slides.Rmd

---
title: "On cats, farts and parrots"
author: "[Laurent Gatto](#laurent-gatto)"
date: "30 May 2024"
output:
  xaringan::moon_reader:
    css: ["default", "default-fonts", "my.css"]
    nature:
      highlightStyle: github
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      countIncrementalSlides: false
---


class: middle
name: cc-by

### Get the slides at [https://bit.ly/2024ai_slides](https://bit.ly/2024ai_slides)

### Get the notes at [https://bit.ly/2024ai_blog](https://bit.ly/2024ai_blog)

This content is available under a **creative common [CC-BY
license](http://creativecommons.org/licenses/by/4.0/)**. You are free
to share (copy and redistribute the material in any medium or format)
and adapt (remix, transform, and build upon the material) for any
purpose, even commercially <img height="20px" alt="CC-BY"
src="./img/cc1.jpg" />.

???

I am not presenting anything new, or original - I will merely be
sharing what I consider being the main take home messages from
information I have been collecting since April 2023. I also realised
that these don’t seem to be widely known among my immediate peers.

---

class: middle, center, inverse

## Who am I?

???

Or rather, or "Why am I here"?

I feel it is important to say a few words to put my talk into
perspective:

- I am a Computational Biologist, heading the CBIO lab at the de Duve
  Institute, UCLouvain. We occasionally use/develop of DL in the CBIO
  lab as part of our research. I am not an expert in AI.
- I have never used ChatGPT or any similar tools, and I'll tell you
  why! I however actively follow discussions on AI and its impact on
  society.
- I will focus on ChatGTP and similar LMs that are released by large
  and very powerful commercial entities for wide public consumption. I
  am not focusing on application of DL, LM, or AI in general in
  research.


---
class: middle


**[On the Dangers of Stochastic Parrots: Can Language Models Be Too
Big?](https://dl.acm.org/doi/10.1145/3442188.3445922)**, Emily
M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret
Shmitchell. March 2021 *Proceedings 2021 ACM Conference on Fairness,
Accountability, and Transparency*.

???

If there's one reference to remember, it is this seminal paper

- Emily Bender is a professor of computational linguistics at
  Washington University. Angelina McMillan-Major is/was a PhD student
  in her lab.
- Timnit Gebru is one of the most well-known and respected Black
  female scientists working in AI. She was a co-lead of Google's
  ethical AI research team. In December 2020, her employment with
  Google ended after Google management asked her to either withdraw
  the paper before publication, or remove the names of all the Google
  employees from the paper.
- Margaret Mitchell was later also fired from Google.

**It is important to note that, in addition to highlight the risks,
the authors do propose paths forward for LM research and
development.**

---

class: middle, center, inverse

## When can I use ChatGPT?

---
class: middle

Following Yves Deville and Christine Jacqmot's recommendations
([ChatGPT : Menace ou opportunité pour l’enseignement
supérieur](https://oer.uclouvain.be/jspui/bitstream/20.500.12279/885.2/14/ChatGPT_UCL_2023_03_28_CC.pdf),
March 2023):


Given that

> LLMs have absolutely no notion of "true" or "false", nor any
> understanding of what it is asked.

Use it if

> 1. You don't can about the validity of the results.
> 2. You are an expert in the field.

???

Note: lots has been said about ChatGPT's "occasional" hallucinations
(beware of the **anthropomorphising** word here). They always
hallucinate. It just happens so that sometimes, what is made up, is
not wrong. I will come back to some of these points in the later
**stochastic parrot** section.

Assuming you still want to use it, what are the **costs**?

---

class: middle, center, inverse

## At what cost?

???

Many have tested ChatGPT. Some ([New AI tools much hyped but not much
used, study says)](https://www.bbc.com/news/articles/c511x4g7x7jo)
possibly make regular use of the free and/or the paid version. It
might be used for important or minor/mundane tasks. But at what costs?

---
class: middle

## Human cost
## Environmental cost
## Intellectual property

???

