La Serena School for Data Science

Intro to DS 2024

Federica Bianco

University of Delaware

Rubin Observatory

Special thanks to A. Bayo

access this presentation at

https://slides.com/federicabianco/lsdss24_intro 

What is data science?

What is data science?

DS = ML

ML:

Machine Learning is the domain that develops, interprets, and applies mathematical model with parameters that are learned from data.

DS:

The discipline that deals with extraction of information from data

DS = ML

ML:

Machine Learning is the domain that develops, interprets, and applies mathematical model with parameters that are learned from data.

DS:

The discipline that deals with extraction of information from data

What are the necessary skills for a data scientist?

What are the necessary skills for a data scientist?

What are your strengths and assets?

What are your strengths and assets?

What is missing??

Life Cycle of a DS project

scientific question

Life Cycle of a DS project

scientific question

Data

collection

Proposal writing

Instrument Building

Calibration

Deployment

Collection

scientific question

Life Cycle of a DS project

scientific question

Data

collection

Proposal writing

Instrument Building

Calibration

Deployment

Collection

scientific question

...Search web for data...

Life Cycle of a DS project

scientific question

Data

collection

Data preparation and preprocessing for broadcast systems monitoring in PHM framework

• DOI: 10.1109/CoDIT.2019.8820370

Data

Engineering

Data Wrangling

Data cleaning

Feature extraction

Feature engineering

Data

exploration

statistical analysis and extraction of statistical properties

DS = ML

ML:

Machine Learning is the domain that develops, interprets, and applies mathematical model with parameters that are learned from data.

DS:

The discipline that deals with extraction of information from data, including all phases of data driven inference from data collection through modeling and communication, and its interpretation in a domain context

The fist data science project:

John Show map of cholera

Idea driven by domain knowledge (he was a doctor)

Data collection

Data exploration

The fist data science project:

John Show map of cholera

Idea driven by domain knowledge (he was a doctor)

Data collection

Data exploration

digitized data accessibel here https://blog.rtwilson.com/john-snows-cholera-data-in-more-formats/

Life Cycle of a DS project

scientific question

Data

collection

Data

Engineering

Data

exploration

Modeling

Apply ML models

Adapt ML model

Create ML model

Modeling

Apply ML models

Adapt ML model

Create ML model

Life Cycle of a DS project

scientific question

Data

collection

Data

Engineering

Data

exploration

Modeling

Modeling

Communication

of results

Visualize data

Visualize results

Tell the story 

Paper

Report

Presentation

Blog post...

The fist data science project:

John Show map of cholera

Idea driven by domain knowledge (he was a doctor)

Data collection

Data exploration

Communication

What are your strengths and assets?

Historical perspective

"Data that does not fit in memory"

astronomical data production

Both data volumes and data rates grow exponentially, with a doubling time ~ 1.5 years

It is also estimated that everyone has access to 50% of the existing data!

Area vs Volume

astronomical data volume

number of sources

Galileo Galilei 1610

Following: Djorgovski

https://events.asiaa.sinica.edu.tw/school/20170904/talk/djorgovski1.pdf

Experiment driven

what drives

astronomy

Enistein 1916

what drives

astronomy

Theory driven | Falsifiability

Experiment driven

Ulam 1947

Theory driven | Falsifiability

Experiment driven

Simulations | Probabilistic inference | Computation

http://www-star.st-and.ac.uk/~kw25/teaching/mcrt/MC_history_3.pdf

what drives

astronomy

what drives

astronomy

Theory driven | Falsifiability

Experiment driven

Simulations | Probabilistic inference | Computation

Ulam 1947

what drives

astronomy

the 2000s

Theory driven | Falsifiability

Experiment driven

Simulations | Probabilistic inference | Computation

Data | Survey astronomy | Computation | pattern discovery

what drives

astronomy

Theory driven | Falsifiability

Experiment driven

Simulations | Probabilistic inference | Computation

Data | Survey astronomy | Computation | pattern discovery

3.2 Gpix Rubin camera

the 2000s

what drives

astronomy

Theory driven | Falsifiability

Experiment driven

Simulations | Probabilistic inference | Computation

Data | Survey astronomy | Computation | pattern discovery

3.2 Gpix Rubin camera

lazy learning

learning by example

(supervised learning)

pattern discovery

(unsupervised learning)

how do the data get big?

