer the question, “Can machines think?” The fundamental premise of the experiment said that if a computer can participate in a dialogue with a human in such a way that an observer can’t tell which participant is human and which is computer, then you can consider that computer intelligent.

Turing’s test proposed that a human evaluator assess dialogues between a human and a machine that was designed to generate human-like responses. The evaluator knows that one of the participants is a machine, but not which one. To eliminate any bias from vocal cues, Turing proposed that the test giver limit the interactions to a text-only medium. If the evaluator found it challenging to distinguish between the machine and the human participant, the machine passed the test. The evaluation didn’t focus on the correctness of the machine’s answers, but on how indistinguishable its responses were from a human’s. In fact, the test’s criteria didn’t make any reference to the accuracy of the answers.

The Turing test: 1960s and beyond

In 1966, well after Alan Turing’s death, German-American scientist Joseph Weizenbaum created ELIZA, the first program that some say appeared to pass the Turing test. Many sources refute that it could pass the Turing test, but it was technically capable of making some humans believe that they were talking to human operators. The program worked by studying a user’s typed comments for keywords and then executing a rule that transformed the user’s comments, resulting in the program returning a new sentence. In effect, the ELIZA, like many programs since then, mimicked an understanding of the world without actually possessing any real-world knowledge.

Taking this development a step further, in 1972, Kenneth Colby, an American psychiatrist, created PARRY, which he described as ELIZA with attitude. Experienced psychiatrists tested PARRY in the early 1970s by using a variation of the Turing test. They analyzed text from real patients and from computers running PARRY. The psychiatrists correctly identified the patients only 52 percent of the time, a statistic consistent with random guessing.

Even to this day, the Turing test gives the world a concise, easily understandable method of assessing whether a piece of technology has intelligence or not. By limiting the test to text-based interactions that require natural language query (conversational English), anyone could easily understand the nature of the test when Turing first introduced it. And by separating out the accuracy of the response from the question of identification, it focused the test on evaluating what truly makes humans more human.

Computers have advanced by leaps and bounds since the time that Alan Turing first proposed the Turing test. But consider this timeline regarding the ongoing development of intelligent technology:

• As recently as 2021, chatbots that much of the world had access to struggled to pass the Turing test consistently. Services such as Siri from Apple, Alexa from Amazon, and Google’s Assistant could speak to us in natural language but would quickly get stumped with some of the most basic of questions. For example, the question “Describe yourself using only colors and shapes?” may prompt the answer “Okay, I found this on the web for describing colors and shapes… .”
• As of 2023, major chat interfaces from the likes of OpenAI, Google, and others, can pass the Turing test. This quick change shows how technological advancements in the field of AI happen in fits and starts, with so much having changed dramatically in just 24 months.

The Dartmouth Conference of 1956

The academic community often considers the Dartmouth Conference of 1956 as the birth of artificial intelligence (AI) as a distinct field of research. Held during the summer of that year at Dartmouth College in Hanover, New Hampshire, the conference brought together luminaries...

Produkt-Details

• Erscheint lt. Verlag: 22.8.2024
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Schlagworte: AI • AI Marketing • Algorithmic marketing • artifical intelligence marketing • Artificial Intelligence • artificial intelligence business • B2B Marketing • Business Intelligence • chatgpt marketing • Digital Marketing • marketing book • midjourney marketing
• ISBN-10: 1-394-23720-0 / 1394237200
• ISBN-13: 978-1-394-23720-3 / 9781394237203