Innovation is Essential and a Misguided Sideshow

Remarkable things can happen. Or not. Can we solve climate change, food shortage, limited healthcare, and other global stresses – all with TikTok videos? Innovation is unpredictable and astonishing – it can address the world’s most critical issues today, from hunger to efficient energy, to devastating diseases. It is also too often misguided, inefficient, and meaningless – creating nothing more than distractions and wastes of time cloaked in an image of technological wonder. Misguided and manipulative business plans sit alongside the groundbreaking disruptions that may address society’s most significant problems. We don’t have time. Even though there is no clear argument for resources going to a new video-sharing platform or immersive game, that is beside the point. Technology delivers something, nothing else can. It is the only way to find solutions to otherwise intractable and potentially devastating global crises. . The freedom to pursue solutions is the essential first step. Letting the best people do their best is still the best policy. It will also generate the best outcome.

First Principles – Disruption’s Source

“Assume no knowledge” (Socrates) No successful company can create or sustain its competitive strength without constantly examining its First Principles. It means defining a problem effectively, understanding the actions needed, and then implementing those plans. This requires a unique combination of perspective, talent, drive, and organizational flexibility. It is rare, but when discovered, it is

Distributed Machine Learning Can Bring Healthcare Breakthroughs

Over the last decade, the dramatic rise of deep learning has led to stunning transformations in dozens of industries. It has powered our pursuit of self-driving cars, fundamentally changed the way we interact with our devices, and reinvented our approach to cybersecurity.

In health care, however, despite many studies showing its promise for detecting and diagnosing diseases, progress in using deep learning to help real patients has been agonizingly slow. All this could change with distributed learning.