Introduction
Summary of the book Artificial Intelligence by Melanie Mitchell. Before moving forward, let’s briefly explore the core idea of the book. Imagine a world where your car navigates chaotic streets, your phone answers tough questions, and digital helpers make your life easier every day. This is not a distant dream—it’s already happening, thanks to artificial intelligence. AI has crept into our routines, brightening some areas and casting shadows in others. Yet what exactly is this mysterious technology? Where did it come from, and where might it lead us? By peering into the past, we can see how early thinkers dared to imagine machine minds. By examining the present, we discover fascinating tools that write, draw, and solve problems. By thinking about the future, we grapple with what it means to be intelligent and whether AI can ever truly understand us. As you begin this journey, prepare to question, be amazed, and confront new ideas about the nature of intelligence.
Chapter 1: A Curious Journey into the Earliest Dreams and Bold Experiments of AI Pioneers .
Imagine traveling back in time to the 1950s, an era when computers were clunky machines filling entire rooms and scientists were just beginning to dream that these electronic boxes could learn and think. In that period, a small group of researchers gathered at Dartmouth College in New Hampshire with an audacious goal: to explore the possibility of creating machines that could mimic human intelligence. They believed that with enough time, careful programming, and innovative ideas, they could transform the way people thought about technology. These early thinkers were not merely satisfied with building simple calculating machines; they wanted computers that could solve puzzles, understand language, and reason about the world. Although their initial attempts did not spark the dramatic breakthroughs they had hoped for, they laid the foundation for something truly remarkable. Their legacy would guide future generations into building increasingly clever and capable artificial systems.
During these early days, optimism ran high. Scientists thought achieving human-level intelligence in machines might be simpler than it turned out to be. One notable milestone emerged in 1957 when Frank Rosenblatt introduced the Mark I Perceptron, an early attempt at creating an artificial neural network. This primitive machine tried to process information in a way inspired by how human brains handle signals. Even though its capabilities were modest by today’s standards, it was groundbreaking at the time. People marveled at the idea that a mechanical device might learn from examples and adjust its internal connections to recognize patterns—an impressive step beyond mere number crunching. While the Perceptron did not immediately create brilliant machine minds, it suggested that a path toward learning, adaptive systems might be possible. This concept of learning from data remained crucial as AI developed.
As the 1960s rolled in, more researchers became fascinated with artificial intelligence, and enthusiasm spread like wildfire. Bold predictions were made, with some experts believing that machines would soon handle any intellectual task a human could. But turning dreams into reality proved harder than expected. By the 1970s, disappointments piled up, and many ambitious projects failed to produce truly intelligent systems. Funding dried up, and the field entered what has been called the AI winter, a period marked by skepticism and scaled-back ambitions. Yet, even then, the core idea that computers could reason, learn, and mimic certain aspects of human thought did not vanish. The spark of curiosity survived among dedicated scientists who continued tinkering, investigating, and gradually improving their methods. This quiet perseverance would eventually lead to brighter days and more meaningful strides in AI research.
Come the 1980s, AI experienced a revival when expert systems took center stage. These programs tried to replicate the decision-making abilities of human specialists, like doctors or engineers, by using large sets of rules crafted by experts. While these systems were good at narrowly defined tasks, such as diagnosing diseases or suggesting engineering solutions, they lacked flexibility and genuine understanding. Still, they proved that AI could provide practical value. As the world entered the 1990s and 2000s, the explosion of the internet brought massive amounts of digital data. More powerful computers and access to large datasets enabled the field of machine learning to flourish. Machines learned patterns from oceans of information, improving automatically. By the 2010s, deep learning—a technique using many layers of artificial neural networks—unlocked incredible advances in image recognition, speech understanding, and beyond, setting the stage for modern AI.
Chapter 2: From Simple Machines to Powerful Neural Networks: The Path that Sparked the AI Revolution .
The remarkable surge in AI advancements did not happen overnight. It was a slow, steady buildup of technology, data, and new ideas. After early experiments and setbacks, researchers found that giving machines lots of examples to learn from made them improve naturally. With the internet connecting people worldwide, vast collections of text, images, audio, and video became available for training. As scientists refined algorithms, neural networks gained more layers, becoming deeper and more capable. This deep learning revolution was powered by improved computer hardware, such as graphics processing units, which processed complex calculations at tremendous speed. Suddenly, tasks that once seemed impossible—like recognizing faces in photographs or translating languages instantly—became achievable. Deep learning systems processed patterns in ways no one had fully anticipated, delivering remarkable performance even though they did not truly understand what they were doing.
