History of Object-Oriented Programming:
Alan Kay coined the term “object-oriented programming” in 1961, but the concept existed in hardware form before that. In 1961, Bob Barton invented a machine that executed byte-coded instructions, similar to Java’s byte-coded VMs. Barton’s machine was the first attempt at building a higher-level machine to reflect the idea of programming in languages higher than C.

Barton’s Influence:
Barton’s work has not yet been fully understood or implemented by Intel. Barton wrote a seminal paper in 1961, “A New Approach to the Functional Design of a Digital Computer,” which laid out the foundation for a felicitous execution environment for higher-level languages. Barton’s machine was notable for its uncrashable nature due to its capability-based hardware, preventing one program from harming another.

Importance of Understanding History:
Kay emphasizes the importance of understanding the history of computing and learning from the founders of the field. He encourages students to read the original papers and not rely solely on current textbooks.

Advanced Systems Design Course:
Kay describes a course he took in 1966 taught by Barton on advanced systems design. Instead of lecturing, Barton gave students a list of papers to read and expected them to understand them thoroughly. The goal of the course was to challenge and destroy students’ preconceived beliefs about computing, allowing them to learn from a fresh perspective.

Beliefs vs. Reality:
Kay argues that humans often confuse their beliefs with reality, which limits their thinking and prevents them from considering alternative perspectives. He emphasizes the importance of challenging beliefs and treating them as opinions rather than absolute truths.

Make and Fix Paradigm:
Kay describes the traditional approach to computing as the “make and fix” paradigm. This paradigm involves starting with data structures and procedures and iteratively refining them until they work correctly.

Architectural limitations:
Software architecture is largely based on fitting gears (APIs) together, which are prone to complications and lack scalability. Tinker Toy-like architectures, while initially functional, can easily break due to misalignment of components.

Mexico City as an analogy for software:
Mexico City represents the chaotic and overwhelming nature of modern software, which is often a result of individual efforts leading to an unbearable totality.

Object-oriented programming shortcomings:
Object-oriented languages with setters turn objects back into data structures, increasing complexity and reducing the benefits of object-oriented design.

Software complexity vs. functionality:
Software systems like Vista and other large software companies’ products contain millions of lines of code, requiring thousands of books to document. The question arises whether such complexity is necessary for the level of functionality provided.

Complication curve vs. complexity curve:
The complication curve, representing software complexity, is significantly ahead of the complexity curve, indicating an imbalance between software complexity and its actual usefulness.

Complexity and Useless Code:
Alan Kay addresses the issue of excessive complexity in software, suggesting that a large portion of code may be useless or redundant, hindering comprehension and maintenance.

Microsoft Word’s Longstanding Bug:
Kay highlights a specific example of a persistent bug in Microsoft Word related to paragraph justification and selection errors, demonstrating the challenges of identifying and fixing such issues in complex software systems.

The Internet’s Simplicity and Success:
In contrast to the complexity of general software, Kay emphasizes the simplicity and elegance of the Internet’s design, controlled by less than 20,000 lines of code, and its ability to self-repair and scale effectively.

Biological Inspiration in Software Design:
Kay emphasizes the influence of biological concepts in the design of the Internet, contributing to its resilience and adaptability.

Challenges in Translating New Ideas into C-based Forms:
Kay discusses the difficulties faced in translating innovative software ideas into C-based forms, leading to the reintroduction of problematic paradigms and hindering progress.

Negotiation with Complex Systems:
Kay proposes the need for software systems capable of negotiation, considering the limitations of human comprehension and the increasing complexity of feedback loops and self-regulatory systems.

Software Development and Life Cycle Costs:
Kay highlights the significant costs associated with software maintenance and changes after deployment, emphasizing the importance of upfront investment in adaptability and flexibility.

Thinking in Biological Terms:
Kay advocates for adopting a biological mindset in software design, considering concepts like ecologies and inter-operation protocols to enhance software adaptability and integration.

Scale Jump from Polymers to Living Things:
Kay draws a parallel between the scale jump from polymers to living things and the challenges faced in creating capable software objects. He suggests that making objects too small may have hindered progress in software development.

