Learning Machines

Attended a sharing session by GIC’s CEO and even though it’s a part of a Adaptive Skills 4.0 Speaker Series and sounds only tangentially related to research / academia, it helped to give some structure to the work I’m doing.

3 key takeaways (based on my own interpretation):

1. Being a learning machine

   It’s always good to have condensed and pre-processed information, like how a country’s leaders can get the best specialists in the area to explain to them about concept they might not know. Most of us aren’t world leaders (yet, haha!), but we can similarly maintain a good cache / database of past knowledge (that’s quickly and easily retrievable) + know people in adjacent fields who we can consult (and of course, you should help them out too). Most importantly, be in a constant process of learning (observe, think, express/apply) - in other words, don’t just be a database. Act on the information you have.

2. Problem stages: Business vs Engineering vs Science

   In academia, we’re mostly working on ‘Science’ problems and we’re in the business of creating new technology. When the ‘Science’ problems are solved / proven to be possible, then it’s a matter of engineering the right solution. Finally, if / when the engineering problems are addressed, it doesn’t mean that it will lead to a viable business. Perhaps the engineering problem is solved, but the product might not be at a viable cost that consumers are willing to pay a premium over it.

   E.g. Selling a bicycle: someone had to figure out the physics of how a bicycle can operate (e.g. friction between wheels and ground, circular motion, torque); then comes the engineering problem (e.g. aerodynamics) ; then it’s a matter of developing a business model that can ensure sustainability of the business (e.g. cost management, procurement, marketing)

   All companies in all industries are in some stage of this process. Some industries are not yet at the business stage, or even the engineering stage (e.g. fMRI). Blockchain is an area where lots of work is still going on in terms of engineering (the science problem is solved to an extent, but some people have tried to rush the business side of things too - that’s possibly why we see the crazy swings it has when more people realize that some of the security issues are not ironed out yet!

   Be in time for the future - not too early.

3. Investing in such a turbulent environment

   • Besides think about what to change, what do you not change? Some organisations have to stick to principles, even in crisis.
   • Diversification is still the only free lunch, but you need more than it to do better
   • Take risks that you understand + you’re compensated appropriately