Polymorphic Applications: Mission-Driven Software, Cognitive Architectures, NEXT-GEN PARADIGMS

Name: Polymorphic Applications: Mission-Driven Software, Cognitive Architectures, NEXT-GEN PARADIGMS
Uploaded: 2023-07-21T11:00:25.000Z
Duration: 1 h 2 min 29 s
Description: All my links: https://linktr.ee/daveshap

Introduction to Polymorphic Applications

In this section, David Shapiro introduces the concept of polymorphic applications and explains their significance in software development and architecture.

What are Polymorphic Applications?

Polymorphic applications, also known as self-changing applications, are a new paradigm in software development.

These applications are designed to adapt and evolve based on a clear, measurable, and purposeful mission.

They go beyond traditional object-oriented programming by focusing on missions as the primary organizing feature.

Key Points:

Polymorphic applications are built around a mission-centric design.

The mission serves as the core driver of the software's behavior, adaptation, and evolution.

These applications are not just tools but active agents pursuing their respective missions.

They require autonomy, agency, adaptability, flexibility, and dynamic tool creation.

Micro Frameworks and configurable frontends/backends enable the application to change itself.

AI Capabilities for Language Technology

This section discusses Microsoft CEO Satya Nadella's keynote speech on AI capabilities enabled by language technology.

AI Capabilities Enabled by Language Technology

Microsoft CEO Satya Nadella highlighted two primary components of AI capabilities:

Natural language interface for voice chat, emails, etc. (front end)

Reasoning engine for brainstorming, planning, problem-solving, coding/testing code (back end)

Key Points:

Language models have advanced beyond generating text for simple tasks like writing emails or fiction.

They possess cognitive abilities such as brainstorming, planning, problem-solving, coding/testing code.

Cognitive architecture has been a focus area for years to harness these reasoning engine capabilities.

Importance of Microsoft Inspire 2023 Keynote

This section emphasizes the significance of watching the Microsoft Inspire 2023 keynote speech, especially for investors, startup founders, product owners, and those working with generative AI.

Importance of Microsoft Inspire 2023 Keynote

The keynote provides valuable insights into the latest advancements in AI and language technology.

It is particularly relevant for individuals involved in generative AI projects.

Watching the keynote can offer strategic guidance and inspiration for leveraging these new capabilities.

Mission-Oriented Programming

This section introduces mission-oriented programming as a paradigm beyond object-oriented programming.

What is Mission-Oriented Programming?

Mission-oriented programming is a software development paradigm centered around clear, measurable, and purposeful missions.

Applications are primarily designed and built to fulfill their respective missions.

The mission drives the software's behavior, adaptation, and evolution.

Key Points:

Mission-centric design replaces traditional object-oriented thinking.

Autonomy, agency, adaptability, flexibility are essential aspects of mission-oriented applications.

Polymorphic applications are often referred to as active agents pursuing their missions.

Dynamic tool creation allows applications to create tools when needed.

Micro Frameworks and configurable frontends/backends enable self-change within the application.

Generative AI as Automation

This section highlights generative AI as a form of automation with new capabilities.

Generative AI as Automation

Generative AI or llms (large language models) can be seen as another type of automation.

It offers new capabilities within the realm of automation for infrastructure engineers, IT professionals, software developers/architects.

Key Points:

llms or generative AI models act as universal middleware that can communicate with various systems in an organization's tech stack.

It provides automation capabilities for tasks such as function calling and API use.

Understanding generative AI as a form of automation simplifies its integration into existing workflows.

Automation Engine and Capabilities

This section explores the automation engine and the range of tasks it can automate.

Capabilities of the Automation Engine

The automation engine, powered by generative AI, can automate various tasks within an organization.

OpenAI's function calling and API use enable llms to communicate with different systems in the tech stack.

Key Points:

The automation engine can automate a wide range of tasks, making it a powerful tool for organizations.

llms act as universal middleware, facilitating communication between different components in the tech stack.

Timestamps are approximate and may vary slightly.

Problem Solving Techniques and Mission-Oriented Programming

In this section, the speaker discusses problem-solving techniques and the concept of mission-oriented programming.

Tree of Thought and Problem Solving

The speaker mentions that there are various problem-solving techniques, such as the "tree of thought" or "chain of thought."

