The AI Conundrum Excerpt

Introduction

Artificial Intelligence Acceleration

Over the past two decades, research, applications, and interest in artificial intelligence (AI) have exploded. The use of AI has significant momentum and is accelerating. Consider the trend of these signposts:

• In October 2018, MIT announced a $1 billion commitment to address the global opportunities presented by the rise of AI—to “reorient MIT to bring the power of computing and AI to all fields of study at MIT, allowing the future of computing and AI to be shaped by insights from all other disciplines.” Going forward, AI is meant to underpin every course in every subject at MIT.1

• In 2020, venture-capital investment in AI was $28 billion, according to Protocol, a firm that tracks venture capital. In the first six months of 2021, $30 billion had already been invested, eclipsing the entire prior year’s investment in half the time.2

• International Data Corporation (IDC), a technology-tracking firm, measured $50.1 billion in global AI spending in 2020 and expected businesses to invest $110 billion in AI software and hardware by 2024.3 A year later, in 2021, it reported that spending had already eclipsed those estimates, growing to an eye-popping $341.8 billion. After its underestimation, IDC revised its forecasts for AI upward, expecting spending will exceed more than half a trillion dollars in 2023.4

• In 2023, OpenAI, a company with less than $40 million in revenue, set a new start-up valuation record when it received a $10 billion investment from Microsoft, valuing the company at $29 billion.5

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Artificial intelligence is expanding in part because the barriers to creating and applying it are decreasing quickly and in part because of the massive profit potential in AI applications. Today, there are “no code” implementations of AI that allow nontechnical developers to build their own AI applications. Extensive AI libraries allow software developers to plug AI into a variety of programs without needing to know the underlying math. Amazon, Google, Microsoft, OpenAI, and other major players have made it easy to run AI in their clouds with a credit card and a few clicks. OpenAI’s chatbot, ChatGPT, added one million users in less than a week following its public release. Thousands of AI start-ups offer products and services spanning the catalog from A to Z—from advertising optimization to zoo management, from autonomous long-haul trucks to zoonotic disease detection intended to identify the next pandemic before it spreads. Researchers are developing new AI advances every day, and businesses and governments are applying AI at breakneck speed.

However, AI has more weaknesses than most people realize. Given the expected growth in the application of AI and the torrent of AI hype that comes from well-funded companies with an economic interest in celebrating AI’s successes, it is important that more people better understand AI’s strengths and inherent weaknesses. AI is often anthropomorphized as having a humanlike ability to take in information and apply humanlike reasoning based on humanlike motivations to produce its answers and insights. But the reality is quite different. To function effectively for a given purpose, AI requires the acquisition of data and a pipeline to load more “experiences” at many orders of magnitude greater than any human requires to learn similar patterns. Even after “learning” these patterns and producing the answer a human wants the AI to output, the AI may not have really learned the underlying meaning of its input. In many cases, it has developed an uncanny ability to imitate understanding without actually understanding the task at all. AI operates on a programmed reward system, which in itself can cause a host of alignment problems. AI can also suffer from bias, which comes from the data it feeds on. Even if there isn’t bias in the data, the mathematics of AI makes it easy for the AI to learn a spurious relationship that doesn’t truly match what we are trying to teach the system. Yet adoption of AI is growing because it can be useful and profitable in a wide range of domains. By the end of this book, we hope that you will hold two simultaneous impressions of AI:

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• Awe: “It is amazing what AI can do.”

• Caution: “It is amazing that AI doesn’t know what it is doing or why it is doing it.”

