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📖 Artificial Intelligence: A Very Short Introduction (2018)

💡 About

This is my first attempt to use LLMs for book summarization. The book was chosen as I want to dig deeper into the history of AI, and the VSI series is a good place to go through deep knowledge in short time. After the summarization process, I found that it could also be meaningful to use LLMs to translate the summaries!

📝 Prompt Used

I want you to act as a professional text summariser. I will send you some texts and you shall summarize it with minimal technical jargons and make sure adults from a different field would understand. Do not write explanations. Forget everything after you have done the summarization. Do not summarize the text with the memory of the previous prompts or answers. Do not start with "In summary" or "In conclusion". Only return the summarized text and do nothing else.

  • Paragraphs existed the maximum length of prompt are manually segmented; texts that violate the TOS of ChatGPT were manually removed.
  • Depth of Summarization: 3


  • The corpus used is Artificial Intelligence: A Very Short Introduction (Very Short Introductions), which can purchased here.


The raw content of the book is the property of Oxford University Press and shall not be redistributed.


Artificial Intelligence (AI) is the use of computers to perform tasks that would normally require human intelligence such as reasoning, perception, prediction, and planning. The ultimate goal of AI research is to create systems with human-like general intelligence, which remains a major challenge in the field. Researchers use a variety of methodologies and techniques such as heuristics, planning, mathematical simplification, and knowledge representation to achieve this goal. The current research focuses on using AI to recognize and respond to human emotions, rather than on creating AI systems that can experience emotions themselves. The backpropagation algorithm is a popular method used to train Artificial Neural Networks (ANNs) which is based on the principle of "backpropagation" of errors and it is found to be effective in training deep neural networks.

人工智能(AI)是使用計算機執行通常需要人類智能的任務,如推理,感知,預測和規劃。 AI研究的最終目標是創建具有人類般通用智能的系統,這仍然是該領域的一個主要挑戰。研究人員使用各種方法和技術,如啟發式,計劃,數學簡化和知識表示來實現這一目標。目前的研究重點是使用AI來識別和回應人類的情感,而不是創建能夠經歷情感的AI系統。反向傳播算法是一種常用於訓練人工神經網絡(ANNs)的方法,基於“錯誤的反向傳播”原理,並且被發現在訓練深度神經網絡中是有效的。


1. What is artificial intelligence? (人工智能是什麼?)

Artificial Intelligence (AI) is the use of computers to perform tasks that would normally require human intelligence such as reasoning, perception, prediction, and planning. It involves the use of virtual machines, which are information-processing systems that the programmer has in mind when writing a program. There are five major types of virtual machines used in AI: classical or symbolic AI, artificial neural networks or connectionism, evolutionary programming, cellular automata, and dynamical systems. AI pioneer Ada Lovelace foresaw part of AI in the 1840s, but how to achieve it was still a mystery. Alan Turing clarified the mystery of AI in the 1940s by showing that every possible computation can in principle be performed by a mathematical system called a universal Turing machine. Early AI research was heavily influenced by the cybernetics movement of the 1940s and 1950s, which focused on biological self-organization and the concept of feedback.


2. General intelligence as the Holy Grail (通用智能是聖杯)

General intelligence as the Holy Grail refers to the ultimate goal of AI research, which is to create systems with human-like general intelligence. The field of AI has made significant progress in achieving specialized tasks, but creating systems with general intelligence remains a major challenge and the ultimate goal of the field.

To achieve this goal, AI researchers use a variety of methodologies and techniques such as heuristics, planning, mathematical simplification, and knowledge representation. Heuristics are problem-solving strategies that involve using shortcuts or educated guesses to find a solution. Planning involves breaking down a problem into smaller, more manageable sub-problems and solving them in a specific order. Mathematical simplification involves using mathematical techniques to reduce the complexity of a problem, making it more manageable to solve. Knowledge representation involves organizing information in a way that makes it more easily accessible and usable for problem-solving.

While these strategies can greatly improve the efficiency of solving problems, they also have limitations and assumptions that can limit the scope of the AI system and may not be applicable in all situations. It is important for AI practitioners to be aware of these limitations and to consider their implications when designing and using AI systems.





