Unleash AI’s Potential: Treat It Like Your Most Valuable Employee

Treat Al as a an exceptionally bright employee: provide all the tools to get the job done and maximal autonomy

Table of Contents

1. Introduction: The Rise of Software 3.0
— Software Evolution: From 1.0 to 3.0
2. Challenges and Solutions in the Evolution of LLMs
— Challenges Facing LLMs
— Bridging the Gap: Software 1.0 Meets Software 3.0
— The Evolutionary Path: From Hacks to End-to-End Solutions
3. Futuristic AI Architectures: Swarms and Hierarchies
4. Conclusion: Envisioning the Future of AI

Introduction: The Rise of Software 3.0

The future of technology with AI can be seen as an embodiment of A.N. Whitehead’s idea:

Civilization advances by extending the number of important operations which we can perform without thinking about them.

LLMs, like ChatGPT, are extending the number of operations we can perform without conscious thought.

For example, just as the transition from manual to automatic transmission in cars has freed drivers to focus more on navigation and less on gear shifting, advancements in AI are streamlining complex tasks, allowing humans to dedicate their cognitive resources to higher-level problem-solving.

Though Software 3.0 has its limitations, its strategic application can exponentially accelerate development, multiplying your company’s momentum by thousands.

Software Evolution: From 1.0 to 3.0

The journey of software development has been a remarkable evolution, marked by significant milestones:

- Software 1.0 represents the era of traditional programming, where every instruction needed to be explicitly defined by the programmer. This is the domain of compilers and interpreters, transforming human-readable code in languages like Assembly, C, or JavaScript into machine instructions. It’s characterized by detailed control flow statements such as “If x do y,” requiring thousands of lines of code to perform complex tasks.

- Software 2.0 is the advent of neural networks, which shifted the paradigm from explicit programming to pattern detection and learning from data. Here, a neural network model is trained on large datasets to infer the rules from the data itself, often encapsulated in a simple line like `y = model(x)`. This approach significantly reduces the lines of code and abstracts the complexity behind layers of learned weights and biases.

- Software 3.0 is the current frontier, powered by Large Language Models (LLMs) like ChatGPT. These models take abstraction to a new level, allowing for complex operations to be executed with minimal input, such as `llm(“if x do y”)`. LLMs leverage vast amounts of data and sophisticated algorithms to understand and generate human-like text, enabling a wide range of applications from automated content creation to coding assistance.

Each iteration of software evolution brings us closer to a more natural and intuitive interaction with technology, where the complexity of the underlying processes fades into the background, allowing us to focus on creativity and higher-level problem solving.

In essence, the evolution of software mirrors the evolutionary path of human interaction with our environment. Just as our ancestors developed tools and language to abstract and simplify their interactions with the natural world, software has evolved to simplify and abstract our interactions with technology.

- Software 1.0 is akin to the early tools, requiring precise and deliberate actions to achieve a desired outcome.
- Software 2.0 reflects the development of language and symbols, allowing us to communicate complex ideas with simple representations.
- Software 3.0 is comparable to the development of societal systems, where individual actions are part of a larger, more complex network of interactions that function with a level of autonomy and sophistication previously unattainable.

As we progress, our technological tools become more like an extension of our natural faculties, enabling us to perform complex operations with ease and intuition, much like our ancestors learned to master their environment for survival and growth while minimizing cognitive load.

Challenges and Solutions in the Evolution of LLMs

Challenges Facing LLMs

Large Language Models (LLMs) like ChatGPT have revolutionized how we interact with technology, offering unprecedented capabilities in generating human-like text. However, they face significant challenges:

- Math: LLMs struggle with mathematical operations due to their inherent design focused on language understanding rather than computational logic.
- Access to External Information: Current LLMs operate within a closed environment, limiting their ability to access or act on information outside their trained dataset.
- Performing Actions in External Systems: While LLMs can generate instructions or code, they lack the capability to perform actions in external systems directly.
- Reasoning, Logic, and planning: LLMs are still not able to reach human-level performance in reasoning, logic, and planning

Bridging the Gap: Software 1.0 Meets Software 3.0

The solution to these challenges lies in the integration of Software 1.0 with Software 3.0, leading to the emergence of more sophisticated AI agents or assistants. This approach combines the strengths of both software paradigms:

- OpenAI’s Assistants API: A prime example of this integration, offering capabilities such as:
— Code Interpreter for Math: Enhancing LLMs with the ability to understand and execute mathematical operations more accurately.
— Retrieval to Access External Information: Allowing LLMs to fetch and incorporate external data into their responses, overcoming the closed environment limitation.
— Function Calling to Act and Retrieve External Information: Enabling LLMs to perform actions in external systems and retrieve information, making them more interactive and dynamic.
— Incorporating Logic from Software 1.0: Integrating procedural memory and decision-making algorithms to enable LLMs to execute complex tasks that require loops, conditionals, and other advanced logic.