Ultimately, understanding the fundamental limitations of the current state of the art in AI provides the reader with the advantage of a more comprehensive, nuanced, and sober grasp of AI. It allows us to look past the hype and determine the applications where AI can best add value while minimizing risk. In terms of strengths, AI’s superpower is its ability to take in any data set and find patterns that can be used for classification, prediction, or generation of new content. Computer scientists call this automatic pattern fitting “learning” and the underlying system of math “artificial intelligence.” The downside of AI’s learning superpower is that AI transforms data and fits patterns in ways that can produce outputs that aren’t precise. AI can be easily fooled when operating in an open environment. It doesn’t offer rationale for the patterns it has learned. And for it to learn the patterns in the first place, it requires massive amounts of data— some would say AI is so data hungry that it is inefficient. This book explains these and other limitations of AI and suggests how researchers are attempting to expand AI toward an intelligence that is more robust. By the end of part I, you will understand how AI operates, and you will have both an explanation for AI’s weaknesses and an appreciation of its strengths. By the end of part II, you will be able to identify areas where AI is a great solution relative to other alternatives as well as areas where AI is risky and where countermeasures are required. You will understand why AI can produce biased output and know the countermeasures that can offset the risk (at least in part).

Business decision-makers and government policy makers will be well served by investing time to understand the AI risk framework to support responsible use. Some in the field of AI today, especially those with an economic interest in it, tend to gloss over its weaknesses. But no one can afford to wear blinders when it comes to artificial intelligence. We all need to study its limits in order to help us avoid risky overestimation of it now, even while potentially building better AI for the future.

Before we explore AI’s weaknesses, let’s celebrate the strengths of AI. Travel back with us to 2017, when we had the privilege of attending MIT’s Northern California Artificial Intelligence Conference.6 One of us (Caleb)

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was an outsider, the other (Rex) an insider invited by the event’s headline sponsor. We were at the event together, and then over the next few years we worked on our separate AI efforts. Five years later, our distinct experiences came together to form this book. We will briefly describe our experiences with AI so you know where we are coming from and how those experiences influenced our perspective in this book.

The Insider’s View (Rex)

As an insider, I had plenty of experience with AI over the previous 25 years. In 1996, I applied neural networks to personalize the experience of a prominent website. In tracking the people who visited the website and the stories on which they clicked, the AI learned to predict the content that would lead people to read more, helping the company to sell more ads. I was fortunate that WIRED wrote about my work in its May 1998 cover story, “The Promise of One to One (a Love Story).”7

Over the years, I was keenly interested in applying AI to marketing and market research applications, such as sussing out bias in population studies and automatically correcting for it. I founded a company to help marketers evolve from making decisions with their guts to using data and machine learning, and some very talented and committed people joined me in the effort. Together, we applied AI to generate automatic segmentations for personalized messaging and to attribute the influence of advertising to consumer behavior. The team applied AI to a wide range of business needs, including an AI that predicted how the Knicks could best fill Madison Square Garden seats depending on 32 factors, such as web traffic, betting odds, social media buzz, day and time of the game, and injuries. The model could tell that the surest way to fill up the Garden was for the Knicks to win more often—but, alas, we couldn’t solve that one for them. We could take the win/loss record as a given and focus on how best to optimize marketing with our AI. Another AI application used data to determine how to adjust advertising spending for maximum impact on an auto manufacturers’ profits. A brilliant graduate student intern developed an AI that could detect a brand logo while watching television to aid in attribution modeling for advertisers.

In my lab in 2014, the team and I created an AI that could analyze media spending for the top-100 advertisers in the United States and identify

The AI Conundrum:

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opportunities to increase sales and profits by tens of millions of dollars. We anthropomorphized the AI by pairing it with a robot and named it MONICA. We hired a Burning Man costume designer to create a dress to cover the robot’s frame. Atop the frame was a Microsoft Surface tablet that displayed a computer-generated female face. MONICA’s lips moved in sync with its text-to-voice speech. Our goal was to shake up the advertising industry by having the AI roll up to prominent marketers at the Association of National Advertisers (ANA) conference with the question about why they weren’t spending more in whatever area the AI identified as way past optimal. Jack Neff with the trade magazine Advertising Age wrote a story titled “Hey, ANA: Monica’s Coming for You and She Knows Who You Are, What You Spend: Robot Roaming at Conference Will Promote Free ‘Marketing Brain.’”8 The case study for my company’s work with Warner Bros. on the film Creed featured the same virtual MONICA robot explaining how the AI boosted sales for the film (for reference, the video is available on this book’s website at www.AI-Conundrum.com). We were grateful for the recognition from the Entrepreneurs Organization for naming our work on marketing attribution as one of the top-ten innovations of 2014 and to I-Com for its awards for the business impact we produced for our customers.

