Geoffrey Hinton Leads Google Brain Representation Similarity Index Research Aiming to Understand Neural Networks

https://medium.com/syncedreview/geoffrey-hinton-leads-google-brain-representation-similarity-index-research-aiming-to-understand-b5d14bf77f49

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For anyone who is interested in non-parametric multivariate data analysis, PhD thesis is publicly available on arXiv now.
https://arxiv.org/abs/1905.10716v1

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#Statistics such as correlation, mean and standard deviation (variance) create strong visual images and meaning. Two different #datasets with the same correlation would sort of look the same. Right?

Not so much.

Each of these very different-looking graphs are plotting datasets with the same correlation, mean and SD. This is why plotting data is so important though oddly so rarely (in my expereince) done.

https://bit.ly/2oZ29MP

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Misconception 4: Explainable #machinelearning is just models of models.

I do like surrogate models. They have several important uses. However, I also REALLY like tools that explain models directly, including:

- ALE: https://lnkd.in/e3mz23V
- ICE: https://lnkd.in/eaQxk_Q
- Friedman's H-stat: https://lnkd.in/emwNcdy
- Partial dependence: https://lnkd.in/ejnkFYN, Section 10.13.2
- Shapley explanations: https://lnkd.in/ewsMxbU

(What did I miss? Any others?)

Moreover, surrogate models & direct explanatory techniques work very well together! See pic below.

Misconception 3: https://lnkd.in/eM3hVyW

Read more/contribute: https://lnkd.in/e8_hciE

#ai #datascience #deeplearning #aiforall #artificialintelligence #datascience #ml #python

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5 Computer Vision Textbooks

Textbooks are those books written by experts, often academics, and are designed to be used as a reference for students and practitioners.

They focus mainly on general methods and theory (math), not on the practical concerns of problems and the application of methods (code).

The top five textbooks on computer vision are as follows (in no particular order):

🔸 Computer Vision: Algorithms and Applications, 2010.
🔸 Computer Vision: Models, Learning, and Inference, 2012.
🔸 Computer Vision: A Modern Approach, 2002.
🔸 Introductory Techniques for 3-D Computer Vision, 1998.
🔸 Multiple View Geometry in Computer Vision, 2004.

Top 3 Computer Vision Programmer Books

Programmer #book s are playbooks (e.g. O’Reilly books) written by experts, often developers and engineers, and are designed to be used as a reference by practitioners.


🔸 Learning OpenCV 3, 2017.
🔸 Programming Computer Vision with Python, 2012.
🔸 Practical Computer Vision with SimpleCV, 2012.

#ComputerVision

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AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence https://arxiv.org/abs/1905.10985 Historically, hand-designed pipelines are ultimately outperformed by entirely learned ones. Will that will be true of creating general AI itself?

Three Pillars are essential for the approach: (1) meta-learning architectures, (2) meta-learning the learning algorithms themselves, and (3) generating effective learning environments.

I argue that either the manual approach or the AI-generating algorithm path could produce general AI first, and both are scientifically worthwhile irrespective of which is the fastest path.

Because both are promising, yet the ML community is currently committed to the manual approach, I argue that our community should increase its research investment in the AI-GA approach. To encourage such research, I describe promising work in each of the Three Pillars.
Because it it may be the fastest path to #AGI and it is inherently interesting to understand the conditions in which a simple algorithm can produce general AI (as happened on Earth), I argue that AI-GAs should be considered a new grand challenge of computer science research.
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Aude Oliva (MIT): "there are about 200 papers using ConvNets to model the activity of the primate visual cortex." She is running a challenge to explain fMRI and MEG data: http://algonauts.csail.mit.edu/challenge.html http://algonauts.csail.mit.edu/challenge.html


Alyosha Efros: "A few year ago I gave a talk whose title was 'the revolution will not be supervised'. Yann has been advertising my slogan. Whenever Yann... https://www.redbubble.com/people/perceptron/works/24771996-the-revolution-will-not-be-supervised-white-font-3d?p=t-shirt
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“An Explicitly Relational Neural Network Architecture” - new work from the DeepMind cognition team takes a step towards reconciling #deeplearning and symbolic #AI
https://arxiv.org/abs/1905.10307
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MIT and U.S. Air Force launch AI accelerator program which will focus on rapid deployment of artificial intelligence innovations in operations, disaster response, and medical readiness. #DataScience #AI #ArtificialIntelligence

