lioned

2y

@emucakes @boobdylan U changed username in october

boobdylan

2y

i knew the song named cowgirl was on it featuring an industry plant

boobdylan

2y

now stop this. have a good weekend

lioned

2y

Hoes

emucakes

2y

I found a use for the Journaling app. Every day I look up a scientist and something they discovered/invented that has changed our lives and a beautiful thing they said. It's been really fun so far. Today is Stephen Jay Gould, a theoretical ecologist/evolutionary biologist.

He wrote an essay called "The Median isn't the Message" where he provides examples of how people being too fixated on statistics has become a blindspot, that the outliers and exceptions can tell us so much about a system as a whole because we don't live in a world ruled by the median, we live in a world full of variation and you can't just take the average of that. And I feel like that's my takeaway message from him. I thought that might be relevant to your work.

Hazyeyefreakmonstr

2y

This is a powerful notion, that outliers are very important to consider especially in biology. Finding interesting systems that work in unique ways is sort of at the heart of biology research and what makes it useful. Biological systems are so complicated lmao, its rare for someones initial intuition to be correct.

I need to dig more into the history behind science, most of my knowledge is skewed towards modern theory and methods but my foundation is not as strong as it should be

emucakes

2y

Don't worry mine isn't either. I know my biologists but I really should know my chemists and mathematicians because so many things in biology are described in their ways and now that AI is getting closer to imitating life through things like neurosymbolic AI, I feel like it's time to catch up and know who is who in the industry. If you have any suggestions on where to start on key figures and ideas on AI, I'm all ears. The only time I ever had exposure to any machine learning was through Bayesian inference of phylogenetic trees to model how new species might be related to others by comparing their DNA and I feel like that is probably one of the most basic first things you learned probably.

emucakes

2y

Don't worry mine isn't either. I know my biologists but I really should know my chemists and mathematicians because so many things in biology are described in their ways and now that AI is getting closer to imitating life through things like neurosymbolic AI, I feel like it's time to catch up and know who is who in the industry. If you have any suggestions on where to start on key figures and ideas on AI, I'm all ears. The only time I ever had exposure to any machine learning was through Bayesian inference of phylogenetic trees to model how new species might be related to others by comparing their DNA and I feel like that is probably one of the most basic first things you learned probably.

WRU

2y

emu hoe ass gettin bullied in random threads

hoopsplayer21

3y

how does one quiet the inner monologue.

Choking

3y

in the dark listening to some lectures on heidegger thinking where it all went wrong

Semi

2y

Damn I was going to @ u but it is u

emucakes

2y

Don't worry mine isn't either. I know my biologists but I really should know my chemists and mathematicians because so many things in biology are described in their ways and now that AI is getting closer to imitating life through things like neurosymbolic AI, I feel like it's time to catch up and know who is who in the industry. If you have any suggestions on where to start on key figures and ideas on AI, I'm all ears. The only time I ever had exposure to any machine learning was through Bayesian inference of phylogenetic trees to model how new species might be related to others by comparing their DNA and I feel like that is probably one of the most basic first things you learned probably.

Hazyeyefreakmonstr

2y

I'm actually pretty new to machine learning in relation to its full history, I started really thinking a lot about machine learning terms in 2020 and by then the majority of noteworthy advancements were already made. I know of groups like OpenAI or some other groups who have developed software I use but I don't have a comprehensive knowledge of its history, I guess that's part of the process I'm in where I'm learning about the fundamental models and approaches.

Bayesian methods (same is true for machine learning) are always a trip because people use the term bayesian for a lot of different things. What you're describing could have been using a hidden Markov model which is bayesian but it's also like it's own little universe and in general bayesian statistics is so massive its sort of vague as a term.

I have a horrible habit of forgetting the names of people whose publications are relevant to my work, I'll have to get back to you with some interesting examples of legendary AI developers.