therajiv

I've tried a few online jeopardy simulators before, but have always just ended up going back to hunching over a laptop together with my friends on j-archive.

This looks much more convincing than ones I've seen before, especially the fact that you can just play prior game boards without any fuss. I'll have to give it a try with my friends, but so far gameplay feels smooth and the jeopardy-like accents are a good touch.

Are applications like batteries, semiconductors, solar panels, etc. bottlenecked by the number of available materials? Also, I wonder if the discovered materials are kind of "interpolating" between materials that are already known, or if they expand the convex hull in some way. (Though perhaps it's difficult to precisely define what the convex hull of materials is.)

Seems like this could have dramatic effects on some professions where 60+ hour workweeks are typical even outside of Korea, e.g. being an early startup employee or adhering to the strenuous pro gamer practice schedule. While it's probably not healthy to work that much, in the short-term, throwing more time at a job depending on the profession can sometimes optimize your reward function.

NYT and Nature News.

I ran into this guy randomly at MIT once, during the offseason (last February). He was such a chill guy. Hearing this news makes me like him even more - he's not letting one of his passions get in the way of another.

TLDR is that models will become more abstract (current pattern recognition will blend with formal reasoning and abstraction), modular (think transfer learning, but taken to its extreme - every trained model's learned representations should be applicable to other tasks), and automated (ML experts will spend less time in the repetitive training/optimization cycle, instead focusing more on how models apply to their specific domain).

You provide great examples, thanks. I guess I was just hoping that the article would spell out those situations as clearly as you did.

Sorry if I came off that way, that's not at all what I meant. My comment was more along the lines of putting a face to statistics; reading about the numbers of people dying is a weaker motivator than hearing the stories behind the lives that are lost to cancer.

It's not clear to me how malicious actors can manipulate this observation to confuse self-driving cars. That said, I don't think this discredits the point of the article; it's important to note how easily deep learning models can be fooled if you understand the math behind them. I just think the example of tricking self-driving cars is difficult to relate with / understand.

I knew Maryam was incredible the first time I heard her give a talk at Stanford. To my fellow cancer researchers out there: this is about as good motivation as we'll ever have to keep plugging along.