IQ and Knowledge:
IQ alone does not guarantee success or innovation. Knowledge can often trump IQ, especially when it comes to leveraging the work of geniuses who invent new ways of looking at things. Calculus, for example, allows those with average IQs to understand and apply complex mathematical concepts.

The State of Computer Science:
Computer science in the United States is in decline due to a lack of emphasis on learning and a focus on clever programming without a strong foundation in knowledge and outlook. While the absolute number of highly intelligent individuals in the field has increased, the relative number has decreased due to the overall growth of the field. The general knowledge of computer science practitioners has only increased slightly despite the vast amount of knowledge available. The number of powerful outlooks in programming has increased, but the general outlook has not improved significantly.

Comparison to Pop Music:
The current state of computer science resembles pop music, where cleverness and short-term popularity are valued over long-term innovation and depth of knowledge.

Conclusion:
Knowledge and outlook are more valuable than IQ in driving innovation and progress in computer science. The field needs to prioritize learning, cultivate powerful outlooks, and move beyond clever programming to address the challenges and opportunities of the future.

Past, Present, and Future of Computing:
Alan Kay draws a parallel between pop culture’s limited use of music’s vast riches and computing’s current state. Universities, particularly in the US, are criticized for perpetuating a narrow focus on incremental progress, hindering the exploration of new possibilities.

Cicero’s Quote on Knowledge and the Present:
Cicero’s quote highlights the importance of understanding the past to gain a broader perspective and avoid being confined to the present’s limitations. McLuhan’s observation emphasizes the tendency to perceive the future as an extension of the present, making it challenging to envision transformative changes.

Randomness and Human Memory:
Kay presents a metaphor of a gully formed by random rainfall, which grows larger due to its efficiency. This model parallels human memory, where certain concepts or ideas gain prominence and influence subsequent learning, making it difficult to perceive alternative perspectives.

The Perils of a Single Programming Language:
Kay cautions against learning only one programming language, as it can lead to a limited understanding of computing. He suggests learning multiple languages simultaneously to gain a broader perspective and relativize the concept of computing.

Academic Paper Writing in the US:
Kay criticizes the emphasis on quantity over quality in academic paper writing in the US, which incentivizes professors and graduate students to produce a high number of papers rather than focusing on groundbreaking ideas.

The Vastness of the Past and the Limitations of the Present:
Kay emphasizes the vastness of the past and the contributions of countless individuals, highlighting the limited perspective of the present. He advocates for challenging and deconstructing the perceived reality based on the past to open up new possibilities.

Creative Thinking and the Pursuit of Vision:
Kay describes a creative process involving a feeling or sensation that leads to exploration of the past and the emergence of a vision. This process results in ideas that transcend incremental progress and originate from vague, even physical sensations.

Historical Progression of Technologies:
Humans and especially primates are natural tinkerers, and engineering emerged as a form of principled tinkering before math and science. Engineering in the past was more practical and intuitive, relying on trial and error rather than formal mathematics and science. The Greeks’ aversion to long cookbooks of rules led to the development of the mathematics we use today. Science, as we know it, is a relatively recent phenomenon, emerging approximately 400 years ago.

Human Ancestry and the Origin of Science:
Our strain of humans descended from a single female who lived around 192,000 years ago, according to mitochondrial DNA studies. Despite our long history, science is a relatively new endeavor, highlighting the gradual evolution of human intelligence.

Diverse Fields and Their Interconnections:
Most professionals in technical fields engage in a combination of tinkering, engineering, math, and science. Temperament and personal preferences often determine the specific field an individual pursues. Computing is primarily characterized by tinkering, with limited engineering, math, and science involvement. True engineering, as exemplified by the construction of the Empire State Building, involves a high level of efficiency and organization, which is often lacking in software development.

Abstractions and Language in Science:
Phenomena, such as hairballs or abstract concepts, are represented using language and symbols to make sense of them scientifically. Computer scientists utilize operations like NAND or NOR to construct language and concepts. The brain’s limitations restrict our understanding to language-based representations rather than direct perception of phenomena.

