Demystifying the Buzzwords#
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In this chapter, we explore the intricate layers of Artificial Intelligence, Machine Learning, and Deep Learning unveil their distinct roles and interconnectedness in shaping the future of technology. By meticulously examining the evolution of AI from its conceptual origins to its current applications, we describe ML’s pivotal role in enabling machines to glean knowledge from data, thereby fostering a new era of innovation. Deep Learning, as a subset of ML, is highlighted for its profound ability to emulate the neural networks of the human brain, offering unparalleled insights and complexities in data analysis. This narrative not only elucidates the technical underpinnings and advancements but also ventures into the ethical considerations and societal impacts, advocating for a judicious balance between technological growth and moral responsibility. It encourages the academic and professional communities to ponder the profound implications of these technologies, urging a collaborative approach towards navigating their future trajectory for the betterment of society.
Deep Learning · Intelligence · Learning · Machine Learning
As we embark on this chapter, I’m eager to delve into the often-misunderstood
buzzwords like Artificial Intelligence, Machine Learning, Neural Networks, and
others. Some of these terms have become increasingly popular since 2010, yet
I’ve noticed a mix-up in their usage, both within my professional circle and
beyond. It’s common to hear these terms being used interchangeably, with
systems labeled as “AI-based” or
“ML-based”, and companies touted as AI-first. But,
do these labels accurately reflect their meanings, or is there more to these
buzzwords than meets the eye?
1 Introduction#
In pursuit of clarity, my first step is to dissect the meanings of
Intelligence and Learning. Here, my focus is on human intelligence, as
it provides a crucial reference point for understanding AI. While
acknowledging that intelligence manifests diversely across different species,
our primary exploration will center on its human aspect. In the context of
computer science, these concepts of intelligence and learning are deeply
intertwined with data — the cornerstone of knowledge and information
processing in this digital age.
This chapter is not just about defining these terms; it’s about peeling back the layers to reveal the nuances and intricacies they encompass. By understanding what each of these buzzwords truly signifies, and how they interplay within the broader landscape of AI, we’ll gain a deeper appreciation of the field. Moreover, in this chapter, we won’t limit ourselves to theoretical definitions alone. Understanding how Artificial Intelligence, Machine Learning and Neural Networks are utilized in various industries and sectors and can provide a more tangible grasp of their relevance and potential. From healthcare and finance to autonomous vehicles and beyond, we will explore how these buzzwords translate into groundbreaking innovations and everyday solutions. This practical lens is crucial as it connects the abstract concepts of intelligence and learning with real-world impact, illustrating the transformative power of AI and ML in shaping our present and future. So, as I dissect these terms, we’ll also be uncovering the myriad ways they’re being employed to solve complex problems, enhance human capabilities, and redefine the technological landscape.
2 General Intelligence#
2.1 Definition and Context#
In my exploration of Artificial Intelligence, one term frequently arises: Intelligence. According to Google…
Intelligence is the ability to acquire and apply knowledge and skills to solve a particular problem at hand.
Some experts describe it as the capacity to solve complex issues or make informed decisions using prior knowledge, akin to personal traits like height or strength. The quest for a definitive understanding of intelligence is complex and multifaceted, and no single definition can capture its entirety. But in the context of AI, our quest isn’t to pinpoint a rigid definition, but rather to grasp the essence of intelligence in all its dimensions. Let’s reflect on this concept again. At its most fundamental level, intelligence involves learning something and then applying that acquired knowledge. This process, simple in description yet profound in implication, is what lies at the heart of both human and artificial intelligence. In AI, this is mirrored through algorithms that learn from data and then utilize these learnings to perform tasks or make predictions. Understanding the nuances of this process — how information is learned, the ways it can be applied, and the varying contexts it operates within — is crucial. This understanding underpins the entire field of AI.
As I dig deeper into this topic, I aim to unravel the layers of what intelligence means, especially about AI. We’ll explore how this fundamental concept of learning and applying not only shapes human cognition but also forms the foundational principle of AI systems. It’s a journey through the intricate relationship between knowledge, application, and the diverse manifestations of intelligence in our increasingly digital world. In addition to understanding the foundational aspects of intelligence, it’s also important to recognize its evolving nature, especially in the context of AI. As we delve deeper into Artificial Intelligence, we see that just like human intelligence, AI is not static; it evolves, adapts, and improves over time. This dynamic aspect of AI, where algorithms learn from new data, adapt to changing environments, and potentially even develop new ways of problem-solving, is a fascinating parallel to human cognitive growth.
2.2 Evolutionary Aspect of Intelligence#
In our exploration, we will not only focus on the current state of AI but also
consider its potential for future development. How does AI’s ability to learn
and adapt today shape what it could become tomorrow? This perspective is
crucial for a comprehensive understanding of AI and for envisioning how it
might continue to transform our world. By keeping in mind both the established
definitions and the progressive nature of intelligence, we can gain a richer,
more nuanced understanding of what it means for a machine to be
intelligent. This exploration is key to appreciating the full scope and
potential of AI, as we continue to witness its remarkable journey from a
concept to a transformative force in our lives. In my experience of what
intelligence truly means, I’ve observed a fascinating aspect of our human
nature and that of some mammals. Throughout our lives, we’re engaged in a
continuous process of learning and adapting. This ongoing development of
skills and knowledge, and their application in various contexts, is the very
essence of intelligence at work. Unlike something tangible and localized,
intelligence is an abstract, multifaceted capability. It encompasses our
ability to analyze, learn, adapt, and wisely use information — a complex
trait that we, as a species, have evolved over thousands of years.
2.2.1 Learning from Cognition#
To put this into perspective, let’s consider a few illustrative examples from our evolutionary past. Picture early humans learning to utilize sticks and twigs, not just as tools but as instruments of defense. Initially, simple bamboo sticks or dried tree branches were employed to fend off predators. However, as these methods gradually lost their efficacy, our ancestors began to innovate — sharpening the ends of these sticks, or even using fire to enhance their defensive capabilities. This evolution of tool usage showcases the adaptive nature of human intelligence, where learning from experiences leads to better, more effective solutions. Another example that resonates with most of us is the instinctive reaction to touching a hot surface. We’ve all experienced that sudden withdrawal upon contact with something scalding. This immediate response, followed by a cautious approach in similar future situations, is a prime example of learning and adapting based on experience. It highlights how intelligence isn’t just about knowledge acquisition but also about applying that knowledge to ensure better outcomes. As I go deeper into the realms of Artificial Intelligence, these reflections on human intelligence provide a vital backdrop. They help us understand the challenges in replicating such adaptive and learning capabilities in AI systems. In AI, we are attempting to emulate this depth and flexibility of human intelligence, understanding that it is not a static trait but a dynamic, evolving one. This journey of understanding the essence of intelligence, both in humans and machines, is not just intellectually enriching but also crucial in our quest to advance AI technologies.