This kind of unified solution drastically cut your development cost, delegating the hard work to the LLM.

The Evolutionary Path: From Hacks to End-to-End Solutions

Historically, technological advancements often begin with a series of “hacks” or workarounds that address specific limitations. Over time, these solutions evolve into more integrated, end-to-end systems. The journey of machine learning, from early vision systems to current LLMs, exemplifies this progression. Initially reliant on a patchwork of specialized algorithms and manual feature extraction, the field has moved towards more holistic models that learn directly from data.

Similarly, the integration of Software 1.0 and Software 3.0 represents a move away from isolated solutions towards a more seamless, end-to-end approach. By combining the precise, rule-based reasoning of Software 1.0 with the nuanced, data-driven insights of Software 3.0, we are paving the way for AI systems that are not only more capable but also more aligned with the complexities of the real world.

The diagram below illustrates how the integration of different AI components can create a more robust system. It shows the interplay between procedural memory, semantic and episodic memory, and a decision procedure that incorporates reasoning and actions based on observations from both digital and physical environments. This reflects a system that can handle complex tasks by using loops and conditional logic, akin to the human brain’s ability to process and act upon information.

In essence, the evolution from hacks to holistic solutions reflects a broader trend in technology: the move towards systems that are more integrated, efficient, and capable of handling complex, real-world tasks with minimal human intervention. As we continue to advance, the distinction between AI and human capabilities will become increasingly blurred, leading us into a future where technology is an invisible, yet indispensable, extension of our natural faculties.

Futuristic AI infrastructure and human-AI interactions: Swarms and Hierarchies

The future of AI is not just in the complexity of individual models like LLMs, but also in how these models interact with each other and with different software systems. The transition from today’s isolated software entities to a more interconnected and hierarchical structure is essential for the next leap in AI capabilities.

From Fragmentation to Fluidity: The Role of LLMs in Harmonizing Software

The image below depicts the evolution from the current state, where individual software systems operate in silos, to a future where these systems are integrated into a cohesive swarm, orchestrated by an LLM.

Civilization advances by extending the number of important operations which we can perform without thinking about them.

Now: We have disparate software systems (Software a, b, c, etc.) that are not inherently designed to communicate with each other. This isolation can lead to inefficiencies and a lack of synergy.

Tomorrow: Envision a swarm of AI entities, where each software component is part of a larger, harmonious system. The LLM sits at the core, facilitating communication and coordination among the various software entities. This integration allows for a more fluid and dynamic interaction between systems, much like the neurons in a brain or individuals in a social network. The end result is that humans are able to achieve more with less cognitive load.

Hierarchical AI Systems: The Company Analogy

Applied AI is converging towards a hierarchical approach to AI systems, akin to the structure within a company:

In this analogy, the ‘big LLM’ can be seen as the executive level, making high-level decisions and delegating specific tasks to ‘smaller LLMs’ or specialized AI systems, similar to department heads and their teams, faster for specific tasks. This hierarchy allows for a division of labor, where complex tasks are broken down into simpler, manageable components, each handled by the most suitable AI entity.

Conclusion and Future Directions

The rapid progression of AI research ensures that the manual rules coded by your programmers will soon be surpassed by AI capabilities:

Treat Al as a an exceptionally bright employee: provide all the tools to get the job done and maximal autonomy

Hope you liked my post!
BTW, I’m Louis, a 2x AI founder, Techstars & OrangeDAO alumni, ex-CIA.
I’m always looking for opportunities to push the boundaries of what’s possible with AI. I’m particularly interested in projects that aim to enhance human capabilities and improve the quality of life through technology. If you’re working on something exciting in the applied AI space, I’d love to connect and explore how we can collaborate.
Schedule a meeting with me: https://cal.com/louis030195/applied-ai.
Or connect on LinkedIn.