General talk on who makes the choice now: machine or human
Discussion based on the book «A Human’s Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control».
Link: https://knowledge.wharton.upenn.edu/article/algorithms-decision-making/
#podcast #general #meta #publicml
Discussion based on the book «A Human’s Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control».
Link: https://knowledge.wharton.upenn.edu/article/algorithms-decision-making/
#podcast #general #meta #publicml
Knowledge at Wharton
Who Made That Decision: You or an Algorithm?
Algorithms now make lots of decisions, but they have their own biases, writes Wharton’s Kartik Hosanagar in his new book. …Read More
On the concept of 'intellectual debt'
There is technical debt — when you know you should rewrite some stuff, or implement some features, but they don't seem critical at the moment. So article introduces a concept of 'intellectual debt', which resies with more broad and common use of #MachineLearning and #DeepLearning (specially, the latter). What happens when AI gives us seemingly correct answers that we wouldn't have thought of ourselves, without any theory to explain them?
Link: https://www.newyorker.com/tech/annals-of-technology/the-hidden-costs-of-automated-thinking
#Meta #common #lyrics
There is technical debt — when you know you should rewrite some stuff, or implement some features, but they don't seem critical at the moment. So article introduces a concept of 'intellectual debt', which resies with more broad and common use of #MachineLearning and #DeepLearning (specially, the latter). What happens when AI gives us seemingly correct answers that we wouldn't have thought of ourselves, without any theory to explain them?
Link: https://www.newyorker.com/tech/annals-of-technology/the-hidden-costs-of-automated-thinking
#Meta #common #lyrics
The New Yorker
The Hidden Costs of Automated Thinking
Overreliance on artificial intelligence may put us in intellectual debt.
Critics: AI competitions don’t produce useful models
Post, suggesting a viewpoint that AI competitions never seem to lead to products, how the one can overfit on a hold out test set, and why #Imagenet results since the mid-2010s are suspect.
Link: https://lukeoakdenrayner.wordpress.com/2019/09/19/ai-competitions-dont-produce-useful-models/
#critics #meta #AI #kaggle #imagenet #lenet
Post, suggesting a viewpoint that AI competitions never seem to lead to products, how the one can overfit on a hold out test set, and why #Imagenet results since the mid-2010s are suspect.
Link: https://lukeoakdenrayner.wordpress.com/2019/09/19/ai-competitions-dont-produce-useful-models/
#critics #meta #AI #kaggle #imagenet #lenet
Luke Oakden-Rayner
AI competitions don’t produce useful models
Ai competitions are fun, community building, talent scouting, brand promoting, and attention grabbing. But competitions are not intended to develop useful models.
Great collections of Data Science learning materials
The list includes free books and online courses on range of DS-related disciplines:
Machine learning (#ML)
Deep Learning (#DL)
Reinforcement learning (#RL)
#NLP
Tutorials on #Keras, #Tensorflow, #Torch, #PyTorch, #Theano
Notable researchers, papers and even #datasets. It is a great place to start reviewing your knowledge or learning something new.
Link: https://hackmd.io/@chanderA/aiguide
#wheretostart #entrylevel #novice #studycontent #studymaterials #books #MOOC #meta
The list includes free books and online courses on range of DS-related disciplines:
Machine learning (#ML)
Deep Learning (#DL)
Reinforcement learning (#RL)
#NLP
Tutorials on #Keras, #Tensorflow, #Torch, #PyTorch, #Theano
Notable researchers, papers and even #datasets. It is a great place to start reviewing your knowledge or learning something new.
Link: https://hackmd.io/@chanderA/aiguide
#wheretostart #entrylevel #novice #studycontent #studymaterials #books #MOOC #meta
Using AI to Understand What Causes Diseases
An overview on applying data science in healthcare
Poster: https://info.gnshealthcare.com/hubfs/Publications_2019/ESMO_GI_Final_Poster_Printed_PD_20.pdf
Link: https://hbr.org/2019/11/using-ai-to-understand-what-causes-diseases
#meta #biolearning #dl #medical #healthcare
An overview on applying data science in healthcare
Poster: https://info.gnshealthcare.com/hubfs/Publications_2019/ESMO_GI_Final_Poster_Printed_PD_20.pdf
Link: https://hbr.org/2019/11/using-ai-to-understand-what-causes-diseases
#meta #biolearning #dl #medical #healthcare
Great community event by OpenDataScience in Dubai 🏝🏙
The first Data Fest in Dubai.
Check the agenda and don't miss the event!
