AI researchers allege that machine learning is alchemy | Science | AAAS
https://www.sciencemag.org/news/2018/05/ai-researchers-allege-machine-learning-alchemy

#ML

Silla CN, Freitas AA. A survey of hierarchical classification across different application domains. Data Min Knowl Discov. 2011;22: 31–72. doi:10.1007/s10618-010-0175-9

A survey paper on hierarchical classification problems. It is a bit old as it didn’t consider the classifier chains, but this paper summarizes most of the ideas in hierarchical classification.

The authors also proposed a framework for the categorization of such problems using two different dimensions (ranks).

#ML

Voss, et al., "Branch Specialization", Distill, 2021. https://distill.pub/2020/circuits/branch-specialization/

TLDR;

- Branch: neuron clusters that are roughly segregated locally, e.g., AlexNet branches by design.
- Branch specialization: branches specialize in specific tasks, e.g., the two AlexNet branches specialize in different detectors (color detector or black-white filter).
- Is it a coincidence? No. Branch specialization repeatedly occurs in different trainings and different models.
- Do we find the same branch specializations in different models and tasks? Yes.
- Why? The authors' proposal is that a positive feedback loop will be established between layers, and this loop enhances what the branch will do.
- Our brains have specialized regions too. Are there any connections?

#DS
Wing JM. Ten research challenge areas in data science. Harvard Data Science Review. 2020;114: 1574–1596. doi:10.1162/99608f92.c6577b1f
https://hdsr.mitpress.mit.edu/pub/d9j96ne4/release/2

#statistics

This is the original paper of Fraser information.

Fisher information measures the second moment of the model sensitivity; Shannon information measures compressed information or variation of the information; Kullback (aka KL divergence) distinguishes two distributions.
Instead of defining a measure of information for different conditions, Fraser tweaked the Shannon information slightly and made it more generic. The Fraser information can be reduced to Fisher information, Shannon information, and Kullback information under certain conditions.

It is such a simple yet powerful idea.

Fraser DAS. On Information in Statistics. aoms. 1965;36: 890–896. doi:10.1214/aoms/1177700061
https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-36/issue-3/On-Information-in-Statistics/10.1214/aoms/1177700061.full

#DS #ML

The “AI Expert Roadmap”. This can be used as a checklist of prelims for data people.

https://i.am.ai/roadmap/#note

#DS

(This is an automated post by IFTTT.)

It is always good for a data scientist to understand more about data engineering. With some basic data engineering knowledge in mind, we can navigate through the blueprint of a fully productionized data project at any time. In this blog post, I listed some of the key concepts and tools that I learned in the past.

This is my blog post on Datumorphism https://datumorphism.leima.is/wiki/data-engeering-for-data-scientist/checklist/

#DS #EDA #Visualization

If you are keen on data visualization, the new Observable Plot is something exciting for you.
Observable Plot is based on d3 but it is easier to use in Observable Notebook. It also follows the guidelines of the layered grammar of graphics (e.g., marks, scales, transforms, facets.).


https://observablehq.com/@observablehq/plot

#career #DS

I believe this article is relevant.
Most data scientists have very good academic records. These experiences of excellence compete with another required quality in the industry: The ability to survive in a less ideal yet competitive environment.
We could be stubborn and find the environment that we fit well in or adapt based on the business playbook. Either way is good for us as long as we find the path that we love.

(I have a joke about this article: To reasoning productively, we do not need references for our claims at all.)

https://hbr.org/1991/05/teaching-smart-people-how-to-learn#

#DS

“Don’t pull down the data. Do it with SQL.”

https://hakibenita.com/sql-for-data-analysis

#ML

An interesting talk:

-------------------

Dear all,

We are pleased to have Anna Golubeva speak on "Are wider nets better given the same number of parameters?" on Wednesday May 19th at 12:00 ET.


You can find further details here and listen to the talk here.

We hope you can join!

Best,

Sven

#DS

https://octo.github.com/projects/flat-data

Hmmm, so they gave it a name.
I've built so many projects using this approach. I started building such data repos using CI/CD services way before github actions was born. Of course github actions made it much easier.
One of them is the EU covid data tracking project ( https://github.com/covid19-eu-zh/covid19-eu-data ). It's been running for more than a year with very little maintenance. Some covid projects even copied our EU covid data tracking setup.

I actually built a system (https://dataherb.github.io) to pull such github actions based data scraping repos together.

#DS

This paper serves as a good introduction to the declarative data analytics tools.

