The Mirror and the Crystal Ball: Harnessing AI’s Potential in a Changing World
When I posted my first blog, I promised a follow-up on utilising GPT-4 to build the web app used in the workshop. However, as is often the case with new technology, Open AI swiftly released functionality that allows users to create GPTs. These are the same things used for the workshop to create personas to show generative AI’s risks and immense potential but integrated into the chat GPT interface. If the functionality had been previously available, I’d have likely used it in my process, so the guide-style blog post felt less relevant, given the new functionality.
Therefore, this post will take a slightly different angle from my original intention but still strongly focus on what Large Language Models (LLMs) do and how to use them effectively. I’m not an academic in AI, nor do I work with them extensively professionally except for making my life easier. However, individual perspectives can be useful and insightful.
Mirror Mirror on the wall
AI developers feed generative AIs like GPT-4 vast quantities of data, most of which they scrape from the internet, rate for suitability, and then incorporate into a training tranche. When looking at technology like generative AI, it’s very easy to forget how incredible the internet is, a globally accessible hub of almost all information we humanity have ever collected, created or imagined. Therefore, it’s no surprise that folk worry about the ethics of LLMs; what if we look in that mirror and don’t like what we see? What if our ways of thinking are not the fairest of them all? The answer depends on the LLM. Some of Llama’s openly available and modified versions will answer queries without restriction, while others, depending on their training, fine-tuning, and configuration, are as restricted as commercial models.
Crystal Gazing
The term “crystal gazing” conjures images of predicting the future, a concept once relegated to fantasy and superstition. Yet, this metaphor finds new relevance in the generative AI domain. Generative AIs, with their advanced algorithms, are not just tools for generating content; they are our modern-day oracles, offering glimpses into potential futures with startling precision.
Consider the familiar task of writing an email, creating a presentation template, or summarising a call. In these scenarios, generative AI acts as a predictive tool, anticipating and formulating responses that align closely with what you might have produced through personal effort. This predictive ability is not limited to text generation; it extends to more complex and dynamic realms, a good example being weather forecasting.
The traditional approach to weather prediction involves massive supercomputers running intricate physics-based algorithms. These models simulate Earth’s atmospheric behavior starting from specific conditions, but factors like spatial resolution and computational time limit them. Here, AI introduces a transformative approach.
AI models can be trained on historical data from atmospheric physics models, actual weather outcomes, and general forecasting knowledge creating a leap in predictive capability vs an atmospheric physics model on its own. When provided with initial atmospheric conditions, such AI systems can generate forecasts for the subsequent days with remarkable accuracy. These predictions are precise and produced in a fraction of the time taken by traditional atmospheric simulations. Take a look at Atmo, Graphcast and experimentation from the European Centre for Medium-Range Weather Forecasts if you’re interested.
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This example illustrates the broader potential of AI in predictive analytics. From anticipating natural phenomena to aiding in decision-making across various sectors, AI is narrowing the cone of uncertainty in ways previously unimaginable. However, remembering that these predictions are probabilities, not certainties, is crucial. The AI relies on patterns learned from past data to make forecasts, and although they are often accurate, they are not infallible.
Narrowing the cone of uncertainty
If you’re reading this, you might be familiar with how LLMs work; they use attention mechanisms in a process called “inference”, where, based on the input, it predicts the likelihood of each subsequent word in its response. Hence, narrowing the cone of uncertainty is exactly what aformetioned GPTs do. They provide additional context to a generalised LLM, allowing it to be primed on a narrowed-down pool of words and probabilities, greatly increasing the relevance and accuracy of its response. Ask an LLM to respond to a legal query in the style of a lawyer, and not only will the response be written in the uncanny style of a lawyer, but its response has a higher chance of being accurate vs being a hallucination (hallucinations being the proper name for the model “making stuff up”).
Layers of Likely vs Leaps of Faith
In generative AI, you will have heard a lot about context; the context is the body of text, images, or data passed to a generative AI to respond to. Focusing on LLMs, we can think of context as layers of information that will improve the focus of its output. This is where the aforementioned GPTs can play a role by increasing default context and so priming the model. When building the web app discussed in my prior post, I used several separate chat sessions, each building out different app elements. Without GPTs for each new session, I had to provide context on what I was doing, why, and prior states of the code. If I’d had access to GPTs, I would have created a GPT containing the what, why and otherwise useful information, such as packages and coding languages used. With each new session, I would only have provided code snippets where the requested functionality would interact and the functionality request itself, leaving out most of the other content, creating a more efficient, productive session. The same logic can be applied to any number of tasks, or be used to create distracting games (Our own Paul K admits this a niche crowd).
A personal journey
How most folk have learnt to use LLMs for the time being is likely confined to personal journeys of discovery — from requesting emails to creating school reports or developing web applications and proofreading formal texts, each application is unique. But I can share some recommendations.
Our journey with technologies like open AIs GPT, Google Gemini, and Meta’s open-source Llama will be a balance between human creativity and algorithmic prowess. This partnership will challenge us to constantly adapt, learn, and reflect. We must harness these tools with a spirit of exploration, balancing our enthusiasm with a mindful approach to their profound capabilities and inherent limitations. Embrace it with curiosity, caution, and an open mind, for the future of AI is not just written by algorithms but by the stories we choose to create with them in this evolving narrative. We are both the authors and the audience, shaping and being shaped by the transformative power of AI.