The ML vs AI breakdown
Chief AI Officer
Head of AI
VP of AI
Director of ML
ML manager
Staff MLE
Senior MLE
Junior MLE
ML intern
| Job | Director of Machine Learning @ Wikimedia |
Chris Albon
@chrisalbon
- 2 Followers
- 38 Following
- 15 Posts
- Director of Machine Learning at Wikimedia
- Makes MachineLearningFlashcards.com
- Writes ML tutorials at ChrisAlbon.com
- Co-founded Yonder.ai
- Advisor at MLOps startup BaseTen.co
- PhD in political science
Is there a good list of #ml people on here?
Fired from the bird site? Or another company? Wikimedia is hiring! Come work on the last best place on the internet.
I’m tempted to remake this account on @thegradient’s Sigmoid Social. Is the local timeline particularly focused on ML?
Terence EdenI don't think she's on Mastodon, but Vicki Boykis has written an excellent essay about the rituals we have when we leave a job:
https://vicki.substack.com/p/the-art-of-the-long-goodbye
I think the thing that I find most interesting is how these transitions happen when we're WFH. Sometimes you're doing a completely different job from yesterday - but in the same room & on the same laptop - but in a slightly different Slack instance.
Robert RitzDiscovery is the biggest problem in Mastodon. Does anyone have a list of data people on Mastodon?
@drewfustin @vicki you screenshot it and post to Twitter
Vicki BoykisSometime I want to start a site that’s just academic papers that seem like they were written either to settle a bet or just pure shitposting.
Here’s the latest, “When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and its Intensity”, which is bascially just the authors watching Friends reruns. https://arxiv.org/abs/2211.01889v1
#machinelearning
When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and its Intensity
Prerecorded laughter accompanying dialog in comedy TV shows encourages the audience to laugh by clearly marking humorous moments in the show. We present an approach for automatically detecting humor in the Friends TV show using multimodal data. Our model is capable of recognizing whether an utterance is humorous or not and assess the intensity of it. We use the prerecorded laughter in the show as annotation as it marks humor and the length of the audience's laughter tells us how funny a given joke is. We evaluate the model on episodes the model has not been exposed to during the training phase. Our results show that the model is capable of correctly detecting whether an utterance is humorous 78% of the time and how long the audience's laughter reaction should last with a mean absolute error of 600 milliseconds.
