Introduction
As artificial intelligence (AI) continues to weave itself into the fabric of our daily lives, it becomes increasingly crucial to develop a nuanced understanding of its intricate nature. This article aims to shed light on the often-overlooked distinction between widely recognized AI interfaces, such as ChatGPT.com or Claude.com, and the families of advanced AI models they utilize, such as OpenAI’s GPT-4 or Anthropic’s Claude 3.5. By doing so, we hope to provide additional clarity on the current state and potential future of AI technologies, especially regarding better decision making for businesses.
The key to unlocking this understanding lies in grasping the concept of transformer architecture. How do LLMs (Language Learning Models) mathematically ‘guess’ the next word? For this, we turn to the seminal paper “Attention is All You Need” (Vaswani et al., 2017). This work introduces the transformer architecture, which has been a central breakthrough leading to modern AI and defines the probabilistic nature of the technology. The paper underscores that LLMs are quite comparable to specific mechanisms in the human brain that similarly transform data.
The Abstraction Layer: Interfaces vs. Actual AI Models
The user-friendly interfaces provided by applications like ChatGPT.com or Claude.com give users a simplified and streamlined experience. However, it’s important to recognize that these applications are designed to cater to the needs of a vast and diverse user base, which can result in limitations when applied to specific real-world use cases. A common misconception arises when users equate these applications with the AI itself, unaware that their interactions are moderated and influenced by the abstraction layer.
This distinction is critical for businesses, especially when making strategic decisions based on their experiences with these AI systems. The abstraction layer, designed to provide a seamless user experience, can inadvertently skew the results and lead to misinformed conclusions.
The Impact of the AI Chat Interface
One of the most significant developments in AI’s recent history is the rise of chat interfaces. When ChatGPT was launched, it quickly captivated users by providing a familiar and intuitive way to interact with AI—through a chat interface similar to SMS or messaging apps. This approach made it easy for users to understand how to engage with the AI, leveraging the GPT-3 technology but with a notable twist: changing the way users perceive and interact with the AI. In other words, GPT3 already existed before ChatGPT, but users couldn’t quite understand how to use it. So in an effort to increase adoption, OpenAI implemented a chat-like interface (an abstraction layer) so that users could just ‘chat’ with the AI easily. 100 million users within a few months.
The Great Misconception
Despite its obvious success, this shift in interface has led to a commonly held misconception: users often believe that each prompt they send is part of a continuous conversation, with the AI taking into account the full context. When the AI’s responses do not meet their expectations, they may blame the technology for its “inconsistency.” The reality is more nuanced. Each interaction with a chat interface like ChatGPT is a separate conversation thread, unless specifically designed otherwise. This means that the AI does not naturally continue a thought or retain all context from previous interactions unless explicitly constructed to do so as is the case with AI chat apps. The ChatGPT application abstraction layer actually provides the chat thread history to the AI model every time the user sends a message. The AI does not naturally ‘remember’ the conversation; it is ‘reminded’ of the thread history for context each time the user sends a new message, even in the same ‘conversation’. Custom tailored apps or abstraction layers can provide proprietary business data as context to the AI model instead of, or in addition to, a conversation thread history and return far more accurate and relevant responses from the AI model for that specific use case.
For example, conducting scientific research might require each prompt and completion to be a completely separate, single-step conversation. The abstraction layer in such cases dynamically constructs highly specific prompts for each interaction, ensuring that all context is relevant, tested, and tailored to achieve the desired outcome. This approach is particularly evident in agentic platforms and Retrieval-Augmented Generation (RAG) systems, where every single prompt is meticulously and dynamically constructed to maintain context and relevancy for the specific use case.
In contrast, relying on a long conversation with the AI can lead to a buildup of irrelevant or superfluous information, which can taint the flow and distract the AI’s focus. If the AI responds with an error and is subsequently corrected, it might not notice the correction in later interactions due to the accumulated context and its’ attention limitations. This can result in skewed responses that do not align with the user’s intent.
Understanding this distinction—between the generalized model and the specific, context-driven prompt—is crucial for anyone aiming to harness the full potential of AI. The abstraction layer, which might include moderation techniques, can introduce biases and misunderstandings, both in the data presented and in the user’s perception of how the AI operates. By grasping these principles, users can more effectively leverage AI for tasks that require precise and context-specific interactions, such as scientific research or strategic consulting.
This knowledge is not just beneficial but necessary for anyone looking to be at the forefront of AI advancements. It ensures that they can consult on AI projects with a deeper understanding and strategize more effectively for future developments.
The Importance of Direct Model Interaction
When users interact with AI systems through abstraction layers, the responses they receive are “tainted” by the layer’s unique system prompts and moderation techniques. This can result in misleading outcomes, especially in research contexts. To obtain unfiltered and more accurate responses, direct model interaction, locally or via APIs such as OpenRouter.ai or Hugging Face, is preferred. By prioritizing this approach, businesses can make data-driven decisions based on reliable and unbiased information.