Learning through experience: How our instinctive reaction to a hot surface mirrors the fundamental process of human learning and adaptation. This everyday example illustrates the essence of intelligence - not only in acquiring knowledge but also in applying it to avoid harm. It reflects the continuous evolution of human understanding, a concept at the core of developing adaptive and responsive AI systems.#
2.2.2 Dynamic and Adaptive Nature of Intelligence#
In the backdrop of these examples and insights, it’s important to acknowledge that the study of intelligence, both in biological and artificial systems, is an area of ongoing research and spirited debate. The field is continually understanding. This dynamic nature of intelligence research is something that I find it particularly intriguing and it forms an integral part of our exploration in L.E.A.R.N. In my exploration of human intelligence, I’ve come to understand its deep roots in our survival instincts. Throughout the millennia, this innate drive has shaped our intellect, continually evolving and improving. It’s vital to appreciate that this journey of discovery and adaptation wasn’t swift; it involved a gradual, often painstaking process of learning from our environment and experiences. Our ancestors faced numerous challenges, and through trial and error, they developed and honed strategies for survival, leading to the sophisticated level of intelligence we possess today.
As I explore further, I’m struck by the ongoing debates among scientists
and researchers about defining and measuring intelligence. Is it a
quantifiable entity, or something more abstract? Furthermore, the intricate
relationship between intelligence and consciousness adds another layer of
complexity. The ability to be aware of and understand our surroundings plays a
crucial role in how we solve problems. This suggests that intelligence isn’t
just about the actions we take but also about our consciousness of the
situation we are in. Therefore, when I reflect on intelligence, I see it as a
symphony of actions and awareness. It’s the entire process of recognizing a
problem, conceptualizing a solution based on past learnings, experimenting,
and then refining our approach based on feedback. This dynamic, iterative
process is what I aim to parallel in the world of Artificial Intelligence.
It’s not solely about creating machines that solve problems; it’s about
imbuing them with a level of awareness and the capacity to learn and
adapt. This perspective on human intelligence, with its intricate blend of
cognitive processes and consciousness, forms the foundation of my approach to
understanding and developing AI. It’s a vast and fascinating field, where each
discovery about our intelligence opens new possibilities for artificial minds.
2.2.3 Parallels with Human Intelligence and Ethical Implications#
In addition to reflecting on the evolution of human intelligence and its profound connection to consciousness, it’s also crucial to consider the implications of these insights for the future trajectory of Artificial Intelligence. As we continue to unravel the mysteries of human cognition and consciousness, we gain valuable perspectives that can guide the development of more advanced, responsive, and perhaps even empathetic AI systems. Understanding the intricate processes behind human learning, decision-making, and problem-solving can inspire new models and algorithms in AI. This knowledge could lead to the creation of AI systems that not only mimic human intelligence more closely but also interact with us in more intuitive and meaningful ways. Furthermore, these insights into human intelligence bring to light ethical considerations that are increasingly important in AI development. As we strive to create machines that resemble human thinking, questions about AI autonomy, decision-making, and the role of AI in society become more pertinent. It’s a reminder that the journey of AI is not just a technical endeavor but also a deeply philosophical and ethical one. In this context, my exploration into AI is not only about understanding or replicating human intelligence but also about envisioning the future of AI and its role in our lives. It’s about shaping a future where AI enhances human capabilities and operates in harmony with our values and ethical principles.
3 Natural Learning#
3.1 Sensory Learning and Retention#
In my exploration of learning, I’ve come to understand it as an intricate
process that goes far beyond mere information acquisition. To me, learning
involves engaging our senses — vision, sound, smell, touch, and taste, to
gather and interpret information about the world around us. This sensory
exploration is fundamental, yet it’s distinct from intelligence. While there
are overlaps, intelligence, in my view, is a more encompassing concept. It’s
not just about learning; it’s about how we use that learned information to
navigate and make decisions in our lives. Learning, then, is a vital subset of
intelligence. It’s about delving deep into a subject, unraveling its
complexities, and expanding our understanding. The information we absorb
through our senses lays the groundwork for further learning actions. Without
this sensory input, our reactions and adaptability to new situations would be
severely limited. This realization highlights the critical role of information
in the learning process. But learning isn’t just about gathering information;
it’s also about retention. The ability to store this information in our memory
is what transforms fleeting experiences into lasting knowledge. This storage
aspect is crucial, as it allows us to build on past experiences and
continuously expand our understanding. As I investigate more thoroughly into
the realm of learning, I see fascinating parallels with how AI systems learn.
Like us, AI systems gather data (their version of sensory input) and store
patterns and information. This process enables them to learn and make
informed decisions based on past inputs. In exploring these parallels, I aim
to shed light on both the human learning process and AI learning mechanisms.
It’s a journey through the multifaceted landscape of cognition, where human and
artificial intelligence intersect, each providing insights into the other.
Moreover, it’s important to emphasize that learning, both for humans and AI is an ongoing and evolving journey. In humans, this continuous process shapes our understanding, behaviors, and skills throughout our lives. We are not just passive recipients of information; we actively engage with and adapt to new experiences and knowledge. This adaptability is a hallmark of our intelligence. Similarly, in the realm of AI, learning is not a one-time event but a continuous process. AI systems are designed to learn from new data, adapt to changing environments, and improve their performance over time. This ability to evolve and refine their knowledge base and decision-making processes is what makes AI both powerful and promising. In this context, exploring learning is about understanding this dynamic process — how we gather, store, and adapt information over time, and how AI systems mirror this ability in their unique way. It’s about recognizing that learning is an ever-changing landscape, full of challenges and opportunities for growth, both for us as individuals and for the AI technologies we develop.