These techniques, like the tree of thought, can significantly enhance problem-solving capabilities by around 900%.

By using these techniques, one can overcome problems and make better decisions.

Mission-Oriented Programming

The speaker explains that with a given objective or mission, an automation engine can make decisions in pursuit of that mission.

They propose the idea of "mission-oriented programming," which involves planning, brainstorming, and pursuing a specific mission.

Having an automation engine capable of these tasks opens up new possibilities for developers.

On-Demand Tools and Tool Factory

The speaker suggests changing our mindset to focus on on-demand tools or tools that create other tools (a tool factory).

They draw a parallel with GPT (Generative Pre-trained Transformer) models generating code as a tool factory for software development.

This ability to synthesize code objects on the fly is crucial for building polymorphic applications.

Polymorphic Applications and Automated Tool Factory

Polymorphic applications require modular pieces that can be printed or fabricated on demand.

By combining these modular pieces generated by an automated tool factory, developers can create versatile applications.

The analogy used is similar to using Legos; with enough blocks (building components), you can build anything.

Plug-and-Play Architecture and Access to APIs

Once all the building blocks are available from a tool factory, they can be easily plugged together.

Aim for plug-and-play architecture where different components seamlessly integrate with each other.

With access to various APIs within an organization (Azure, AWS, Google Cloud, etc.), the possibilities for integration are vast.

Cognitive Architecture and Autonomous Cognitive Entities

In this section, the speaker introduces the concept of cognitive architecture and its role in creating autonomous cognitive entities.

Importance of Cognitive Architecture

The speaker emphasizes the need for a cognitive architecture to guide decision-making and tool utilization.

They refer to Sachin Nadella's point about having a reasoning or automation engine as part of this architecture.

ACE Model: Autonomous Cognitive Entities

The speaker presents their ACE model (Autonomous Cognitive Entities) as a framework for next-gen applications.

ACE stands for autonomous, cognition, and entity.

The goal is to create entities with a higher degree of autonomy that utilize reasoning and cognition.

Six Layers of ACE Model

Aspirational Layer: Concerned with mission, values, purpose, ethics, and morals.

Perception Layer: Involves sensing and understanding the environment through various sensors.

Memory Layer: Stores information and experiences for future reference.

Reasoning Layer: Utilizes logic and reasoning to make decisions based on available information.

Learning Layer: Enables continuous learning from data and experiences to improve performance over time.

Action Layer: Executes actions based on decisions made by the reasoning layer.

Creating Polymorphic Applications with ACE Model

The ACE model provides a conceptual framework for building polymorphic applications like Commander Data or C-3PO.

It enables developers to create entities that can adapt and change shapes based on different requirements.

Timestamps have been associated with relevant bullet points as requested.

Agent Model and Capabilities

This section discusses the importance of the agent model in understanding the capabilities of a machine. The agent model helps the machine know what it is, how it works, and what it is capable of in order to make decisions.

The agent model is concerned with the capabilities of the machine.

It helps the machine understand its limitations and how to overcome them.

The agent model needs to be able to change itself based on its goals and mission.

A comprehensive agent model is necessary for the machine to pursue its mission effectively.

Polymorphic Nature of Agent Model

This section explores the polymorphic nature of the agent model, which allows it to adapt and change based on its understanding of limitations or requirements.

The polymorphic nature of the agent model enables it to recognize limitations and seek solutions.

It can determine if external help or permissions are needed to access certain resources.

The agent model should be capable of self-improvement and problem-solving.

Aspirational Layer: Mission and Values

This section introduces the aspirational layer, which sets the mission, values, and purpose for an autonomous cognitive entity.

The aspirational layer serves as a moral compass and guiding North Star for the entity.

It defines the highest values, principles, vision, and mission for decision-making.

Examples include missions for lawyers (zealously advocating for clients) or doctors (achieving best health outcomes).

Executive Function: Planning and Resource Management

This section focuses on the executive function layer responsible for planning, resource management, and decision-making based on available resources.

The executive function layer involves making plans and considering potential obstacles.

It requires knowledge about external resources such as computing power and access to data.

Planning is essential for organizing tasks and allocating resources effectively.

