Artificial Intelligence. The Beginning of a New Technological Revolution: Challenges and Opportunities

By Ruslan Makov

Step into the future with this captivating exploration of the technological revolution that's transforming our world in unimaginable ways. In this eye-opening book, the author guides you through the fascinating realm of intelligent systems, unveiling their remarkable capabilities, untapped potential, and the profound impact they're set to have on every aspect of our lives.

Prepare to be amazed as you uncover how advanced algorithms are poised to revolutionize the way we work, play, learn, and even build relationships. From the office to our personal lives, this comprehensive exposé leaves no stone unturned in its examination of the machine intelligence uprising that's already in motion.

But this book is more than just a commentary on the takeover by smart machines - it's your essential guide to navigating the uncharted territory ahead. Within these pages, you'll discover the tools, strategies, and thought-provoking insights you need to not merely survive, but flourish in the age of intelligent systems.

Whether you're an entrepreneur seeking to harness the power of innovation driven by cutting-edge algorithms, a curious student eager to peek into the future, or simply someone trying to understand this transformative technology, this book is your passport to the forefront of the technological revolution.

Prepare for an unforgettable journey - once you've glimpsed the future powered by intelligent machines that awaits, you'll never look at the world the same way again. This book is more than just a read; it's a perspective-shifting experience that will redefine your understanding of the world and your place within it.

Don't hesitate another moment to immerse yourself in the captivating pages of this essential read. Secure your copy today and become part of the vanguard shaping the future driven by machine intelligence.

• Language: English
• Publisher: Publishdrive
• Release date: Mar 27, 2024

Book preview

Artificial Intelligence and the New Technological Revolution: An Introduction

Throughout human history, we have sought to create machines and mechanisms capable of facilitating our lives, taking on some of our tasks and functions. From the earliest and simplest tools to the most complex modern computers, all these inventions were designed to expand human capabilities and enhance our intellectual and physical abilities. And now, we stand on the threshold of a new stage in this eternal pursuit – the creation of artificial intelligence (AI), a machine mind capable not merely of executing algorithms set by humans, but of learning independently, making decisions, and creating.

The concept of artificial intelligence has captivated the minds of scientists, philosophers, writers, and ordinary people for many decades. From amusing stories about talking robots to grim dystopias about machine uprisings, the image of a thinking computer has firmly entered our culture and mass consciousness. But behind these fantasies and fears lies a real scientific and technological revolution unfolding right now, before our very eyes. A revolution capable of changing virtually every aspect of our lives – from economics and industry to medicine and education, from art and creativity to human relationships and the very nature of the mind.

Artificial intelligence is not just another new technology in a long line of others. In terms of its potential impact on civilization, it is comparable to such epochal inventions as the wheel, electricity, the computer, or the internet. Perhaps its influence will prove to be even more profound and all-encompassing. After all, all previous technologies were merely tools in human hands, while AI for the first time gives us the opportunity to create something equal to ourselves in intelligence, and in the future, perhaps even surpassing our own cognitive abilities.

Already today, systems based on machine learning and neural networks are demonstrating astonishing results in areas such as image and speech recognition, big data analysis, playing chess and other intellectual games, automatic translation, and even creativity. They are helping doctors make diagnoses, biologists study the genome, and physicists model the universe. They control the most complex production processes, optimize logistics, and forecast economic trends. There is practically no sphere of human activity where intelligent algorithms could not bring benefits and achieve results unattainable by humans.

And this is only the beginning. With each passing year, machine intelligence technologies are becoming more sophisticated, encompassing ever new areas. Today's narrowly specialized systems are gradually evolving towards more universal, flexible, and autonomous solutions. In laboratories around the world, work is underway to create neuromorphic chips that replicate the structure of biological neurons, quantum computers capable of solving problems in seconds that would take traditional machines millennia, and hybrid systems combining the capabilities of natural and artificial intelligence. The horizons for the development of these technologies seem truly limitless.

But the more powerful artificial minds become, the more questions and challenges they pose for us. How will the labor market and education system change in a world where many intellectual professions are accessible to machines? How can we ensure the safety and reliability of increasingly autonomous systems? Where is the line between AI assisting humans and enslaving them in a world of total automation? Finally, is a truly strong, general AI that is not inferior to human intelligence possible, and what will happen when it is created? All these questions require deep reflection today.