It was intriguing to see people react to our MONICA robot. During the main session of the ANA conference, when Kraft’s chief marketing officer presented the company’s strategy and opened the floor to questions, the MONICA robot rolled up to the microphone and observed, “Based on my analysis of your advertising spending data, you are not spending enough on digital media. You are underspent by 15.7 percent and that is costing your shareholder’s value. You are especially underinvesting in social media. Why is that?” It was a curious moment: a human expert having to justify actions to a robot. We hoped to inspire curiosity in AI and what we saw as a coming wave of innovations from artificial intelligence.

Inspiring people to look at data and technology a little differently led me to present a TED Talk on how AI can help humans shift from being knowledge workers to being insight workers.9 We trained AI to become aware of what a person in business is working on so that the AI could offer bestpractices recommendations to the human partner. The team and I appreciated the validation that came from being financially backed by one of the best early-stage AI funds in Silicon Valley, Zetta Ventures. I personally appreciated that Zetta’s founder invited me to attend the MIT AI event,

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where Zetta was the title sponsor, and to bring my son, Caleb, as he began to experiment with AI.

By the time I sold the controlling interest in my company in 2019, the patent office had issued us five patents for AI data analytics and knowledge management. After exiting the company, I volunteered my data science and technology experience to education, health-care, and law enforcement agencies. When COVID struck, I was able to help on a local level with Immunize Nevada and on a national level with the Ad Council and the COVID Collaborative on the vaccination effort. I dove in and created a model that was among the most accurate at predicting hospitalizations and deaths 30 and 90 days out as well as immunization rates and the factors that influenced them. We open-sourced the model, shared it daily with our partners, and published it in Research World on a monthly basis. We used the model to help guide Ad Council public-health advertising. We published research with our state public-health lab in Nevada with academics from Harvard and Brown Universities. In these efforts, I partnered with ArtsAI, an AI advertising technology firm, to implement AI-based communications and will be forever grateful for its engagement. We estimate that our collective efforts saved about 3,500 lives and kept more than 20,000 people out of hospitals—we wish we could have done more.

Considering my experience, it is safe to say I am a strong proponent of AI. I appreciate that AI has power, but I also noticed some unsettling characteristics in my journey with AI. Some of AI’s challenges were obvious, such as the need to invest lots of human time to label data in order to develop a controlled vocabulary for media classification, even though our human brains have no problem sorting out the meaning without labels; the challenge of how processor intensive (and expensive) AI is for simple tasks, such as recognizing a company logo in a 30-second TV ad; and the fact that we needed a human driver to help the MONICA AI robot maneuver and to press “enter” when it was time for the AI to speak. The MONICA robot was more of a puppet controlled by a human than it superficially appeared to be.

Other issues were less obvious, such as the way bias could sneak into our data sets in ways that were hard to detect, the way we might be simultaneously predicting and influencing someone’s behavior with our segmentation and subsequent personalized advertising, and the way that such advertising might polarize a population over time. For all AI’s strengths, I had glimpses of weaknesses. It felt like seeing motion out of the corner of

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my eye, but by the time I turned to look, I couldn’t quite see what had initially caught my attention. I couldn’t quite define it yet, but I saw enough glimpses to know something wasn’t quite right with AI.

In the spring of 2021, when I heard my coauthor, Caleb, present his thesis, “The Fundamental Limitations of AI,” I knew he had put his finger on what had been gnawing at me. At the end of his presentation, I turned to my friend, a senior executive from Google who was also interested in Caleb’s thesis paper, and we had the same reaction: “Wow.”

Caleb was explaining the shortcomings we were seeing in AI inside our companies but hadn’t yet heard these shortcomings articulated or explained. For all of AI’s advantages (and there are many), its weaknesses need to be well understood and offset with countermeasures in specific areas.