3. Language, creativity, emotion (語言,創造力,情感)

The current research focuses on using AI to recognize and respond to human emotions, rather than on creating AI systems that can experience emotions themselves. This is a challenging task as emotions are complex and multi-faceted, and there is still much debate among researchers about how they should be represented in AI systems and how they should be used to influence the behavior of the AI. However, some researchers argue that incorporating emotions into AI systems could lead to more natural and human-like interactions, which would be beneficial for a wide range of applications such as healthcare, education, and customer service. Overall, while progress has been made in the field, much more research is needed to fully understand and model emotions in AI systems.


4. Artificial neural networks (人工神經網絡)

The backpropagation algorithm is a popular method used to train ANNs. It uses the gradient descent method to adjust the weights of the connections between units in the network, in order to minimize the error between the network's output and the desired output. This algorithm is based on the principle of "backpropagation" of errors, where the error is propagated backwards through the network, starting from the output units, and adjustments are made to the weights of the connections accordingly. This method has been found to be effective in training deep neural networks, which have many layers of interconnected units and are capable of learning complex patterns and features.

Deep learning is a subfield of machine learning that is focused on the development of deep neural networks and the application of these networks to various tasks such as image and speech recognition, natural language processing, and decision making. The main advantage of deep learning is its ability to automatically learn features and representations from large amounts of data, without the need for explicit feature engineering. This has led to breakthroughs in many areas of AI, such as computer vision and natural language processing.

Overall, ANNs and deep learning are important areas of AI research that have led to many practical applications and have also had theoretical implications for the understanding of the brain and intelligence. The field is constantly evolving, with new techniques and architectures being developed to improve the performance of these networks.




5. Robots and artificial life (機器人和人工生命)

Artificial Life (A-Life) is a branch of AI that focuses on modeling and simulating the characteristics and behaviors of living organisms. It combines ideas from computer science, biology, and other fields to create artificial systems that exhibit characteristics of life, such as self-organization, adaptation, and evolution. Robotics is one of the key applications of A-Life in AI, as robots are quintessential examples of AI. Recent advances in robotics have come from a shift from psychology to biology, leading to the development of "situated" or "behavior-based" robots that can navigate rough terrain and adapt to unexpected situations. Evolutionary AI is another subfield of AI that uses techniques from evolutionary biology to generate solutions to problems. Self-organization is a key feature of biological systems and is the spontaneous emergence of order from a less ordered state, and it is an important concept in the field of A-Life and AI in general.


6. But is it intelligence, really? (但這真的是智能嗎?)

The question of whether AGI systems can truly possess intelligence, understanding, creativity, selves, moral standing, free choice, and consciousness is a philosophical one that is highly controversial and has no unchallengeable answers. However, careful arguments and use of AI concepts by philosophers can help to shed light on these questions and provide a more nuanced understanding of the nature of real minds. The Turing Test, proposed by Alan Turing in 1950, is a test to determine whether a machine can be considered truly intelligent by being able to hold a conversation with a human and not be distinguished from a human. However, it has been criticized by philosophers as it does not prove that a machine is truly intelligent or conscious. The problem of consciousness, also known as the "hard problem," refers to the question of how and why we have subjective experiences or qualia. The field of machine consciousness (MC) aims to build computer models of consciousness, focusing on functional consciousness rather than phenomenal consciousness. However, despite some progress, the question of consciousness remains a philosophical morass with many unresolved issues.


7. The Singularity (科技奇點)

The concept of whole-brain emulation, also known as mind uploading or mind transfer, is related to the Singularity hypothesis. It refers to the idea of transferring the information in a biological brain to a computer or artificial substrate, resulting in a form of artificial intelligence. The idea is that this could be a way to achieve true AGI, and some proponents argue that it could be a way to achieve the Singularity. However, many experts in the field are skeptical of this idea, as it raises numerous technical, ethical, and philosophical challenges. There is no consensus on whether whole-brain emulation is possible or desirable, and it remains a topic of ongoing debate and research.