Intelligence artificielle

Item

Title
Intelligence artificielle
Item sets
Concepts et mots-clés

Linked resources

Items with "has top concept: Intelligence artificielle"
Title Class
« Rappelons donc que le terme automate vient du grec ancien "automatos" (αὐτόματος) qui signifie "agir de sa propre volonté" étant dirigé de l'intérieur et capable de s'animer par lui-même. C'est une machine philosophique. Donc, l'automate, qui hérite des problèmes du rapport du corps et de l'esprit, circule de Platon jusqu'à Descartes, puis de l'intelligence artificielle. L'automate reconduit ce fantasme millénaire de transcender la condition mortelle et prolonge le rêve d'une altérité qui se joue à travers une figure sans cesse reconduite du double faite à l'image de l'homme. » author self citation
« Selon la perspective du sommet fondateur de Dartmouth sur l'intelligence artificielle tenue en 1955, la part de hasard que manifeste notre créativité ne serait pas un privilège de l'humain ou de la vie puisqu'elle participe à cette aptitude de la matière à s'organiser spontanément. La notion de créativité ne permet plus de distinguer l'humain de la machine. »  author self citation
« StyleGAN—you know the deepfakes of people—there's a website [called] ThisPersonDoesNotExist that's all using a GAN, using StyleGAN, and the way the GAN works is that we have these two networks, a generator and a discriminator. When I'm teaching about this I sort of introduce this like one person is a forger—that's the generator—and then the discriminator is a detective. So if someone's forging money, trying to make fake bank notes, then the discriminator is the person at the bank trying to figure out is this real money or is this fake money, and these are extremely effective. So the discriminator is trying to tell what's a real image compared to what's being generated, what's fake, and the generator's got this adversarial opposite objective which is: I want to generate things that trick the discriminator, passed off as being real. In this setup, the generator has one objective, and that objective is: I just need to trick this other network, I just need to get my bank notes passed off as real money, so to speak, or get my images of people or whatever passed off as being real, training data. » author self citation
« There are many definitions [of computer vision] but one kind of all-encompassing one is that this is an interdisciplinary field that uses artificial intelligence tools and that enables computers to interpret and make decisions based on visual data, and it somehow needs to emulate how we as humans perceive and understand images. And even taking it a step further what the objective is is to automate the capabilities of the human visual system and to be able to take information, informational insights, or rather informative insights as I say here, from visual input and then do as you will from there. » author self citation
« There are two things really when we're thinking about images and studying images at large scale and using these big models and how they've been trained on massive data set, there's kind of two things to be concerned about. One is data privacy, of course. So we don't really know a lot of times where these images are coming from, what's going on, and this is something that others that have presented about generative AI, this is a real problem that they face. But I'm going to focus a little bit more on this bias in predictions. What we find is that because a lot of these models have been trained using human labeled images, the sort of predictions that we get might convey the inherent biases that might exist among the coders. » author self citation
« Très lié à la question qui intéresse ce séminaire, qui est le faux et puisqu'on essaie après de faire convergence avec l'IA, c'est aussi la très célèbre définition de sémiotique donnée par Umberto Eco, six ans après McLuhan. Donc, vous voyez, ils étaient des contemporains. Eco disait que la sémiotique c'était la discipline qui étudie tout ce qui peut être pour mentir. Si quelque chose, si le signe ne peut pas être utilisé pour mentir, alors il ne peut pas non plus être utilisé pour dire la vérité. Donc, au final, on peut l'utiliser pour rien, donc ce n'est pas un signe. Il mettait en avant le problème de comme quoi les signes, c'est quelque chose de conventionnel, c'est quelque chose de culturel. C'est quelque chose qui appartient à un système de règles partagé, social, etc. Mais au final, on peut discuter, on peut critiquer, cela fait cinquante ans. Aujourd'hui, les sciences cognitives, la perception est étudiée, comment le cerveau des individus influence également comment on perçoit les signes, c'est beaucoup plus important aujourd'hui. Avec Eco, c'est intéressant de voir comment il analysait le succès ou l'échec de la communication. » author self citation
« Ultimately, I kind of came to this realization that what I was trying to do was I was trying to train a generative model without modelling data which is an oxymoron, it's a complete contradiction. It took me about a year to really figure this out but once I did, that was quite helpful, quite constructive, it made me rethink the problem, reframe the research I was trying to do, and so then I started thinking: "ok, we're not trying to train something from scratch to do something that isn't modelling data, how do we just push or nudge a general model away from an existing data set?" So how do we take something that's already been trained, and how do we kind of move it in a direction. And I ended up—instead of looking at very theoretical statistics in cognitive science—just looking at the actual structure of the models and really just thinking about their component parts. How can we play around with this? The talk is kind of an hacker's guide, and I started taking a hacker's mentality to working with generative AI where I was really just trying to pull these things apart, figure out what you can do, figure out the unintended ways that you could use them, figure out ways of using them that you weren't really supposed to do. » author self citation
