While scrolling through my Instagram reels feed, I came across a reel of Jensen Huang, NVIDIA’s CEO, talking about the need to fail fast, which motivated me to write a post. ‘Fail fast’ is a recent piece of advice I have been hearing since I embarked on my PhD; fail fast on the research directions that we plan to pursue so that we can understand the difficulties and limitations of the research problems and methods used which will in turn give us more time to finetune our problem and develop more nuanced approaches. Since childhood, most of us have been taught that failures eventually lead to success and that persevering towards success is critical. However, one thing that I could not come to terms with is the narrative of several failures ‘magically’ leading to success. If you were destined to be successful, why would you even fail? And also, for every failure-to-success story we hear, there are many other stories of failure that we don’t.
Continue readingAuthor Archives: Arun Raja
On National AI strategies
Recently, I have become quite interested in how countries have been shaping their national AI strategies or frameworks. Since the launch of ChatGPT, several concerns have been raised about AI safety and how such groundbreaking AI technologies could augment or adversely affect our daily lives. To address the public’s concerns and set standards and practices for AI development, some countries have recently released their national AI frameworks. As a budding academic researcher in this space who is keen to make AI more useful for medicine and healthcare, there are two key aspects from the few frameworks I have looked at (specifically the US, UK and Singapore) that are of interest to me, namely, the multi-stakeholder approach and focus on AI education which I will delve further into in this post.