3.2 Role of Memory in the Learning Process#
In my exploration of the learning process, I’ve come to recognize the pivotal role of memory. Memorization is not merely about storing information; it’s a fundamental aspect of learning that enables us to build upon what we’ve previously encountered. This concept becomes vividly clear when observing a toddler learning to walk. To me, a toddler represents an embodiment of the learning journey, a journey marked by persistence, experimentation, and the crucial role of memory. Consider the early stages of a toddler’s life. The initial challenge of rolling over might seem insurmountable to the little one, but with each attempt, the child learns and adapts. Through repetition and recall of what works and what doesn’t, progress is made. Once rolling over is mastered, the toddler ventures into crawling, using hands and feet in coordination. This phase is more than mere movement; it’s a complex integration of previously learned skills, all stored and retrieved from memory. The journey continues as the toddler attempts to stand. Each faltering step is a learning experience, a trial that feeds into the child’s growing repository of knowledge. Memory plays a key role here, as each attempt, successful or unsuccessful, informs the next. Eventually, the child stands and then walks. These milestones are not just physical achievements but are the culmination of a sophisticated process of learning and memorizing.
The evolution of learning and memory in action: Tracing a toddler’s journey from rolling over to walking. This sequence captures the essence of gradual skill acquisition and the pivotal role of memory in each phase. It begins with mastering rolling over, progresses to the coordination required in crawling, advances through the challenge of standing, and culminates in the triumphant first steps of walking. Each stage represents not just physical growth, but the intricate process of learning, remembering, and building upon past experiences.#
3.3 Parallels Between Human and AI Learning#
In drawing parallels to Artificial Intelligence, I see a reflection of this
process. AI systems, in their way, learn by gathering data, processing it,
and remembering patterns. This mimicry of human learning and memorization
is fascinating and offers profound insights into the potential of AI. It’s a
reminder that learning, whether in humans or machines, is an intricate
tapestry woven from experiences, trials, and the continuous process of
building upon past knowledge. Furthermore, it’s important to acknowledge the
challenges inherent in translating these natural learning processes into AI
systems. While the parallels between a toddler’s learning journey and AI’s
learning mechanisms offer valuable insights, the replication of human-like
learning in machines presents a unique set of complexities. AI systems, though
capable of processing and remembering vast amounts of data, still face
limitations in replicating the nuanced and adaptive nature of human learning.
We need to understand the limitations of AI in mimicking human learning
processes, such as the understanding of context, the application of learned
knowledge in varied situations, and the ability to learn from minimal input.
It’s about recognizing the remarkable capabilities of AI while also being
mindful of the journey ahead in achieving a level of learning and
understanding that truly mirrors human cognition. This acknowledgment not only
informs our approach to AI development but also fosters an appreciation for
the intricate and sophisticated nature of our learning abilities.
3.3.1 Learning in the Natural World#
In my studies and observations of the natural world, I’ve been continually fascinated by the universal nature of learning. It’s a phenomenon that transcends human boundaries, evident in nearly all forms of life. From the terrestrial mammals that instinctively learn to walk and swim to the birds that master the art of flight, learning is an integral part of life’s tapestry. This process, which seems so intuitive and ingrained, has always intrigued me. Observing a baby as it navigates its first steps or a bird as it takes to the skies, one can’t help but marvel at the innate ability that drives these milestones. These instinctive behaviors, which occur without formal instruction, suggest a profound intelligence embedded within each species. It raises compelling questions about the nature of intelligence — Is it the manifestation of deeply rooted instincts, or is it something that transcends innate behaviors? This conundrum is one that researchers and scientists grapple with, as the boundary between instinctual behavior and learned intelligence often blurs into a fascinating gray area.
3.3.2 Instinct versus Learned Behavior#
Some theories propose that these innate abilities are the result of evolutionary processes, honed over generations for survival and efficiency. As I delve deeper into this subject, I find myself pondering the intricate relationship between instinct, learning, and intelligence. It’s a relationship that not only defines the survival and development of a species but also highlights the remarkable adaptability and complexity inherent in nature. In exploring these ideas, I aim to capture the essence of learning in its broadest sense — not just as a human endeavor but as a fundamental characteristic of life. The way a child learns to walk, or a bird learns to fly, isn’t merely a function of biology; it’s a testament to the intelligence that permeates the natural world. These natural phenomena, often overlooked in their simplicity, are in reality profound expressions of life’s inherent wisdom and adaptability.
4 Distinctive Aspects of Intelligence and Learning#
In reflecting upon the essence of intelligence and learning, I’ve come to view intelligence as a collective process intricately woven from various elements, central to problem-solving. Intelligence, as I see it, is the orchestration of skills such as analyzing, adapting, and applying knowledge. Learning, in contrast, is the foundational process of acquiring this knowledge through persistent trial and error. It is a critical component of intelligence, akin to gathering the building blocks that will be used to construct solutions. While exploring these concepts, I recognize that intelligence is not just a binary process of learning and applying. It encompasses more nuanced components like planning, creativity, rationalization, and socializing. These facets, often overlapping with learning, enrich the tapestry of intelligence, making it a dynamic and multi-dimensional attribute. We will delve deeper into these aspects in our journey, uncovering how each contributes to the broader picture of intelligent behavior.
4.1 Intelligence as an Array of Cognitive Skills#
Approaching intelligence as a toolbox is a metaphor that resonates with me. This toolbox contains an array of tools, each with its unique purpose and function in the realm of cognition. Among these tools is learning itself, a fundamental instrument in our intellectual arsenal. The challenge, and my focus, is to understand how to utilize these tools effectively. It’s about learning to harness the power of this toolbox, not just in theory but in practical, real-world scenarios. As we progress, I aim to explore not only the individual tools of intelligence but also the skillful art of employing them in concert. This exploration is about more than comprehending concepts; it’s about mastering the application of intelligence and learning in diverse situations. It’s a journey through the intricate landscape of the mind, where I hope to unlock the potential of these cognitive tools and share insights on how to wield them with precision and effectiveness.
Additionally, it’s worth contemplating how these cognitive tools of intelligence and learning apply beyond individual problem-solving, extending into various realms of our lives and work. The skills we develop through learning and intelligence are not confined to personal growth alone; they influence how we interact with others, how we approach challenges in our professional fields, and how we contribute to society. In different scenarios, be it in scientific research, artistic creation, business strategy, or social interactions, the ability to effectively use these tools can lead to groundbreaking innovations and solutions. Understanding the nuances of intelligence and learning becomes particularly vital in fields like education, psychology, and even artificial intelligence, where these principles guide the development of more effective teaching methods, deeper psychological insights, and advanced AI algorithms. As I continue to delve deeper into these topics, I intend to not only explore the theoretical underpinnings of intelligence and learning but also to highlight their practical applications. It’s about connecting the dots between the cognitive processes we cultivate and the real-world impact they can have. This perspective is crucial for anyone looking to harness their intellectual abilities to their fullest potential, making a meaningful impact in their personal and professional endeavors.