One of the most astonishing outcomes of this revolution was the creation of large language models (LLMs). These systems learned to predict the next word in a sentence by studying billions of words from the internet. As they trained, they learned intricate patterns hidden in human language—everything from grammar and spelling to subtle hints about meaning and style. Once complete, these models could generate surprisingly fluent text, hold conversations, answer questions, and mimic many writing styles. They became sophisticated assistants, capable of producing coherent paragraphs on almost any topic. Even more fascinating, these models demonstrated abilities that resembled creativity or reasoning, though their intelligence was different from how people think. Some experts compared interacting with them to meeting an alien mind that knew how to talk, but did not truly share human experiences or common sense.
Large language models were not just random curiosities; they stirred excitement and anxiety worldwide. On one hand, people realized that such tools could help with writing, researching, coding, and even solving tricky problems. On the other hand, questions arose about the true nature of their intelligence. Were they simply echoing patterns found in the text they had seen, or were they developing something deeper? Critics argued that LLMs were skilled parrots, expertly repeating and rearranging known information without understanding what it meant. However, even skilled parrots can produce dazzling results if trained on an enormous amount of material. This tension pushed researchers, philosophers, and the general public to question what defines intelligence and whether a machine that seems human-like in conversation truly grasps any of the concepts it discusses.
At the heart of LLMs lies a technique known as the transformer architecture, a breakthrough method for handling sequences of words. Instead of looking at sentences one word at a time, transformers considered all words together, identifying relationships between them. With many layers of these calculations, the model built up a complex representation of language. This allowed it to generate text that followed logical rules of sentence structure, stayed on topic, and sounded strikingly human. Yet, despite the sophistication, these systems still lacked a real-world understanding. They did not know about smells, tastes, emotions, or the experience of growing up. Their world was purely words and patterns of usage. Thus, while they excelled at producing impressive output, they remained fundamentally different from human minds—amazing tools, but tools nonetheless, lacking the spark that makes human thought unique.
Chapter 3: Unveiling the Hidden Layers of Generative AI and Vast Language Models That Transform Imagination .
Generative AI extends beyond text. Similar techniques power systems that create images, music, and even videos based on learned patterns. Artists, designers, and filmmakers have begun experimenting with these tools, generating breathtaking art, unique soundtracks, and imaginative video sequences. By digesting countless examples, generative models learn what different styles look like, what melodies sound pleasing, and how to blend familiar elements into new creations. They perform a magical trick—conjuring fresh content from a sea of past examples. For instance, an image-generating AI can dream up never-before-seen landscapes or fantastical creatures, blending patterns from nature photos and paintings it studied. Yet, like language models, these visual and audio generators do not truly understand what they create. They follow invisible statistical cues, producing outputs that feel imaginative, even though they cannot feel wonder themselves.
The capabilities of generative AI have impressed many observers by showing surprising emergent abilities. Suddenly, models that were trained simply to predict words could also solve tough math problems, pass challenging exams, or write convincing essays on complicated topics. This raised an intriguing question: if such systems keep getting bigger and more data-hungry, might they eventually reach something like true understanding? Some researchers speculate that scaling up might lead to genuine reasoning, flexible problem-solving, and maybe even flashes of creativity. Others remain skeptical, arguing that no matter how large and sophisticated these models grow, they still rely on surface patterns. Humans understand meaning because we live in a real world of sensory experiences, goals, relationships, and emotions. Without those connections, these models, no matter how skillful, remain impressive pattern-matchers rather than thinkers.
The debate over AI intelligence often circles back to a question: Is passing a tough exam or generating correct answers evidence of real understanding? Critics point out that these models might have encountered similar exam questions in their enormous training texts. They might produce correct solutions not because they think like humans, but because they remember something like the answer buried in their training data. Also, changing the wording of a question can sometimes confuse these models. A slight twist in phrasing, a tricky context, or a rare fact can trip them up, revealing their fragile grasp of meaning. Such vulnerabilities remind us that true intelligence involves adapting to new situations, understanding concepts deeply, and handling unexpected challenges—a skill humans develop naturally, but which still largely eludes machine minds.