- Human cost is real and current. It is not a potential
  science-fiction picture of AI vs humanity. Such a picture diminishes
  the current human cost of AI, as is force-fed by big tech.

---
class: middle

## Human cost

- [TIME](https://time.com/6247678/openai-chatgpt-kenya-workers/): OpenAI Used Kenyan Workers on Less Than $2 Per Hour.
- [The
  Guardian](https://www.theguardian.com/technology/2023/aug/02/ai-chatbot-training-human-toll-content-moderator-meta-openai):
  ‘It’s destroyed me completely’: Kenyan moderators decry toll of
  training of AI models.
- [Business
  Insider](https://www.businessinsider.com/openai-kenyan-contract-workers-label-toxic-content-chatgpt-training-report-2023-1):
  Kenyan Workers Paid $2/hr Labeled Horrific Content for OpenAI.
- [Fortune](https://fortune.com/2024/05/03/google-search-raters-wages-benefits-contractors-tech-ai-employment/):
  I’m paid $14 an hour to rate AI-generated Google search
  results. Subcontractors like me do key work but don’t get fair wages
  or benefits.

**Already marginalised communities suffer the highest human cost.**

???

- Human cost is real and current. It is not a potential
  science-fiction picture of AI vs humanity. Such a picture diminishes
  the current human cost of AI, as is force-fed by big tech.


- Note that this isn't specific to ChatGPT. Similar **workers
  exploitation** has been documented for Meta/Facebook reviewers from
  the Global South.

---
class: middle

## Environmental cost

- [Nature Machine
  Intelligence](https://www.nature.com/articles/s42256-020-0219-9):
  The carbon impact of artificial intelligence.
- [technologyreview.com](https://www.technologyreview.com/2022/11/14/1063192/were-getting-a-better-idea-of-ais-true-carbon-footprint/):
  We’re getting a better idea of AI’s true carbon footprint.
- [Nature Climate
  Change](https://www.nature.com/articles/s41558-022-01377-7):
  Aligning artificial intelligence with climate change mitigation.
- [nature.com](https://www.nature.com/articles/d41586-024-00478-x):
  Generative AI’s environmental costs are soaring - and mostly secret.
- [nature.com](https://www.nature.com/articles/d41586-022-01983-7):
  How to shrink AI’s ballooning carbon footprint.
- [theconversation.com](https://theconversation.com/ai-has-a-large-and-growing-carbon-footprint-but-there-are-potential-solutions-on-the-horizon-223488):
  AI has a large and growing carbon footprint, but there are potential
  solution on the horizon.
- [The
  Guardian](https://www.theguardian.com/technology/2023/jun/08/artificial-intelligence-industry-boom-environment-toll):
  As the AI industry booms, what toll will it take on the environment?
  (citing - [Estimating the Carbon Footprint of BLOOM, a 176B
  Parameter Language Model](https://arxiv.org/abs/2211.02001))
- [tomshardware.com](https://www.tomshardware.com/tech-industry/artificial-intelligence/ai-may-eventually-consume-a-quarter-of-americas-power-by-2030-warns-arm-ceo):
  AI may eventually consume a quarter of America's power by 2030,
  warns Arm CEO.
- [bloomberg.com](https://www.bloomberg.com/news/articles/2024-05-15/microsoft-s-ai-investment-imperils-climate-goal-as-emissions-jump-30):
  Microsoft’s AI Investment Imperils Climate Goal As Emissions Jump
  30%.

---
class: middle


[The
Guardian](https://www.theguardian.com/technology/2023/jun/08/artificial-intelligence-industry-boom-environment-toll):
As the AI industry booms, what toll will it take on the environment?
(citing - [Estimating the Carbon Footprint of BLOOM, a 176B Parameter
Language Model](https://arxiv.org/abs/2211.02001))

   > [Luccioni et al.] tallied the amount of energy used to train [...]
   > Bloom, on a supercomputer; the energy used to manufacture the
   > supercomputer’s hardware and maintain its infrastructure; and the
   > electricity used to run the program once it launched. They found
   > that it generated about 50 metric tons of carbon dioxide emissions,
   > the equivalent of an individual taking about 60 flights between
   > London and New York.