The path of artificial intelligence development is also a path of understanding our own human nature. By modeling the mind in silicon and algorithms, we unwittingly ask ourselves – what is the mind itself? What makes us human? Mind, self-awareness, free will, emotions, creativity – is it possible to reproduce all this in a machine, and if so, will it then be fundamentally different from ourselves?

In this book, we will try to understand the phenomenon of artificial intelligence from all sides – technological, scientific, economic, social, and philosophical. We will trace the history of the idea of thinking machines from the first naive concepts to the latest scientific developments. We will look at how the development of machine mind technologies is changing and has already changed various spheres of human activity. We will discuss the emerging prospects and potential risks and threats. We will try to glimpse into the future of intelligent systems and our own coexistence with them.

Artificial intelligence is perhaps the main challenge and the main opportunity facing civilization today. The shape of our world in the coming decades depends on how we respond to this challenge and realize this opportunity. To comprehend what is happening and prepare for the impending changes is the task this book sets for itself. Join our exploration – the future is beginning right now

Part I: Artificial Intelligence as a Technology

Chapter 1: The History of the Creation and Development of Artificial Intelligence

The idea of creating thinking machines capable of performing complex tasks and even surpassing the human mind has its roots deep in antiquity. For centuries, philosophers, scientists, and inventors have tried to understand the nature of intelligence and reproduce it in mechanical devices. This long journey, full of amazing discoveries, grandiose designs, and bitter disappointments, ultimately led to the birth of the modern science of artificial intelligence.

1.1 Ancient ideas about automatons and mechanisms: the philosophical aspect

Already in ancient philosophy, we find the first reflections on the possibility of creating artificial beings endowed with reason. For example, the ancient Greek thinker Aristotle, in his treatise On the Soul, discusses different types of souls – vegetative, animal, and rational, the latter being inherent only in humans. However, he allows that some functions of the rational soul, such as the ability to judge and reason, can be reproduced in inanimate objects.

In Plato's dialogue Euthyphro, the main character talks about the mechanical statues of gods created by the legendary inventor Daedalus. These statues could move and even make sounds as if they were alive. Although Plato uses this image more in a metaphorical sense, it reflects the ancient human dream of creating artificial life.

In the Hellenistic era, the first real automatons appeared – mechanical devices capable of independently performing assigned functions. For example, the ancient Greek mathematician and engineer Heron of Alexandria created many amazing machines, including an automatic puppet theater powered by a system of counterweights and levers, and an automaton for selling holy water in temples, operating on the principle of a coin acceptor.

In ancient China, skillful mechanisms imitating the movements of living beings were also known. For instance, the treatise Shu Jing mentions Emperor Mu Wang (10th century BC), who ordered the creation of a mechanical bird capable of flying and singing. And in the 2nd century AD, the inventor Ma Jun created a wooden doll musician that could play melodies on the stringed instrument qin.

Medieval Islamic scholars made a great contribution to the development of automation and mechanics. The Banu Musa brothers in the 9th century wrote the Book of Ingenious Devices, which described dozens of amazing mechanisms, including automatic musical instruments, fountains, and even a humanoid robot that served to entertain guests at feasts. The outstanding Persian scholar Al-Jazari at the beginning of the 13th century created a number of programmable automatons, such as a robot musician and a servant for serving drinks.

In the Renaissance, the idea of thinking machines took on new meaning in light of humanistic ideas about the limitless possibilities of the human mind. Leonardo da Vinci left many drawings and notes about mechanical devices, including a humanoid robot knight capable of moving its arms, head, and opening the visor of its helmet. Although it is unknown if this robot was ever built, the very idea of a mechanical man captured the minds of many thinkers of that era.

Philosophers and scientists of the modern era continued to reflect on the nature of the mind and the possibility of reproducing it in a machine. In his treatise Discourse on the Method, René Descartes draws a clear line between humans and animals, arguing that the latter are nothing more than complex biological automatons devoid of thought and self-awareness. At the same time, he allows that a perfect artificial human is theoretically possible, albeit extremely unlikely in practice.

Gottfried Leibniz, one of the greatest minds of his time, was fascinated by the idea of creating a universal logical language and a calculating machine capable of solving any problem through rigorous mathematical calculations. Although his project remained unrealized, it anticipated some key ideas of modern computer science and artificial intelligence.