telescope size

FoV

camera size

resolution

fainter, more distant

more sky area at once

more data units

more objects/details

filters

variety (complexity)

ground based

optical

"The Sloan Digital Sky Survey has created the most detailed three-dimensional maps of the Universe ever made, with deep multi-color images of one third of the sky, and spectra for more than three million astronomical objects. Learn and explore all phases and surveys—past, present, and future—of the SDSS."

2.5m

6 sq degree

4Mpix

1''/pix

5 bands

optical: data releases

The SDSS map of the Universe. Each dot is a galaxy; the color bar shows the local density.

2019 DR16

273 TB

photometric parameters for 53 million unique objects.

optical: data releases

2019 DR2 PanSTARRS

1.6 Pb

 The amount of imaging data is equivalent to two billion selfies, or 30,000 times the total text content of Wikipedia. The catalog data is 15 times the volume of the Library of Congress.

optical PS1

1.8m

7 sq degree

1.4Gpix

0.26''/pix

6 bands

optical: data releases

optical: DES

4m

3 sq degree

96 Mpix

0.2''/pix

570 MP camera with a 3 deg2 field of view installed at the prime focus of the Blanco 4 m

0.5Gpix

0.2''/pix

5 bands

optical: ZTF

1.2m

0.5Gpix

1.2m

47 sq degree

1''/pix

2

band

optical: Rubin LSST

1.2m

3.2Gpix

8m (6.5 effective)

9 sq degree

0.2''/pix

6 bands

optical

Nightly data rates

optical: Rubin LSST

OPINION:

what astro did right about BD

FITS files: universal data storage

Strong pressure on making data public

Strong tradition of collaboration

OPINION:

what astro did right about BD

Still lack of trust in cloud services

sparse collaboration between institutes generating solutions, a ton of platforms that work differently

slow integration of methods

What does astronomy have to do with it??

slide credit: A. Bayo

  We propose to meet the need for scientists with experience in using these tools and techniques by beginning to train advanced undergraduates and beginning graduate students today.  

Country of Affiliation

Country of Nationality

Astronomical Data Acquisition,
Introductory Probability and Statistics,
Data Processing Pipelines and their output,
Astronomical Databases,
Tools of the Virtual Observatory,
Tools of High Performance Computing,
Advanced Statistical tools applied to Astronomy.

What is missing??

The main skill that is missing in the portfolio of our new hires is data ethics

NGC 4565 is an edge-on spiral galaxy about 30 to 50 million light-years away. The faculty at the LSSDS used a AI model (emulator) to predict the hidden physical parameters of the Galaxy wrongfully estimating the DM content of NCG 4565.

NGC 4565 is an edge-on spiral galaxy about 30 to 50 million light-years away. The faculty at the LSSDS used a AI model (emulator) to predict the hidden physical parameters of the Galaxy wrongfully estimating the DM content of NCG 4565.

The galaxy had been banned from all astrophysics media appearances. The galaxy claims emotional damage and loss of revenue

https://www.chathamhouse.org/sites/default/files/2022-11/2022-11-11-regulating-facial-recognition-in-latin-america-caeiro.pdf

https://restofworld.org/2024/facial-recognition-government-protest-surveillance

We use astrophyiscs as a neutral and safe sandbox to learn how to develop and apply powerful tool. 

Deploying these tools in the real worlds can do harm.

Ethics of AI is essential training that all data scientists shoudl receive.