The Outsider’s View (Caleb)

As an outsider in 2017, the field looked promising. I had recently learned to program Lisp, designed in 1958 by John McCarthy of MIT. It is heavy on mathematical notation and became a favored AI language in AI’s early development. I had tried my hand at creating AI from scratch and started voraciously reading research papers. I was excited to attend the MIT AI conference and absorb the content. A morning session entitled “Ghost in the Machine” discussed AI in vehicles and promised fully autonomous cars in the near future. The venture capitalist Mark Gorenberg gave a compelling explanation of the AI virtuous loop, in which the ingestion of more data generates more value, which in turn generates more data, thus expanding the value of AI decisions. Each presentation noted the fast pace of progress and projected an encouraging future.

I read about how AI has achieved impressive results in recent decades, which demonstrate it can exceed human capabilities. AI achieved superhuman levels in chess—laying waste to every human competitor. In the far more complex game Go, DeepMind’s AlphaGo AI beat the 18-time world champion, winning four games out of five. An AI called AlphaFold2 managed to achieve success on the notoriously difficult problem of protein folding. It created the first complete database of the known protein universe, covering 200 million proteins from one million species. Human researchers had managed to cover only a fraction of that number and primarily only for humans, mice, and a few other mammalian species.

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At the same time, AI has captured the interest of more and more people— Google searches for “AI” have gradually increased since 2004, accelerating even faster since 2016. In the press, AI dazzled reporters. I calculated that the number of stories in the New York Times mentioning AI has more than doubled in the past five years compared to the five years before that. A natural-language-processing engine, GPT-2, was even interviewed by the Economist and featured in the New Yorker 10 The Cornell math professor Steven Strogatz described AlphaZero, a chess-playing AI application, in this way: “Most unnerving was that AlphaZero seemed to express insight. It played like no computer ever has, intuitively and beautifully, with a romantic, attacking style. It played gambits and took risks. . . . Grandmasters had never seen anything like it. AlphaZero had the finesse of a virtuoso and the power of a machine. It was humankind’s first glimpse of an awesome new kind of intelligence.”11 As a fan of Dr. Strogatz’s videos that dive deeply into math methods, I wanted to delve into what he learned about the inner workings of AI. My imagination was engaged. I set out to expand my understanding of the underlying math of neural networks so I could build more sophisticated AI from scratch, without the use of AI libraries.

To augment what I learned in biology class, I created genetic algorithms to better understand the evolution of single-cell organisms—I was intrigued by how well the simple goal-seeking AI rules autonomously evolved into artificial life forms with many similarities to our real-world single-cell organisms. I created self-driving bots to navigate randomly generated racing tracks. I was impressed by how the AI utilized an oversight in my code that allowed for negative inputs to “invent” a way to reverse itself when it worked itself into a corner—an outcome I hadn’t expected. I built computer-vision AI for a robot, so the robot could see and grab the right blocks and move them to the right place in the physical world—it worked remarkably well in the predictable confines of a garage. I created a kill bot that uses real-time object detection to align the scope on an enemy target in a first-person-shooter game—it killed the other team more efficiently than I ever could. I created AI art—including the design in the table of contents of this book. I created a language AI that scraped Quora for questions, learned from the popularity scores, and developed its own questions—it received more than half a million views from humans.

As I worked with math and code, I began to appreciate the strengths and weaknesses of AI. Although the AIs that I built could drive, ask questions,

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produce art, kill virtual targets, and produce generations of artificial life, I uncovered many weaknesses in the process. As I read more news about AI, I began to worry that there wasn’t enough understanding of AI’s weaknesses.

Consider the implications of two of my projects. One of my projects emerged when I was invited to the Quora partner program, which allowed users to post questions and get paid based on the ad revenue the views generated. I instantly saw an opportunity: What if I created an AI that could generate the questions automatically?

I wrote a bot that perused Quora, looked for popular questions, and added them to a database. This database fed into the training of an AI built to produce new questions that my AI predicted would be popular. In a few weeks, I had amassed more than half a million views on “my” questions. The AI had fooled people—almost. Upon close (and sometimes not so close) inspection, it was possible to find the weakness in the AI. The questions often sounded right but sometimes were absurd. For example: “How should I prepare my two-year-old for the SAT?”

