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#historyofscience

3 posts3 participants0 posts today
Public
Harald Sack

Last week, our students learned how to conduct a proper evaluation for an NLP experiment. To this end, we introduced a small textcorpus with sentences about Joseph Fourier, who counts as one of the discoverers of the greenhouse effect, responsible for global warming.

github.com/ISE-FIZKarlsruhe/IS

#ise2025 #nlp #lecture #climatechange #globalwarming #historyofscience #climate @fiz_karlsruhe @fizise @tabea @enorouzi @sourisnumerique

Slide of the Information Service ENgineering lecture 03, Natural Language Processing 02, section 2.6: Evaluation, Precision, and Recall Headline: Experiment Let's consider the following text corpus (FOURIERCORPUS): 1 In 1807, Fourier's work on heat transfer laid the foundation for understanding the greenhouse effect. 2 Joseph Fourier's energy balance analysis showed atmosphere's heat-trapping role. 3 Fourrier's calculations, though rudimentary, suggested that the atmosphere acts as an insulator. 4 Fourier’s greenhouse effect explains how atmospheric gases influence global temperatures. 5 Jean-Baptiste Joseph Fourier's mathematical treatment of heat flow is essential to climate modeling. 6 Climate science acknowledges that Fourier helped to understand the atmospheric absorption of heat. 7 Climate change origins often cite Fourier's mathematical work on radiative heat. 8 J. Fourier published his "Analytical theory of heat" in 1822. 9 Fourier analysis is used in signal processing. 10 Fourier series are key in heat conduction math. 11 Fourier and related algebras occur naturally in the harmonic analysis of locally compact groups. 12 The Fourier number is the ratio of time to a characteristic time scale for heat diffusion. The corpus is available at https://github.com/ISE-FIZKarlsruhe/ISE-teaching/blob/b72690d38911b37748082256b61f96cf86171ace/materials/dataset/fouriercorpus.txt On the right side in the background is a portrait engraving of Joseph Fourier
Public
Harald Sack

Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.

T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
arxiv.org/abs/1301.3781

#NLP #AI #wordembeddings #word2vec #ise2025 #historyofscience @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi

Slide from the Information Service Engineering 2025 lecture, lecture 02, Natural Language Processing 01, NLP Timeline. The timeline is in the middle of the slide from top to bottom, indicating a marker at 2013. On the left, a diagram is shown, displaying vectors for "man" and "woman" in a 2D diagram. An arrow leades from the point of "man" to the point of "woman". Above it, there is also the point marked for "king" and the same difference vector is transferred from "man - > woman" to "king - ?" asking, what might be the appropriate completion. Right of the timeline, the following text is displayed: Word2Vec neural network based framework to learn distributed representations of words as dense vectors in continuous space (word embeddings) was developed by Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean at Google. These language models are based on the Distributional Hypothesis in linguistics i.e. words that are used and occur in the same contexts tend to purport similar meanings. Bibliographical reference: T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781
Public
Harald Sack

Building on the 90s, statistical n-gram language models, trained on vast text collections, became the backbone of NLP research. They fueled advancements in nearly all NLP techniques of the era, laying the groundwork for today's AI.

F. Jelinek (1997), Statistical Methods for Speech Recognition, MIT Press, Cambridge, MA

#NLP #LanguageModels #HistoryOfAI #TextProcessing #AI #historyofscience #ISE2025 @fizise @fiz_karlsruhe @tabea @enorouzi @sourisnumerique

Slide from Information Service Engineering 2025, LEcture 02, Natural Language PRocessing 01, A Brief History of NLP, NLP timeline. The timeline is located in the middle of the slide from top to bottom. The pointer on the timeline indicates 1990s. On the left, the formula for conditional probability of a word, following a given series of words, is given as a formula. Below, an AI generated portrait of William Shakespeare is displayed with 4 speech buubles, representing artificially generated text based on 1-grams, 2-grams, 3-grams and 4 grams. The 4-grams text example looks a lot like original Shakespeare text. On the right side the following text is displayed: N-grams for statistical language modeling were introduced and popularised by Frederick Jelinek and Stanley F. Chen from IBM Thomas J. Watson Research Center, who developed efficient algorithms and techniques for estimating n-gram probabilities from large text corpora for speech recognition and machine translation. Bibliographical reference: F. Jelinek (1997), Statistical Methods for Speech Recognition, MIT Press, Cambridge, MA.
Public
Harald Sack

Next stop on our NLP timeline (as part of the #ISE2025 lecture) was Terry Winograd's SHRDLU, an early natural language understanding system developed in 1968-70 that could manipulate blocks in a virtual world.