4.2 Observations from Animal Kingdom#
In my journey to elucidate the essence of intelligence and learning, I find the natural world to be a source of profound insights. Let me illustrate this with an example that has always fascinated me: the distinct survival strategies of squirrels and cows. Squirrels, as I’ve observed, instinctively hoard food for the winter, a behavior developed through evolutionary learning and adaptation. This strategic accumulation of resources is a testament to their intelligence, honed through generations of trial and error. In contrast, cows, especially those that are domesticated, rely less on resource accumulation and more on social interaction for survival. They have learned, in their own way, that proximity to humans ensures sustenance, irrespective of the season. These contrasting behaviors in the animal kingdom provide a vivid illustration of how intelligence and learning are not monolithic concepts but are tailored to specific needs and environments. For squirrels, survival hinges on foresight and preparation for scarce times, while for cows, it’s about the learned behavior of co-existing with humans.
Adaptive intelligence in action: The squirrel’s instinctive strategy for survival. This depiction from “Ice Age” illustrates the squirrel’s innate behavior of resource hoarding, a skill perfected over generations through evolutionary learning. It’s a playful yet poignant reminder of how intelligence in the animal kingdom is shaped by environmental challenges and survival needs.#
Furthermore, reflecting on these examples from the natural world, I’m struck by the importance of adaptability and flexibility — qualities that are crucial both in nature and in the realm of AI and ML. Just as squirrels and cows have adapted their behaviors to their respective environments and needs, our approach to AI and ML development must also be adaptable and responsive to changing circumstances and requirements. This adaptability in AI and ML is not just about creating systems that can handle a variety of tasks; it’s about developing solutions that can learn, grow, and evolve as the world around them changes. In my work, I constantly remind myself of this principle. The goal is to create AI and ML systems that are not static, but dynamic — capable of adjusting to new data, different environments, and unforeseen challenges. It’s about building intelligence that is not rigid but fluid, mirroring the ever-evolving nature of the intelligence we observe in the natural world. In essence, the lessons from nature extend beyond mere problem-solving strategies; they encompass the broader theme of adaptability and evolution. As I continue to explore the realms of AI and ML, these lessons from nature guide my approach, ensuring that the solutions I develop are not only effective but also resilient and adaptable in the face of change.
4.3 Translating Learning to AI Development#
Drawing parallels from this to my field of AI and ML engineering, I see a
crucial lesson about the importance of context and purpose in developing
solutions. As an engineer, it’s not just about the technical prowess of
creating AI or ML systems; it’s about understanding the why behind what
we’re building. Are we developing technology that meets a genuine need, or are
we simply chasing the novelty of advanced tools? This discernment is vital.
Just as squirrels and cows have adapted their behaviors to their environments,
we too must design AI and ML solutions that are fit for purpose, addressing
real-world problems effectively. It’s about focusing our resources and efforts
on necessities, not just desires. In my reflections, I am continually reminded
that the key to effective solution-building, whether in nature or technology,
lies in understanding and adapting to the context. It’s about asking the right
questions — Do we need AI, ML, or another approach? This understanding is
what guides us in making informed, impactful decisions in the realm of
technology development.
5 Artificial Intelligence#
5.1 Current System and Future Prospects#
In my quest to understand and articulate the essence of Artificial
Intelligence, I often return to the fundamentals of human intelligence or
general intelligence as a reference point. To reiterate, at its core,
intelligence is about analyzing, comprehending, and learning from information,
and then using this knowledge to craft solutions. Artificial Intelligence, as I
perceive it, mirrors these capabilities but within an artificial framework
created by human ingenuity. When I speak of AI, I’m referring to a system
designed to understand and adapt to problems, and then forge a path to solve
them. Its operation bears similarities to the intelligence we witness in
living beings, yet it’s distinctly different. Unlike humans or animals, AI
doesn’t rely on organic senses for information acquisition. Instead, it
processes data — vast and varied — as its means of sensing the world. Its
learning process is grounded in algorithms that enable it to test, adapt, and
evolve. This iterative process is reminiscent of the trial-and-error approach
inherent in natural learning. In conceptualizing AI, I see it as a system that
simulates aspects of living entities’ intelligence. This simulation isn’t an
exact replication of human cognition, but rather an emulation of various
intelligent behaviors observed in nature. The adjective “artificial” is
crucial in this context. It signifies that AI, while inspired by natural
intelligence, is fundamentally a human creation, a product of programming and
design, not an organic emergence.
5.1.1 Ethical Considerations and Societal Impact#
As my inquiry deepens into the realm of Artificial Intelligence, I aim to
explore how these artificial systems emulate cognitive functions and
consider the broader implications of such technology. It’s a journey into
understanding how AI, as a product of human creation, can execute tasks, solve
problems, and learn, in ways that are both similar to and distinct from the
intelligence found in nature. This exploration is not just about technical
understanding but also about appreciating the nuances and potential of AI as
it intertwines with the tapestry of human intellect and creativity.
Furthermore, as I reflect on the advancements and potential of Artificial
Intelligence, I’m increasingly mindful of the ethical considerations and
challenges that accompany this technology. The development of AI is not just a
question of what can be achieved technically, but also what should be pursued
in line with ethical guidelines and societal values.
Questions around data privacy, algorithmic bias, the impact of AI on employment, and the moral implications of autonomous decision-making by AI systems are critical. These issues require careful consideration and proactive measures. As we advance in the realm of AI, ensuring that these technologies are developed and used responsibly becomes paramount. In exploring AI, it’s crucial to engage with these ethical dimensions, fostering a dialogue that includes not just technologists and engineers but also ethicists, policymakers, and the broader public. This multidisciplinary approach is vital for creating AI solutions that are not only innovative and effective but also aligned with ethical standards and beneficial for society as a whole. As I continue to delve into the world of Artificial Intelligence, I aim to not only understand and contribute to its technological growth but also to be an active participant in the conversation about its ethical and societal impact. This balance is essential for ensuring that the advancement of AI is both responsible and sustainable.
