AGI Success Unlocked: 3 Essential Tactics for Strategic Edge, Unassailable Moats, and Revealing Hidden Knowledge

Welcome to this week's issue of The Prompt.

As attempts to create Artificial General Intelligence (AGI) gather pace and with the predicted technological singularity inching closer, it's crucial to stay informed and prepared.

In this issue, we bring you 3 actionable insights, 2 practical tips, and one thought-provoking question that will help you excel in the AGI era, shedding light on competition, building a moat to solidify your advantage, and harnessing the potential of AGI-backed language models to unleash hidden knowledge.

As the stoic philosopher Epictetus once said:

In the realm of AGI, diversifying our knowledge and strategies will help us navigate the uncertain future effectively. So without further ado, let's dive into our first three insights.

3 Insights

I. Intelligent Agents and the Cusp of Self-Improvement

We're beginning to witness the reality of Artificial General Intelligence (AGI) - AI systems that can understand, learn, and perform any intellectual task a human can.

Cutting-edge technologies like GPT-4 are showcasing a broad spectrum of capabilities, from generating images to executing code, and simple systems like BabyAGI and AutoGPT, illustrate how these AI systems can handle diverse tasks, retrieve relevant information, and implement code.

A groundbreaking observation in the paper "Sparks of Artificial General Intelligence: Early experiments with GPT-4" reveals GPT-4's near-human performance in solving a wide range of complex tasks, offering a glimpse into the nascent dawn of AGI.

Remarkably, these systems show a budding capability to improve their own code, a feature that while not yet matching human proficiency, is set to do so as AGI continues to learn, adapt, and evolve.

These early sparks of AGI mark the rise of intelligent agents capable of self-evolution and autonomous decision-making. As we stand on the cusp of this transformative era, recognizing and adapting to these paradigm shifts across economic, social, and ethical landscapes is crucial.

We are not merely looking at a distant possibility; we're facing a developing reality. The AGI era, with its self-improving capabilities, promises transformative breakthroughs that necessitate our responsible guidance to ensure a beneficial impact for all of humanity.

II. Unlocking the Human Moat: Harnessing AGI to Illuminate Dark Data

In an era increasingly influenced by artificial general intelligence (AGI), human knowledge - that is, our unique store of explicit and tacit information - becomes the 'moat' protecting us. In this context, the 'moat' serves as a safeguard, differentiating humans from AGI, primarily because AI has access only to public knowledge, not the specialized and intimate information unique to individuals or controlled sources.

This human knowledge, however, is not entirely straightforward. Much like an iceberg, it contains a vast portion of 'dark data,' with about 65% (as per Databerg) hidden within networks, people, and machines. This data, though underutilized due to lack of suitable tools and proficiency in handling unstructured data, holds immense value.

The advent of AGI might seem like a threat to humans, given the potential for job automation and industry disruption. However, it is more accurate and beneficial to view it as a catalyst rather than a competitor. AGI presents a unique opportunity to illuminate the dark data, empowering us to utilize and expand our unique knowledge base. By harnessing the capabilities of AGI, we can not only protect our moat but also enhance it, thus improving decision-making processes and creating more value.

Therefore, in this rapidly digitalizing world, the key is not to pit human jobs and knowledge against AGI, but to see how we can leverage AGI to amplify our strengths. This ensures that our human moat becomes a beacon of growth and resilience, maintaining its relevance and significance in the face of automation and digital advancement.

III. The Advent of AI Colleagues: Towards Collaborative and Proactive AI in Workplaces

Envision an AI colleague of the future, not just a tool, but a comprehensive understanding entity, grasping your tasks, emotions, and expectations. Harnessing a 'Machine Theory of Mind', this AI colleague interprets human beliefs and intentions, and proactively makes decisions.

In a complex project, the AI aligns tasks, manages processes, predicts your needs, and adapts to team dynamics. It grasps human emotions and fosters improved collaboration, marking itself as a trustworthy team player. Additionally, one of the major issues humans face is coordination. Instead of simply bolting on AI to perform specific tasks, AI can become a ubiquitous entity that coordinates and elevates our work performance.

