Reflecting on ChatGPT’s Anniversary
What is the path forward after a year of revolutionary strides in conversational AI?
ChatGPT celebrates its first birthday on November 30, 2023. Exactly one year ago, OpenAI announced the birth of ChatGPT in a blog post, which you can find here.
In December 2022, the ChatGPT craze took off immediately, with hundreds of thousands of users and developers (like myself) eagerly engaging with this innovative technology.
I must confess that before ChatGPT was born, I was skeptical of large language models (LLMs) and statistic-based AI technologies in general. I followed the debates surrounding GPT-3 in 2022 with keen interest, and I must admit I was quite perplexed about the potential successful evolution of what some referred to as ‘statistic parrots’.
However, everything changed with the release of GPT-3.5 by OpenAI and subsequent models. These new models were extensively trained not only with human texts, including narratives and conversations, but also with programming code (InstructGPT). This has been a game-changer, in my opinion. These LLMs have been ‘filtered’ (with RLHF techniques, etc.) and fine-tuned for a chat experience interface (ChatGPT), not only demonstrating a nearly perfect natural language syntax but also exhibiting remarkable semantics, as acknowledged even by Walid Saba.
Inspired by amazing David Shapiro’s live coding videos, I delved into prompt engineering techniques in December 2022. Throughout 2023, I focused on exploring the applications of large language models, specifically in the realms of natural language processing and conversational interfaces, especially chatbots.
In my free time, I also studied a lot and participated a little bit in the growth of some excellent open-source Python development frameworks like LangChain, LlamaIndex, LiteLLM, just to name a few. I have been overwhelmed by the sheer number of academic papers published at an insane rate throughout the year.
Now, a crucial development is that the latest GPT-based models have demonstrated the capability to perform some reasoning-based tasks.
Many researchers are currently grappling with the challenge of constructing ‘autonomous’ agents, or more accurately, copilot assistants — systems designed to interact in real-time with users and collaboratively execute workflows alongside humans. This is a topic that has interested me for quite some time, as I developed in 2020 a research project concerning a voice cobot in logistics industrial real-time workflows.
I am currently intrigued by the prospect of utilizing LLMs as foundational layers in cognitive architecture frameworks to develop a new era of goal-oriented conversational agents. These systems engage in real-time conversations with users, perhaps in voice-first augmented-reality applications, incorporating voice, chat, vision, physical sensors, actuators, to achieve user goals without depending on hard-coded logic (an ‘imperative-driven’ software written in some programming language).
Perhaps we have reached a juncture where we can design these systems, setting ‘business requirements’ almost entirely in natural language, maybe by the final user directly (or probably helped by a prompts design expert). Are we witnessing the realization of the ‘no-code’ dream that some of us fantasized about just a few years ago?
Surely, 2024 will be an exciting year for the conversational AI community!
What are your thoughts?
