Commit 11fea5d6 by PLN (Algolia)

Content: slides1; tp1 bases

parent 467051bb
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......@@ -5,7 +5,7 @@ color: #eee
colorSecondary: #333
backgroundColor: #111
paginate: true
footer: "ML101 | Paul-Louis NECH | INTECH 2022-2023"
footer: "ML101 | Paul-Louis Nech | INTECH 2022-2023"
---
# <!-- fit --> Hello World
......@@ -42,9 +42,10 @@ $$ I_{xx}=\int\int_Ry^2f(x,y)\cdot{}dydx $$
<!-- Les dangers, un sujet pour un cours entier -->
<!-- Biais, feedback loop foireuses, Risque existentiel?... -->
![bg right](https://uproxx.com/wp-content/uploads/2015/05/angry-bender.jpg?w=650)
<!-- Image Credit: Bender from Futurama, created by Matt Groening and David X. Cohen -->
###### - [the Fallacy of generalization from Fictional evidence](https://www.lesswrong.com/posts/rHBdcHGLJ7KvLJQPk/the-logical-fallacy-of-generalization-from-fictional)
<!-- The more details, the less likely! -->
---
## Encore que...
......@@ -98,6 +99,13 @@ $$ I_{xx}=\int\int_Ry^2f(x,y)\cdot{}dydx $$
![bg left 100%](./img/01-dungeon.jpg)
---
### Comment les modèles... peuvent halluciner
##### - [DeepDream](https://en.wikipedia.org/wiki/DeepDream)
![bg left 100%](../tp/img/intech_dream.png)
----
### Comment ils nous comprennent
......@@ -153,7 +161,13 @@ _color: black
---
## C'est quoi ce cours ?
<!-- Plan du cours / TDs / eval -->
<!-- Plan du cours: -->
<!-- TD: Quali aujourd'hui, plus pratique en janvier -->
<!-- Eval: Par les rendus, pas de piège :) -->
<!-- Format / interactions welcome -->
---
#### Des connaissances
......@@ -188,6 +202,8 @@ Conditionnement opérant (Pavlov et sa cloche)
---
<!-- _footer: "" -->
![bg](https://image.beeplaza.com.au/wp-content/uploads/2019/12/bee-dance.jpg)
<!-- Image Credit: BeePlaza Beekeeping Shop; beeplaza.com.au -->
<!--
Acquis vs Inné : danse des abeilles vs culture des chimpanzés
-->
......@@ -195,6 +211,7 @@ Acquis vs Inné : danse des abeilles vs culture des chimpanzés
---
<!-- _footer: "" -->
![bg](https://i0.wp.com/www.throwcase.com/wp-content/uploads/2014/12/monkeys-e1419187612146.jpg?fit=1000%2C692&ssl=1)
<!-- Image Credit: Throwcase's Net Madness section - https://www.throwcase.com/2014/12/21/that-five-monkeys-and-a-banana-story-is-rubbish/ -->
---
......@@ -241,6 +258,7 @@ _backgroundColor: white
## Learning
![bg right 90%](./img/01-reinforcement.png)
<!-- Image Credit: [Megajuice](https://en.wikipedia.org/wiki/Reinforcement_learning#/media/File:Reinforcement_learning_diagram.svg) -->
---
### Le par-coeur, un problème ?
......@@ -268,6 +286,7 @@ Unfortunately, many students in traditional schools become accustomed to guessin
![bg right 90%](./img/01-information.png)
<!-- Image(s) Credit: https://towardsdatascience.com/information-theory-of-neural-networks-ad4053f8e177 -->
<!--
Initially,weights are randomly initialised.
......@@ -364,6 +383,8 @@ REPLICATION CRISIS! Probleme commun dans toute la science, incentives, game theo
> _unsexy_ problems
> **before the sexy ones**
<!-- Publish or Perish, ça vous parle ? -->
<!-- Replication Crisis ? -->
---
......@@ -372,19 +393,20 @@ https://galactica.org/explore/
[Try it live ;)](https://huggingface.co/spaces/morenolq/galactica-base)
[LeCun hyped](https://twitter.com/ylecun/status/1592619400024428544)
![bg right 80%](./img/00-tweet-lecun-hyped.png)
---
[GALACTICA failed](https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science/
)
![bg right](img/00-galactica-failed.png)
<br />
> Like all language models, Galactica is a mindless bot that cannot tell fact from fiction.
