Low Code Data Scientist - learning from Grace Hopper
I very much recommend David Knott 's newsletter and the last episode was especially insightful for me
Two quotes from David's post above
As Hopper observed in a 1976 interview, “Well, you see, someone learns a skill and works hard to learn that skill, and then if you come along and say, ‘you don’t need that, here’s something else that’s better,’ they are going to be quite indignant.”
However, I think that, when we feel the temptation to react in this way, we should look in the mirror and ask ourselves: are we behaving like Grace Hopper’s enthusiastic crew of collaborators, who help make efficient compilers a reality? Or are we reacting like the sceptical assembly and machine code developers, defensive of our craft, and hostile to tools that abstract away our skills?
I have read Grace Hopper's biography - precisely to understand how she was a such a major change agent - while being an outsider in essence
This resonates with my teaching
We, in my course at the #universityofoxford have some experience in this area see
In our recent AI in cybersecurity - I tried this technique - ie using using prompts and conceptual knowledge graphs to create an AI risk register using prompts alone for cyber experts ie non developers.
This made me think, like Grace hopper references, that the simpler we make things - the more inclusive we we will be
Specifically, in creating an AI risk register, the output was constrained. Creating a structure for the output helped us to create the artefact using prompts more easily.
I think the outcomes of a low code data scientist could be
领英推荐
1) Dashboards
2) Synthetic data
3) Workflows
4) Agents
5) Data driven Decisions
6) A system itself end to end
All developed through code as an intermediary
In a recent article, McKinesey proposes that with genAI - we should now be looking beyond effectiveness to efficiency
I think we are entering an exciting new world driven by AI !
If you want to study with me at #universityofoxford see our course on #AI #genAI and #mlops
?
?
My job was a general worker. The time I was coaching. I was helping teachers to learn from them. That's why I say I was coaching start.
3 个月Deep Leaming This skill enables data scientists to develop sophisticated models that can learn from vast amounts of data, driving advancements in Al and providing cutting-edge solutions in various fields