5.1.2 Artificial Narrow Intelligence#
In my endeavor to demystify Artificial Intelligence, I often begin by
simplifying it to its essence — a system. This term, system, is broad and
multifaceted in the context of AI. It could manifest as a computer program
designed for specific tasks, an intricate network of computers communicating
with each other, or even a robotic framework tailored for specialized
activities. At its core, AI is this: a system adept at simulating aspects of
human intelligence or functioning in a human-like manner to solve problems.
Over the past decade, the evolution and diversification of AI have been
remarkable. We’ve seen AI systems mastering complex games, engaging in
meaningful dialogues with humans, and much more, each application a testament
to the strides we’ve made in this field. Delving deeper, these AI systems,
which we interact with or hear about, are primarily examples of Artificial
Narrow Intelligence, or ANI. These are systems expertly crafted to excel in
specific domains — whether it’s a game, language processing, or predictive
analytics. ANI represents a focused application of AI, where the system’s
prowess is confined to a particular area. In contrast, there lies a more
ambitious and elusive goal in the realm of AI — the creation of Artificial
General Intelligence, or AGI. AGI aims to replicate the comprehensive
cognitive abilities of humans, an endeavor that pushes the boundaries of AI
far beyond narrow specializations.
Constructing ANIs and AGIs is a journey marked by both marvel and complexity. While ANIs demonstrate our ability to infuse machines with remarkable task-specific intelligence, AGIs represent the pinnacle of AI research — the quest to create a system that mirrors the general intelligence of humans. This pursuit involves not only the replication of intellectual tasks but also the emulation of nuanced human traits like adaptability, emotional understanding, and creative thinking. As I venture further into the depths of AI, my focus is not only on understanding these systems but also on appreciating the intricate challenges they present. Building AI, be it ANI or AGI is a process that intertwines technological innovation with deep insights into human cognition. It’s a venture that calls for a blend of precision, creativity, and ethical consideration, as we step into an era where the lines between artificial and natural intelligence increasingly blur. This exploration is vast and profound, encompassing the technicalities of AI development and the broader implications of such technologies in our lives and societies. Moreover, as I reflect on the current state and prospects of Artificial Intelligence, I find it essential to consider the potential advancements on the horizon. AI is not a static field; it’s rapidly evolving, with breakthroughs and applications emerging regularly. The future may bring AI systems that not only replicate human intelligence more closely but also integrate seamlessly into various aspects of our lives, from personalized healthcare to advanced automation in industries. In my exploration of the AI landscape, I’ve observed a predominant presence of Artificial Narrow Intelligence (ANI). These systems, which are abundant, demonstrate capabilities akin to human intelligence but only within their specific domains. ANIs are remarkable in their designated tasks, yet they are intrinsically limited. They cannot venture beyond the realms for which they have been trained, a stark contrast to the theoretical concept of Artificial General Intelligence (AGI).
5.1.3 The Quest for Artificial General Intelligence#
AGI represents an aspirational frontier in AI research, envisaged as a system
capable of comprehensive and autonomous problem-solving, akin to a human’s
versatile intelligence. The idea of AGI extends to it having a form of
subconscious processing, enabling a profound understanding and ability to
debug and solve a wide spectrum of problems. However, as of now, AGI remains a
concept rather than a reality. While there have been claims, such as those
from some researchers in the field, suggesting advancements toward AI
consciousness or sentience, I approach these assertions with cautious
skepticism. The journey to AGI is not just a technological leap but also an
ethical and philosophical odyssey, fraught with complex challenges that are
yet to be surmounted. That said, the current generation of AI, the ANIs,
continually astounds me with their problem-solving skills. Their ability to
provide solutions to complex problems, often in ways that are both innovative
and efficient, underscores the significant progress in the field of AI. These
achievements, however, should be recognized for what they are —
advancements within the scope of narrow intelligence. They signify the strides
we have made in specific areas of AI but do not yet cross the threshold into
the realm of general intelligence.
In my reflections, I am constantly reminded that AI, in its current state, serves as a powerful tool, one that has the potential to revolutionize countless aspects of our lives. Yet, the pursuit of AGI, the creation of a system that not only simulates but also independently matches human intelligence, remains a distant goal. This pursuit is not just about technological innovation; it’s about reshaping our understanding of intelligence itself and responsibly navigating the ethical landscapes it presents. Furthermore, as I delve deeper into the realms of ANI and AGI, I increasingly consider their potential societal impact and the ethical ramifications. The advancements in ANI have already begun to transform industries, from healthcare diagnostics to financial analysis, demonstrating the profound influence AI can have on our daily lives. However, with these advancements come responsibilities and ethical considerations. Issues such as data privacy, algorithmic bias, and the displacement of jobs due to automation are just a few examples of the challenges we face as we integrate these technologies into society.
Echoing a timeless truth: With Great Power Comes Great Responsibility. This iconic phrase from Uncle Ben in Spiderman resonates deeply with the ethical challenges in AI development. It serves as a poignant reminder that advancing AI technology not only brings immense possibilities but also significant responsibilities to ensure privacy, security, and the positive societal impact of these intelligent systems.#
5.2 Ethical Responsibilities in AI Development#
With these advancements comes a significant responsibility. The ethical considerations in AI development cannot be overstated. As we push the boundaries of what AI can achieve, questions about privacy, security, the societal impact of automation, and the moral implications of decision-making by AI systems become increasingly important. It is imperative that as a developer and researcher in this field, I, along with my peers, remain vigilant and proactive in addressing these ethical challenges. In sum, my exploration of AI is an ongoing journey, one that encompasses not just the technical aspects of creating intelligent systems but also a deep consideration of their long-term implications. The goal is to contribute to an AI future that is not only technologically advanced but also ethically sound and beneficial to society as a whole.
Looking towards the future and the possibility of AGI, these considerations become even more pronounced. The development of systems with general intelligence — capable of independent reasoning and decision-making — raises profound questions about their integration into a human-centric world. How will such advancements affect human relationships, work, and societal structures? How do we ensure that these technologies are developed responsibly and used for the betterment of society? These are questions that occupy my thoughts as I explore the field of AI. My aim is not just to contribute to the technological advancements in AI but also to engage in the critical dialogue surrounding its ethical and societal implications. It’s about envisioning a future where AI, in any form, is developed with foresight and responsibility, ensuring that its integration into our world enhances, rather than diminishes, the human experience.