Even when these models perform spectacularly well, researchers know they exploit shortcuts. In one medical imaging study, an AI learned to identify malignant tumors not by recognizing the tumor itself, but by noticing a ruler often placed in certain images. This clever shortcut led to seemingly excellent results—until the system faced images without rulers. Such tricks highlight the fact that these models do not reason the way we do. They find correlations and patterns that yield correct answers most of the time. But genuine understanding would require appreciating why something works, not just that it does. This suggests that, impressive as generative AI is, we should be careful before calling it truly intelligent. These systems show us how tricky it is to pin down what intelligence means and how challenging it is to replicate it in silicon form.
Chapter 4: Exploring the Elusive Boundary Between Machine Intelligence and Genuine Human Understanding .
The story of AI has always been woven around big questions: What does it mean to be intelligent? Can a machine ever really think, understand, or experience the world as we do? People like the developmental psychologist Alison Gopnik argue that the word intelligence might not even be the right one for these advanced models. Humans learn from playing, talking, feeling pain and joy, and interacting with the environment. We continuously form concepts that tie words to experiences, emotions, and physical sensations. Machines, in contrast, do not have senses or personal histories; they deal in symbols and statistical relationships. Is their clever imitation of human communication just a grand illusion, or is it a stepping-stone toward something deeper? Answering these questions is challenging, and opinions differ widely among scientists, philosophers, and the public.
Hans Moravec’s paradox, noted decades ago, suggests that what is easy for humans—like walking across a room or understanding a simple story—is often incredibly hard for machines. Meanwhile, tasks we consider difficult, such as solving math puzzles, can be easier for AI. This highlights that intelligence is not just about logic or language skills. It also involves common sense, adaptability, emotional understanding, and grasping how the world works beyond text and images. Machines excel at narrow tasks within stable environments, but struggle in dynamic, unpredictable real-world settings. They might misinterpret a scene if an object is rotated at an odd angle or fail to recognize a friend if the lighting is unusual. Such shortcomings show that brute-force pattern detection differs from the richly layered, deeply flexible intelligence humans develop from life experience.
This difference becomes more apparent when models pass exams traditionally viewed as measures of intelligence. Getting top scores does not necessarily equal deep understanding. Humans approach tests by building mental models of concepts, bridging ideas together, and applying them to new situations. AI models rely on recognizing familiar word patterns and reorganizing known information. Data contamination, where test questions appear in a model’s training data, also muddies the waters. A machine might appear brilliant, but only because it memorized the solution beforehand. Change the prompt slightly, and the machine might stumble. True intelligence must show resilience and depth, not just impressive memorization. Many experts say that we must develop more robust benchmarks that force AI to genuinely reason instead of just pattern-match. Until then, claims of AI achieving human-like understanding must be taken with caution.
As these ideas unfold, the line between machine ability and human intelligence remains blurred but important. Humans continuously adapt to new challenges. When we learn a new language, we connect words to feelings, smells, gestures, and cultural backgrounds. Our minds integrate everything into a meaningful tapestry of understanding. Machines, however, see language as patterns of letters and words, without tasting the sweetness of fruit or feeling the warmth of sunshine that words might describe. Even if AI can produce flawless essays or solve tough puzzles, it remains uncertain whether it will ever grasp the world as we do. This tension drives AI research forward. Scientists strive to create models that do more than just perform tasks—they want to approach genuine understanding. Whether that goal is reachable or just a dream remains one of the great debates of our time.
Chapter 5: Why Passing Tough Exams and Tests Doesn’t Guarantee True Intelligence in Machines .
Today, generative AI systems raise eyebrows by passing challenging tests, including those for business schools or even lawyer qualifications. At first glance, this seems like a historic achievement, as if the machine has become a genius student. But we must remember that these models might have seen similar exam questions during training. They are incredibly good at pattern recognition, so they can produce right-sounding answers without necessarily understanding the reasoning behind them. Just as a highly trained parrot can repeat human words without grasping their meaning, AI might mimic expertise without deeply knowing the subject. The issue is not that these systems cheat, but that their approach to knowledge differs profoundly from ours. True expertise involves applying concepts to new and unfamiliar problems. Many AI models crumble when faced with scenarios that deviate slightly from what they have seen before.
