How Cheppy thrills and acceleraties us at AMIS-and what it does not yet do
ChatGPT is a bit of a “tongue twister” so I will speak of Cheppy.
AMIS has a long history of spotting, exploring, embracing and rolling out new concepts and technologies . It something we like doing — a trait of the organisation- and are good at and successful with. That means we are not afraid of new things — we welcome them in. But we are critical too: what can it do for us? What is fad or fiction, what is fact and perhaps fate?
Unsplash — Powertools — David Siglin
Cheppy had been coming for some time — or at least something like Chappy. The large language models that could generate images (StableDiffusion, DALL-E *), MIdjourney, GPT) or the AI powered tools for software developers (GitHub Copilot, AWS Code Whisperer, Google Alphacode, BlueDiff Cover, CodeGeeX) have been around for some time now. We felt close to an inflection point, where capabilities and uptake of these technologies would explode. When Cheppy arrived on the seem, it seemed that point may have been reached. Especially the versatility of Cheppy made it interesting to a wide range of people — from my son to my mother, my tennis team and almost anyone I talk with. (most of the conversations at some point touch on the shortsighted rearguard battles in some universities where Cheppy is tried to be banned)
At AMIS, we started early explorations in what Cheppy could do for us professionally as soon as the service became available. On internal forums we shared our own findings and some of the amazing stories found online (“pretend you are a Linux terminal, now execute these commands”, “explain this story to me as if I were a five year old and do it in language X”). We compared Cheppy to Copilot (we prefer Cheppy), tried to find clues that our own blog (3500 articles) had been a source for Cheppy (it probably was, but no actual proof) and pushed each other for more extravagant and also more useful applications.
In January we organized a Code Café where two colleagues summarized all our findings and discussed the origin of Cheppy and lookalikes, the data sets they were trained on and some history and workings of the large language models. We also looked at the road ahead. And we shared experiences and brainstormed on further applications…