6 Machine Learning#
6.1 Understanding the Basics#
As we delve into the fascinating realm of Machine Learning or ML for short,
it’s akin to embarking on a journey of discovery, not unlike the way we humans
learn from our experiences. Picture this, just as a child learns to recognize
shapes and colors by observing and interacting with the world, Machine
Learning enables computers to learn and make decisions based on the data
they encounter. To put it simply, Machine Learning or in this case, Deep
Learning is a type of Computer Science where a machine can learn and adapt
based on data, much like how we learn from our daily experiences. Imagine your
smartphone gradually understanding your preferences and habits; this is a
basic example of Machine Learning or Deep Learning in action. Now, let’s break
it down. A quick note, when I say machine, I’m simply implying a program or a
piece of software.
Note for the reader
I’m prefacing the significance of Deep Learning over traditional Machine Learning as a lot of people that I’ve come across tend to steer over the former over the latter in terms of their interest in learning about this technology. In doing so, I’ll be using the terms Machine Learning and Deep Learning interchangeably on purpose to keep the idea simple and to simply differentiate them from their broader counterpart, that is Artificial Intelligence.
6.1.1 The Learning Process in Machines#
The process starts with input data — this could be anything from pictures
and texts to sounds. Think of this as the machine’s way of sensing the
world. In the early days of Deep Learning or DL for short, the lack of
sufficient data was like trying to understand a story with half the pages
missing. But today, thanks to the internet, data is abundant, which is like a
vast library of books for the machine to read and learn from. However, just
having data isn’t enough. It’s akin to memorizing a recipe without
understanding the techniques of cooking. Here’s where computational power
or simply put, the computer’s ability to process and make sense of this data
plays a crucial role. It’s like having a quick-thinking brain that can hold
and analyze large volumes of information.
As I navigate through the intricacies of Machine Learning, more
specifically Deep Learning, I often find parallels between how we humans learn
and how machines do. Let’s take the same example of a toddler from before.
There’s a lot of trial and error involved — crawling, standing, falling,
and then trying again. Similarly, in Deep Learning, the system or the software
tries to understand the data, makes mistakes, learns from them, and improves
over time. This process, known as Iterative Learning, is fundamental to
both humans and machines while learning. But how do we know if the machine has
learned correctly? In our world, we test our knowledge against known facts or
experiences, some might even say right or wrong answers or behaviors. In the
world of ML, this is done by comparing the machine’s decisions or predictions
against a set of correct answers, known as ground truth. When the
machine’s predictions match the ground truth, it’s a sign that the learning
has been successful. Now, let’s consider real-world applications. From voice
assistants like Siri and Alexa to recommendation systems on Netflix and
Amazon, Machine Learning or Deep Learning is becoming an integral part of our
daily lives. These systems learn from the vast amount of data we provide
through our interactions and continually evolve to serve us better.
6.1.2 Historical Context and Evolution#
As I probe more deeply into the intriguing world of Machine Learning, I often find myself marveling at its journey from a mere concept in the mid-20th century to an integral part of our daily lives today. It’s a tale of how technology, fueled by human curiosity and ingenuity, has evolved to mimic our way of learning and decision-making. To understand Machine Learning or Deep Learning, it’s essential to differentiate it from its broader counterpart, Artificial Intelligence. AI is the overarching concept of machines smartly performing any given task. Machine Learning or Deep Learning, however, is a specific application or a subset of AI that involves feeding machines data and letting them learn for themselves for a specific niche use case. It’s like teaching a child to solve a particular set of puzzles by showing examples rather than dictating rules. In my exploration of ML, I discovered it’s not a one-size-fits-all approach. There are different types, each with its charm. Supervised learning, much like a teacher guiding a student, involves learning from labeled data. Unsupervised learning, on the other hand, is like a child exploring a room, finding patterns and categorizing objects without explicit instructions. Then there’s reinforcement learning, akin to training a pet by rewarding desired behaviors.
The historical context of ML is as fascinating as its types. From the inception of AI concepts in the 1950s to the current explosion of data and computational power, the journey of ML has been transformative. It’s a narrative of how necessity, coupled with human creativity, has birthed a field that now redefines how we interact with technology. Yet, this progress brings its own set of ethical dilemmas and challenges. Bias in data, for instance, can lead to skewed learning, making it imperative to approach ML with a sense of responsibility. Privacy concerns and the implications of automated decision-making also loom large, reminding us that with great power comes great responsibility. Looking ahead, the future of Machine Learning is exhilarating. From revolutionizing healthcare with personalized treatments to powering self-driving cars, the possibilities are boundless. It’s a future where technology not only complements but enhances human capabilities.
6.2 Drawing Parallels with Human Learning#
As I delve deeper into the world of Machine Learning, I’m struck by a profound parallel between how we, as humans, learn and how these intelligent machines process information. Just like us, ML or DL programs have their learning limits, contending with a seemingly boundless universe of data but restricted by finite memory and processing capabilities. Let me paint you a picture from my own experience.
6.2.1 Chunk-Based Learning Approach#
Imagine preparing for an important exam. You’re faced with a hefty textbook, brimming with complex concepts and detailed illustrations and you have no idea about the topics of interest. It’s impractical, if not impossible, to absorb all this information in one sitting. So, what do I do? I break it down, chapter by chapter, section by section, ensuring each piece is small enough to fit within the confines of my memory. This methodical approach isn’t just about managing workload; it’s about enhancing comprehension and retention. This is precisely how Deep Learning algorithms operate. Faced with vast pools of data, they don’t attempt to process it all in one continuous stretch. Instead, they dissect it into smaller, more manageable batches. This process, for me, resembles how I segment chapters of a textbook. Each batch is like a section of a chapter, enabling the algorithm to focus, learn, and adapt incrementally. Take, for instance, how social media platforms use ML to curate content. It’s like how these algorithms study and learn user preferences in batches, similar to how we break down subjects when studying for exams. The platforms analyze interactions, segment them like chapters in a book, and then use these insights to personalize what we see. It’s a practical example of ML at work, mirroring our methods of digesting large volumes of information.
