i'm quite embarrassed and wanted to sweep it under a rug, but let me share what happened behind this, largely for my own record/reminder and for a small hope this might raise awareness.
1st&foremost, it was totally my oversight to miss that the keynote speaker lineup was entirely composed of male speakers, including myself, which would've reinforced the lack of diversity and also potentially sent out a wrong sign to many participants and others, in our fields.
i've been calling out similar cases myself earlier pretty both publicly & privately (see e.g. slide 80 in drive.google.com/drive/u/0/fold…), but i've apparently fallen into the trap of seeing others' faults while failing to see my own. how embarrassing and eye-opening!
, i realized but then hesitated for a moment: what should i do? should i do anything? after all, i accepted the invitation w/o knowing the lineup. if i stay silent, wouldn't it just go away and only a few (if any) remembers?
you know.. producing an infinite series of possible excuses in my head. but, as they say, better late than never. i decided to see if i could somewhat alleviate this issue, while expecting the worst: offending organizers, alternative speakers &/or perhaps even participants.
thankfully, @hhexiy kindly understood the situation & my intention and agreed to replace me. i'm super excited to hear from her how to "Unlearn dataset bias for robust language understanding" super exciting! i highly recommend everyone to attend and listen to her talk.
the organizers @sedak99 were very open to this last-minute suggestion. i'm thankful they decided to take this extra step in the last minute. the workshop looks fantastic, and i'm looking forward to attending it next week: vectorinstitute.ai/event/natural-…
of course, i must thank @tarfandy for calling it out. this was a great opportunity for me to look at myself rather than at others.
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we all want to and need to be prepared to train our own large-scale language models from scratch.
why?
1. transparency or lack thereof 2. maintainability or lack thereof 3. compliance or lack thereof
and because we can, thanks to amazing open-source and open-platform ecosystem.
(1/12)
we have essentially lost any transparency into pretraining data.
(2/12)
we are being force-fed so-called values of silicon valley tech co's, ignoring the diversity in values across multiple geographies, multiple sectors and multiple groups.
this semester (spring 2024), i created and taught a new introductory course on causal inference in machine learning, aimed at msc and phd students in cs and ds. the whole material was created from scratch, including the lecture note and lab materials;
now that the course is finally over, i've put all the lab materials, prepared by amazing @taromakino, @Daniel_J_Im and @dmadaan_, into one @LightningAI studio, so that you can try them out yourselves without any hassle;
as i tweeted last week, Prescient Design Team at gRED within @genentech is hiring awesome people. in particular, we have the following positions already open and ready:
[Engineering Lead] we want you to work with us to build a team for creating an ML infrastructure that seamlessly integrate between ML and bio: gene.com/careers/detail…
[Machine Learning Scientist] we have a ton of challenging problems inspired & motivated by biology, chemistry & medicine that are waiting for your creativity, knowledge and ingenuity in ML/AI: gene.com/careers/detail…
denoising in a discrete input has always fascinated me ever since i read jmlr.org/papers/volume1… by Vincent & @hugo_larochelle et al., and yoshua has always motivated me to look into denoising for sequence modeling ever since 2013.
just watched the Social Dilemma netflix.com/title/81254224. to anyone who's been thinking about and following various stories about social media and other "attention-grabbing" services, this documentary won't have too much new stuffs, ...
although it works as a great reminder that these services that are effectively surveilling us 24/7 and profiting by selling who we are are embedded in every aspect of our lives. ...
it's an interesting watch after reading Steven Levy's Facebook: The Inside Story (amazon.com/dp/B07V8CL7RH/).
Two ironies:
1. i'm obviously writing about this documentary immediately after watching it on "Facebook" and "Twitter"