Pour un rétro-Design de l’attention. La prise en compte de l’expérience utilisateur par les systèmes numériques pose la question de l’attention de ceux qui sont amenés à les utiliser. Comme l’a très bien pointé le designer Tristan Harris dans son article « Comment la technologie pirate l’esprit des gens », les services et produits numériques portent la question de la conception comportementale à des niveaux de sophistication inédits, notamment en s’appuyant sur l’exploitation de nos biais cognitifs et de nos interactions sociales, puissants moteurs d’activation de nos comportements. Il montre comment le design « capte », « accroche » les utilisateurs en stimulant leurs comportements.
Trop souvent, la seule réponse qu’obtiennent les utilisateurs à ces problèmes attentionnels est qu’elle relève de leur responsabilité, alors que des milliers de designers agencent toujours plus finement la frontière entre l’incitation et la manipulation, en façonnant chaque boucle de rétroaction du moindre de leurs outils. Comment ? Prof. Nick Bostrom - Artificial Intelligence Will be The Greatest Revolution in History. Three Cognitive Dimensions for Tracking Deep Learning Progress. A common unexamined assumption about the evolution of AGI, that is self-aware sentient automation, will follow the path of ever more intelligent machines and thus accelerate towards a super intelligence once human level sentient automation is created.
I argue that this likely will not be the case and that there will be a initial divergence in research on three kinds of artificial general intelligences. A recent research paper titled “Morphospace of Consciousness” by Ariswalla et al. present 3 distinct dimensions to explore consciousness. These are: autonomous, computational and social. The autonomous dimension reflects the adaptive intelligence found in biological organisms. The computation dimension involves the recognition, planning and decision making capabilities that we find in computers as well as in humans. The authors examine various technologies and show how they can be presented in a 3 dimensional space: High-level intelligence is not necessary for survival. 1506.05869v2. New Theory Cracks Open the Black Box of Deep Neural Networks. Intelligence. Deep Learning is Splitting into Two Divergent Paths. A common incorrect assumption about the evolution of Artificial General Intelligence (AGI), that is self-aware sentient automation, will follow the path of ever more intelligent machines and thus accelerate towards a super intelligence once human level sentient automation is created.
I’m writing this article to argue that this likely will not be the case and that there will be an initial divergence of two kinds of artificial intelligences. First, let us establish here that the starting point will come from present day Deep Learning technology. More specifically, I refer these as intuition machines (see: Intuition Machines a Cognitive Breakthrough ). There will be a fork in the evolution of more intelligent machines. One branch will be one that builds super-human narrow intelligence. In the first branch, we will see continued specialization of machines to solve specific narrow problems. This optimized intelligence path will develop automation that works well in highly complex domains. Are Biological Brains Just Made up of NAND Gates? – Intuition Machine – Medium.
I’ve come up with perhaps a controversial opinion as to how biological brains work. I am posting this to facilitate more discussion. I have two opinions, the second more surprising than the first. My first opinion is that biological brains, more specifically human brains, are intuition machines. Intuition is that parallel cognitive process that we develop by learning using induction. Said differently, we learn from experience. We can’t just upload knowledge of Kung Fu and instantly master the art. The failure of Good Old Fashioned AI (GOFAI) may precisely be due to the fact that human cognition is not based on logic. Computers are logic machines, computers have several orders of magnitude more capable in performing logic than humans. The second opinion is that brains function using discrete computation.
The conclusion is clear, the brain works more like a computer than like a continuous system like the weather. To answer the question of this post. New Theory Cracks Open the Black Box of Deep Neural Networks. Three Cognitive Dimensions for Tracking Deep Learning Progress. Forget Killer Robots—Bias Is the Real AI Danger - MIT Technology Review. Google’s AI chief isn’t fretting about super-intelligent killer robots. Instead, John Giannandrea is concerned about the danger that may be lurking inside the machine-learning algorithms used to make millions of decisions every minute. “The real safety question, if you want to call it that, is that if we give these systems biased data, they will be biased,” Giannandrea said before a recent Google conference on the relationship between humans and AI systems. The problem of bias in machine learning is likely to become more significant as the technology spreads to critical areas like medicine and law, and as more people without a deep technical understanding are tasked with deploying it.
Some experts warn that algorithmic bias is already pervasive in many industries, and that almost no one is making an effort to identify or correct it (see “Biased Algorithms Are Everywhere, and No One Seems to Care”). Giannandrea has good reason to highlight the potential for bias to creep into AI. AI needs a human touch to function at its highest level | VentureBeat. Io9.gizmodo. Monica Anderson - Slides from various presentations I've... L’intelligence artificielle va-t-elle rester impénétrable ? [1609.08144] Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.