At an abstract level, this is a good question—questions about educating kids and getting them into college are highly engaging on Quora. The AI knew the right concepts to ask about but didn’t understand what it meant to be a two-year-old, nor did it realize the inappropriateness of preparing a two-year-old for the SAT.

The insight I gained from AI’s generation of absurd questions is that the meaning in our words is derived from our broader human experience, which AI doesn’t yet possess. Like many AI applications, mine was fed only text. My AI lacked the multitude of human senses that allow us to find meaning in our world. The meaning in our words is deeper than our deepest digital neural networks can go today.

A second AI project to consider is the AI kill bot. A few of my friends introduced me to the first-person-shooter game Counter-Strike: Global Offensive. It is a military-style game that pits two teams, terrorists and counterterrorists, against each other and arms them with weapons. In one mode, the terrorists attempt to plant a bomb and the counterterrorists attempt to take out the terrorists. Unfortunately, no matter how hard I tried, I was terrible at the game—I was down in the bottom 10 percent of players. Practice barely helped, so I decided that the best way to improve would be to teach an AI to take over the aiming and shooting decisions. I meticulously gathered and labeled thousands of images of the characters in the game and trained the

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AI to recognize them. I found a way to hijack control of my mouse and slow it down so the AI had humanlike speed. It was important that my AI appear human in game play because the creators of the game, Valve, have code that attempts to find players using hacks to gain an advantage in the game. The end result: my AI vaulted me to the top 3 percent of players—my AI was a killing machine. However, in the process I saw glimpses of something fundamentally wrong with the AI.

My AI would register a particular oddly shaped small plant it saw as an enemy with nearly 100 percent certainty. The AI would fire relentlessly at the offending foliage. A certain lamp it saw on one map was also reliably classified as an enemy. It’s not that we can’t fix the AI’s mistakes in these cases—it’s that these mistakes are a tell that the AI doesn’t really understand what an enemy or a lamp or a plant is. In this case, the AI was fed only images and lacked the broader understanding of what the objects represented and the context for why it was aiming and firing.

As I considered what I built and AI’s weaknesses, I wondered, “What if modern warfare used an AI for targeting and shooting? Might it also occasionally misclassify and destroy civilians?” Without understanding the context, the AI afforded a significant survival and performance advantage—I moved from the bottom 10 percent to the top 3 percent with the assistance of AI. But the AI didn’t understand the context of war, so if this type of AI were applied in the physical world, it could make terrible mistakes. Would society accept the trade-off in performance versus survival as worth the collateral damage? Humans are capable of collateral damage, too. How many hunters have mistaken another hunter for a deer? Should we hold AI to a standard of perfection? Or is a standard of “merely more efficient than humans” good enough?

My two projects with Quora and with the kill bot are a microcosm of the weaknesses of AI, which we cover in part I of this book. The weaknesses raise important questions we need to carefully consider.

Our Perspective

Artificial intelligence is a powerful tool, and it often does a great job at the tasks we ask of it. However, it has also failed to deliver on many of its promises. Its use in image identification in the legal system has led to false arrests. The release of unlimited autonomous cars is continuously delayed

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due to a range of shortcomings. AI can even fail miserably at playing a video game it has perfected in the past if just a few extra pixels are added to the input—a change that would not affect a human’s game play for more than a second. The bottom line is that many AI systems show alarming failures, and some have caused and may cause serious harm.

For humans to use AI effectively and responsibly, it is important for us to understand both its strengths and its weaknesses. Understanding the limitations of AI today may help us develop better AI for tomorrow and beyond. Accordingly, the book is organized as follows:

• Chapter 1: Rather than anthropomorphizing AI and assuming it has humanlike characteristics, we ask the reader to consider what it is like to be an AI. Artificial intelligence isn’t given the context or shared experiences humans often take for granted, which hampers its abilities in many situations.

• Chapter 2: Here, we explain how AI fits patterns and why AI’s structure introduces some fundamental weaknesses that prevent it from gaining a robust understanding of its inputs.

