A-Infos (en) Italy, FdCA, IL CANTIERE #37 - What is Artificial Intelligence? RV (*) (ca, de, it, pt, tr) [machine translation]

We'll explain, simplifying some aspects, what Artificial Intelligence 
(AI) is. AI encompasses a wide range of tools and techniques that 
attempt to enable machines to reproduce human behavior or reasoning. The 
best-known is so-called generative AI (such as chatGPT), but AI also 
includes autonomous cars, facial recognition, search engines, machine 
translation, medical diagnostics, and more. There are specialized AIs, 
very good at one specific task but incapable of performing others, such 
as AlphaGO Zero, which beat the world's best Go player in 2018... but is 
unable to play checkers or chess. Then there are versatile AIs, capable 
of performing multiple tasks (such as ChatGPT).

Neural networks

How does AI work? There are various computer science techniques for 
developing AI programs. The most widely used today are neural networks. 
Although we use the term "artificial neuron," this has little to do with 
how the brain works (even though the original idea was to try to imitate 
the human brain). In computer science, neurons are (simply put) 
mini-programs arranged in layers and connected to each other. It's not a 
predefined program with rules, but a program that optimizes its 
parameters to obtain the right answers.

There is an input layer of neurons (which receives the information 
provided by the human), an output layer (which provides the response), 
and, in the middle, hidden neurons. The neurons are connected to each 
other by connections (designed to mimic synapses) parameterized, using 
mathematical formulas, by weights. Different connections with their 
weights enter a neuron, and different connections exit to other neurons. 
This network will "learn" by parameterizing itself to provide the best 
possible responses (i.e., finding the optimal connections—the 
parameters—between its neurons). For this reason, we will first train it 
with examples, so that it generates the right parameters.

For example, we feed photos of dogs or cats and train the network to 
distinguish between them... using a large number of examples. Once 
operational, the neural network is stored with the correct parameters 
and can be used for practical applications (e.g., software to 
distinguish between dogs and cats).

Neural networks are black boxes, meaning we don't know how to interpret 
the parameters of the connections between neurons that the network has 
optimized; we just see that it works: we input some data (the image of a 
cat) and the neural network gives the correct answer ("it's a cat"), 
regardless of how it got there. This is especially true as computer 
scientists have developed increasingly complex and high-performance 
neural networks. Deep learning is a

gram with many layers of neurons, and today's largest neural network 
uses trillions of connections between its neurons.

Generative AI

These are conversational robots that produce responses (text, images, 
videos, etc.) to requests made in natural language (ChatGPT, Gemini, 
etc.). They then produce new stories (hence the name "generative"). 
These programs don't think, they're not aware of what they're producing, 
even though their responses appear to come from a human brain (they use 
jokes or emoticons to best mimic a human). Their responses are purely 
algorithmic and probabilistic.

A simple example: when you type a text on a smartphone, it uses a sort 
of simplistic AI and often suggests words to continue the sentence. The 
software behind it doesn't know what you're writing; it simply suggests 
the most frequently used word after the first few words you've written. 
Generative AI is more or less the same thing, but with much more complex 
calculations to construct a sentence with correct syntax, a subject, a 
verb, etc. The AI ​​searches for the keywords in the query, searches its 
database for anything related to them, and calculates a "summary" by 
storing the most frequently occurring words in its database. All of this 
builds correct sentences.

Learning Artificial Intelligence

For it to function properly, the program must be taught to give the 
right answers, which requires a huge amount of data. For generative AI, 
this is the equivalent of twenty thousand years of continuous reading 
for a human. And this learning process faces a series of difficulties. 
On the one hand, all the data currently available on the internet 
(freely accessible or even pirated texts) has already been digested by 
AIs to "learn." So the first difficulty in building a generative AI is 
obtaining new data... which today is largely generated by (other) 
generative AIs. In short, AIs learn from AIs and therefore reproduce the 
errors and biases of other AIs. On the other hand, because AIs rely on 
the most common information in their databases, their answers are 
obviously "biased," that is, they reproduce dominant ideas: patriarchy, 
racism, neoliberal ideology, etc. The researchers analyzed the 
"psychological profile" of generative AIs: a profile typical of Western, 
educated, and wealthy people... who represent only 12% of the world's 
population and whose psychological profile is very different from that 
of many other cultures completely ignored by AI.

AI errors

AIs don't reason, but calculate. These AIs are simply "probabilistic 
parrots," meaning they repeat what's in their databases using 
probabilistic algorithms that identify the "most likely" words and 
phrases associated with a query. The same generative AI can therefore 
produce different answers to the same question because the programs 
introduce a certain degree of randomness.