Cognitive Control: Task Selection and Switching

This section discusses the cognitive control layer, which involves task selection, task switching, and adapting based on frustration signals.

Cognitive control determines the order of operations and when to switch tasks.

Frustration signals help identify when a task is not working or needs adjustment.

Task selection and switching are driven by cognitive damping and frustration signals.

Task Prosecution: Task Execution and Evaluation

This section explores the task prosecution layer responsible for executing tasks one at a time and evaluating their success or failure.

The task prosecution layer focuses on executing individual tasks.

It detects whether a task has been successfully completed or has failed.

Information about task outcomes is reported back to the cognitive control layer for adjustments.

The remaining sections will be summarized in subsequent chapters.

Strategic Thinking and Global Perspective

This section discusses the importance of strategic thinking and having a global perspective in autonomous cognitive entities.

Strategic Thinking and Scope

Autonomous cognitive entities, such as lawyer or doctor robots, need to consider the entire context of their work, not just their immediate environment.

They should be aware of global medical research, market trends, and competitors.

The CEO layer of the entity maintains a high-level perspective and is anchored to the real world.

It has a long-term outlook, considering decades or even centuries ahead.

It takes into account the entire planet Earth and beyond.

Agent Model and Capabilities

This section focuses on the agent model layer of autonomous cognitive entities and its role in understanding capabilities.

Understanding Self

The agent model layer represents the ego of the machine, where it knows and believes about itself.

It confers functional sentience to the entity.

The software architecture understands its configuration, capabilities, operational conditions, and resources.

It knows what it can do, what it can't do, and what it's allowed to change about itself.

Functions are autonomics that can be steered based on learning models.

Executive Function for Planning

This section explores the executive function layer of autonomous cognitive entities that focuses on planning.

Planning for Action

Once an entity has a mission, vision, global context awareness, and knowledge of its capabilities, it moves to the executive function layer for planning.

This layer acts as an internal project manager or director responsible for creating a plan to achieve goals.

Large language models excel at this task by generating plans based on given information.

The transcript does not provide further sections beyond this point.

New Section

This section discusses the different layers of cognitive control and their role in task selection, task switching, frustration management, and task execution.

Cognitive Control Layers

The first layer is focused on creating a project plan for a specific mission. It involves gathering materials, money, energy, and data to pursue the mission. Link to timestamp

The second layer is cognitive control, which involves task selection and task switching. It helps determine the correct order of operations based on the plan and capabilities. Individuals with ADHD may experience executive dysfunction in this layer. Link to timestamp

The third layer provides cognitive control to prevent context or task switching without proper readiness or necessity. It is similar to finite state machines used in robotics and video games where there are a finite number of tasks or states to switch between. Link to timestamp

Frustration management is an important aspect of cognitive control driven by the rate of failures versus successes. If the failure rate is high, it indicates the need for a new plan or strategy at higher layers. Link to timestamp

Cognitive damping involves slowing down and thinking through tasks deliberately to avoid mistakes or regrets. It includes considering failure points and having backup strategies in place. Link to timestamp

Task prosecution refers to low-level hands-on tasks such as executing commands or performing specific actions related to the project or mission at hand. Successes or failures in these tasks provide feedback for higher layers of cognitive control. Link to timestamp

New Section Understanding Lang Chain and Ethos Project

In this section, the speaker discusses the concepts of Lang Chain and the Ethos project. They explain how Lang Chain is about workflows and its limitations in being linear. The Ethos project is introduced as a higher-order microservice that provides alignment and justification.

Lang Chain Workflow and Limitations

Lang Chain is a cognitive control and task prosecution framework.

It focuses on building different components but lacks cyclical capabilities.

It can be integrated into an infinite loop but requires understanding paradigms of pass versus fail and changing directions.

Linear nature of Lang Chain is one of its biggest limitations.

The Ethos Project

The Ethos project exemplifies mission-oriented development within the Lang Chain framework.

It serves as a higher-order microservice that provides alignment and justification.

The Ethos project is modular, following the principles of microservices architecture.

Examples: Baby AGI and Chaos GPT

Baby AGI and Chaos GPT are mentioned as examples of attempts at upper layers of the stack within the Lang Chain framework.

These attempts lacked clear mission, cognitive control, planning, or executive function.