Why did I give up my academia career after Ph.D. and build a start-up?

Why did I give up my academia career after Ph.D. and build a start-up?

dev.to - Jan 23

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I just got my Ph.D. in Electrical Engineering in 2022 from Nanyang Technological University and Denmark Technical University, with 22 research papers published in 4 years.

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I also received a couple of faculty position offers from universities worldwide. But I was hesitant then.

My Ph.D. journey was tough, like a truly "permanent head damage". It was hard because:

- The knowledge gap in AI was huge for me.

My Ph.D. topic was to research How Artificial Intelligence can be applied in the electric power industry. I was trained to be an electrical engineer with no computer science or artificial intelligence background. Thus, learning from scratch about AI and adopting the methodology to real use cases in a brand new problem, was hard. I spent the entire 2 years finishing my first research work and publishing the 1st journey paper.

- The data collection procedure was painful.

As you know, AI models require data for training. To build a surrogate model for the circuit, if using the conventional approach, I needed to collect about 10k data points while 1 data point means 1 set of circuit experiments (taking about 30min). This was a very time-consuming process.

As a Ph.D. candidate, what I have was TIME. Thus I had 4 years on this.

Imagine this happens to a company, like a startup or an MSE which is looking for high growth. What if they want to develop their custom AI model to empower their business,

  • Do they have enough talents to bridge the knowledge gap?

  • Do they have enough resources for data collection and model training?**

As reported, to build the ChatGPT model, more than 3 billion US dollars have been spent on it.

Should AI development be always such a luxury?

I wanted to make a change. I believe entrepreneurship was the bridge to bring technology from the lab to real products and people's real lives.

With Ailiverse, we are holding a vision to make deep learning accessible to ALL. With our award-winning breakthrough in domain adaptation along with few shot learning, Ailiverse NeuCore can make computer vision development easier than ever:

  • Only 10% of the training data is required.
  • Training can speed up to 10X.
  • No expertise in AI is required to build a custom model.

With this, you can build your computer vision models in Minutes!
We are just launching our Beta product, looking forward to any kind of feedback!


PS: Ailiverse has recently been featured by Sequoia India and Southeastern Asia Spark Program, which is an important recognition of our vision!