MIT and U.S. Air Force launch AI

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Need a PhD writing template? Download our free one now. It's the simplest way to structure your thinking and see your entire PhD on one page. All part of our goal to make PhD life easier, one thesis at a time.

template


✴️ @AI_Python_EN

Data science is an ever-evolving field. As data scientists, we need to have our finger on the pulse of the latest algorithms and frameworks coming up in the community.

So, if you’re a:
Data science enthusiast
Machine learning practitioner
Data science manager
Deep learning expert

or any mix of the above, this article is for you.

Pranav Dar loved putting together this month’s edition given the sheer scope of topics we have covered. Where computer vision techniques have hit a ceiling (relatively speaking), NLP continues to break through barricades. Sparse Transformer by OpenAI seems like a great NLP project to try out next.

What did you think of this month’s collection? Any data science libraries or discussions I missed out on? Hit me up in the comments section below and let’s discuss!

more to read : https://bit.ly/2Jb2JoB

#machinelearning #datascience #deeplearning

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Implementing a user friendly interface without the need to dig into the code for fastai

https://github.com/asvcode/Vision_UI

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Even when involved in the design of the research, statisticians normally must spend time checking, cleaning, and setting up data, as well as on exploratory data analysis.

Often this can amount to a considerable amount of time. For projects that are repeated, much of this can be automated and should be, when feasible.

The first time, though, trying to cut corners at this stage can be a huge mistake.

As a general rule, we should never delete, recode, or transform a variable unless we know what it is and what it means. When we have missing data, we should try to find out why it is missing. Blind imputation is very risky.

It goes without saying that we shouldn't analyze it either until we know what it is and what it means.

A variable might look like a garbage field, but might actually be telling us that we have the wrong data or that something is seriously amiss with it. If we mechanically delete it, we will not know this.

All this janitorial work pays off in other ways too, by helping us get to know the data, for example.

✴️ @AI_Python_EN

"Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor"
Do word embeddings really say that man is to doctor as woman is to nurse? Apparently not!
Nissim et al.: https://arxiv.org/abs/1905.09866
#ArtificialIntelligence #MachineLearning #NLProc #bias

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Understanding Hinton’s Capsule Networks. Part I: Intuition.
Blog by Max Pechyonkin:
https://medium.com/ai³-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
#MachineLearning #DeepLearning #ArtificialIntelligence
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AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
https://arxiv.org/abs/1905.10985
#ArtificialIntelligence #ArtificialGeneralIntelligence

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A curated list of gradient boosting research papers with implementations.

https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers

✴️ @AI_Python_EN

All You Need About Common #MachineLearning Algorithms

Here is the list of commonly used machine learning algorithms. The code is provided in both #R and #Python. These algorithms can be applied to almost any data problem:

✅Linear Regression
✅Logistic Regression
✅Decision Tree
✅SVM
✅Naive Bayes
✅kNN
✅K-Means
✅Random Forest
✅Dimensionality Reduction Algorithms
✅Gradient Boosting algorithms
✔️GBM
✔️XGBoost
✔️LightGBM
✔️CatBoost


#ai #datascienece

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TensorFlow Graphics Library for Unsupervised Deep Learning of Computer Vision Model

github: https://github.com/tensorflow/graphics

#machinelearning #deeplearning #computervision

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SimpleSelfAttention

The purpose of this repository is two-fold:
-demonstrate improvements brought by the use of a self-attention layer in an image -classification model.
introduce a new layer which I call SimpleSelfAttention


https://github.com/sdoria/SimpleSelfAttention

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website, which will provide you all up-to-date and necessary information on #ArtificialIntelligence, #MachineLearning, Deep Learning and some brain activities. You will also find TED Talks, Lectures and academic writings on these issues.

https://www.newworldai.com/

✴️ @AI_Python_EN