• Chapter 3: This chapter discusses how AI uses gradient descent and why AI is prone to taking shortcuts to provide the answers we seek. The shortcuts are essentially unfounded correlations that can result in a host of mistakes.

• Chapter 4: AI generates what we call associative intelligence. It can produce impressive output, but it requires massive amounts of data to make the associations, and even after consuming copious amounts of data, compressing the data, and finding patterns, the AI may still be found lacking in certain use cases, such as math and engineering.

• Chapter 5: AI has many strengths but also key weaknesses that need to be well understood. This chapter answers why AI struggles with precision, can fail in open environments, and can’t easily provide a rationale for its decisions.

• Chapter 6: We offer a framework for analyzing risk that is based on three criteria: the need for precision, amount of control over the inputs the AI uses to operate, and whether a rationale is required for the system’s decisions. This chapter also explains an approach to compare AI to the next-best alternative to achieve a business’s purposes. And, finally, we conclude with an explanation of why the availability of data, economics,

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and competitive pressures will likely drive adoption of AI even when its application is risky.

• Chapters 7 through 9: We present specific case examples of AI’s strengths and weaknesses in business applications such as language translation, autonomous vehicles, and automated trading. To address AI’s weaknesses, we suggest countermeasures that can reduce risk.

• Chapter 10: Again using case studies, we take a deep dive into AI bias that is inherent in many systems and suggest approaches to assess and reduce bias.

• Chapter 11: We conclude by recommending approaches to training, governance, and accountability as well as a service-architecture approach to human workflow that will make applying ever-improving AI easier. We suggest approaches to safety, including the use of authenticated identity for AI that has access to the internet.

The book’s website (www.AI-Conundrum.com) includes the following resources:

• Risk Analysis Checklist. We offer a set of 25 questions that business decision-makers should consider as part of AI planning and governance. This worksheet can facilitate analyzing risk. For those applying AI in riskier scenarios, the worksheet offers countermeasures that may offset AI’s weaknesses or that may lead a decision-maker to refrain from using AI because the risks are too high.

• A look at why AI polarizes in social media. The documentary The Social Dilemma (Jeff Orlowski, 2020) offers a broad explanation of the polarizing effects of social media algorithms, but a deeper understanding of AI and how it interacts with the economic model in media provides a more complete explanation. A discussion of possible solutions is also included.

• A deep dive into AI bias, how to analyze if an AI system is producing biased output, and various strategies to mitigate bias.

• Videos, interactive tools, and labs to get hands-on experience with AI.

• Blog of updates to the state of the science in AI and trends we find noteworthy.

In this book, we consider, for the most part, the artificial intelligence of today, which is characterized mostly by a single input type, such as text or

The AI Conundrum:

Harnessing the Power of AI for Your Organization—Profitably and Safely August 2024

images. The AI of tomorrow will broaden the range of inputs and introduce new mathematical structures that may lead to a new generation of AI, but some of the weaknesses will likely linger. For the purposes of today’s readers and given the current state of artificial intelligence, we focus on current machine-learning techniques, such as gradient descent and reinforcement learning, as well as on structures such as neural networks, transformers, and generative adversarial networks. We touch on the different outcomes AI produces, such as classification, clustering, and regression, as well as on different techniques for developing AI, such as supervised, unsupervised, and few-shot learning. Our goal is to demystify AI and give you a foundation for understanding AI conceptually.

We look forward to seeing if the challenges described in this book can be overcome in the future. Until then, our hope is this book will be useful to diverse audiences—including business decision-makers, students across a broad range of majors, and government officials—to allow them to

• Better recognize AI’s weaknesses and strengths.

• Ask better questions of their advisers and agents about the AI developed by them or used by their business.

• Aid in evaluating risks and rewards in a business’s or government’s application of AI throughout its organization.

• Ensure effective and ethical use of AI.

• Avoid risky applications of AI—or at least build in countermeasures to offset the risk.

• Demystify what AI is for everyday citizens and journalists so we can communicate AI’s strengths and weaknesses and appreciate why AI can sometimes fail.