Why? What went wrong? I'm a bit out of the loop, I only know them through PGs essays and guidance videos. They've been an excellent learning resource for me.
> The volume of a sphere grows faster than the surface area. But if traversing the interior is instant and frictionless, what does that imply?
It's nearly frictionless, not frictionless because someone has to use the output (or at least verify it works). Also, why do you think the "shape" of the knowledge is spherical? I don't assume to know the shape but whatever it is, it has to be a fractal-like, branching, repeating pattern.
Meaning a GPT but next token is a live sensor reading or a servo angle or accelerometer state. Then connect that GPT with an actual LLM as a controller and you (hopefully) have a physical machine with arms, legs and a mind.
I think it's a combination of multiple factors. I worked with GPU kernel codes before and the code that you write has a tendency of never being updated or modified. once it works it works perfectly and you do not change it. if you get new hardware you're going to fully rewrite it. so, typically readability is just not useful. also, you're never working with variables that make sense to humans. it's never something tangible. it's always tiles, offsets, indices. i do not think, at least when I was writing the code for GPUS to waste space visual space on better variable naming was worthwhile.
Not related to this article, but this post seems to attract so many cranks. Just look at other comments. The amount of weird (out-of-touch) comments on this post is fascinating.
What might be the motivation for upvote farming on HN? There is the personal satisfaction of a carefully written comment being acknowledged and validated, but otherwise?
You do get some minimal upgrades with enough karma, like the ability to downvote. But it's not like you get any priority or special treatment, and the numbers are not exposed unless you open individual profiles.
I suppose it could be useful to give legitimacy to bot accounts to be able to inflate upvotes of some posts, but from what I've seen vote-ring detection is really good on HN.
I love this. I think of mathematics as writing programs but for brains. Not all programs are useful and to use AI for writing less useful programs would generally save humans our limited time. Maybe someday AI will help make even more impactful discoveries?
From "I've spent the last six months working on a deep learning system to improve virtual screening for drug discovery"
To "I’m currently working on a new startup in the blockchain space with a couple co-founders."
I don't get why invest time in PhD if your work afterwards seems totally unrelated to your expertise. Is this how most of the PhD stories end, working at a completely-unrelated-to-your-expertise job for a good pay?
Your question applies to me. I can only speak for myself, but I wanted to be able to call myself a proper researcher at least once, and I got the PhD because I was curious about the subject. At the same time it disillusioned me about the field, more than I expected, so the decision to leave academia was an easy one. I also realized I simply wasn't cut out for that kind of work in a highly competitive field. The pay barely played a role in my decision.
His bio gives a more detailed description of the startup: "after his PhD, Bharath co-founded Computable a startup that built better tools for collaborative dataset management", which seems to not exist anymore.
But he came back to drug discovery:
"Bharath is currently the founder and CEO of Deep Forest Sciences, which is building an AI-powered suite for drug and materials design and discovery."
I can't speak for PhD, but BSc in computer science has changed my mind and altered my perception of the world in ways that I can't express but deeply feel, in the best possible way.
I have a hard time saying that in a way that does not make vocational schools worth less. I appreciate people having done theoretical things, spending time are university is well spent time.
There are mechanics who did not do any theoretical work at all that later in life really need some university. My point is that some people really should go back to university because their work gets better after.
Doing a PhD later in life is a cool thing to do too.
I went into a biosciences/AI PhD with CS/AI background because I wanted to dedicate a few years of my work life to science. So did quite a few other CS / AI grads around me and supervised by me. Few expected to bother with the academic career track and ridiculous conditions afterwards, they all expected to go straight into stable industry or gov AI jobs afterwards.
Bart was my intern at Google (working on drug discovery) and I asked him about this. I think he ended up leaving the blockchain company (possibly scammy or just not technically strong) and returning to drug discovery.
He was one of my strongest interns and I learned a lot from him. We selected him to join the team because he was highly recommended by Vijay Pande (creator of Folding@Home), whose work I've followed for several decades (Vijay left academia for VC and I think he's currently starting a new fund (https://www.wsj.com/articles/healthcare-investors-vijay-pand...) in tech/healthcare.
> I don't get why invest time in PhD if your work afterwards seems totally unrelated to your expertise
Depending on how the supervision chain is arranged, a PhD can a journey of discovery, of new science but first and foremost of yourself and your interests. It can be very self-directed and the only mandate is to discover something new. For this reason it is common for people to dip their toes in a few distinct but related subfields during those years until they find something that sticks (if at all), and the person that comes out of a PhD can be very different from the person who started it.
Typst is almost as easy as markdown, but my only gripe is with the web editor app: the inability to insert links quickly, i.e. with a copy-paste shortcut.