In the 18th century, so-called automatons became widespread – mechanical devices capable of imitating the movements of humans and animals. The most famous are the works of the French inventor Jacques de Vaucanson, who created a mechanical duck that could quack, flap its wings, peck at grain, and even digest food. Although these automatons were purely mechanical and did not possess any signs of intelligence, they paved the way for further research in the fields of robotics and AI.

1.2 The Emergence of Computers and the Foundations of Artificial Intelligence

A real breakthrough in the development of the idea of artificial mind occurred in the 20th century with the advent of the first digital computing machines. The mathematical theory of computation, developed by Alan Turing, Claude Shannon, and other pioneers of computer science, laid the foundation for modeling human thought processes on computers.

In 1950, Alan Turing published his famous paper Computing Machinery and Intelligence, where he proposed an empirical test (later called the Turing test) to verify a machine's ability to think. The essence of the test is that a human judge conducts a dialogue with two invisible interlocutors, one of whom is human and the other a computer program. If, based on the results of the dialogue, the judge cannot determine which of the interlocutors is a machine, then this machine is considered to have passed the test, that is, demonstrated behavior indistinguishable from human intelligence.

Although the Turing test remains a subject of philosophical debate and criticism, it became an important milestone in the history of AI, setting a criterion for assessing the intelligence of machines and stimulating further research in this area.

In 1956, at a conference at Dartmouth College (USA), the official birth of artificial intelligence as a scientific discipline took place. The organizers of the conference - John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon - proposed an ambitious project to create machines capable of using language, forming abstractions and concepts, solving problems now only feasible for humans, and improving themselves. Although many of these goals still remain unattained, the Dartmouth workshop set the direction for the development of artificial intelligence for decades to come.

In the 1960s and 1970s, attempts to develop the first intelligent programs were dominated by an approach based on symbolic computation and logical programming. Researchers tried to formalize the processes of human thinking using rigorous mathematical rules and algorithms. The first programming languages for AI, such as Lisp and Prolog, were developed, and the first systems capable of proving theorems, playing chess, and understanding simple human speech were created.

However, it soon became clear that this approach had serious limitations. Many aspects of human intelligence, such as perception, learning, and common sense, proved too complex to formalize in the form of clear rules and algorithms. Artificial intelligence encountered the so-called knowledge problem - the difficulty of loading into a machine the entire volume of information necessary to solve real problems.

In the 1980s, new approaches to development based on mathematical statistics, probability theory, and neural networks came to the fore. Instead of trying to manually program intelligence, researchers began to train machines on large datasets, allowing them to independently find hidden patterns and make decisions. Although the idea of artificial neural networks imitating the structure of the biological brain was proposed back in the 1940s, only with the advent of powerful computers and large amounts of data did this approach begin to yield impressive results.

The late 20th and early 21st centuries saw a real boom in the development of AI. Thanks to the exponential growth of computing power, the availability of huge datasets, and breakthroughs in deep learning algorithms, machines have learned to recognize images and speech, translate texts, drive cars, diagnose diseases, and even create works of art at a level close to or even surpassing that of humans.

Today, artificial intelligence has transformed from a realm of science fiction into a real technology that is penetrating deeper into our daily lives. Voice assistants in smartphones, recommendation systems in online stores, chatbots in support services, face recognition algorithms in security systems - these are all examples of the practical application of this technology that we encounter every day.

However, despite impressive successes, modern artificial intelligence still remains narrow and specialized, capable of solving only specific tasks on which it is trained. The creation of strong AI, comparable in universality and flexibility to human intelligence, remains a matter for the future. But the pace of technological development suggests that this future may not be so distant.

The history of artificial intelligence is the story of the human mind's struggle to understand itself, to reproduce its abilities in the material world. From the first naive mechanical automatons to modern neural networks, each step along this path has brought us closer to understanding how our own thinking works and to creating its artificial likeness. And although the ultimate goal - a machine indistinguishable from humans in intelligence - may still be far off, the path to it has already changed our world and our perception of ourselves. In the following chapters, we will take a closer look at the current state and prospects for the development of this fascinating technology.

Chapter 2: The Current State of Technology Development

2.1 Modern Approaches to AI Development: Machine Learning and Deep Learning

Artificial intelligence today is a rapidly developing field in which new approaches and technologies are constantly emerging. However, most modern systems are based on two key concepts: machine learning and deep learning.