1606.04199. P15 1166. Schuster97 BRNN. 1409.1259v2. 1409.0473v7. [1409.3215] Sequence to Sequence Learning with Neural Networks. Bengio03a. Science. Unsupervised icml2012. The Great A.I. Awakening. Four days later, a couple of hundred journalists, entrepreneurs and advertisers from all over the world gathered in Google’s London engineering office for a special announcement. Guests were greeted with Translate-branded fortune cookies. Their paper slips had a foreign phrase on one side — mine was in Norwegian — and on the other, an invitation to download the Translate app. Tables were set with trays of doughnuts and smoothies, each labeled with a placard that advertised its flavor in German (zitrone), Portuguese (baunilha) or Spanish (manzana). After a while, everyone was ushered into a plush, dark theater. Photo Sadiq Khan, the mayor of London, stood to make a few opening remarks.
Pichai was in London in part to inaugurate Google’s new building there, the cornerstone of a new “knowledge quarter” under construction at King’s Cross, and in part to unveil the completion of the initial phase of a company transformation he announced last year. Until today. The new wave of A.I. 1. 2. 3. 4. Lecun 01a. Untitled. Eric Berlow and Sean Gourley: Mapping ideas worth spreading | TED Talk Subtitles and Transcript. Eric Berlow: I'm an ecologist, and Sean's a physicist,and we both study complex networks.And we met a couple years ago when we discoveredthat we had both given a short TED Talkabout the ecology of war,and we realized that we were connectedby the ideas we shared before we ever met.And then we thought, you know, there are thousandsof other talks out there, especially TEDx Talks,that are popping up all over the world.How are they connected,and what does that global conversation look like?
So Sean's going to tell you a little bit about how we did that. Sean Gourley: Exactly. So we took 24,000 TEDx Talksfrom around the world, 147 different countries,and we took these talks and we wanted to findthe mathematical structures that underlythe ideas behind them.And we wanted to do that so we could see howthey connected with each other. SG: Absolutely. And so there's a lot of exciting stuff we can do here,and I'll throw to Eric for the next part.
So this is great. Thank you. (Applause) 2012b ComCogSysMan rpwl final. Richard Loosemore. Stanford Center for Professional Development. We Need Algorithmic Angels. Editor’s note: Jarno M. Koponen is a designer, humanist and co-founder of media discovery startup Random. His passion is to explore and create audacious human-centered digital experiences. A lot has been written on how algorithms are manipulating this and that in today’s Internet. However, there haven’t been many concrete proposals about how to create more human-centered algorithmic solutions. For example, do we need algorithms that are on our side? The lost algorithmic me Digital products are moving from our pockets to our skin and finally inside of us. Through personalization we become parts of algorithmic systems that we don’t control. Personalization algorithms are not accessible for an individual. Personalization algorithms are used to affect and guide your behavior. Personalization algorithms are neither neutral nor objective.
Personalization algorithms don’t capture or understand you as a complex individual. Today our algorithmic selves are beyond our control. Algorithmic angels. Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter. Artificial intelligence has gone through some dismal periods, which those in the field gloomily refer to as “AI winters.” This is not one of those times; in fact, AI is so hot right now that tech giants like Google, Facebook, Apple, Baidu, and Microsoft are battling for the leading minds in the field.
The current excitement about AI stems, in great part, from groundbreaking advances involving what are known as “convolutional neural networks.” This machine learning technique promises dramatic improvements in things like computer vision, speech recognition, and natural language processing. You probably have heard of it by its more layperson-friendly name: “Deep Learning.” Few people have been more closely associated with Deep Learning than Yann LeCun, 54. More recently, Deep Learning and its related fields grew to become one of the most active areas in computer research. Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter. Evolutionary Multiobjective Optimization. Models vs. Patterns. A New Direction in Artificial Intelligence Research. Model Free Methods Workshop. Monica Anderson is CEO of Syntience Inc. and originator of a theory for "true" learning in animals, humans, and computers.
Syntience is actively researching computer based systems that can understand the meaning of language in the form of text. This is a video of an interactive workshop about the use of Model Free Methods in the Life Sciences and elsewhere. These methods are very important for AI research since (according to Ms. Anderson's theory) the creation of an Artificial General Intelligence requires that implementations be restricted to only use Model Free Methods. The case for this theory is made in other videos; this one merely discusses how other disciplines are already using Model Free Methods. Ms. These video recordings are sponsored by Syntience Inc.
Artificial Intelligence Videos. How to Make a Mind. Can nonbiological brains have real minds of their own? In this article, drawn from his latest book, futurist/inventor Ray Kurzweil describes the future of intelligence—artificial and otherwise. The mammalian brain has a distinct aptitude not found in any other class of animal. We are capable of hierarchical thinking, of understanding a structure composed of diverse elements arranged in a pattern, representing that arrangement with a symbol, and then using that symbol as an element in a yet more elaborate configuration.
This capability takes place in a brain structure called the neocortex, which in humans has achieved a threshold of sophistication and capacity such that we are able to call these patterns ideas. We are capable of building ideas that are ever more complex. We call this vast array of recursively linked ideas knowledge. Only Homo sapiens have a knowledge base that itself evolves, grows exponentially, and is passed down from one generation to another. Consider the benefits. Artificial Intelligence.