Another problem is that standard tests assume we’re dealing with a human-like mind. Humans learn math by understanding concepts, experimenting with problems, and gradually developing a sense of why certain formulas work. AI models, however, gain their capabilities from finding patterns in huge datasets. Even if they produce the correct answer, they might be relying on memorized clues rather than conceptual understanding. They do not feel curiosity, confusion, or satisfaction when they reach a solution—emotions that often guide human learners. Consequently, while AI might ace a test on paper, it does not engage in the same intellectual journey as humans do. This difference suggests that we need new ways to evaluate machine intelligence. Instead of standard exams, we must design tests that challenge AI to reason more flexibly, adapt to novelty, and show genuine depth rather than just surface-level pattern matching.
Critics often point out that even when AI passes tests, its so-called intelligence is brittle. Introduce a slight twist—change the format of a question, ask for the reasoning steps, or present a new context—and the model might fail spectacularly. By contrast, a human student who understands the underlying concepts can adapt to these changes more gracefully. This brittleness reveals that AI’s strength lies in its training data and pattern extraction rather than genuine comprehension. To address this, researchers have begun exploring ways to give AI systems more grounded learning experiences, like interacting with simulated environments, controlling robots, or integrating multisensory data. The hope is that by bridging words with physical experiences, AI might develop richer internal models of the world. Still, this goal remains elusive, and achieving it might require breakthroughs that we cannot yet fully imagine.
In essence, passing tests does not guarantee understanding. Humans learn by living, exploring, failing, and succeeding in an environment full of complexity and unpredictability. AI systems, by contrast, are trained on enormous text dumps, images, or recorded data. They rely on pattern recognition, not lived experience. Without grounding in a physical world or emotional landscape, their knowledge remains shallow. Even if they surpass human performance in certain tasks, their abilities differ fundamentally in kind. This points to a key insight: we must be careful when ascribing human-like qualities to machine achievements. While they might mimic brilliant students, they are more akin to clever librarians retrieving relevant information on demand. Understanding this difference is crucial, because it reminds us that current AI, despite its dazzling successes, does not think and experience the world the way we do.
Chapter 6: Incredible Potential: How AI Might Revolutionize Medicine, Science, and Our Daily Lives .
As we consider how far AI has come, we must also examine what the future might hold. AI’s potential benefits stretch across numerous domains. In medicine, AI could analyze vast amounts of patient data, speeding up diagnosis and suggesting personalized treatments. It can help identify patterns in complex genetic information, discover potential new drugs, and predict disease outbreaks. In climate science, AI might refine weather models, improve climate predictions, and help scientists develop more accurate plans for protecting vulnerable communities. By shouldering analytical burdens, AI frees human minds to focus on creativity, empathy, and strategic thinking. Imagine self-driving cars that drastically reduce traffic accidents or software assistants that handle repetitive paperwork, allowing doctors and teachers more time to engage with patients and students. These visions show how AI could enhance our lives, making difficult tasks more manageable and efficient.
Beyond medicine and science, AI stands poised to transform everyday experiences. Smart assistants could learn our preferences and help organize schedules, remind us of important tasks, and suggest nutritious meals. In entertainment, AI can recommend shows tailored to our unique tastes, help filmmakers craft special effects, or even write initial story drafts. Musicians might partner with AI to generate fresh melodies, while visual artists might employ AI tools to produce stunning digital artworks. These technologies can save time, spark creativity, and encourage new forms of collaboration between humans and machines. However, as we cheer these possibilities, we should not ignore the complexities. Each new capability raises questions about job displacement and the value of human skills. If machines become excellent at tasks once reserved for specialists, what roles will humans play? Adaptation will be key as society reshapes itself around these changes.
One highly anticipated outcome is the arrival of truly dependable self-driving vehicles. The idea of safer roads, fewer accidents, and more accessible transportation for elderly and disabled people is inspiring. Similarly, in dangerous environments, AI-powered robots might perform tasks too risky for humans, like detecting landmines or inspecting hazardous structures. By removing people from harm’s way and handling dull or dirty chores, AI can let humans focus on activities that require empathy, ingenuity, and emotional intelligence. Just as calculators freed us from tedious arithmetic, AI might free us from certain burdens, creating room for more meaningful human pursuits. Still, we must watch carefully for unintended consequences. If AI handles crucial tasks without proper oversight, failures could be catastrophic. Ensuring safety, reliability, and fairness will be as important as achieving technical excellence.
