Summary
It was an absolute treat to learn more about the cutting-edge application of machine learning to mathematics. Hearing about the work that Petar and his colleagues are doing in solving complex mathematical and scientific problems is very impressive.
I believe that the use of graph representation learning is still in its infancy in industry, and it was great to hear details about its range of applications from one of the leading researchers in the field – including practical tips and insights on implementing it. Notably, Petar believes that AGI could be in reach within our lifetimes.
Petar offered some useful advice in helping bridge the gap between research/academia and industry. The most important point is to clearly understand the interests and incentives of each party and to negotiate a compromise when incentives are misalinged. Finding the right balance can help unlock potential in creating research with a huge impact. For instance, the incentives for researchers...