Title is misleading

Is this approach used on all three levels of training? Base instruct ane chat fine-tuning? And are there different things to be considered for the above?

Very nice short informative video. I'm looking to create a distilled model on reasoning tasks for games which could run locally. This will help 😊 thanks

free?

Well done!

Programmatic labeling of images at scale is so cool. 🎉

Wow! Preprocessing is 🔥🔥

What does Wayfair do... Intro would be helpful to understand and connect with the content.

I came here to understand precisely how the Snorkel software assists with the issue. However, you discussed a general RAG system and mentioned that Snorkel expedited your process in different ways without specifying what Snorkel AI actually does. In your description, you stated, 'We explore how Snorkel Flow accelerated development of[...]'

Thanks for the information, but listening to you talking like: 'aaaaaaa eeeeee anddddddddd' was really frustrating

Thank you Snorkel for putting this channel together! All of your videos + guests have been compact and informative -- really good brand marketing, I think.

When you talk about distilation requiring large, unlabeled datsets... to be clear for my understanding, it's not necessarily that they're unlabeled data, it's more like we don't care about the dataset's labels, and instead use the teacher model's output distribution as the replacement pseudolabel. I guess you COULD create a distilled model by training against some data distribution that the teacher wasn't itself trained on... but I can't imagine why you would want to do that😄

Very informative, thanks

Awesome video! Data-centric AI is really awesome, and is a tractable space for the open source community to work in.

Complicated Nonstationarity is really horrific for a wide range of methods/models

Link to the blog code or ppt would be good

where can we get PPT?

I simp so hard for Emad

Hi, thanks for the nice sharing! Could you please provide the sildes you use in the video so I can further study?

Tip 4: Toss out noisy examples. More data is not always better! Should be rephrased; Toss out non-decisive/opaque examples while keeping variability of examples.

Can she train on the video data of my entire life

Why does she pattern match so hard with Fred sala?

Does California Institute of Technology pay women lesser than men then - per Anima's comment ?

Hey guys. I want to save this video to my youtube playlist. Can you please open save / add to playlist option?

Tried HoloClean on bigger datasets, tensors just explodes out of memory. Any pointers which can help?

Why use AI when you have simpler models that can be easy to explain???

It is sad that there is almost no point in using an Apple Pencil any more. Or rather, if you were a digital pencil user... Your job went poof. People simply don't care about digital paintings any more.

This is amazing. KZfaq need to send more traffic to this!

Thank you for this great summary! .. Could you please share the slides?

Thank you for sharing this event.

Very useful!