Real-world Consequences of Ignoring the Abstraction Layer
Dismissing the role of the abstraction layer can have significant repercussions. For instance, consider Microsoft’s Co-Pilot, a widely adopted AI assistant. Without a thorough understanding of prompt injection risks, an attacker could manipulate the system by injecting malicious prompts through a website, leading to potential disruptions or security breaches. As AI becomes increasingly integrated into organizations, exposing sensitive data, businesses must be vigilant about these risks.
Without insight into the abstraction layer, research efforts can be unwittingly tainted. System prompts, moderation techniques, and the business strategies of interface providers can influence the results, leading to biased outcomes. To truly unlock the potential of AI models, it is essential to have transparency and visibility into the underlying infrastructure, something that most closed-source solutions do not provide, as they are focused on the generalized interface and not on your specific use case. Open-source models and tools, on the other hand, offer flexibility and the ability to customize for unique use cases with enhanced visibility and control.
Market Competition and the Future of AI
Large corporations often promote the idea that AI is evolving at such a rapid pace that current programming efforts will soon become redundant. While this may seem like an inevitable reality, it is important to recognize that this narrative can also be a form of market capture, dissuading potential competitors. In truth, AI’s current state is already incredibly powerful and leveraging existing data and models can provide significant advantages to businesses.
Moreover, while larger corporations may offer seemingly convenient all-in-one solutions, they do not guarantee total control over the AI models. This opaqueness maintains a knowledge gap, discouraging users from exploring more customizable and tailored solutions. The future of AI lies in its proliferation and diversity, empowering businesses and individuals with an array of specialized tools rather than a handful of oversimplified applications.
The Human Retina Analogy
To further illustrate the complexity of AI, let’s consider an analogy with the human retina. Just as the retina converts light waves into electrical signals sent to neurons, LLMs (Language Learning Models) process and transform data. However, connecting a million human retinas will not create a human brain, but rather many different systems working together will.
As we have been saying, a common misconception about AI, particularly chat interfaces like ChatGPT, arises from the way they are presented. Companies like OpenAI want us to think about AI as if we are interacting with a person, albeit an artificial one. This mental model leads us to believe that the computational processes behind the responses are akin to human thought processes when dealing with text. However, this is an inaccurate representation of what is actually happening under the hood.
In reality, the technology underlying AI, specifically in the context of transformer models, is perhaps more comparable to the way the human retina functions. The retina provides mechanisms for converting wavelengths of light into neural impulses, transforming one form of data into another through a complex and probabilistic process. Similarly, AI models, such as those used by ChatGPT, transform text into meaningful responses using mathematical algorithms that mimic this transformation process.
When users interact with a chat interface like ChatGPT.com, they often believe they are engaging with a system that understands and processes information in the same way a human brain does. This belief can lead to a deep misunderstanding, particularly when making high-level business decisions. The truth is that AI models are more akin to specific aspects of human cognition, like the retina, rather than the entire brain.
It is crucial to recognize that AI models are fundamentally about transforming data—in this case, text—into other types of data. This transformation is based on probabilistic calculations and mathematical expressions of meaning, which, for a computer, are simply sequences of ones and zeros devoid of “true” meaning. This capability is technologically remarkable, but it is essential to understand its limitations.
AI Models as Isolated Functions in Larger Systems
Viewing AI models as isolated functions within a broader system helps us better conceptualize their role. Just as the retina is one part of the complex system that is the human brain, AI models are single components within a more extensive framework of AI capabilities. To create an effective AI system for your organization, you need to consider multiple models and steps, much like how the human brain functions with its multitudes of interconnected systems.
For instance, to simulate the complexity of human cognition, an AI system should include not just a “retina”-like ability to process text but also other specialized models that mimic different aspects of the brain, such as world simulation and expert knowledge as well as trained and acquired skills. Implementing an AI system in this way requires a deeper understanding of how each model functions and how they can be integrated to work together seamlessly.
In summary, recognizing that AI models, like ChatGPT, are more akin to specific cognitive functions rather than the whole human brain is vital. This understanding allows us to design AI systems that are not limited to single models but rather incorporate multiple models and steps, resulting in a more comprehensive and effective solution tailored to the needs of your organization.
Move Beyond the Monolithic Model
In conclusion, a nuanced understanding of AI’s layers of complexity is vital for businesses seeking to harness its full potential. By distinguishing between user interfaces and the underlying AI models, recognizing risks and benefits, and embracing existing AI capabilities, organizations can make well-informed decisions. AI technologies have the potential to revolutionize industries, but only if we move beyond the simplified view of AI as a monolithic entity and instead embrace its multifaceted nature.