Let's start with machine learning. In essence, it's an approach to creating intelligent systems in which a machine is not explicitly programmed to solve a specific problem, but learns to solve it on its own using a large array of examples. Instead of manually prescribing all the rules and algorithms, the developer simply feeds the program a huge amount of data and allows it to find patterns and develop a solution strategy on its own.

Imagine you want to teach a computer to distinguish cats from dogs in photographs. The classical approach would suggest that you manually describe all the key features of these animals: the shape of the ears, the length of the tail, the characteristic coloring, etc. Then you would encode these features in the form of strict rules and conditions: if the ears are triangular and the tail is fluffy, it's a cat; if the ears are droopy and the tail is short, it's a dog. It's easy to see that such an approach would be extremely time-consuming, and the resulting system would be fragile and inflexible. The slightest deviation from the given patterns would cause it to fail.

Machine learning offers a fundamentally different path. Instead of teaching the computer specific features, we give it thousands of photos of cats and dogs and simply tell it which animal is depicted where. The program itself, through trial and error, selects the features and patterns that best distinguish these two classes. In essence, it learns from experience, like a child who has been shown cats and dogs many times and has been told what they are until he learns to recognize them on his own.

This is a very powerful idea that has radically changed the approach to creating artificial intelligence. Instead of relying on a human expert who must formalize their knowledge in the form of rules (which is not always possible), we rely on data and the machine's ability to learn on its own. Of course, the key point here is the availability of a high-quality training sample - a sufficiently large and representative set of examples. But in today's world of Big Data, where every click, purchase, or search engine query is saved and analyzed, there is usually no shortage of such samples.

The scope of machine learning is colossal - from speech and image recognition to stock price forecasting and disease diagnosis. In fact, wherever there is a certain array of data that reflects some part of reality, this approach can be applied to find non-obvious relationships and patterns in that data and predict the future behavior of the system.

Among the latest impressive achievements of machine learning is the creation of neural network language models capable of generating meaningful and coherent texts on any topic, almost indistinguishable from those written by humans. Or DeepMind's AlphaFold algorithm, which has learned to predict the three-dimensional structure of proteins from their genetic sequence - a task that the best minds in bioinformatics have been struggling with for decades.

Diversity of Methods and Algorithms

Machine learning is a whole family of approaches and algorithms. Depending on the type of problem being solved, the nature of the available data, and the desired result, different learning strategies are used.

Supervised learning is perhaps the most common type of machine learning. In this case, we have a labeled dataset - a set of examples for each of which the correct answer is already known. For example, a collection of photos where it is indicated for each one whether it is a cat or a dog. The algorithm's task is to find a function that will map the input data (the matrix of image pixels) to the output class labels (cat/dog) as accurately as possible. After training, this will allow classifying new, previously unseen photos.

Unsupervised learning deals with unlabeled data. There is no explicit classification or prediction task here; instead, the algorithm tries to independently find some structure and patterns in the data array. For example, clustering objects so that similar ones are in the same group and dissimilar ones in different groups. Or reducing the dimensionality of the data by highlighting their key features. Such analysis often helps to better understand the nature of the objects and processes being studied.

Reinforcement learning is an approach inspired by behaviorist psychology. Here, the learning agent (e.g., a robot or game AI) learns through interaction with an environment. It takes actions and receives rewards or punishments from the environment depending on the outcome. The agent's goal is to develop a behavior strategy that maximizes the total reward. This is the principle behind the training of the famous AlphaGo AI, which beats world champions at Go.

Deep Learning and Neural Networks - A Breakthrough that Changed Everything

Deep learning deserves special mention - a subset of machine learning associated with artificial neural networks. Although neural networks themselves have been known since the 1940s, it is in the last decade that, thanks to the growth of computing power and data volumes, they have made a real revolution, increasing the efficiency of AI many times over in areas such as computer vision, natural language processing, robot control, and many others.

Neural networks are a special kind of algorithms that structurally imitate the structure of the biological brain. They consist of many simple computational units - neurons - connected by synaptic connections. Each neuron receives signals from others, sums them with certain weights, and when the activation threshold is reached, sends its own signal further down the network. Training a neural network consists of selecting these weights so that when certain data is fed to the input, the desired result appears at the output.