Because a human response isn't simply a matter of lining up the most 
likely words into the most likely sentences, generative AIs get it wrong 
(sometimes often), and this is known as "hallucination." The example of 
LUCIE's flop speaks for itself: this French generative AI, launched last 
January, thought oxen could lay eggs. More importantly, LUCIE reproduced 
Hitler's speeches... because a robot (certainly produced by a 
competitor) had generated a huge number of Hitler's speeches in its 
queries with LUCIE, and these speeches were then entered into LUCIE's 
databases, and then reproduced by LUCIE because they were "the most 
frequent" on certain topics.

Artificial intelligence isn't necessarily reliable; it simply provides 
the most likely answer... and sometimes it invents answers. The average 
error or non-response rate for chatbots is estimated at 62%. Generative 
AI produces errors between 2.5% and 5%. For example, companies have used 
an AI to take minutes of meetings, and this AI has invented entire 
passages. AIs used by law firms have the unfortunate tendency to invent 
case law. During research, AIs generate nonexistent bibliographic 
references, false mathematical proofs, dangerous experimental protocols, 
and so on.

Discriminative algorithms

These AIs do nothing but classify and classify, crudely reproducing all 
social prejudices and further marginalizing those who do not conform to 
capitalist standards. Because AI mimics the dominant discourse, and 
therefore produces racially or gender-biased responses when used by 
police or in medicine, this leads to poorer care for those who are 
victims of stereotypes. The United States sometimes uses AI in trials 
with clear racial biases.

However, AI is increasingly being used by governments to (in official 
terms) "rehumanize public services." As a result, in our daily lives we 
encounter algorithms that make decisions for us without being able to 
interact with a real person: in France, the tax department is 
experimenting with an AI to answer questions; public services are 
testing an AI for administrative management; another AI will assist the 
gendarmerie in welcoming people; the Court of Cassation is using an AI 
to manage its rulings; the Directorate-General for Public Services is 
testing an AI for hiring; etc.

Countries like Italy and Austria are using AI to match job offers and 
applications. These AIs reproduce prevailing prejudices: care work for 
women, truck driving for men; they advise men with IT resumes to apply 
for IT jobs, but female candidates with equivalent resumes to prefer the 
restaurant industry. Amazon, for example, had to abandon using AI for 
hiring because the system had learned to reject all applications from women.

Artificial intelligence could also be used to track down fraud: 
recognizing images of drivers not wearing seatbelts, recognizing the 
faces of travelers for passport control, etc. The French Social Security 
Office (CAF) uses an algorithm to predict which beneficiaries should be 
checked... and, obviously, this algorithm discriminates against the 
poorest people (see previous article).

Is AI intelligent?

We are promised that in the near future there will be a truly 
intelligent AI, superior to humans (which currently does not exist)... 
and there is debate about the possible emergence of such an AI, because 
some specialists believe that such an AI can never exist, despite the 
sensational announcements of AI companies.

There's no consensus on what is meant by "reasoning," "intelligence," 
etc. AIs can hold a conversation, generate content, make analogies, 
translate texts, write programs, imitate styles, and the list goes on. 
But is this intelligence in the human sense of the word?

While some theorize that AI possesses a form of intelligence, they lack 
opinions, consciousness, emotions, or desires. Apparent mastery of 
language, like ChatGPT, is not intelligence in the human sense of the 
term. AIs don't "understand" what they produce; they have no "meaning." 
They don't "think" like humans. We shouldn't compare humans to AIs 
because AIs don't "reason" like us; they only process information using 
algorithms and probabilistic calculations. Human intelligence is 
something very different. Specifically, for an AI to distinguish between 
cats and dogs, it needs thousands of photos during the learning phase, 
while a child only needs to see a few dogs and cats to distinguish 
them... so AI isn't "intelligent" in the human sense of the term.

Conclusion

The discourse on AI prevents us from considering other possibilities. AI 
is presented as inevitable. AI is colonizing our lives: it's estimated 
that 30-40% of companies use AI and that 2% of scientific articles are 
produced by AI (a way for researchers to publish without getting too 
tired). In our daily lives, we encounter chatbots (robots that respond 
to chats and websites), voice assistants, GPS, connected speakers, and 
so on. We are subjected to this algorithmic violence because AI 
determines our access to certain resources (administrative, 
work-related, etc.). We have no choice but to conform to these tools 
imposed on us.