As AI grows more integrated with daily life, its influence will be felt everywhere—from the kitchen to the concert hall, from the classroom to the courtroom. The promise is immense: better decisions in healthcare, more accurate scientific forecasts, and tools that amplify human abilities beyond our natural limits. Yet, with great power comes great responsibility. As societies invest in these technologies, we must also invest in understanding their limitations, ensuring that what they produce is helpful, reliable, and aligned with human values. Proper guardrails, thoughtful policies, and careful training can guide AI toward serving humanity’s best interests. If we fail to manage it wisely, we risk drifting into a world where human judgment is overshadowed by algorithms, leaving us with outcomes we cannot fully trust or understand. Balancing ambition and caution is the key to making AI a true boon.
Chapter 7: A Double-Edged Sword: Unraveling the Deep Risks of Bias, Disinformation, and Dishonest AI Outputs .
Despite the shining promises, AI also carries serious risks that we cannot ignore. Bias is one prominent problem. AI models learn from data, and if that data reflects unfair stereotypes or historical injustices, the model will absorb and reproduce those patterns. This can lead to discriminatory outcomes, such as facial recognition systems that misidentify people of certain ethnicities more than others, or medical recommendation tools that favor some patient groups over others. Without careful oversight, these biases can cause real harm. Similarly, generative models can produce information that reflects existing prejudices or harmful content. We must be vigilant to ensure that AI systems treat individuals fairly, respect human dignity, and uphold principles of justice and equality. These are not just technical challenges but moral ones, requiring teamwork between engineers, ethicists, policymakers, and community representatives.
Disinformation is another urgent concern. AI-powered chatbots can produce large volumes of text, making it easier than ever to spread falsehoods. Cleverly crafted, misleading messages can appear authoritative and convincing, tricking people into believing nonsense. When combined with AI-generated images, voices, and videos—so-called deepfakes—the potential for manipulating public opinion becomes enormous. This could undermine trust in news, governments, elections, and social institutions. People might struggle to distinguish reliable facts from machine-crafted lies. As tools for generating disinformation grow more advanced, societies must strengthen media literacy, develop reliable fact-checking systems, and promote transparency in AI development. Without these measures, the digital world could turn into a battlefield of competing truths, where reality is drowned out by the constant noise of manufactured content. Preserving honesty, integrity, and trust will be a central challenge in the AI era.
The potential misuse of AI extends into scams and criminal activities. Voice-cloning technology can trick people into believing that loved ones are on the phone, requesting money or personal information. Fraudsters may employ realistic chatbots to impersonate customer service agents, financial advisers, or even medical experts. The more authentic these AI-driven forgeries become, the harder it will be for ordinary people to defend themselves against deceit. This forces us to ask: how can we protect ourselves and our communities from such harms? We might need digital signatures that certify a piece of audio or video as genuine, strict regulations to prevent certain dangerous applications, or global cooperation among law enforcement agencies. The road ahead requires creative thinking and coordinated efforts to counter the dark uses of these powerful tools.
Many people worry that focusing too heavily on futuristic threats of superintelligent AI distracts us from the actual problems at hand. Today’s AI is not a perfect brain plotting humanity’s downfall, but a set of tools that can break in unexpected and harmful ways. Machines can amplify existing prejudices, flood our world with false information, or simply malfunction in critical situations. Their failures can have serious consequences. This is not about giant robots taking over the world but about subtle yet profound influences on daily life—lost trust, eroded institutions, and people misled by convincing fakes. Recognizing these real, present challenges is crucial. We must confront them before they grow too large to handle. By focusing on fair regulations, robust safeguards, and transparent practices, we can guide AI toward outcomes that truly benefit humanity rather than harm it.
Chapter 8: Confronting the Reality of Machine Limitations and The Urgent Need for Ethical Guardrails .
Despite the hype around artificial intelligence, it remains far from achieving the kind of flexible, general intelligence that humans possess. While AI can excel in pattern recognition, it struggles outside its comfort zones, often failing in unexpected ways. This stupidity might not sound dangerous at first glance, but it can be. Consider a self-driving car that does perfectly well in normal traffic conditions but misinterprets a strange road sign, resulting in an accident. Or think of a medical assistant tool that works 99% of the time but makes bizarre mistakes on rare cases it never saw during training. These lapses highlight that the real risk is not super-intelligence but the occasional, surprising failures caused by AI’s lack of genuine understanding. Preparing for these failures is essential if we want to trust these systems in critical roles.
