In the startup world, calling an idea “incremental” is somewhat of an insult. Startup founders are constantly filled with cliches of “going big or going home”, “disrupting industries,” “building monopolies” and “creating unicorns. According to the script, incremental is boring. It’s unimaginative, It goes against the sprit of innovation. And it’s certainly not something you want to be accused of. If you’re a tech entrepreneur. Especially one raising venture capital.
This world of anti-incrimental, right or wrong, has been the world I’ve lived in for the last 10 years. But today, in a supervisor meeting talking about my thesis work, it was suggested that my current plans are potentially too complicated for a Masters degree. I should save that idea for my PHD, and focus on something incremental instead. I was taken a bit aback by this. I’ve become instinctually dismissive of anything but completely novel approaches in the past (Note – I’m not trying to pretend I’m Elon Musk here creating missions to mars … I’ve started my fair share of fart apps in the past decade. But I do so always with a bit of shame). I need to think about it a bit more, but I think an incremental approach – at least to a masters thesis – makes good sense.
Again – I need to do a bit more navel gazing – but the problem with a completely novel approach within scientific research (in my case, I am considering a new algorithm for discovering a cluster of beneficial general value functions within a reinforcement learning agent), is a matter of scope. Coming up with the algorithm, running a few experiments, demonstrating results is perhaps the easy part. Explaining and justifying each decision point of the algorithm, competing algorithms is the hard part. Not to mention that in a wide open research topic such as discovery (of features), the related work is immense. Each related paper and idea should be thoughtfully considered. For all these reasons, the scope explodes and perhaps exceeds that of a Masters thesis. I shouldn’t make the blanket statement that one can’t invent a new algorithm / architecture within the scope of a masters thesis. But I do believe it to be likely more appropriate for a PHD thesis.
Again, this idea that creating something new is too grand in scope, is foreign in startups. Sure, there is the lean startup manifesto which guides its followers to build things in small increments. But these increments all are intended to add up to something truly disruptive and novel. In the startup world, you could invent a new algorithm / service. It either works or it doesn’t (Based on engagement). But in the scientific world, whether something “works” or not, isn’t measure as by user engagement. More thought needs to be dedicated to addressing each decision point, and comparisons with other approaches. Note, that achieving either (user engagement vs. a comprehensive description of the thought process and scientific steps taken to achieve a result) can be difficult. In the case of the former, it’s more of a dice roll. Like catching lighting in a bottle. You can get lucky and create something delightful for users in a couple months and “be done.” The idea that you need to go beyond creating something, but define, and defend each decision point, is something I’m still getting acclimatized to.
The nice thing about doing something more incremental during a masters thesis is that much of the groundwork has been laid for you. For example, the Deepmind unreal paper has drawn a lot of attention from us at the University of Alberta (because of our interest in GVFs and our many ties with Deepmind). It’s a fascinating body of work. It challenges the idea of a predictive feature representation – instead using the auxiliary tasks to sculpt the feature representation directly (by finding generally useful features across tasks). But many scientific questions arise because of this work. How would auxiliary tasks compare with a predictive representation in an environment like the compass world or cycle world? What is the sensitivity to the parameters in the auxiliary tasks? What types of environments do auxiliary tasks work best in? These are just a few of the questions that could be thought of as incremental. They’re not creating anything new. But they are contributing many meaningful insights and knowledge towards the field. And could form the basis of a good masters thesis.
That said, thinking about this has only increased my desire to contribute something completely novel to the field. Perhaps the appropriate path to do so is in a PHD once some of the foundation has been laid within a Masters thesis. The work from a masters lends credibility to a PHD author creating something truly novel, not to mention is truly informative to the work done towards a PHD.