Building AI-first Products: Designing for Single and Multiplayer Modes

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Hatched by Glasp

Aug 17, 2023

4 min read

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Building AI-first Products: Designing for Single and Multiplayer Modes

Introduction:

In the rapidly evolving world of technology, paradigm shifts have consistently revolutionized the way we interact with products. From the days of the 286 PC with a 4mhz CPU and 1mb of memory to the advent of 56 kb/s connectivity, simple advancements have had profound impacts. Today, as we venture into the era of AI-first products, there is immense potential waiting to be explored. By thinking beyond traditional human-language interfaces and incorporating artificial domain intelligence (ADI), we can create groundbreaking products that were previously hindered by human costs, scalability, or technical limitations.

Containing the Problem Space: Thinking in Domains

When it comes to building AI products, it is essential to define the problem space and focus on specific domains. Existing foundation models already possess a wealth of domain-specific knowledge, making them a valuable resource. By fine-tuning these models for specific domains, such as artificial domain intelligence (ADI), we can create tangible products and experiences that deliver business outcomes. Whether it's a broad cross-domain experience or a deep focus on a specific domain, clarity in the intended domain-space is crucial for successful AI product development.

Constructing the UX: Breaking the Skeuomorphic Barrier

Bolting AI onto existing products and interfaces often falls short of the mark. To truly harness the power of AI, we must redefine the problem context and rethink solutions with new paradigms. This shift in thinking not only transforms the interface design but also prompts us to reconsider the need for human input in the workflow. AI-native solutions often lead to simpler, more efficient interfaces that hide the complexity behind the scenes. By embracing this new approach, we can unlock the full potential of AI in product design.

Composing the Product Stack: Simulating Proto-AGI

In order to deploy AI models at scale, we must simulate proto-AGI (artificial general intelligence) within the application realm. This involves creating structural scaffolding, workflow handling, and data management techniques that enable reliable AI pipelines and experiences. One of the challenges of using models in production is their probabilistic nature. To overcome this, we can decompose problems into stages and build optimized pipelines that produce the desired output. Machine-interface models (MiMs) also play a crucial role in interfacing directly with machines, eliminating the need for human intervention. Additionally, federation and multiplexing techniques allow us to leverage multiple models, prompts, and functions to tackle complex problems.

Correcting Errors: Guarding for Technical Limitations

While language models are powerful, they lack conceptual understanding and can produce errors or biased outputs. To ensure the reliability and accuracy of AI products, we need robust tooling, methodologies, and processes. Safeguarding against factual errors, bias, and other risks is crucial, especially in critical services like healthcare or search engines. Incorporating programmatic reinforcement features at the application layer can help identify and address negative outputs, improving the overall performance of AI models.

Capturing Value: Building AI Businesses

To build sustainable AI businesses, we must optimize for three key moats. Firstly, a unique product infrastructure that leverages domain insights and enables better services through AI. Secondly, access to proprietary data that can be used to train and fine-tune models, giving a competitive edge. Finally, access to compute power and talented individuals who can accelerate the development and scaling of AI products. By strategically applying AI to existing processes and identifying insertion points, we can capture value and stay ahead of the competition.

Conclusion:

Building AI-first products requires a shift in mindset and a deep understanding of the potential and limitations of AI. By thinking in domains, breaking the skeuomorphic barrier, redefining solutions with AI-native approaches, guarding against technical limitations, and leveraging AI where it creates the most value, we can unlock the true potential of artificial intelligence. As we continue to explore the possibilities, it is crucial to focus on creating products that not only deliver business outcomes but also enrich the lives of users in both single-player and multiplayer modes.

Actionable Advice:

  • 1. Define a clear problem space and focus on specific domains when building AI products. This will enable you to leverage existing domain-specific knowledge and tailor your solutions effectively.
  • 2. Embrace AI-native approaches and rethink the user experience beyond traditional interfaces. Redesign workflows to hand-off tasks from humans to machines, simplifying interfaces and maximizing the magic happening behind the scenes.
  • 3. Ensure the reliability and accuracy of AI products by incorporating robust tooling, methodologies, and processes. Safeguard against errors, bias, and risks, and continuously improve the performance of AI models through reinforcement features at the application layer.

By implementing these actionable advice, you can pave the way for successful AI-first product development and create transformative experiences for your users.

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