Winograd, T. Procedures as a Representation for Data in a Computer Program for Understanding Natural Language. MIT AI Technical Report 235.
dspace.mit.edu/bitstream/handl

#nlp #lecture #historyofscience @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi #AI

Slide from the Information Service Engineering 2025 lecture, Natural Language Processing 01, A Brief History of NLP, NLP Timeline. The picture depicts a timeline in the middle from top to bottom. There is a marker placed at 1970. Left of the timeline, a screenshot of the SHRDLU system is shown displaying a block world in simple line graphics. On the right side, the following text is displayed: SHRDLU was an early natural language understanding system developed by Terry Winograd in 1968-70 that could manipulate blocks in a virtual world. Users could issue commands like “Move the red block onto the green block,” and SHRDLU would execute the task accordingly. This demonstration highlighted the potential of NLP in understanding and responding to complex instructions. Bibliographical references: Winograd, Terry (1970-08-24). Procedures as a Representation for Data in a Computer Program for Understanding Natural Language. MIT AI Technical Report 235.
Public
Harald Sack

With the advent of ELIZA, Joseph Weizenbaum's first psychotherapist chatbot, NLP took another major step with pattern-based substitution algorithms based on simple regular expressions.

Weizenbaum, Joseph (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Com. of the ACM. 9: 36–45.

dl.acm.org/doi/pdf/10.1145/365

#nlp #lecture #chatbot #llm #ise2025 #historyofScience #AI @fizise @fiz_karlsruhe @tabea @enorouzi @sourisnumerique

Slide from the Information Service Enguneering 2025 lecture slidedeck, lecture 02, Natural language processing 01, Excursion: A Brief History of NLP, NLP timeline On the right side of the image, a historic text terminal screenshot of a starting ELIZA dialogue is depicted. The timeline in the middle of the picture (from top to bottom) indicates the year 1966. The text left of the timeline says: ELIZA was an early natural language processing computer program created from 1964 to 1966 at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum which simulated conversation giving users an illusion of understanding on the part of the program based on pattern matching and pre-scripted response templates. Bibliographical reference: Weizenbaum, Joseph (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM. 9: 36–45.
Public
Harald Sack

Next stop in our NLP timeline are the (mostly) futile tries of machine translation during the cold war era. The rule-based machine translation approach was used mostly in the creation of dictionaries and grammar programs. It’s major drawback was that absolutely everything had to be made explicit.

#nlp #historyofscience #ise2025 #lecture #machinetranslation #coldwar #AI #historyofAI @tabea @enorouzi @sourisnumerique @fiz_karlsruhe @fizise

Slide from Information Service Engineering lecture 02, Natural Language Processing 1. Title: NLP Timeline The indicated era on the timeline is 1954-1966. On the right side of the timeline, an AI generated picture of a military parade with mobile missiles in front of the Kremlin basilica is sketched, overlapped with the following machine translation example: English: "The spirit was willing, but the flesh was weak". This sentence was automatically translated to Russian. Then, it was translated back again into English with the following result: "The vodka was good, but the meat was rotten." The text left of the timeline says: 1954 - 1966 Futile cold-war motivated efforts in rule-based machine translation from Russian to English. The rule-based machine translation approach was used mostly in the creation of dictionaries and grammar programs. It’s major drawback was that everything had to be made explicit. Bibliographical references: John A.Kouwenhoven ‘The trouble with translation’ in Harper's Magazine, August 1962 and W. John Hutchins, Machine Translation: Past, Present, and Future, Longman Higher Education, 1985, p. 5.
Public
Harald Sack

Next step in our NLP timeline is Claude Elwood Shannon, who already laid the foundations for statistical language modeling by recognising the relevance of n-grams to model properties of language and predicting the likelihood of word sequences.

C.E. Shannon ""A Mathematical Theory of Communication" (1948) web.archive.org/web/1998071501

#ise2025 #nlp #lecture #languagemodel #informationtheory #historyofscience @enorouzi @tabea @sourisnumerique @fiz_karlsruhe @fizise

Slide from the Information Service ENgineering lecture 02, Natural Language Processing 01. Title: NLP Timeline. A black & white portrait picture of Claude Elwood Shannon (1916-2001) is shown on the left side of a timeline marked with "1948". Shannon is depicted in front of an old 1950s "electronic" computer. The text on the right side of the timeline says: Claude Shannon proposed the idea of using n-grams as a means to analyse the statistical properties of language in "A Mathematical Theory of Communication" (1948). While Shannon's primary focus was on communication and information transmission, he recognised the relevance of n-grams in modeling language and predicting the likelihood of word sequences. BIbliographical reference: Shannon, Claude Elwood (July 1948). A Mathematical Theory of Communication, Bell System Technical Journal. 27 (3): 379–423.
Public
The Inquisitive Biologist

This week's #NewBooks at the library: A second-hand copy of an original 1961 hardback of Arthur O. Lovejoy's The Great Chain of Being from Harvard University Press, a review copy of The Lives of #Bats from @princetonupress @princetonnature, and a second-hand copy of @matthewcobb's The Genetic Age: Out Perilous Quest to Edit Life from Profile Books.

