Programming Language Diversity:
Thousands of programming languages have been designed and implemented over the years. At any given time since the early 60s, 150 to 200 languages have been in standard use. Languages differ in some ways and are similar in others, making generalizations challenging.

Defining Programming:
Programming is the simple concept of “we do something, and the computer does something.” Success is achieved when the computer does what was intended. Programming languages are the usual vehicle for this process.

User Interfaces as Programming Languages:
User interfaces can be seen as impoverished programming languages. Richer user interfaces allowed programming actions to be part of the interface.

Live Coding:
Live coding allows for immediate feedback and response from the computer. It predates the punch card Unix approach and black screen, Vi-terminal style of programming. Live coding involves creating things in various ways and observing the computer’s response. There is no visible compiling or editing.

eToys and Symbolic View:
eToys is a programming language used by millions of children. It features live coding and a symbolic view of objects. The symbolic view displays properties and behaviors of objects. The interface is reflexive, allowing changes in one view to be reflected in others.

Creating Objects with Behaviors:
Objects can have properties and behaviors. Behaviors can be triggered by user actions or scripts. Scripts can be created by dragging out behavior blocks and connecting them. Compilation happens sub-second in the background, enabling instant feedback.

Introduction of Graphical Objects and Steering:
Alan Kay introduces the concept of graphical objects, represented as cars, that can be manipulated on a screen. A “steering wheel” object is created to control the movement of the car, demonstrating the use of different costumes and behaviors for objects. Kay emphasizes the importance of having a programming language that allows for quick and easy creation of objects and their manipulation.

Demonstration of Multitasking and Infection Programming:
Kay showcases automatic multitasking, where multiple programs (cars) can run simultaneously. He creates a simple infection programming scenario where blue cars can infect red cars, and the infected cars change color. This demonstration highlights the concept of system dynamics, where the interactions between objects can lead to emergent behavior.

Exploring System Dynamics through Simulation:
Kay encourages children to experiment with the infection programming scenario by varying the number of cars and observing the resulting dynamics. He emphasizes the importance of understanding system dynamics, particularly in the context of biological systems and contagious diseases. Kay uses AIDS as an example to illustrate how a disease with a long time constant can still have devastating consequences if not addressed early.

Importance of Systems Dynamics in Today’s World:
Kay highlights the relevance of systems dynamics in today’s interconnected world, especially in biological systems. He suggests that policymakers should experiment with system dynamics models to gain a better understanding of complex systems and their potential instability.

Introducing Sensors and Robot Cars:
Kay transitions to the creation of a simple robot car that can navigate a track. He adds a sensor to the car to track its position and demonstrates how a program can be created to control the car’s movement. Kay mentions the pedagogical value of such experiments, where children learn about feedback loops and navigation strategies.

Finding the Wall:
Alan Kay emphasizes the importance of immediately creating a program to detect the wall in a simulated environment to avoid losing track of its location.

Program Design:
He introduces a simple program that continuously moves the car forward and checks if it touches the wall. The program uses the car’s blue color and the wall’s brown color to determine when a collision occurs.

Testing and Troubleshooting:
Kay demonstrates the program and asks the audience to identify its flaw. He explains that the program may lose track of the wall if the car makes a sharp turn and is unable to regain contact with the wall due to its fixed radius.

Feedback Mechanism:
Kay suggests adding a feedback mechanism to the program. He proposes slowing down the car or even stopping it when it loses the wall to increase the chances of reestablishing contact.

Creating Complex Behaviors:
Kay expresses his intention to transform the program into a “wriggly worm.” He explains that this will require additional coding and design.

Introduction to eToys:
eToys is a programming environment designed for children to learn and explore programming concepts. It features a user-friendly interface with visual elements and intuitive controls.

Creating Animated Objects:
Alan Kay demonstrates the creation of a wriggly worm object using two different pictures. He introduces the concept of a data structure to store and alternate between the two pictures, creating the animation effect.

