1.) We should not see our surrounding as served up on a plate. Things can change in the blinking an an eye. Nature can punish us out of the blue or a man made economic or global emergency crisis can leave us all stranded.
This means we should ALWAYS have a Plan B and enough resources for a “black day”
2.) A paramount importance of health and healthcare. All our innovations, be it intelligent phones or self-driving cars, will be of little use if we are all dead or hopelessly sick. While we have progressed in many important areas, healthcare remains in the dark ages. It is in this domain that “man has no superiority over beast, since both amount to nothing”, as in the days of Ecclesiastes.
It doesn’t help that the really smart guys and girls study maths, physics and computer science to become traders and software developers, whereas chemistry and biology (and, by implication, medicine) lag behind. In order for this to change, medicine needs to become digital. Genuinely digital. Much more so than finance.
Thankfully, there are people who are advancing this agenda. Bharath Ramsundar, Peter Eastman, Patrick Walters and Vijay Pande have recently published Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery & More. Bharath also leads the development of DeepChem, which has become the NumPy or pandas of biomolecular deep learning, whereas MoleculeNet has established a new benchmark for testing machine learning methods on molecular properties.
3.) We should see opportunities in things we have gotten used to resenting. Too many people are currently mining bitcoin. The current health crisis highlights the need to utilise more and more CPU and GPU FLOPS for drug discovery – indeed, drug mining (using the aforementioned DeepChem). The pharmaceuticals, rather than viewing this as a threat, should embrace this opportunity of employing armies of data scientists for advancing medicine worldwide. With any luck, drug mining should extend to vaccines, too. One can learn about these innovations at the Thalesians’ Intensive Summer School in ML/AI.
4.) ML/AI are helpless without the generally available Open Data. The need for this becomes evident at the times of a global pandemic. John Hopkins lead the way in this field. They have made their COVID-19 data openly available. Cuemacro’s Saeed Amen has written an article on this dataset.
5.) We should strive for a new standard of hygiene – in catering, hospitality, and in the most unhygienic area of all – money. The advent of Blockchain and cryptocurrencies and, more broadly, cashless payments, should make our transactions more hygienic. We should also rethink how we use our phones. Perhaps in the future, aimed with new sensors, they will become a tool for achieving a higher standard of hygiene, instead of being a health hazard.
6.) We should rethink hospitality and conferencing. Thankfully, many public events (including our Summer School and the lectures of the Machine Learning Institute) are available remotely. Streaming should reduce the need for public gatherings in the time of a health crisis, such as the present one. Technologies such as AnyMeeting, Adobe Connect, Google Hangouts, GoToMeeting, Panopto, Skype, TeamViewer, Cisco WebEx, Zoom and others help take much of the decision making and education online. This includes the lectures that I’m delivering to the MSc Mathematics and Finance students at Imperial College London.
7.) Robotisation should become more widespread. Already today, many jobs have been affected, and many more are yet to be affected, by robotisation. In the future, much of the work will be doable from home, through neocybernetic monitoring and control. This includes many of the jobs that currently require being present in person. Neocybernetic applications will help reduce the economic impact of such global pandemics to a minimum.
