jason
@agikoala
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ai is life. @agihippo is a buddy of mine
San Francisco
Joined March 2023
Kinda depressing but i was in hospital for ~40 hrs for a minor surgery and i found it very relaxing. Despite physical pain and being confined to bed, i didn't have to work, and crucially---i didn't feel bad about not working. It was a great way to recharge. Back to work now
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My post-AI career interests: - Raw travel youtuber - Train in soccer and see if I can get to college level - Learn to sing - Doing science experiments on my own body (current one is how to improve nasal breathing flow, which I measure with an instrument)
Been chatting with a few friends in AI and you have no idea how often people admit to having the same intrusive thought of quitting to open a cafe / coffee shop when the ai wars are over 😂
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@agihippo And this
Upon further reflection, I have a few quips about your writing course: 1. Putting "however" at the beginning of the sentence is actually better. Although the flow may sound better with "however" at the end, putting it at the beginning lets the reader know earlier that you're making a contrast, and reducing cognitive load for the reader is more important than sounding better. 2. Animations on slides are perfectly fine. 3. Redundant information in figures is also fine. 4. I would have gotten summa cum laude at Dartmouth if I had just fixed the darn capitalization in my bibliography for the final report for your class! Overall, I enjoyed the course though, thanks for teaching it :)
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@agihippo i thought this was pretty cringe
In the past weeks I received many questions (from undergrads especially) about AI research, so I'm putting together a "Ask Me Anything" doc. Add any questions to the doc, I'll answer all of them: Yes, I'll actually answer them all, because writing answers to questions scales way better than talking to people individually, and you know I love scaling. (Also, I have a few long flights coming up.) Here are some of the questions that I've written up draft answers to (see the doc). Also open to feedback on my answers! 1. How did your journey in AI begin? 2. What research direction should I work on? 3. Where/how did you learn most of the stuff you needed to conduct effective research? Is it better to spend more time learning, or to jump straight into research if you have interesting ideas? 4. What would you say is the most important trait you need for research? 5. How much does self-learning (e.g. taking these more recent courses that you mention, etc.) play into the whole research process? Do you wish you went to a school that offered more in terms of academics and coursework? If so, do you wish there were more options or that the courses were more rigorous? 6. Is OpenAI offering internships? 7. I’m particularly interested in working with ML in an engineering way rather than a research way (working with pre-existing research rather than doing research on new things), so does OpenAI offer a full-time software engineering role that doesn’t require a graduate degree?
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Science is broken. I bought a tempur-pedic mattress, wear blue-light glasses, don't eat after 7pm, take melatonin and still can't get a good night's sleep. Meanwhile my girlfriend eats mcdonalds and drinks coffee right before bed and sleeps like a baby
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5. Never seen people in the world so passionate about something as soccer. The Argentine kids next to us, from Argentina, went to 3 group stage games and stay in the US. How do so many people afford tickets to games like this? Even as an American $700 even feels like a lot
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you know you made it in life when you schedule your meetings with people without looking at when they're free
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It is amusing to send a blog post draft to friends and get comments in multiple places like "this could trigger people". Does that mean I'm doing something right?
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when i'm too upset to do real work i just fix mypy errors and write unit tests for ` files
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Back in the day we developed new, creative neural architectures to do specific tasks. We’ve now moved up one level in the stack: we develop new prompting techniques, each with tradeoffs for different tasks, implementation overhead, amount of compute used, etc.
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