The Digital Actuaries of Our Time: AI Transformers

Key Terminology

Before delving into AI transformers and transformer architecture, let’s establish some key terms:

Transformer: A neural network architecture designed for processing sequential data, particularly effective in natural language processing (NLP).

Attention Mechanism: A technique that allows the model to focus on different parts of the input when producing each element of the output.

Self-Attention: A specific form of attention where the model relates different positions of a single sequence to compute a representation of the same sequence.

Feedforward Neural Network: A type of neural network where information moves in only one direction, from input nodes through hidden nodes to output nodes.

Encoder-Decoder Structure: A framework where the input is first processed (encoded) into a dense representation, which is then used to generate the output (decoded).

If you are still with me, let’s start.

The Digital Actuaries of Our Time?

General purpose transformers (GPTs) can be thought of as digital actuaries.

While traditional actuaries rely on actuarial intuition built through years of experience, transformers employ an attention mechanism to process and analyze vast amounts of data.

This parallel is not merely a quirky analogy; it is why I chose to study actuarial science back in 2008.

The foundations of transformer architecture share surprising common roots with actuarial science. Both fields focus heavily on statistical modelling and probability theory.

The primary difference lies in their application: actuarial science applies these principles to assess risk and uncertainty in insurance and finance, while transformer architecture uses similar mathematical concepts to understand and generate complex sequences in language and beyond.

This connection becomes clearer when we consider that both fields aim to make predictions based on historical data and patterns.

Actuaries assign different weights to risk factors, much like how transformers weigh the importance of different inputs via the attention mechanism.

The Birth and Evolution of Transformers?

Transformers were first introduced in 2017 with the groundbreaking paper “Attention Is All You Need” by Vaswani et al.

Initially applied to machine translation tasks, they quickly outperformed previous state-of-the-art models. This capability enabled them get adopted across various natural language processing tasks.

Their power lies in their ability to handle much longer sequences of data compared to previous architectures. For instance, while earlier models might struggle with documents longer than a few paragraphs, transformers can process entire books, maintaining context and coherence throughout.

One of the transformers’ key advantages is their ability to process all parts of the input simultaneously through parallelization.

This dramatically speeds up training and inference, making them much more efficient than their predecessors, such as recurrent neural networks.

Perhaps the most intriguing aspect of transformers is their capacity for transfer learning.

Pre-trained models like OpenAI’s GPT series or Anthropic’s Claude can be fine-tuned for specific tasks, significantly reducing the need for task-specific training data.

This versatility has opened up new possibilities in AI application across various industries, including insurance.

Just Predicting Text?

While earlier language models were indeed “just predicting text,” transformer technology has evolved at a breathtaking pace.

Today’s transformers can capture long-range dependencies and nuanced contexts far beyond simple word prediction. They can understand the subtle interplay between words separated by paragraphs or even pages, grasping context in a way that mimics human comprehension.

Recent advances have pushed transformers into multi-modal learning territory. They can now process and generate not just text, but also images, audio, and even code.

This multi-modal capability allows them to predict not only the next word but to generate a constellation of ideas around that word, creating rich, interconnected outputs.

The increased mathematical sophistication of transformers has also improved their reasoning capabilities.

Through techniques like few-shot learning and chain-of-thought (CoT) prompting, these models can perform complex reasoning tasks and synthesize knowledge to generate novel insights.

This capability is particularly relevant in the insurance industry, where complex risk assessments often require nuanced understanding and creative problem-solving.

Is Mathematics of Transformers Ripe for Advancement?

Every advancement in human knowledge depends on mathematical progress. The field of transformer technology is no exception.

Despite their impressive capabilities, we’re still at the beginning of understanding the full potential of these models.

Researchers are actively working on reducing the quadratic computational complexity of self-attention. For example, a team at Google Research is developing more efficient attention mechanisms that can scale to much longer sequences.

This advancement could allow transformers to process entire insurance policy documents or decades of claim histories in one go, potentially revolutionizing risk assessment and policy underwriting processes.

Interestingly, while we know transformers work exceptionally well, we still lack a comprehensive theoretical framework explaining why.

This mirrors our understanding of the human brain – we know it’s amazing, but we don’t fully understand how it works. However, researchers at MIT and Stanford are working to demystify the inner workings of these models, which could lead to even more powerful and efficient architectures in the future.

Another area of focus is increasing the attention span of these models.

Startups and research labs are developing mathematically sound ways to make attention mechanisms more efficient for extremely long sequences.

This advancement is crucial for applying transformer technology in critical industries like insurance.

With these improvements, purpose-built transformers could comprehend complex insurance claims, such as illustrating a lifetime payment plan resulting from a car accident where the optional accident benefit of the first party’s insurance company is responsible.

Interdisciplinary Knowledge is the Future?

The future of AI and transformer technology will be shaped not just by developments in hardware, but by advancements in both mathematics and algorithms.

Deeper mathematical understanding will be crucial for improving model efficiency, interpretability, and generalization. Novel algorithms for training, optimization, and architecture design will continue to play a vital role.

However, the most significant breakthroughs may come from combining insights from mathematics, computer science, neuroscience, and other fields.

The transformer architecture serves as a prime example of how this interdisciplinary approach can lead to groundbreaking advancements in artificial intelligence.

For the insurance industry, this means staying at the forefront of these developments.

As transformers become more sophisticated, they could revolutionize underwriting processes, claims management, fraud detection, and customer service.

Insurance professionals who understand and can leverage these technologies will be well-positioned to lead their organizations into the future.

In conclusion, while creating “the best mathematics” is indeed crucial, the future of AI will likely belong to those who can synergize mathematical insights with innovative algorithms and interdisciplinary knowledge.

For the insurance industry, this presents both a challenge and an opportunity to reimagine traditional processes and create more efficient, accurate, and customer-centric solutions.