The Journey of an AI Scientist With Stewart Hu
We recently sat down for a quick Q&A with Stewart Hu, AI scientist at Sixfold. Our conversation ranged from his career journey to how he stays current in the field, as well as the tasks on his daily agenda.
Let’s get this started! In your own words, what does your job as an AI scientist involve?
AI scientists engage in a lot of practical work. Despite our 'scientist' title, our roles often overlap with those of developers or research engineers. In fact, over 50% of our tasks are typical software engineering activities. We develop software grounded in foundational models, employing a range of techniques, not just AI.
Previously, AI encompassed anything linked to machine learning, but now it's more commonly associated with large language models like GPT. Our role includes integrating these models into software applications, utilizing models such as GPT-4, and even fine-tuning our custom models. Additionally, we apply both traditional machine learning and deep learning methods. This involves creating classifiers with techniques predating neural networks, like gradient boosting machines or random forests. At our core, we are software engineers crafting machine learning algorithms to address real-world challenges.
How did you get into the world of Generative AI?
My fascination with AI really took off with GPT-3's emergence. But it was the debut of the stable diffusion model in August 2022 that truly captivated me. This revelation prompted me to pivot my career towards a tech startup specializing in deep learning and AI.
In the early stages of my career, I worked as a software engineer. This was followed by a ten-year journey in data science, beginning with statistical learning and gradually evolving into machine learning, deep learning, and finally AI. Essentially, I devoted my first decade to hardcore software development, and the next decade explored the realms of data science and machine learning.
Could you give some insights into what's on a typical to-do list for you?
My work is basically divided into three key areas.
Firstly, there's data management: sourcing appropriate data, organizing it properly, and conducting thorough analyses. A major chunk of our time is dedicated to dealing with data - acquiring, scrutinizing, and delving into it.
Secondly, I engage in software development, where my goal is to craft software that's not only reusable but also adaptable to growing complexities. This involves strategic software design to ensure it can be easily scaled up.
The third area is AI, particularly focusing on 'retrieval augmented generation’ . This entails extracting pertinent details from extensive document collections to accurately contextualize models like GPT-4. My day-to-day involves juggling these three components.
How would you distinguish a purpose-built AI tool from a generic one?
AI often gets hyped up with flashy demos requiring little coding. However, Sixfold is a purpose-built gen AI tool, our focus is on crafting solutions that address real-world business problems, not just making eye-catching demos. We use AI to make underwriters work faster, more accurate, and enjoyable. By taking over repetitive tasks, AI allows underwriters to focus on the more engaging aspects of their job.
Our platform is built with a strong emphasis on accountability, not just on interpretability or explainability. This means our solutions cite sources when making recommendations and provide actual source documents for our classifications. It's a practical, business-centric approach that boosts confidence in underwriting decisions.
What excited you the most about joining Sixfold?
Two things particularly drew me to Sixfold. First, the experienced team leading the company. The founders have a proven track record of creating substantial business value, blending tech knowledge with sharp business insight. Second, on a personal level, my wife has been in the insurance industry for over ten years, and I've always found it fascinating. Joining Sixfold presented a chance to dive deeper into this sector.
It was the combination of the seasoned leadership and the company's expertise in insurance and underwriting that ultimately convinced me to become part of the team.
How do you stay engaged with the AI community?
My go-to resource is X (formerly known as Twitter), where I've created a list named ‘AI Signals.' This list features over 100 experts deeply engaged in the field, tackling everything from fine-tuning models to enhancing the speed of large language model inference. While some of these individuals may not be widely known, their insights are incredibly valuable.
Previously, I would follow arXiv for academic papers, GitHub for trending repositories, and Papers with Code to find research papers with their corresponding code. However, X has become my most essential tool. I regularly check updates from my list there to keep up-to-date with the latest developments.
That sounds like a great list! Can we share it with the readers?
Of course, happy to share it - here you go!
How can people best follow your work?
I haven't been active on my blog lately, but I do maintain a GitHub repository named 'LLM Notes.' It serves as a practical guide for data scientists and machine learning practitioners. This repository is a compilation of the knowledge and insights I've gathered throughout my career. A few months back, I uploaded a wealth of information there, including lessons learned, common pitfalls, and personal experiences. It's a good resource for anyone interested in the field.
Thanks for your time, Stewart! We’ll let you get back to your to-do list now.
If you’d like an opportunity to work at Sixfold, check out our vacancies.