Tackling complexity, one page at a time: Just like Simpson grappling with a hefty textbook, Deep Learning algorithms break down vast data into manageable “chapters” for efficient learning and comprehension. This strategy mirrors our approach to studying complex subjects, where segmenting information helps in better understanding and retention.#
Well, this is odd. You might wonder, why are these batches of data selected at random rather than following a particular sequence like humans do. To keep this answer short, this randomness is more strategic than it appears. It ensures that the learning process is comprehensive and not skewed towards any particular pattern or trend within the data. It’s akin to me choosing different topics from various chapters to study, thus gaining a well-rounded understanding of the subject matter. The more I explore the intricacies of ML, I’ve come to appreciate the subtleties of this learning process. It’s not just about feeding data into a system; it’s about how this data is segmented, processed, and ultimately internalized by the algorithm. This method mirrors our learning strategies, revealing a fascinating intersection between human cognition and artificial intelligence. So, whenever I interact with technologies powered by Machine Learning or Deep Learning — be it a smart assistant, a recommendation engine, or a predictive text feature — I’m reminded of this incredible process. These systems, much like ours, are on a journey of continuous learning and improvement, one batch of data at a time.
6.2.2 Importance of Data Diversity in Learning#
Another intriguing aspect is the visual learning analogy. Imagine how we often
grasp concepts better with visual aids. Similarly, ML or DL algorithms can be
trained using batches of visual data, such as images or videos, allowing them
to see and understand the world in a way that’s remarkably similar to
our visual learning process. The diversity of data is another cornerstone of
effective ML. Just as a well-rounded education encompasses a variety of
subjects, ML algorithms thrive on varied datasets. This variety is crucial for
avoiding biases and ensuring the accuracy of the learning process. It’s like
expanding one’s horizons by reading different genres of books, each offering a
new perspective and dimension of knowledge.
6.2.3 Human Revision and Machine Learning Processing#
As my inquiry deepens into the intricacies of Machine Learning, I often find myself marveling at its resemblance to the human learning process. Just as we engage in the rigorous exercise of studying for an exam, DL algorithms undergo a similar journey of learning and refining their understanding. For instance, take the previous example of sitting with a hefty textbook the night before a major test. You pore over each section, trying to absorb and understand the material. Initially, some concepts don’t quite stick, and your practice answers might be far from perfect. This is where the beauty of repetition comes in. You revisit the chapters and re-read the text, and each time, your understanding deepens. Your answers begin to align more closely with the correct information, improving with each iteration. An ML program starts by processing a selected batch of data. It’s akin to reading a chapter from a textbook for the first time. The program then attempts to draw conclusions or make predictions based on this initial processing. This stage is crucial, and it’s where I see a direct parallel to taking a practice test after a round of studying.
Now, just as I compare my practice answers to the correct ones, the ML
algorithm does something similar. It compares its results with a known set of
correct answers or the ground truth. If the algorithm’s predictions
deviate significantly from this ground truth, it’s a clear indicator of
loss — a term we use in ML to describe the gap in the accuracy of the
learned information. The next step, much like my revising chapters that I
didn’t quite grasp, involves the algorithm revisiting the data. With each
iteration, it learns from its previous errors, adjusting its approach and
refining its predictions. This continual process of learning, adjusting, and
improving is what fascinates me the most about Machine Learning. It’s not just
a static program running through data; it’s an evolving, learning entity, much
like a student striving to master a subject. As I reflect on this, I’m struck
by the realization that ML is not just about algorithms and datasets. It’s
about a journey of growth and improvement. Each iteration, each revisited
batch of data, brings the program closer to accuracy, mirroring our path of
learning, where we continuously strive to better our understanding and reduce
our errors. This harmonious blend of human-like learning in the realm of
artificial intelligence not only enhances the capabilities of these programs
but also opens up a world of possibilities for their application in various
fields.
6.2.4 Understanding Ground Truth and Loss#
I often think of the realm of Machine Learning as a journey of continuous
improvement, much like our own learning experiences. In this journey, two key
concepts play a pivotal role — ground truth and loss. To
understand these, I like to compare the ground truth to the answer key of
an exam, providing the correct answers against which the ML algorithm’s
predictions are measured. Loss, then, represents the difference between
the algorithm’s predictions and this ground truth, much like the gap
between a student’s response and the correct answer in a test. One of the most
relatable examples of this iterative learning process in action is how voice
recognition software improves over time. With each interaction, it learns from
the user’s voice patterns, gradually enhancing its accuracy. Similarly,
recommendation algorithms on streaming platforms adapt to predict viewer
preferences more effectively, learning from the viewers’ choices and feedback.
Feedback, too, plays a pivotal role in ML, akin to how we learn from our
experiences. As these algorithms process data and produce results, they
receive feedback on their accuracy. This feedback loop is essential for
refining their learning and ensuring continuous improvement and adaptability.
However, this process isn’t without challenges. The need for substantial data and computational resources is a significant hurdle. Moreover, there’s a risk of overfitting, where an algorithm becomes too attuned to the specific data it’s trained on, losing its ability to generalize and perform well on new, unseen data. If you don’t understand overfitting, don’t worry.
6.3 Challenges and Human Intervention in Machine Learning#
In the world of ML, the human element remains crucial. Data scientists and experts often step in to adjust algorithms based on their performance, ensuring that these learning processes remain on track. This blend of human expertise and machine processing underscores that ML isn’t a purely automated realm but a collaborative effort between human intelligence and technological capability. Looking ahead, the future of iterative learning in ML appears bright and full of potential. With advancements in technology, we might see more sophisticated forms of AI emerging, leading to more autonomous and efficient learning systems. These developments could transform how machines learn, making them even more integral to our daily lives and various industries.
In essence, as I delve into the nuances of ML, I’m continually fascinated by how it mirrors our learning process. From the initial struggles to the gradual mastery of concepts, the journey of an ML algorithm is not just a technical process; it’s a reflection of growth and evolution, a testament to the extraordinary capabilities that can emerge from the synergy of human intelligence and machine learning. Yet, the journey of ML is not without its challenges and limitations. Processing extremely large datasets efficiently remains a hurdle, and ongoing research is focused on overcoming these obstacles. It’s a field that’s continually evolving, pushing the boundaries of what’s possible. For those intrigued by Machine Learning, there are numerous online tools and resources where you can see these concepts in action. Engaging with interactive platforms can demystify ML, making it more tangible and understandable.