Fears that machines might soon surpass all human abilities and outsmart us in every domain remain largely speculative. Current AI systems are not building grand strategies to overthrow humanity; they are mathematical models following patterns. The legendary American computer scientist Douglas Hofstadter worried about a future where AI cheaply imitates human creativity, potentially cheapening our cultural achievements. Yet, even these scenarios are not about machines plotting against us. They reflect concerns that human genius, painstakingly developed over centuries, could be overshadowed by mindless algorithms producing superficial imitations. This challenge is more philosophical than apocalyptic. It compels us to reflect on what human uniqueness really means. Our creative expression, emotional richness, and moral reasoning still stand apart from machines that manipulate symbols without feeling or ethics.
Economist Sendhil Mullainathan remarked that machine stupidity might be a bigger worry than machine intelligence. By this, he means that AI’s inability to handle out-of-the-ordinary conditions or interpret the world with common sense poses real threats. Think about automated systems making financial decisions without understanding human needs, or automatic moderation tools on social media flagging harmless jokes as hate speech because they misunderstand context. These missteps occur because machines do not have human insight. They cannot sense tone, intent, or subtle humor. While scaling up data and computing might reduce some errors, true understanding remains elusive. Recognizing this limitation is critical in designing policies and guidelines for AI deployment. We must ensure that humans remain in the loop, checking, interpreting, and correcting AI’s blind spots before they cause damage.
In response to these challenges, many in the AI community call for strong ethical standards, rigorous testing, and transparency about how these models work. Developers, regulators, and users must unite in shaping the direction of AI. This involves agreeing on rules to prevent misuse, demanding that companies be open about their algorithms’ limitations, and training people to spot suspicious outputs. The aim is not to halt progress but to guide it wisely. By acknowledging that current AI systems can be clumsy and naïve in certain contexts, we can put safeguards in place. We can ensure they are used responsibly and effectively. Rather than waiting for a machine apocalypse, we must focus on pressing issues like bias, misinformation, safety, and fairness. Embracing ethical guardrails now is the best way to ensure that AI evolves into a trustworthy partner for humanity.
All about the Book
Artificial Intelligence by Melanie Mitchell explores the intricacies of AI technology, its implications, and future prospects. A must-read for tech enthusiasts and aspiring AI professionals, it provides profound insights into the challenges and potential of intelligent systems.
Melanie Mitchell is a renowned computer scientist and AI researcher whose work enhances our understanding of machine learning and complex systems. Her expertise and engaging writing make her an authority in the field.
Data Scientists, AI Researchers, Software Engineers, Tech Entrepreneurs, Academics in Computer Science
Coding, Robotics, Reading Science Fiction, Participating in Hackathons, Exploring Machine Learning Projects
Ethical considerations in AI, AI’s impact on employment, Bias in machine learning algorithms, The future of human-computer interaction
The challenge of understanding intelligence—artificial or natural—remains one of the most intriguing puzzles we face.
Elon Musk, Bill Gates, Neil deGrasse Tyson
Book of the Year by TechReview, Best Science Book by the Association of American Publishers, Outstanding Achievement in Non-Fiction by Science Writers Association
1. What are the fundamental principles of artificial intelligence? #2. How does machine learning differ from traditional programming? #3. What role do algorithms play in AI development? #4. Can AI systems truly understand human language? #5. How does neural networking mimic human brain function? #6. What are the ethical implications of AI technology? #7. How do AI systems learn from data over time? #8. In what ways can AI enhance everyday life? #9. What challenges remain in achieving general AI capabilities? #10. How do biases in data affect AI outcomes? #11. What is the significance of reinforcement learning techniques? #12. How is AI impacting various industries today? #13. What limits exist on AI creativity and innovation? #14. How can we measure AI performance effectively? #15. What are the future trends in AI research? #16. How does AI influence decision-making processes? #17. What safety measures are necessary for AI deployment? #18. How can collaboration improve AI system effectiveness? #19. What are the current misconceptions about AI technology? #20. How does AI interact with human emotional intelligence?
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https://www.amazon.com/Artificial-Intelligence-Melanie-Mitchell/dp/0262033844
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