   > By contrast, limited publicly available data suggests about 500
   > metric tonnes of CO2 were produced just in the training of ChatGPT’s
   > GPT3 model 3 – the equivalent of over a million miles driven by
   > average gasoline-powered cars, the researchers noted.

   > Even more unclear is the amount of water consumed in the creation
   > and use of various AI models. [...]. One non-peer-reviewed study,
   > led by researchers at UC Riverside, estimates that training GPT3
   > in Microsoft’s state-of-the-art US data centers could potentially
   > have consumed 700,000 liters of freshwater.

---
class: middle

[theconversation.com](https://theconversation.com/ai-has-a-large-and-growing-carbon-footprint-but-there-are-potential-solutions-on-the-horizon-223488):
AI has a large and growing carbon footprint, but there are potential
solution on the horizon.

  > Since the AI boom started in the early 2010s, the energy
  > requirements of AI systems known as large language models (LLMs) –
  > the type of technology that’s behind ChatGPT – have gone up by a
  > factor of 300,000. With the increasing ubiquity and complexity of
  > AI models, this trend is going to continue, potentially making AI
  > a significant contributor of CO₂ emissions. In fact, our current
  > estimates could be lower than AI’s actual carbon footprint due to
  > a lack of standard and accurate techniques for measuring
  > AI-related emissions.

???

An important point here is **increasing ubiquity and complexity**. (1)
do we need AI for every mundane application? and (2) there research
going on to limit the complexity of the models.

---
class: middle

[bloomberg.com](https://www.bloomberg.com/news/articles/2024-05-15/microsoft-s-ai-investment-imperils-climate-goal-as-emissions-jump-30):
Microsoft’s AI Investment Imperils Climate Goal As Emissions Jump 30%.

  > "The company’s goal to be carbon negative by 2030 is harder to
  > reach, but President Brad Smith says the good AI can do for the
  > world will outweigh its environmental impact."

**How ironic!!**

---
class: middle

## Environmental cost

- [Nature Machine
  Intelligence](https://www.nature.com/articles/s42256-020-0219-9):
  The carbon impact of artificial intelligence.
- [technologyreview.com](https://www.technologyreview.com/2022/11/14/1063192/were-getting-a-better-idea-of-ais-true-carbon-footprint/):
  We’re getting a better idea of AI’s true carbon footprint.
- [Nature Climate
  Change](https://www.nature.com/articles/s41558-022-01377-7):
  Aligning artificial intelligence with climate change mitigation.
- [nature.com](https://www.nature.com/articles/d41586-024-00478-x):
  Generative AI’s environmental costs are soaring - and mostly secret.
- [nature.com](https://www.nature.com/articles/d41586-022-01983-7):
  How to shrink AI’s ballooning carbon footprint.
- [theconversation.com](https://theconversation.com/ai-has-a-large-and-growing-carbon-footprint-but-there-are-potential-solutions-on-the-horizon-223488):
  AI has a large and growing carbon footprint, but there are potential
  solution on the horizon.
- [The
  Guardian](https://www.theguardian.com/technology/2023/jun/08/artificial-intelligence-industry-boom-environment-toll):
  As the AI industry booms, what toll will it take on the environment?
  (citing - [Estimating the Carbon Footprint of BLOOM, a 176B
  Parameter Language Model](https://arxiv.org/abs/2211.02001))
- [tomshardware.com](https://www.tomshardware.com/tech-industry/artificial-intelligence/ai-may-eventually-consume-a-quarter-of-americas-power-by-2030-warns-arm-ceo):
  AI may eventually consume a quarter of America's power by 2030,
  warns Arm CEO.
- [bloomberg.com](https://www.bloomberg.com/news/articles/2024-05-15/microsoft-s-ai-investment-imperils-climate-goal-as-emissions-jump-30):
  Microsoft’s AI Investment Imperils Climate Goal As Emissions Jump
  30%.

**Already marginalised communities (will) suffer the highest
environmental cost.**


???