« Un des éléments qui m'a intéressé ou étonné, disons, dans cette recherche sur l'action de l'intelligence, c'est la sorte de récurrence ou réapparition des mêmes questionnements, approches ou postures vis-à-vis des technologies. J'en ai choisi un parmi d'autres, c'est, par exemple, cet aspect réflectif des technologies. C'est-à-dire que ces technologies pourraient augmenter, approfondir ou complexifier, par exemple, notre connaissance de l'intelligence. C'est ce que défend Yann Le Cun avec cette idée que, finalement, l'intelligence artificielle, au-delà de toutes les applications, ça va nous permettre de découvrir les mécanismes sous-jacents et les principes à l'œuvre dans l'intelligence naturelle ou artificielle. Cette intelligence artificielle va nous aider pour certaines choses, mais va surtout nous aider à avoir une meilleure compréhension de l'intelligence elle-même. C'est assez étonnant, parce que ce type de raisonnement, on peut le retrouver dès les années 1960 avec Norbert Wiener, mais si je le restreins au domaine de l'architecture, donc avec Nicolas Negromonte, entre autre, quelqu'un d'assez important dans le domaine de l'architecture. Dans les années 1970, il y avait à peu près la même idée, on peut dire. C'est-à-dire que les technologies, c'était même l'ordinateur, permettraient de nous faire découvrir des moyens, de découvrir les mécanismes de la conception, donc on arriverait à mieux découvrir les critères de qu'est-ce que la conception. Évidemment, l'intelligence artificielle amène des connaissances différentes, mais, à la fois, la manière de poser les questions et la manière d'interagir ou de réfléchir sur ces technologies. J'ai le sentiment qu'il y a une sorte de redondance depuis une soixantaine d'années et je pense que c'est intéressant de voir que, finalement, s'il y a des progressions sur certains points, il y a des éléments sur les manières de penser, notre relation avec les technologies qui me semblent relativement similaires. » author self citation
« We can remember a lot of [images] because we can simply memorize them as tokens. Of course, since the invention of paper, we can record it, and leave it behind, even if no one can draw it. And the amazing thing is that we can represent images. I believe this is the essence of art. Letting AI draw has clearly given me this insight. » author self citation
« We try to inspire students with this history of narrative methods and generative narrative methods that were pre-digital. Things like sonets, some of the Ulipo experiments. We have a picture [on the slide] of an early plot robot. An other example of just ways that people used different templates or models to create stories or try to automate the creation of stories before computers. Also history of mutimedia, narratives and stories from AI is a source and contexte. Also, I have started, and I'm hoping this year to do more with. This is what I've started teachins, a long history of story generators, pre-digital, then also digital ones that have led up to large language model of AI, so my contemporary students understand that every story generator has a model of language of storytelling that it realizes. You know, Plato assumes that a story is a plot and so, by looking at these different attemps to model storytelling, we can look at some of the ideological assumptions about what's important in a story, and we can also critique those and try to come up with better models of story as a result. And I think that Samuel'S ecosystem is another good example of trying to encourage our students to think about the different ways that you can generate stories and come finding some of these different methods as, maybe, a future goal, rather than the large language model AI and the predictive AI technology. » author self citation
TEST 2 "Neque porro quisquam est qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit..." "There is no one who loves pain itself, who seeks after it and wants to have it, simply because it is pain..." What is Lorem Ipsum? Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. author self citation
Items with "has broader: Intelligence artificielle"
Title Class
Algorithmes Concept
Items with "has narrower: Intelligence artificielle"
Title Class
Arts trompeurs Concept
Items with "Ressource(s) associée(s) (ressource physique, actant, conceptuelle): Intelligence artificielle"
Title Class
Artistes virtuels: bon ou mauvais? Annotation
Disparition de l'artiste? Annotation
Naevis: nouvelle dans le monde réel Annotation
PLAVE, un groupe hors du commun Annotation
Items with "a comme concept: Intelligence artificielle"
Title Class
‘I am not a robot’ At the uncanny valley Article
« Chom5ky vs Chomsky », le vrai et le faux de l’intelligence artificielle | Le Devoir Article
« La Vallée de l’étrange » de Stefan Kaegi Article
At the Edge of Presence and Absence in an Uncanny Valley: On Rimini Protokoll’s Unheimliches Tal / Uncanny Valley Academic Article
Bacteria to AI: Human Futures with our Nonhuman Symbionts Book
Chomsky Vs Chomsky by Sandra Rodriguez Article
Items with "a comme concept: Intelligence artificielle"
Title Class
ANTICORPS Performance
CHOM5KY vs CHOMSKY Film

Annotations

There are no annotations for this resource.