Declarative analytics performs data analysis using a declarative syntax instead of functions for specific algorithms. Using declarative syntax, one can “describe what you want the program to achieve rather than how to achieve it”.
To be declarative, the declarative language has to be specific on the tasks. With this, we can only turn the knobs of some predefined model. To me, this is a deal-breaker.

Anyways, this paper is still a good read.

Makrynioti N, Vassalos V. Declarative Data Analytics: A Survey. IEEE Trans Knowl Data Eng. 2021;33: 2392–2411. doi:10.1109/TKDE.2019.2958084
http://dx.doi.org/10.1109/TKDE.2019.2958084

#fun

Germany, birthplace of the automobile, just gave the green light to robotaxis



https://fortune-com.cdn.ampproject.org/c/s/fortune.com/2021/05/28/germany-automobile-legalize-robotaxi-autonomous-vehicle/amp/

#ML

The Bayesian hierarchical model provides a process to use Bayesian inference hierarchically to update the posteriors.
What is a Bayesian model? In a Bayesian linear regression problem, we can take the posterior from the previous data points and use it as our new prior for inferring based on new data. In other words, as more data coming in, our belief is being updated.
However, this is a problem if some clusters in the dataset have small sample sizes, aka small support. As we take these samples and fit them onto the model, we may get a huge credible interval.
One simple idea to mitigate this problem is to introduce some constraints on how the priors can change. For example, we can introduce a hyperprior that is parametrized by new parameters. Then the model becomes hierarchical since we will also have to model the new parameters.

The referenced post, "Bayesian Hierarchical Modeling at Scale", provides some examples of coding such models using numpyro with performance in mind.

https://florianwilhelm.info/2020/10/bayesian_hierarchical_modelling_at_scale/

#DS

A library for interactive visualization directly from pandas.

https://github.com/santosjorge/cufflinks

#ML

Geometric Deep Learning is an attempt to unify deep learning using geometry. Instead of building deep neural networks ignoring the symmetries in the data and leaving it to be discovered by the network, we apply the symmetries in the problem to the network. For example, instead of flattening the matrix of a cat image and have some predetermined order of the pixels, we apply a translational transformation on the 2D image and the cat should also be a cat without any doubt. This transformation can be enforced in the network.

BTW, If you come from a physics background, it is most likely that you have heard about the symmetries in physical theories like Noether's theorem. In the history of physics, there was an era of many theories yet most of them are connected or even unified under the umbrella of geometry. Geometric deep learning is another "benevolent propaganda" based on a similar idea.


References:

1. Bronstein, Michael. “ICLR 2021 Keynote - ‘Geometric Deep Learning: The Erlangen Programme of ML’ - M Bronstein.” Video. YouTube, June 8, 2021. https://www.youtube.com/watch?v=w6Pw4MOzMuo.
2. Bronstein MM, Bruna J, LeCun Y, Szlam A, Vandergheynst P. Geometric deep learning: going beyond Euclidean data. arXiv [cs.CV]. 2016. Available: http://arxiv.org/abs/1611.08097
3. Bronstein MM, Bruna J, Cohen T, Veličković P. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. arXiv [cs.LG]. 2021. Available: http://arxiv.org/abs/2104.13478

#ML

A Turing lecture article by the three famous DL guys.
It's an overview of the history, development, and future of AI. There are two very interesting points in the outlook section:
- "From homogeneous layers to groups of neurons that represent entities." In biological brains, there are memory engrams and motifs that almost do this.
- "Multiple time scales of adaption." This is another key idea that has been discussed numerous times. One of the craziest things about our brain is the diversity of time scales of plasticity, i.e., different mechanisms change the brain on different time scales.

Reference:
Bengio Y, Lecun Y, Hinton G. Deep learning for AI. Commun ACM. 2021;64: 58–65. doi:10.1145/3448250
https://dl.acm.org/doi/10.1145/3448250

#fun

GitHub Copilot · Your AI pair programmer
https://copilot.github.com/

This is crazy.

What is GitHub Copilot? GitHub Copilot is an AI pair programmer that helps you write code faster and with less work. GitHub Copilot draws context from comments and code, and suggests individual lines and whole functions instantly. GitHub Copilot is powered by OpenAI Codex, a new AI system created by OpenAI. The GitHub Copilot technical preview is available as a Visual Studio Code extension.

How good is GitHub Copilot? We recently benchmarked against a set of Python functions that have good test coverage in open source repos. We blanked out the function bodies and asked GitHub Copilot to fill them in. The model got this right 43% of the time on the first try, and 57% of the time when allowed 10 attempts. And it’s getting smarter all the time.