Note that this is also relevant for other cloud services, such as
video on demande (detail for Netflix here).

---
class: middle

## Intellectual property

Where does all that training data come from?

- What about the credit and licensing of text, voice and images of
  those that produced that data used for training.


---
class: middle, center, inverse

## Stochastic parrot

???

I'll borrow here directly from the paper, to highlight specific issues
with the vast amounts of data needed to train these large models, and
the (absence of) meaning output by the models.

---
class: middle, center, inverse

##  Unfathomable training data

???

(insondable)

- Size doesn't guarantee diversity: from initial participation, to
  data filtering, the data reflect the hegemonic viewpoint.
- Data is static data, but social views change.
- Biais is encoding and amplified in the training data, in particular
  stereotypical associations and negative sentiment towards specific
  groups.
- Large data and the lack of curation, documentation and
  accountability (responsabilité) lead to a major documentation debt
  (dette), that can't be addressed after the fact.

---
class: middle, center, inverse

##  Unfathomable training data

### **Systematic biais against already marginalised communities.**

???

**Systematic biais against already marginalised communities.**

---
class: middle, center, inverse

## Coherence is in the eye of the beholder

???

La cohérence est dans l’œil du spectateur

---
class: middle

## Coherence is in the eye of the beholder

- There is no meaning, no model of the world, no intend to communicate
  in ChatGPT's output.
- Perceived "fluency" and "confidence" give the illusion of (implicit)
  meaning and expertise.
- We tend to mistake the coherence of LLM outputs for meaningful text
  or expertise.

???

- signification
- aisance

> Contrary to how it may seem when we observe its output, an LM is a
> system for haphazardly stitching together sequences of linguistic
> forms it has observed in its vast training data, according to
> probabilistic information about how they combine, but without any
> reference to meaning: a stochastic parrot.

It is important to note that, in addition to highlight the risks, the
authors do propose paths forward for LM research and development.

---
class: middle, center, inverse

## AI contamination

---
class: middle

AI-generated text is already ubiquitous on-line, and it becomes more
and more difficult to identify AI-generated text. How long until
AI-generated (meaningless) text (including as answers in Q&A sites),
will be (or are) re-used for training.

Outlets are terminating journalist contract to replace them by AI, and
independent writers are 'competing' against AI.

---
class: middle

We have all faced AI chat-bots in so-called help-desks. But [AI
chatbots are intruding into online communities where people are trying
to connect with other humans](https://theconversation.com/ai-chatbots-are-intruding-into-online-communities-where-people-are-trying-to-connect-with-other-humans-229473).

> Both of these responses were lies. That child does not exist and
> neither do the camera or air conditioner. The answers came from an
> artificial intelligence chatbot.

> According to a Meta help page, Meta AI will respond to a post in a
> group if someone explicitly tags it or if someone “asks a question
> in a post and no one responds within an hour.”

---
class: middle, center, inverse

## AI contamination - **enshittification**

???

> Enshittification is the pattern of decreasing quality observed in
> online services and products such as Amazon, Facebook, Google
> Search, Twitter, Bandcamp, Reddit, Uber, and Unity. The term was
> used by writer Cory Doctorow in November 2022, and the American
> Dialect Society selected it as its 2023 Word of the Year. Doctorow
> has also used the term platform decay to describe the same concept.

---
class: middle, center, inverse

## ChatGPT in research

---
class: middle

Reproducibility? [AlphaFold3 — why did Nature publish it without its
code?](https://www.nature.com/articles/d41586-024-01463-0)

> When AlphaFold2 was published, the full underlying code was made
> accessible to all researchers. But AlphaFold3 comes with
> ‘pseudocode’ — a detailed description of what the code can do and
> how it works.

> [...] for AlphaFold2, the DeepMind team worked with the European
> Molecular Biology Laboratory’s European Bioinformatics Institute
> [...] Now, DeepMind has partnered with Isomorphic Labs, a
> London-based drug-development company owned by Google’s parent,
> Alphabet. In addition to the non-availability of the full code,
> there are other restrictions on the use of the tool — for example,
> in drug development. There are also daily limits on the numbers of
> predictions that individual researchers can perform.