The key feature of neural networks that distinguishes them from other machine learning methods is the ability to automatically extract hierarchies of features from raw data. While a conventional ML algorithm needs to be fed pre-made, human-highlighted features of objects (e.g., petal length for iris classification or word frequency for text analysis), a neural network can work directly with raw data - image pixels, text letters, unprocessed sound - and find deep abstract features in them on its own.

It is this property that makes neural networks so effective and versatile. Multilayer, or deep neural networks (hence the name Deep Learning) are capable of extracting incredibly complex and abstract patterns and representations - such as the concept of a cat in a set of pixels or the semantics of a sentence in a sequence of words. At the same time, they show amazing flexibility and transferability between tasks: trained on one type of data, they can be successfully applied to analyze other, related types.

The explosive progress of deep learning in the 2010s is associated with the emergence of new neural network architectures, such as convolutional networks for image processing, recurrent networks for working with sequences (texts, time series), transformers and attention mechanisms for analyzing structured data, and many others. Hardware development, especially graphics processors (GPUs), ideally suited for massively parallel matrix computations on which neural networks are based, also played an important role.

It is deep neural networks that underlie such impressive AI systems of recent years as:

Neural network translators that are not inferior to professional linguists (Google Translate, DeepL);

Systems for generating realistic images and works of art from textual descriptions (DALL-E, Midjourney, Stable Diffusion);

Algorithms for playing poker and other games with incomplete information that surpass the strongest human players (Pluribus, ReBeL);

Self-learning language models capable of coherent dialogue and reasoning (GPT-4, LaMDA, RETRO);

Neural network programming assistants that automatically generate and correct code (GitHub Copilot, AlphaCode).

This list could go on for a long time, and it is updated literally every week. Deep learning has revolutionized AI, demonstrating that computers are capable of solving tasks that were previously thought to be only within human capabilities, and often doing it better and faster than us.

At the same time, for all their power and efficiency, neural networks also have a number of serious limitations and problems. One of their main drawbacks is the opacity of their operation. While conventional ML algorithms make decisions based on fairly clear and interpretable rules and features, trained neural networks are a typical example of a black box. We see their impressive results, but often have no idea how they were obtained, what exactly the network relied on in its decision. This creates problems of control, trust, and debugging of such systems.

Another difficulty is the dependence of the quality of neural network training on the volume and quality of data. To achieve good results, modern neural networks need truly gigantic datasets, several orders of magnitude larger than those required by classical ML algorithms. And the quality of this data must be very high, since neural networks tend to capture and amplify the slightest patterns and noises in the training sample. Collecting, labeling, and cleaning such data is a complex and costly process.

Finally, trained neural networks are not flexible enough and do not generalize well to data that is very different from the training examples. If a cat recognition algorithm is shown a picture of a dog, it will most likely classify it as a cat, since it has never seen dogs before. Whereas a human would easily transfer the once-learned concept of pet to a new object. So far, neural networks are not able to learn quickly and transfer knowledge between tasks, as we do.

However, the rapid progress of deep learning does not stop for a minute, and many of these limitations are gradually being overcome. Approaches are being developed to create more transparent and explainable neural networks, transfer learning and meta-learning algorithms, techniques for working with small and unbalanced samples. Recent breakthroughs in training huge language models on gigantic arrays of textual data have led to an unexpected reaction - the emergence of properties and abilities in them (such as common sense, logical inference, explanation of their actions) that no one explicitly taught them. This is already very close to the so-called strong or general AI (Artificial General Intelligence, AGI) - that is, intelligence comparable in flexibility and universality to human intelligence. Many researchers believe that deep learning combined with ideas from neurobiology, cognitive science, evolutionary computation, and other related disciplines is the path that will ultimately lead to the creation of AGI.

Wet Code and Neuromorphic Processors - On the Way to an Artificial Brain

It is worth mentioning a few more promising and exciting areas at the intersection of AI and neuroscience. First, there are attempts to combine artificial and biological neural networks into a single system - the so-called hybrid neural networks or wet code. The idea is to grow living neurons on microchips and make them exchange signals with artificial, silicon neurons. Scientists hope that such a combining of the brain and the computer will make it possible to take advantage of both types of computing systems: the speed and accuracy of electronic circuits and the adaptability and energy efficiency of biological neurons. There are already some initial successes in this area - for example, IBM's neuromorphic