News Feature: What are the limits of deep learning?

Nice article summarizing recent progress in deep learning. It can be renamed into «Recent progress in deep learning leaves DL critics searching for new things to criticize».

Link: https://www.pnas.org/content/116/4/1074

#DL #Meta

State-of-the-art (SOTA) collection of Paperswithcode

A great site, worth spreading word about: Papers With Code now includes 950+ ML tasks, 500+ evaluation tables (including SOTA results) and 8500+ papers with code. Probably the largest collection of NLP tasks, including 140+ tasks and 100 datasets.

Link: https://paperswithcode.com
Sota link: https://paperswithcode.com/sota

#Meta #collection #sota #useful

Text-based game to feel yourself in not so distant future where real AI exists.

We all now, how DS and #AI products affect our life and how they actually influence our lifestyle. But in the future our relationship will become more tense. It is rather nice experience to play this game, not only because it reminds of old-school games, but also because it looks shookingly possible.

Link: https://www.theverge.com/2019/1/31/18140796/wake-word-algorithm-text-game-ai-artificial-intelligence

#interactive #Meta

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

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

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

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

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

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

​​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

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

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

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