> ~_MIT Tech Review_
[_Paper_](https://arxiv.org/abs/2211.09085)
> Like all language models, Galactica is a **mindless bot** that _cannot tell fact from fiction_.
[See Research _Paper_](https://arxiv.org/abs/2211.09085)
---
......@@ -394,20 +416,68 @@ https://huggingface.co/spaces?sort=modified&search=galactica
---
---
> Narrator voice: LMs have no access to "truth", or any kind of "information" beyond information about the distribution of word forms in their training data.
Emily M. Bender, Faculty Director, Computational Linguistics
[Emily M. Bender, Faculty Director, Computational Linguistics](https://twitter.com/emilymbender/status/1592993757498331136)
---
https://twitter.com/emilymbender/status/1592993757498331136
> In all cases, it was wrong or biased but sounded right and authoritative. I think it's dangerous.
[~ Michael Black](https://twitter.com/Michael_J_Black/status/1593133722316189696)
https://twitter.com/Michael_J_Black/status/1593133722316189696
---
<!-- Vous savez comment un scientifique réagit quand on lui dit que son projet est dangereux ? -->
[LeCun not happy](https://twitter.com/ylecun/status/1593293058174500865)
![bg right 100%](img/01-tweet-lecun-not-happy.png)
---
# ChatGPT
![bg right 100%](./img/00-chatgpt.png)
- [Blog post](https://openai.com/blog/chatgpt/)
---
ChatGPT:
[Linda Problem](https://twitter.com/dggoldst/status/1598317411698089984)
---
<!-- But maybe assumption? Language ambiguous! -->
[Assumptions?](https://twitter.com/dggoldst/status/1598737445780164635)
---
[Computing, Fast & Slow](https://twitter.com/stefanmherzog/status/1598397009161060359)
---
[Math is hard tho](https://twitter.com/batwood011/status/1598389323979800576/photo/1)
---
[Risk preferences](https://twitter.com/mdahardy/status/1598139462122622976/photo/1)
---
[Conjuction fallacy](https://twitter.com/mdahardy/status/1598139470641262592/photo/1)
---
- See more: https://www.alignmentforum.org/posts/fFF3G4W8FbXigS4gr/cognitive-biases-in-large-language-models
---
[Physical Reasoning: Windows](https://twitter.com/mdahardy/status/1598139488873897984/photo/1)
- But https://twitter.com/mdahardy/status/1598139493856743424
---
[Physical Reasoning: Big Macs](https://twitter.com/mdahardy/status/1598139498357231616/photo/1)
---
[Physical Reasoning: Napoleon](https://twitter.com/mdahardy/status/1598139507559518208)
---
TAY
......@@ -632,4 +702,8 @@ footer: ""
![bg right 120%](./img/01-lesswrong.png)
---
Conclusion:
---
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Arguments:
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paginate: true
footer: "ML101 | TP0: Introduction | Paul-Louis Nech | INTECH 2022-2023"
---
# <!-- fit --> TP1: Introduction
<!-- ### Définitions & Histoire du ML
### méthodes et outils ;
### critiques, biais et limites
### applications et perspectives d'avenir -->
---
Objectifs :
- Se familiariser avec les bases du ML
- Se faire une intuition de ses applications possibles
- Découvrir ses limites avec quelques gros fails
---
Format: Rendu écrit (fichier Markdown ou Doc)
Sur l'intranet ou à formation@nech.pl
**DEADLINE : 15 Décembre 23:59:59**
<br />
> _Le cachet de mon mailserver faisant foi_.
---
## Lvl 0: Jouer avec des LMs
<br />
<br />
> Consigne: Entrez dans le [AI Dungeon](https://play.aidungeon.io/), faites une partie, et notez:
- Une phrase de son output que vous trouvez excellente _(expliquez en quelques mots pourquoi celle là)_
- Une phrase de son output que vous trouvez risible _(expliquez pourquoi : qu'est-ce qu'il a si mal compris ?)_
---
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