This is brave, important work. I’m grateful to have happened across this article. Just today, I reviewed news of dismissal of the accusations that members of the Fairfax, VA (US) Police Department protected a sex trafficking circle. The charges were dismissed due to evidence that the accuser (a Jane Doe) had identified as a consenting escort worker in her history of involvement with the individual at the center of the alleged trafficking ring. One thing that occurred to me in thinking about the efficacy of content skimming of sites hosting ads that recruitment hooks may be nested within, is that a lot of trafficking is off-web and involves youth that are trafficked (sold/bought) through in-person processes involving economic incentive pressure, false-choice coercion and direct threats levied against vulnerable people by powerful buyers in trafficking networks, and activities such as kidnapping and in-person drugging of victims, luring and enticement that leads to hostage involvement in the most dangerous networks in human/sex trafficking. Are there identified characteristics of heightened likelihood of trafficking in certain geographic areas - e.g. your mention of high rates of homeless youth being trafficked? Other possible metrics of likelihood might be having a known sex-tourism market, access to ports over-water transport (private boats, larger cargo/industry watercraft), and various socioeconomic measures (poverty increases, changes in industries due to disaster events or climate/seasonal tourism increase or decline/loss of protective factors such as NGOs serving vulnerable women and children, local and regional law enforcement presence being protective of victims or protective of perpetrators, stringency of port records and identification of international visitors, conflict or war-related events…etc. etc. When I reflected on the strong recruitment-trafficking line between Australia and wealthy western countries, I wondered about whether that activity may be showing secondary-broker activity relating to the conspicuously absent (non-English speaking, not online) SE Asian and Pacific Island recruitment/victim markets? I’m sure that the investigative authorities are looking into this, especially after the 200+ global child pornography/brutality arrests of last year…and, yet, because some of the buyer-markets in the trafficking economy are exceptionally covert in their activities, are there ways to identify potential likely areas of risk-of-victimization by some array of metrics that could help local/national/intl. authorities put measures in place to discourage trafficking and/or apprehend perpetrators, like port and water-access security cameras w 3rd party monitoring (because local law enforcement is known to be protective of some criminal networks in some places), access to responsive helplines and reporting lines in local languages, in-person safety measures and environmental protective measures in addition to security cameras at points of entry and exits connecting trafficking markets to the rest of the world (overland and by water) - things like lighting or businesses, police kiosks, clearing of possible low-visibility routes of on-foot transit for people who may be kidnapping or buying young people for trafficking, automobile traffic check-points? Similar metrics may be of use to discover new or transitory victim markets in the US - such as the Gulf Coast (which has a large population of SE Asian immigrants and workers in the shrimping and other port-based trade+travel+hospitality industries, as well as a lot of vulnerable people that may become increasingly vulnerable following destabilizing weather events, slow-season economies, increased costs of living, etc. South American coastal areas (and inland areas with road/river connections to buyer markets and export routes on both Caribbean and Pacific coasts) that have factors that increase risk of predator activity can be easily accessed by boat and transport to/from the US mainland and secondary transport+sale brokers may not be as heavily investigated as perhaps it ought to be…? I deeply appreciate the work you’ve done on data tracking to help show/predict active trafficking activity. I’m grateful that there are committed intl. investigators working to discover and end sex trafficking and child trafficking. Please be careful out there, because the people involved in some of these networks are very powerful and very dangerous, as you are likely aware. If you ever need someone to contract-work on reviewing and labeling data sets, I have training and experience in content analysis, theme-labeling, rubric-based scaling of similar content to hone specificity and gauge possible secondary/tertiary needs for additional or sub-theme labels. I love that sort of bot-mind work and I am 💯 % on board with the effort to use remotely-available information to shine a spotlight on the nodes and mechanics of online & offline trafficking network activities. Much appreciation - and be safe out there. Godspeed and much protection from all the good in the world.* *this is another way of saying ‘…all those nasty mf’er and all their evil ways are gonna be brought down like a sledgehammer-heavy bolt of lightning hitting the ground, and so they better not f* with anyone ‘cause they got eyes on ‘em from all over the sky.’ Cheers…and thanks for the good thinking and the chance to share thoughts here. 😁

Paper link?

Nice summary video

7.53 The PAC rule doesn't really apply here. What if we are able to label only a few type of easy cases. This means we are not uniformly labelling samples from the original data distribution.

At 28:00, it was not clear to me why accuracies are independent? Is that an empirical evidence? Is that a weak assumption?

Great presentation - thank you!

Julien is very awesome

Excellent presentation. Very lucid. Give this lady a raise.

Holy god!! that's an awesome work thanks for sharing

Excellent talk

Thanks for sharing been waiting for this. Great work by authors!!

Thanks for compiling the list on GITHUB, Sebastian. As an NLP enthusiast I want to know the current state of the art in NLP (English) and how could I use those techniques in Arabic.

Tip 1: Make the labels y consistent Tip 2: Use multiple labelers to spot inconsistencies Tip 3: Clarify labeling instructions by tracking down ambiguous examples Tip 4: Toss out noisy examples. More data is not always better! Tip 5: Use error analysis to focus on subset of data to improve

Great Content!

Good best wishes

Great session! Thanks for sharing all that insight into building a valuable product.

Thank you so much for sharing this event.Great work.👍

Great work with this, really enjoyed it!