- Top talks from renowned experts in their fields
- Lots of new insights, skills and know-how
- Best networking with the professional community
Location: Hult International Business School
Link: https://fest.ai/dubai/
#event #dubai #ml #meta #dl
The first Data Fest in Dubai.
Check the agenda and don't miss the event!
- Top talks from renowned experts in their fields
- Lots of new insights, skills and know-how
- Best networking with the professional community
Location: Hult International Business School
Link: https://fest.ai/dubai/
#event #dubai #ml #meta #dl
fest.ai
December 7, Data Fest Dubai
Community Data Science Conference in Dubai, Hult IBS
What we learned from NeurIPS 2019 data
x4 growth since 2014
21.6% acceptance rate
Takeaways:
1. No free-loader problem: Relatively few papers are submitted where none of the authors invited to participate in the review process accepted the invitation
2. Unclear how to rapidly filter papers prior to full review: Allowing for early desk rejects by ACs is unlikely to have a significant impact on reviewer load without producing inappropriate decisions. Likewise, the eagerness of reviewers to review a particular paper is not a strong signal, either.
3. No clear evidence that review quality as measured by length is lower for NeurIPS: NeurIPS is surprisingly not much different from other conferences of smaller sizes when it comes to review length.
4. Impact of engagement in rebuttal/discussion period: Overall engagement seemed to be higher than in 2018.
#Nips #NeurIPS #NIPS2019 #conference #meta
x4 growth since 2014
21.6% acceptance rate
Takeaways:
1. No free-loader problem: Relatively few papers are submitted where none of the authors invited to participate in the review process accepted the invitation
2. Unclear how to rapidly filter papers prior to full review: Allowing for early desk rejects by ACs is unlikely to have a significant impact on reviewer load without producing inappropriate decisions. Likewise, the eagerness of reviewers to review a particular paper is not a strong signal, either.
3. No clear evidence that review quality as measured by length is lower for NeurIPS: NeurIPS is surprisingly not much different from other conferences of smaller sizes when it comes to review length.
4. Impact of engagement in rebuttal/discussion period: Overall engagement seemed to be higher than in 2018.
#Nips #NeurIPS #NIPS2019 #conference #meta
Data Science by ODS.ai 🦜
Three challenges of Deep Learning according to Yann LeCun
Yann LeCun's talk slides and video
Slides: https://drive.google.com/file/d/1r-mDL4IX_hzZLDBKp8_e8VZqD7fOzBkF/view
Video of the talks: https://vimeo.com/390347111
- 1:10 in for Geoff Hinton's keynote,
- 1:44 for Yann LeCunn's,
- 2:18 for Yoshua Bengio's,
- 2:51 for the panel discussion moderated by Leslie Pack Kaelbling
#talk #meta #master
Slides: https://drive.google.com/file/d/1r-mDL4IX_hzZLDBKp8_e8VZqD7fOzBkF/view
Video of the talks: https://vimeo.com/390347111
- 1:10 in for Geoff Hinton's keynote,
- 1:44 for Yann LeCunn's,
- 2:18 for Yoshua Bengio's,
- 2:51 for the panel discussion moderated by Leslie Pack Kaelbling
#talk #meta #master
Popular example of application AI to fashion
Ai can be used for chair design. Some generative models can definately be used in the fashion industry.
Link: https://qz.com/1770508/an-emerging-japanese-startup-is-mining-tradition-to-create-a-more-sustainable-fashion-future/
#aiapplication #generativedesign #meta
Ai can be used for chair design. Some generative models can definately be used in the fashion industry.
Link: https://qz.com/1770508/an-emerging-japanese-startup-is-mining-tradition-to-create-a-more-sustainable-fashion-future/
#aiapplication #generativedesign #meta
Data Science interview questions list
List, compiled from medium article and peer-provided contributions.
Github (questions and answers): https://github.com/alexeygrigorev/data-science-interviews/blob/master/theory.md
#interview #questions #meta
List, compiled from medium article and peer-provided contributions.
Github (questions and answers): https://github.com/alexeygrigorev/data-science-interviews/blob/master/theory.md
#interview #questions #meta
GitHub
data-science-interviews/theory.md at master · alexeygrigorev/data-science-interviews
Data science interview questions and answers. Contribute to alexeygrigorev/data-science-interviews development by creating an account on GitHub.
TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction
TLDR: Prediction of the brain response based on the audio and visual content input.
ArXiV: https://www.arxiv.org/pdf/2507.22229
#Brain #Meta #MRI
TLDR: Prediction of the brain response based on the audio and visual content input.
ArXiV: https://www.arxiv.org/pdf/2507.22229
#Brain #Meta #MRI
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