Metaprogramming in eToys:
Kay introduces the idea of metaprogramming, which involves creating a new language within an existing one. He explains his intention to create a language specifically for particle system simulations within the eToys environment.

Implementation of the Particle System Language:
Kay utilizes the animation system of eToys to execute instructions for the particle system language. He creates a test instruction that moves a blue guy and another instruction that turns both a blue guy and a red guy red if they collide.

Learning Programming through Metaprogramming:
Kay emphasizes the importance of children learning how to program from an early age. He suggests that designing programming languages with children in mind can provide valuable insights for creating powerful and easy-to-use languages.

Computer Architecture and Virtual Machines:
Kay discusses the different layers of computer architecture, from logic gates to microcode and virtual machines. He explains how a virtual machine like Smalltalk can make the computer behave like a Smalltalk machine, simplifying programming tasks.

The Role of Smalltalk and eToys:
Kay briefly mentions Smalltalk as a programming language with a development system written in itself. He clarifies that the eToys system, which is used to present the slides, is also written in Smalltalk.

Different Approaches to Problem-Solving in Programming:
Kay highlights two approaches to solving programming problems: writing tons of programs to address specific issues or finding languages that fit the problems gracefully. He suggests that the latter approach can lead to a more efficient and elegant solution.

Turing’s Idea of Simulation:
Kay references Alan Turing’s concept of simulating any mechanism on a simple mechanism with limited logic and ample memory. He relates this idea to the eToys environment, which allows children to simulate complex systems using simple programming constructs.

Measuring Code Complexity:
Alan Kay introduces “lines of code” as a metric for measuring code complexity and programmer effort. He compares the number of lines in a 400-page book (approximately 20,000 lines) to the lines of code in software systems.

Lines of Code in Notable Structures:
A foot of books stacked up represents around 300,000 lines of code. A meter of books stacked up represents approximately 1 million lines of code.

The Empire State Building Analogy:
The Empire State Building is approximately 22,000 books high, which translates to 440 million lines of code. This analogy highlights the immense scale and complexity of modern software systems.

Examples of Large-Scale Software Systems:
Government systems often consist of hundreds of millions of lines of code. Microsoft’s operating system alone contains around 120 million lines of code. Microsoft Office adds another 100 million lines of code to the equation, totaling 220 million lines of code.

Complexity Challenges:
The sheer volume of code in large software systems makes it challenging for any single person or group to comprehend and maintain the entire codebase. Much of the code may not even be actively used, but it remains in the system due to uncertainty about its potential impact if removed.

Code Complexity:
Alan Kay discusses the complexity of software code, specifically questioning why 220 million lines of code are needed for certain features. He mentions that the original implementation at Xerox PARC only required 10,000 lines of code, highlighting the exponential increase in complexity.

Financial Products Company Example:
Kay cites an unnamed large financial products company with over 400 million lines of code in its primary customer offerings. He emphasizes the outdated nature of their user interface, which still uses screens reminiscent of mainframe days. The company maintains 170,000 screens, equivalent to approximately 20,000 books or an Empire State Building’s worth of code.

Job Market Demand:
Kay highlights the job market demand for programmers to maintain and manage these complex codebases. He emphasizes that businesses are hiring programmers not for innovation but to address the existing mess of legacy code.

Examples of Solutions:
Kay intends to present examples of potential solutions to address the problem of code complexity within the remaining time of his presentation.

Machine Code and Higher-Level Languages:
Machine code requires billions of lines of code to perform complex tasks due to its one-instruction-per-line nature. Higher-level languages like C allow for a more compact representation, with hundreds of millions of lines of code potentially replacing billions in machine code. Advanced higher-level languages can further reduce the code count to thousands of lines, simplifying programming tasks.

Programming Effort and Language Levels:
The programming effort decreases as we move to higher-level languages. Computer science has not adequately addressed the need for invention in creating programming systems that significantly reduce programming work. The amount of work required for packaging, documentation, and debugging remains significant, regardless of the programming language used.

The Current State of Business Programming:
Most business programming in the US is still done at the low level, similar to the practices of the 1960s. New programming languages have not substantially elevated the level of abstraction in business programming.