#Books #Scicomm #Bookstodon #HistoryOfScience #ScienceHistory #HistSci #ScalaNaturae #Chiroptera #Mammals #Genetics @bookstodon

A top-down photo of three books lying down on a small, brown, wooden table. At the top, lying down a wooden bookstand, The Great Chain of Being, opened on its title page (the cover is plain dark-red cloth binding so not very interesting to show). At the bottom left, The Lives of Bats, showing a bat in flight against a black background. At the bottom right, The Genetic Age, showing a simplified, schematic diagram of a DNA double helix with green and yellow nucleotides and a red-blue sugar-phosphate backbone. One of the two strands of the backbone turns into the head of a snake with a blue tongue flicking out. The background shows a beech-coloured laminate floor.
Public
Harald Sack

We are starting #ISE2025 lecture 02 with a (very) brief history of #NLP pointing out only some selected highlights. Linguist Ferdinand de Saussure was laying the foundations of today's NLP by describing languages as “systems.” He argued that meaning is created inside language, in the relations and differences between its parts.

Course in general linguistics. ia600204.us.archive.org/0/item

#linguistics #historyofscience @fiz_karlsruhe @fizise @enorouzi @tabea @sourisnumerique @KIT_Karlsruhe #AIFB

Slide from the ISE2025 lecture. Headline: NLP Timeline. On the left side a sepia-toned old portrait picture of Swiss linguist Ferdinand de Saussure (1857-1913) is shown. In the middle is a timeline depicted as a ray from top to bottom with an indicator at "1916". The text says: Ferdinand de Saussure, Professor at the University of Geneva, developed an approach describing languages as “systems.” Saussure argued that meaning is created inside language, in the relations and differences between its parts. A shared language system makes communication possible. After his death in 1916, his colleagues Albert Sechehaye and Charles Bally published “Cours de Linguistique Générale” from Saussure’s manuscript notes and lecture notes from his students. Bibliographical references: Saussure, Ferdinand. Course in general linguistics. Eds. Charles Bally & Albert Sechehaye. Trans. Wade Baskin. NY: The Philosophical Society, 1959.
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Rita Singer

It's publication day!

Enwogion o fri: Diversity Project 2023-2025

Our free #DiversityProject anthology for the #Bywgraffiadur has just dropped on KC Works. Over 40 authors contributed more than 60 articles about the most fascinating people in #Welsh #history you could possibly imagine.

We've covered #BAMEHistory #LGBTQ_ and #DisabilityHistory, #WomensHistory, #ArtHistory, the #HistoryOfScience, #HistoryOfReligion and #Wales

Frankly, there's not a single article in this collection that's not bound to be of interest to someone.

Get your own copy here as PDF or epub. And because we're in Wales, we even offer you two versions.

English: works.hcommons.org/records/dtb
Cymraeg: doi.org/10.17613/mmwvm-ryh93

Book cover for Enwogion o fri, showing two African and South Asian students of Harlech College in the 1960s. They're reading a piece of paper -- and clearly enjoy it.
#BlackHistory#OA#OpenAccess
Public
Mark McCaughrean

Niche question for astronomers & historians of astronomy, particularly those at Harvard perhaps.

Can anyone tell me the birth & death dates of Edith F. Reilly, who worked with Bart Bok in the 1940's & co-authored the discovery paper on "small dark nebulae", now called "Bok globules"?

I've looked online, but have drawn a blank. They'd be good to have for my book.

adsabs.harvard.edu/pdf/1947ApJ

#SpaceScience#Astronomy#HistoryOfScience
Public *
Nangpa

"We know how to live in a world without tuberculosis. But we choose not to live in that world. "- John Green

Robert Koch discovered the bacteria responesible for tuberculosis and goes public with his results on this day, 24 march, in 1882.

britannica.com/biography/Rober

germanhistory-intersections.or

Encyclopedia BritannicaRobert Koch | German Bacteriologist & Nobel Laureate | BritannicaGerman physician and one of the founders of bacteriology
#tuberculosis#bacteriology#pathology
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