6.3.1 Initial Exposure and Learning Curve#
As I’ve expressed earlier, the more I explore the intricacies of Deep Learning or Machine Learning, I’m constantly reminded of its parallels with our human learning process. It’s a fascinating journey that begins with understanding the significance of the initial learning phase. Just as a novice embarking on learning a new language or musical instrument requires a substantial amount of time to grasp the basics, training a Machine Learning program from scratch demands a similar, if not greater, investment of time to achieve acceptable results. This initial phase is critical, setting the foundation for all future learning.
6.3.2 Impact of Data Quality on Learning Outcomes#
Reflecting on my own experiences, I realize how the quality of learning is
often influenced by the quality of the sources or teachers we rely on. In the
realm of Machine Learning, this concept is encapsulated in a principle widely
recognized as garbage in, garbage out. The essence of this principle is
strikingly simple yet profound. If the input data fed into an ML algorithm is
of high quality, accurate and well-structured, the algorithm is more likely to
yield reliable and effective results. On the other hand, if the input data is
poor, biased, or flawed, the outcome is inevitably compromised. It’s akin to
trying to learn from a textbook riddled with errors — the learning is
bound to be flawed.
This understanding leads me to appreciate the meticulous care required in curating the data used for training ML algorithms. Just as a student seeks out the best books and teachers, an ML practitioner must ensure the data is of the highest quality. This involves not only selecting the right data but also preparing it in a way that makes it most beneficial for the algorithm. The process is intricate, requiring a keen eye for detail and an understanding of the nuances of both the data and the learning model. In my journey through the realms of Deep Learning, this realization of the symbiotic relationship between the quality of input data and the efficacy of learning outcomes continually shapes my approach. It’s a powerful reminder that in the world of artificial intelligence, just like in human learning, the building blocks of knowledge are crucial for any meaningful and accurate understanding.
6.3.3 Exploring Through Analogies#
As I immerse myself in the world of Deep Learning or Machine Learning, I often
find that the best way to unravel its complexities is through relatable
analogies from everyday life. Consider, for instance, the process a chef
undergoes to perfect a recipe, constantly tweaking ingredients based on
feedback. This is akin to how Deep Learning algorithms refine their
understanding through continuous data processing and learning. Reflecting
on the principle of garbage in, garbage out, I’m reminded of how vital the
quality of input is in determining the outcome. This is vividly illustrated in
navigation apps like Google Maps, where the accuracy of traffic data and user
feedback directly influences the effectiveness of route suggestions. Just as
reliable data leads to better navigational advice, high-quality data is
essential for effective deep-learning outcomes.
Drawing parallels with my personal experiences in learning new skills, I’ve noticed a similar pattern. When I started learning a new language, the initial phase was challenging, and the quality of my learning materials significantly impacted my progress. This mirrors the initial stages of training a Deep Learning model, where starting from scratch with quality data is crucial for successful learning. Looking ahead, the prospects of Deep Learning are both exciting and vast. In healthcare, for instance, Deep Learning could revolutionize diagnostic processes by analyzing medical images with precision, aiding in early disease detection. Similarly, in environmental science, predicting climate change patterns using these algorithms could lead to groundbreaking discoveries.
In conclusion, as I navigate the vast and evolving landscape of Machine Learning, I’m struck by its complexity and potential. From practical applications in everyday technology to its ability to learn and adapt in ways that mirror human cognition, ML is not just a field of study; it’s a window into a future where technology and human intelligence converge in fascinating ways. In sum, my journey through the realms of Deep Learning is not just a technical exploration but a deeper understanding of how quality data, ethical practices, and real-world applications intertwine to shape this dynamic field. It’s a world where technology meets everyday life, creating a future brimming with possibilities and innovations.
If you read it completely, I would like to thank you for your time!
References
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Whether it be machine learning, large language models, smart applications and appliances, digital assistants, synthetic media software, or autonomous vehicles, companies that aren’t investing in AI products and services risk becoming obsolete. Countless companies stand to benefit from AI, but a handful of stocks have AI and automation as a central part of their businesses, https://builtin.com/artificial-intelligence/ai-companies-roundup
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Forecast to grow to nearly US$2T by 2030, machine learning (ML) can be key to unlocking the value of corporate and customer data. According to MIT, ML is a subfield of AI that gives computers the ability to learn without explicitly being programmed, https://builtin.com/artificial-intelligence/machine-learning-companies
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From Crawling to Walking: A Journey of Persistence and Memory. This captures a pivotal moment in a toddler’s life, embodying the essence of learning through trial, error, and memory. Each stage, from rolling over to taking those first unsteady steps, is a testament to the complex process of building upon past experiences, guided by the persistent and experimental nature of human learning. It’s a visual representation of how memory serves as the foundation for acquiring new skills, showcasing the remarkable journey from dependency to independence., https://lovevery.com/community/blog/skills-stages/walking/
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Human beings are not the only creatures living on earth who are capable of demonstrating and computing intelligence. Fortunately enough for us, human intelligence is usually considered as — by far — the highest in the environment, https://medium.com/dataseries/ai-nature-are-they-interlinked-e7e0ae0061dc
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Scrat is an acorn-obsessed saber-toothed squirrel that lived during the ice ages, attempting to store his prized acorn, https://iceage.fandom.com/wiki/Scrat
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Privacy in the context of AI has different considerations to data privacy in general. One of the challenges of protecting privacy in artificial intelligence concerns how to create suitable regulations that protect privacy without stifling advances in AI technology. The data contexts at stake are both the scanning mechanisms that enable the AI tools to learn about their environments, as well as the nature of the data itself and how it is used to create the AI capability, https://www.isaca.org/resources/news-and-trends/isaca-now-blog/2021/beware-the-privacy-violations-in-artificial-intelligence-applications
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Learn what is artificial intelligence (AI), https://www.ibm.com/topics/artificial-intelligence
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South Park trolls Amazon Alexa owners in this week’s episode. Cartman, ever the prankster, starts the show’s 21st season off by triggering a virtual Amazon Alexa, and adding disgusting items to its shopping list, https://www.theverge.com/2017/9/16/16318694/south-park-amazon-alexa-google-home
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While some research suggests people are capable of storing between five and nine units of information, more recent research posits that short-term memory has a capacity for about four chunks of information, https://www.verywellmind.com/chunking-how-can-this-technique-improve-your-memory-2794969
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Garbage In, Garbage Out (GIGO) is also used to describe failures in human decision-making due to faulty, incomplete, or imprecise data, https://en.wikipedia.org/wiki/Garbage_in,_garbage_out