A Call for Research and Innovation:
Despite the dominance of low-level programming, there are opportunities for researchers and innovators to explore higher-level abstractions. Studying the history of programming reveals exceptional individuals who transcend the limitations of their time.

Overview:
This chapter discusses the 50th anniversary of the first interactive computer graphics system, Sketchpad, created by Ivan Sutherland in 1962. It highlights the system’s groundbreaking features, including interactive graphics, object-oriented design structures, and constraints for programming.

Inventor and Context:
Ivan Sutherland, the inventor of computer graphics and object-oriented design systems, developed Sketchpad in one year as his PhD thesis. Sketchpad ran on a massive air defense computer, occupying a space comparable to the Franz Hall building. It was only accessible for use from 3 am to 6 am.

Features and Functionality:
Sketchpad allowed users to create and manipulate graphical objects using a light pen and a simulated graphics display on an oscilloscope. It introduced the concept of constraints for programming, enabling users to define relationships between objects that the system would automatically maintain. Sketchpad featured the first implementation of a clipping window, allowing users to view a specific portion of a drawing. Objects drawn in Sketchpad could be designated as “masters,” and instances of those masters could be created with different sizes and orientations. The system also supported the creation of dynamic simulations, as demonstrated by a bridge simulation that calculated the bridge’s behavior based on physical constraints.

Legacy and Impact:
Sketchpad’s contributions include the introduction of interactive computer graphics, object-oriented design structures, and constraint-based programming. The system’s influence can be seen in modern graphical user interfaces (GUIs), computer-aided design (CAD) software, and object-oriented programming languages. Sketchpad’s innovative ideas laid the foundation for the development of modern computer graphics and interactive computing.

Visualizing Structural Mechanics with Dynamic Simulations:
Alan Kay demonstrated a dynamic simulation of a bridge structure, showcasing the ability to manipulate objects and observe the resulting stresses and strains. By dropping a weight onto the bridge, viewers could observe the ripple effect of the force across the structure, demonstrating realistic behavior similar to actual bridges.

Enhanced Display Capabilities:
Modern displays allow for improved visual representation of simulations, including the addition of weight to letters and enhanced rendering capabilities.

Three-Constraint Programming Approach:
The simulation was programmed using three constraints: gravity, spring tension, and pin constraints. Disabling or adjusting these constraints allowed for the exploration of different structural behaviors and the impact of various forces on the bridge.

Dynamic Behavior and Realistic Simulations:
The simulation demonstrated the dynamic behavior of structures, including the ongoing oscillations and vibrations observed in real-world structures. Turning off constraints allowed for the observation of the structure’s collapse, highlighting the importance of these constraints in maintaining structural integrity.

Overall Summary:
Alan Kay emphasizes the importance of creating programming languages that are closely aligned with mathematical formulas and concepts, allowing for more efficient and understandable code. He demonstrates this by showcasing a graphics rendering system developed at Viewpoints, highlighting its compact programming language and visual debugger.

Programming Close to Mathematical Formulas:
Kay advocates for programming languages that closely resemble mathematical formulas, enabling programmers to express their ideas more directly and concisely. He presents an example of a graphics rendering formula, along with a programming language that closely mirrors this formula. This approach reduces the amount of code needed to implement complex graphics operations, making it easier to understand and debug.

Visual Debugging Tools:
Kay introduces a visual debugger automatically generated from the code, allowing developers to easily trace the flow of data and identify the source of errors. This visual debugger provides a graphical representation of the code, enabling developers to see how different parts of the program interact and affect the final output. By hovering over specific pixels or elements in the graphical representation, developers can quickly identify the corresponding code and data responsible for rendering those elements.

Importance of Debuggers in Future Languages:
Kay emphasizes the need for programming languages of the future to include integrated debuggers. He argues that the development of debuggers should be an integral part of language design, rather than an afterthought. By providing visual and interactive debugging tools, developers can more easily understand and troubleshoot their code, leading to faster and more efficient development cycles.