Science and Engineering: A Harmonious Relationship:
Science studies phenomena, regardless of their origin, whether natural or human-made. Engineering creates artifacts and systems, and its principles can be applied to understand and improve existing creations. The Akashi Kaikyo Bridge in Japan exemplifies the harmonious integration of engineering and science, demonstrating the immense capabilities of human ingenuity.

Computing: A Blend of Technologies:
Computing encompasses hardware, software, and theoretical foundations, combining elements of tinkering, engineering, math, and science. John McCarthy, a pioneer in computer science, contributed significantly to the field’s development and understanding.

Abstraction and the Lisp Interpreter:
Alan Kay highlights the significance of abstraction in programming, showcasing the Lisp interpreter as a prime example. The Lisp interpreter, small enough to fit on a t-shirt, represents one of the greatest achievements in the field of computer science.

Personal Computing and Code Efficiency:
Kay questions the efficiency of personal computing, emphasizing the need to minimize code complexity. He suggests that the empire state building worth of code from Microsoft should be condensed into a few t-shirts worth of code.

Frankenstein’s Monster Systems:
Kay describes the creation of “Frankenstein’s monster” systems, which are complex and convoluted but serve as stepping stones towards more efficient systems. These systems allow for experimentation and enable the development of baby steps towards capturing millions of lines of code in a few tens of thousands of lines.

Viewpoints Research Institute (VPRI) and the Math Wins Principle:
Kay introduces the Viewpoints Research Institute (VPRI) and its website (vpri.org), where more information about their projects can be found. He presents the “math wins” principle, which involves utilizing tiny Model T t-shirt programming, particles, and fields.

Dan Amelang’s Mathematical Approach to Computer Graphics:
Kay discusses Dan Amelang’s work on computer graphics for personal computers, involving millions of lines of code. Amelang collaborated with his mother, a high school geometry teacher, to develop a new mathematical approach for handling the interaction of arbitrary polygons with pixels. This approach resulted in a 40-line mathematical formula that provides perfect anti-aliasing.

Executable Language for Mathematical Expressions:
Amelang created an executable language based on the mathematical language, allowing for the execution of mathematical expressions. This language, with 80 lines of code, can render high-quality characters and simple shading.

The Compositing Rules:
Amelang and his brother developed 26 standard compositing rules used in Cairo, with all of them totaling around 90 lines of code. The simplicity of the code is attributed to the use of the right kind of language, which allows for direct expression of mathematical relationships.

Building a Graphics Language in Under 400 Lines of Code:
Alan Kay achieved a remarkable feat, developing a graphics language in just under 400 lines of code. This is a significant reduction compared to the millions of lines of code typically required for graphics engines like Cairo.

The Role of Compiler-Compiler Languages:
To make the graphics language operational, Kay introduced the concept of compiler-compiler languages. These languages allow for the creation of domain-specific languages (DSLs) with ease.

Alex Worth’s Contribution to Ometa:
Alex Worth played a crucial role in developing Ometa, a compiler-compiler language used to create the parser for the graphics language. Ometa consists of 130 lines of code, with an additional 700 lines for the optimizer and compiler to convert the DSL into a language like C.

Advantages of Compiler-Compiler Languages:
Compiler-compiler languages offer several benefits: They facilitate the creation of DSLs for specific problem domains. They enable self-creation and optimization with minimal code (76 and 30 lines of code, respectively).

The Significance of Point of View in Problem Solving:
Kay emphasizes the importance of finding the right point of view when approaching a problem. This perspective can significantly simplify the problem representation and lead to more effective solutions.

Historical Context and Terminology:
The concept of DSLs was prevalent 50 years ago, known as problem-oriented languages (POLs). However, this technique was overshadowed by the commercialization of personal computing in the early 1980s.

The Need for a Broader Perspective in Learning:
Kay encourages learners to challenge conventional knowledge and explore alternative techniques. This open-mindedness can lead to significant discoveries and innovations.