LinkedIn‘s Global Talent Trends report states that job posts mentioning AI or Generative AI (GAI) have more than doubled (2.2x) globally in the last two years
And applications for these posts have grown 17% faster compared to those that don’t mention AI or GAI.
This trend is set to continue
If you are looking at growing in this space, here are a few pointers
👉 #1 Focus on the value and not on the shiny objects
A long while ago my team worked on leveraging language models for understanding top customer issues. I had a bunch of PhDs on my team and we explored the latest in the space at that point in time, from BERT to various LDA models. In the end, the one that worked was a combination of SME driven heuristics + supervised approach
Rarely does success lie in the algorithm alone. Understanding the context of the data, business, risks and preparing to fail fast helps tremendously.
As a leader, work towards creating a culture that allows this to happen
👉 #2 Leader or IC, get your hands dirty
There is nothing more demotivating for the teams than to have strategic plans thrown at them which rarely takes into consideration the ground realities.
Given the speed at which things move in this space, whether you are an individual contributor or a leader, the best way to learn is to get into the weeds to understand why and how things work.
It helps provide better guidance to the teams + teams like working for technical leaders
👉 #3 Learn Tech in the context of business implications
A lot of folks seek to learn the tech – from how multi-head attention models work to deploying at scale. Whilst this is important, put it in context of the business implications. When an LLM is open sourced, think about what this means to the way it could commoditise that layer – not just how one can now access it easier
👉 #4 Teach
The best way to learn, according to Feynman is to teach. Explaining things to others is hugely clarifying. Form close knit learning communities to share what you learn on a regular basis
👉 #5 Build
A mentor once told me, ‘prepare to fail well’.
Rarely does AI work in one iteration. Build. Fail. Learn. Iterate.
What would you add to the list?
Read the report here
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