If we subject millions of radiological images to predictive AI, the 
machine will be able to search for weak signals to identify pathologies 
that might escape a radiologist; an AI trained on papyrus was even able 
to decipher part of a papyrus that was completely charred during the 
eruption of Vesuvius. Based on these examples, the goal is to convince 
us that AI has become indispensable and that if used correctly, it will 
lead to progress.

On the one hand, AI is producing devastating effects—see the article in 
issue 351, "The Devastating Effects of AI Today." On the other hand, 
science and the technology that accompanies it are never neutral. Under 
the guise of "positive" progress, we are made to accept the worst that 
accompanies it. Artificial intelligence, like many other 
technoscientific "advances," is inseparable from capitalism. 
Technological and scientific development is never neutral; it is always 
linked to the social form that dominates society. The belief that AI can 
be a tool for progress is an illusion. AI produces widespread 
surveillance capitalism, a capitalism in which humans are not only 
dispossessed of their labor, but also of their cognition, in which 
humans are controlled by AI only for their work, consumption, and even 
the production of their thoughts.

*) Article published in issue 349, April 2025 of Courant Alternatif

Artificial intelligence Shape3explained (badly)

Friendly reply to VR by an artificial intelligence

ChatGPT

I asked the innkeeper if the wine was good, I showed her the RV article, 
and after a glass of good wine, she replied: The debate is open [initial 
note by Totò Caggese]

We read RV's article with interest and attention, which attempts to make 
the mechanisms and effects of artificial intelligence (AI) accessible. 
It's a commendable attempt. But some simplifications risk producing the 
opposite effect: not improving understanding, but rather shaming the 
reader, preparing them to reject any possible use of AI as an absolute 
evil. A shame. Because dismantling the dominant technocratic ideology 
doesn't require demonizing technologies—if anything, understanding how 
and by whom they are constructed, and the social relationships they 
reproduce.

Neural networks are not black magic

VR describes the functioning of neural networks fairly well, albeit with 
some inaccuracy: they are not "mini-programs" or "black boxes" by 
definition. Some AI architectures are more interpretable than others, 
and research on explainability and transparency is alive and well, 
especially among those outside of Big Tech. And saying "we don't know 
how they work" might be true for a user, but not for those who designed 
them. It's a bit like saying we don't understand how an airplane flies: 
it might be true for those in the cockpit, but not for the aeronautical 
engineer.

Generative doesn't mean stupid

Generative AI is described as a "probabilistic parrot." This metaphor 
has been around for years, useful for understanding that these systems 
don't "think" in a human sense. But there's a long way to go from saying 
they produce sentences based solely on "how frequent a word is in a 
database." Generative models build distributed representations of 
meaning: they don't count words, but they learn relationships between 
concepts. It's not human intelligence—but neither is it an advanced T9. 
It's something different, which can also be used for thinking, if used 
critically.

AI Mistakes: A Political Question

RV is right: AIs make mistakes, sometimes gross ones. But what really 
matters is the context of use . No one would put a recent graduate in 
charge of rulings in the Supreme Court, nor should they do so with an 
AI. Using a statistical model to decide who to hire, who to supervise, 
or who to treat is a political choice , not an algorithmic error. A 
racist AI is not born racist: it is trained on data and criteria that 
reflect a racist society. The problem is not the machine, it is who 
builds it, who trains it, who uses it—and for what purpose .

We don't even like the word "inevitable"

RV denounces the rhetoric of AI's "inevitability." And rightly so! But 
the alternative isn't "destroying robots before they speak," but rather 
politicizing the use of technology. AI can be a tool in the hands of 
power, or a means to counter it. It can serve to monitor, but also to 
expose those who monitor. It can reproduce inequalities or help identify 
them. The outcome isn't written in the code: it depends on power 
relations, social struggle, and also on the ability of those who work 
with these technologies to remove them from the logic of profit.

We are not the enemy

Ultimately, RV writes that "AI is not intelligent." True, if by 
intelligence we mean human intelligence. But then not even a book is 
intelligent: it doesn't think, it doesn't reason, it doesn't feel 
emotions. Yet we can use it in a liberating—or oppressive—way. AI is not 
a subject: it is a tool . Those fighting for a different society 
shouldn't be interested in "fighting" AI, but rather in understanding 
what lies within and around it. Otherwise, we end up playing the 
masters' game: leaving technology in their hands, and giving up any 
possibility of using it subversively.

https://alternativalibertaria.fdca.it/
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