---
class: middle

- Paper writing (paper mills) and reviews (ChatGPT is
  [polluting](https://www.nature.com/articles/d41586-024-01106-4)/[influencing](https://arxiv.org/abs/2405.02150)
  peer review).


- [Science journals ban listing of ChatGPT as co-author on
    papers](https://www.theguardian.com/science/2023/jan/26/science-journals-ban-listing-of-chatgpt-as-co-author-on-papers)


---
class: middle, center, inverse

## Who benefits from ChatGTP/AI?

---
class: middle

## Who benefits from ChatGTP/AI?

AI, as a [hyped-up surveillance business
model](https://www.helmut-schmidt.de/aktuelles/detail/die-rede-der-zukunftspreistraegerin),
force-fed by big tech.


**Already marginalised communities likely to benefit the
least. Privileged communities to benefit the most.**

???

- In search engines (Google's "AI Overviews").  Not the users.
- Use your social media photos, posts, info, ... to train AI. Not the
  users.
- Facial recognition. Not the citizens.
- Microsoft Windows Recall. Not the employees.

Already marginalised communities likely to benefit least **on
average**
- there are examples set up to specifically benefit minorities.


---
class: middle, center, inverse

## What about regulations?

???

- In the light of what has been said so far, I think it is reasonable
  to wonder whether **regulations shouldn't be put in place**, to
  address current and future impact and scope of the technologies put
  in place, their concrete risks and harms, and their implications in
  terms of systematic (private) data collection and use.

- Every major big tech company is **investing vast amounts of money**
  in AI technologies, data centres, and data collection. And they are
  demanding returns on these investments.

- These same companies are actively **lobbying** to assure support in
  their vested interested. This becomes clear when reviewing their
  implications in various working groups and how AI is framed and
  communicated to the public and various stakeholders.

---
class: middle

- [OpenAI forms safety council as it trains latest artificial
  intelligence
  model](https://www.theguardian.com/technology/article/2024/may/28/openai-safety-council-chatgpt?CMP=Share_AndroidApp_Other):
  The safety committee is filled with company insiders, including Sam
  Altman, the OpenAI CEO, and its chairman, Bret Taylor, and four
  OpenAI technical and policy experts. It also includes the board
  members Adam D’Angelo, who is the CEO of Quora, and Nicole Seligman,
  a former Sony general counsel.

- [How Big Tech Manipulates Academia to Avoid
  Regulation](https://theintercept.com/2019/12/20/mit-ethical-ai-artificial-intelligence/):
  The discourse of “ethical AI” was aligned strategically with a
  Silicon Valley effort seeking to avoid legally enforceable
  restrictions of controversial technologies.

???

Here are two examples, one very recent from the Guardian, and one that
directly relates to the influence of Silicon Valley on academia:

---
class: middle, inverse

## AI isn't useless. But is it worth

> AI can be kind of useful, but I'm not sure that a "kind of useful"
> tool justifies the harm.

**Molly White**

???

Despite some notable failures with 'AI for public consumption' , one
can't ignore that there there are also success stories, and possibly
still untapped opportunities. But ...

---
class: middle
name: laurent-gatto

.left-col-50[
<img src="./img/lgatto3b.png" width = "180px"/>
### Laurent Gatto
<i class="fas fa-flask"></i>&nbsp;[Computational Biology Group](https://lgatto.github.io/cbio-lab/)<br />
<i class="fas fa-map-marker-alt"></i>&nbsp;de Duve Institute, UCLouvain<br />
<i class="fas fa-envelope"></i>&nbsp;laurent.gatto@uclouvain.be<br />
<i class="fas fa-home"></i>&nbsp;https://lgatto.github.io<br />
]

.rigth-col-50[
## Thank you for your attention
]

### Slides
### [https://bit.ly/2024ai_slides](https://bit.ly/2024ai_slides)

### Blog post
### [https://bit.ly/2024ai_blog](https://lgatto.github.io/ia-irss/)