2023 was an exceptionally busy year, largely due to the swift pace of innovation in the AI space and the sheer challenge of keeping up.

So, how did the year go? My objectives for the year, as outlined in my 2022 year-in-review post, included a greater emphasis on research, writing, and community contributions, as well as learning new things and maintaining personal health/fitness.

TLDR;

This year, my personal blog writing decreased as I dedicated significant time to staying current with AI/ML developments, leaving little room for other activities. While I spent sometime researching how to improve my web dev skills (e.g., digging into state management tools like Zustand, and node based diagramming with React Flow), I wish I got to dig into more net-new technologies.

Overall, I am hopeful for, and excited about the year ahead.

Related post

Year In Review 2022

2022 has been a fairly interesting year! October 2022 made it exactly 1 year at Research Software Engineer, @MSR. It has been a period of intense learning and growth. I've been fortunate to work with amazing, generous colleagues and learn from them. A technical highlight of the year has been getting to make some contributions to GitHub CoPilot ..

A Turbulent Year. A Busy Generative AI Year.

2023 has been a turbulent year for the tech industry, with a significant number of layoffs. The AI space also witnessed an unprecedented pace of innovation, which has led to some degree of AI anxiety.

Several important developments occurred this year in the AI space, particularly in Generative AI, that I consider highly significant. A simplification of these important events, in my opinion, includes:

• RLHF Fine-Tuning: ChatGPT demonstrated that LLMs, when fine-tuned to align with human feedback, can become overall more helpful tools. These models become even more valuable when paired with the right user experience. This has inspired dozens of research projects on how to replicate the outcome of helpful models, such as RLAIF, DPO, etc.
• OSS Models and Parameter-Efficient Fine-Tuning: Models like LLAMA and Mistral provided OSS (weights) access to competitive foundational models. Techniques like LORA and QLORA made it possible to rapidly fine-tune base foundational models on consumer GPUs. The combination of these developments has led to an explosion of experiments, research, and progress in the AI space. There are currently thousands of open-source LLMs (mostly in the 7B to 13B parameter range) on Hugging Face.
• AI and Product: There have been dozens of new product features that integrate LLMs (e.g., GitHub Copilot, Microsoft Product CoPilots, etc.), as well as startups exploring novel products and problem spaces with AI. A shout-out to all the teams that worked under tight deadlines to deliver many of the interesting results we see today.

As a research software engineer, a side effect of the surge in progress in the AI space is that I have been busy. This includes keeping up with reading new papers, running new experiments that integrate these new approaches, and rewriting existing tools to accommodate new updates, among other tasks.

Research Writing

Published 2 research papers this year at leading natural language processing research venues - ACL and EMNLP.

Blog and Writing

Mixed results here. I was hoping I could write on my blog and newsletter atleast once per month (12 articles each). That did not pan out really well. Instead, I got to write:

• 5 personal blog articles (vs 16 last year)
  • Implementing Auth in Firebase Hosting
  • Implementing Dark Mode in Gatsby + Tailwind while Avoiding Flash of Unstyled Content (FOUC)
  • How to Build a Similar Posts Feature for Your Gatsby (JAMSTACK) Website using Machine Learning (Document Similarity)
• 6 full length newsletter posts (vs 6 posts last year)
  • Multi-Agent LLM Applications | A Review of Current Research, Tools, and Challenges
  • Top 5 AI Announcements (and Implications) from the 1st OpenAI DevDay
  • A Defacto Guide on Building Generative AI Apps with the Google PaLM API
  • Understanding Size Tradeoffs with Generative Models - How to Select the Right Model (GPT4 vs LLAMA2)?
  • How to Generate Visualizations with Large Language Models (ChatGPT, GPT4)
  • Practical Steps to Reduce Hallucination and Improve Performance of Systems Built with Large Language Models

The newsletter readership did grow - from 100 subscribers to over 600 subscribers (Thank you!). Still very small, but progress. I was also hoping to make significant progress on my book (Designing with Machine Learning). Didn't make much progress here, but there is a chapter on Generative AI available.

Community

I gave 7 talks, created LIDA - an open source tool to automatically generate data using LLMs, core contributor on AutoGen - a framework for building multi-agent LLM applications.

Conferences and Talks

• In Person Talks.

  • GDG Seattle DevFest (Automated Data Visualizations)
  • Keynote on AutoGen at the Ai.dev/Cassandra Summit.

• Virtual Talks:

  • Lancaster University (Automated Data Visualizations)
  • Deaking University AI Festival (Automated Data Visualizations)
  • Stanford (Introduction to OpenAI apis, DataViz)
  • Microsoft MLADS Conference
  • Snowflake BUILD Conference (Automated visualizations with LLMs and Streamlit)

OSS Contribution

LIDA - a library for automated generation of visualizations using LLMs - is definitely the most exciting OSS project I worked on this year. I built an initial version of LIDA in August 2022 (before ChatGPT, Code Interpreter), demonstrating how a chat interface paired code execution could enable novel approaches to generating visualizations. Since then, LIDA has gone on to become an OSS project (over 2000 stars as at time of writing).

LIDA was impactful to internal teams looking to apply LLMs to data visualization (designing a reliable pipeline, UX, evaluation metrics) and has been used by many external teams since becoming open source. There is still much to be studied here and I look forward to extending this work in 2024.

I am also actively contributing to building AutoGen, a framework for building multiagent LLM applications.

Learning New Skills

This year, I just have not had time to delve into any completely new areas. I tinkered around with some new javascript/React framework (e.g., Reactflow for flow diagrams, zustand for better state management, building UI interfaces with Streamlit).

Work Life Balance

• Personal and Work Travel: Participated in two family/vacation trips this year, in addition to other personal and work-related travel.

• Family Time Commitment: Maintained a consistent focus on family during weekends, creating many specialmemories with my son. Activities included accompanying him to Wushu classes and observing his progress through belt rankings, teaching him math concepts, working on his drawing and reading skills, and guiding him in learning to ride a bicycle. Witnessing the moment when bike riding just "clicks" after persistent practice is profoundly rewarding.

• Physical Fitness and Well-being: I can now do a half-decent wheel pose. A bit of improvement from the glute bridge I started working on in 2022. Also got consistent with working out at least 4-5 hrs per week.

I am grateful for the opportunity at this stage in my career to balance professional work with personal enrichment. I hope to maintain this balance in the coming year.

Cheers to a Better New Year Ahead!

For 2023, I'd like to continue improving on the same things - research, writing, community contributions, learning new things, personal health/fitness. I'd love to grow the LIDA project, AutoGen project and build out some interactive tools to demonstrate some of my thoughts on the UX for Generative AI applications.

I am thankful for all who have helped make 2023 a wonderful year. Thank you! Cheers to everyone who made it to the end of the year. You did great, you are awesome, you are appreciated, you rock! Happy New Year!