Video: Palantir demonstrating AI to be used to coordinate military operations.

Advancements in AI, such as GPT-4, have demonstrated an ability to solve 95% of Theory of Mind tasks, indicating a near future where AI systems will significantly contribute to our work and lives. This transition from hypothesis to reality is already underway.

2 Actionable Tips

I. Uncovering Hidden Knowledge: Leveraging GPT-4 for Personal and Enterprise Knowledge Management

Language models such as OpenAI's GPT-4 and ChatGPT promise to revolutionize knowledge management with their language and document comprehension capabilities. The pain point lies in managing large volumes of knowledge effectively and efficiently.

Three ways AI systems encapsulate knowledge are explicit text, fine-tuned AI model "memory", and information accessible through APIs. These methods provide a dynamic approach to knowledge management.

ChatGPT's strength is its intelligent interaction with data. It can create structured documents, generate note-taking templates, and convert text snippets into digestible, structured information. This feature simplifies capturing insights from various sources.

Here's a simple plan to harness ChatGPT using a personal knowledge management system like Obsidian.md:

1. Use ChatGPT’s internal knowledge to request a template for the QEC note-taking system in Obsidian.

2. Generate an Obsidian template for storing ChatGPT's information.

3. Paste a text snippet (e.g., an article or a book) and ask ChatGPT to structure it using the template.

4. Store the output in Obsidian.

Each step can be done by simply asking ChatGPT. Still unsure? Just paste my newsletter as context.

ChatGPT can effectively process and manage different types of information, such as code or technical documents. This aids in easy storage and retrieval of knowledge, improving productivity.

To conclude, integrating language models like ChatGPT for knowledge management offers immense benefits in managing and accessing knowledge efficiently, making it highly valuable for personal and enterprise use.

Why can't we leverage AGI to handle all of these steps for us and work proactively? In fact, that's exactly what I'm working on! Check out Aloy.so to learn more.

II. Empowering Development Tasks: Unleashing the Potential of GPT-4 as a Developer

Language models like GPT-4 have the potential to outperform tools such as Copilot in the realm of coding, thanks to one fundamental feature: context specificity. The key to leveraging AI for coding lies in providing the same information you would require to solve problems in code.

Understanding the Contextual Needs of AI Systems:

For AI to solve problems in coding effectively, it requires a specific set of information that mirrors the insights you have gained as an employee. This encompasses an understanding of the company's vision and purpose, your department's functions, the relevant IT systems, user requirements, user stories, dependencies on other systems, technology choices, and more.

Structuring Prompts for Efficient Coding:

To maximize the effectiveness of coding with GPT-4, prompts should be structured in an incremental way, with context extending from the broader to the specific. A potential structure could look something like this:

• Company Overview

• Processes Involved

• Systems in Use

• Details of the Current Software

• Relevant Requirements

• Relevant Functions from Other Files

• The Current File Code

• The Specific Change Required

Implementing the Right Approach:

Two main approaches can be used to provide this context: pasting in the specifically required information or setting up a system like BabyAGI that retrieves the information as needed. The key is to provide relevant information without overloading the AI.

For instance, when generating a simple function in a file, you may not need to provide an entire company overview.

To illustrate this point, consider coding for a webpage analytics tool. In this case, the AI wouldn't need to know anything about the company's payroll system. Thus, the inclusion of context should be targeted and specific.

Conclusion:

The power of AI coding tools like GPT-4 is unlocked through the appropriate provision of context. By understanding and structuring the contextual requirements effectively, we can optimize the use of AI in coding tasks, achieving enhanced efficiency and precision.

1 Thought-Provoking Question

How might the competitive landscape fundamentally transform as companies begin to employ self-improving AI that can not only enhance code, but autonomously adapt and optimize all aspects of business operations, and what are the potential ripple effects of this shift in your industry?

Additionally, what strategic preparations or pioneering steps might be necessary to navigate or lead this imminent change?

I consistently curate and incorporate top-tier ChatGPT prompts and resources into our collection.

What are your thoughts on this issue? Reply to this email and let me know!

— Alexander