November 30, 2022

will be made available to developers through Google Cloud’s API from December 13, 2023

unexpected consequences of NLP models

https://transformer.huggingface.co/

GPT-3

Vinay Prabhu exposes racist bias in GPT-3

unexpected consequences of NLP models

unexpected consequences of NLP models

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell 

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell 

We have identified a wide variety of costs and risks associated with the rush for ever larger LMs, including:

environmental costs (borne typically by those not benefiting from the resulting technology);

financial costs, which in turn erect barriers to entry, limiting who can contribute to this research area and which languages can benefit from the most advanced techniques;

opportunity cost, as researchers pour effort away from directions requiring less resources; and the

risk of substantial harms, including stereotyping, denigration, increases in extremist ideology, and wrongful arrest, should humans encounter seemingly coherent LM output and take it for the words of some person or organization who has accountability for what is said.

https://dl.acm.org/doi/pdf/10.1145/3442188.3445922

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell 

When we perform risk/benefit analyses of language technology, we must keep in mind how the risks and benefits are distributed, because they do not accrue to the same people. On the one hand, it is well documented in the literature on environmental racism that the negative effects of climate change are reaching and impacting the world’s most marginalized communities first [1, 27].

Is it fair or just to ask, for example, that the residents of the Maldives (likely to be underwater by 2100 [6]) or the 800,000 people in Sudan affected by drastic floods pay the environmental price of training and deploying ever larger English LMs, when similar large-scale models aren’t being produced for Dhivehi or Sudanese Arabic?

https://dl.acm.org/doi/pdf/10.1145/3442188.3445922

While the average human is responsible for an estimated 5t CO2 per year, the authors trained a Transformer (big) model [136] with neural architecture search and estimated that the training procedure emitted 284t of CO2. [...]

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell 

4.1 Size Doesn’t Guarantee Diversity The Internet is a large and diverse virtual space, and accordingly, it is easy to imagine that very large datasets, such as Common Crawl (“petabytes of data collected over 8 years of web crawling”, a filtered version of which is included in the GPT-3 training data) must therefore be broadly representative of the ways in which different people view the world. However, on closer examination, we find that there are several factors which narrow Internet participation [...]

Starting with who is contributing to these Internet text collections, we see that Internet access itself is not evenly distributed, resulting in Internet data overrepresenting younger users and those from developed countries [100, 143]. However, it’s not just the Internet as a whole that is in question, but rather specific subsamples of it. For instance, GPT-2’s training data is sourced by scraping outbound links from Reddit, and Pew Internet Research’s 2016 survey reveals 67% of Reddit users in the United States are men, and 64% between ages 18 and 29. Similarly, recent surveys of Wikipedians find that only 8.8–15% are women or girls [9].

https://dl.acm.org/doi/pdf/10.1145/3442188.3445922

https://dl.acm.org/doi/pdf/10.1145/3442188.3445922

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell 

4.3 Encoding Bias It is well established by now that large LMs exhibit various kinds of bias, including stereotypical associations [11, 12, 69, 119, 156, 157], or negative sentiment towards specific groups [61]. Furthermore, we see the effects of intersectionality [34], where BERT, ELMo, GPT and GPT-2 encode more bias against identities marginalized along more than one dimension than would be expected based on just the combination of the bias along each of the axes [54, 132].

https://dl.acm.org/doi/pdf/10.1145/3442188.3445922

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell 

https://dl.acm.org/doi/pdf/10.1145/3442188.3445922

The ersatz fluency and coherence of LMs raises several risks, precisely because humans are prepared to interpret strings belonging to languages they speak as meaningful and corresponding to the communicative intent of some individual or group of individuals who have accountability for what is said.

A global initiative led by Cohere For AI involving over 3,000 independent researchers across 119 countries. Aya is a state-of-art model and dataset, pushing the boundaries of multilingual AI for 101 languages through open science.

RAISE ALL VOICES

There is a different way!

A global initiative led by Cohere For AI involving over 3,000 independent researchers across 119 countries. Aya is a state-of-art model and dataset, pushing the boundaries of multilingual AI for 101 languages through open science.

RAISE ALL VOICES

There is a different way!

Thank you!

Federica B. Bianco

University of Delaware

Physics and Astronomy 

Biden School of Public Policy and Administration

Data Science Institute

Rubin Observatory Construction

Deputy Project Scientist

Rubin Observatory Operations

Interim Head of Science

fbianco@udel.edu

thank you!

University of Delaware

Department of Physics and Astronomy

Biden School of Public Policy and Administration

Data  Science Institute

federica bianco

fbianco@udel.edu