Until Cepheus came along. Bowling and his team instructed the computer to play billions of poker games against itself. Initially, they taught Cepheus only the basic rules of Texas Hold’em. The computer started off playing randomly, but eventually it began to learn. Cepheus started compiling lists of “regrets”—situations in which it could have folded or bluffed or bet differently, and won more money by doing so. The researchers then programmed Cepheus to begin acting on its most serious regrets, while ignoring its more minor regrets.
Ultimately, Cepheus whittled its list of regrets nearly down to zero. Now the program can bet and bluff with the best. “If you do this in a precise mathematical way, you can prove your regrets are guaranteed to go down to zero,” Bowling says. “And in the process of approaching zero, you must be approaching perfect play.”
Cepheus isn’t perfect, but it is guaranteed not to lose in the long run. That’s about as good as it gets for a game that still relies partially on chance. Cepheus’ performance has other experts in the field of artificial intelligence excited. “It’s a really interesting paper, with a convincing argument that a particular form of poker has been essentially solved,” says Howard Williams, a computer scientist and doctoral student at Queen Mary University of London, who was not involved in the study.
Beyond poker, Bowling envisions a new set of algorithms that could help security officers optimize checkpoints, random searches and placement of air marshals on flights. In these situations, a program like Cepheus could be taught to view potential terrorists as other players in a high-stakes game rife with variables. “That’s very close to what we have achieved here for the game of poker. It’s a strategy guaranteed not to lose,” he says.
If, however, you find yourself tempted (I know I am), Bowling and his team have set up a website where you can try your luck against Cepheus itself—the one computer program that always knows when to hold ‘em and when to fold ‘em.
It could be "strange" to show interest to this article here, but IA's behavior is one of the most difficult part of robot's studies. being able to create something same as bluff. And, as show last research regarding IA, this behavior is not all programmed, but a reszponse to experience and creativity, a sort of real learning... really impressive.
Je n'ai pas l'habitude généralement dans ce scoop it de partager des articles aussi loin de mes préoccupation (je préfère généralement les partager directement sur twitter... je ne peux d'ailleurs que vous conseiller d'aller voir amazing science). Cependant cet article parle d'un des defi les plus important de la création d'intelligence artificielle, la façon dont on a fait évoluer leur "personnalité" et leur réaction vers des comportements qui pourraient sembler non rationnel, comme ici le bluff. Et comme ces dernière années, il ne s'agit pas d'un comportement programmé directement mais d'une façon d'apprendre de construire un comportement en réaction a des expérience, des essai et des échecs...
Somos criaturas favorecidas de una manera especial por la evolución. Mientras otros seres del planeta se han adaptado a los diferentes medios mejorando los sistemas de camuflaje, de defensa o de ataque, a nosotros la evolución nos ha premiado con un cerebro más desarrollado. Esa innovación ha resultado ser notablemente ventajosa, hasta el punto que, siendo criaturas físicamente mucho más débiles que otras, gracias a nuestro cerebro nos hemos convertido en depredadores implacables.
Lucile Debethune's insight:
Une fois n'est pas coutume, le lien d'aujourd'hui est en espagnol... et il ne s'agit pas d'un texte, mais d'un podcast très intéressant sur la science.
Les automates intelligents: robotique, vie artificielle, réalité virtuelle - Revue mensuelle, par Jean-Paul Baquiast et Christophe Jacquemin
Lucile Debethune's insight:
Je suis fascinée par le cerveau, et par les récentes recherches autour de la mémoire, de la création du sens, des relation entre le conscient et l’inconscient, la façon dont passent les message entre les synapses, la résilience après un choc, etc
J’essaie donc de me tenir au courant, je ne pense pas être jamais capable de lire le livre de S. Dehaenne (bien sûr si je me lance, une fois le livre en français, j’essaierai de faire un résumé/une réaction ici) car il y a bien trop de concepts qui me sont inconnus, mais pour ceux qui s’intéressent au sujet, quelques points qui me semblent intéressant :
L’utilisation de stimulus pour essayer de mapper les signatures de la conscience (ou les leviers qui entraine une réaction consciente)et donc aussi déterminer les facteurs qui font qu’information perçue reste inconsciente.
La description qu’il fait des échanges globaux au sein du cerveau et notamment de la partie consciente
Mais bon, l’article ci-joint fais une meilleure recension du livre que moi ^^
L’être humain avait déjà un cerveau magnifique, il en a maintenant un deuxième, bien caché au creux de l’intestin. Vous pouvez l’appeler système nerveux entérique. Ses quelques 200 millions de neurones pourraient laisser penser que, dans le ventre, chacun de nous accueille l'équivalent du cerveau d'un petit chien...
Lucile Debethune's insight:
J’avoue, on s’éloigne de mon thème de prédilection qui tourne plutôt autour de la re-création du cerveau (ou de certains éléments du cerveau au niveau actuel) par des systemes informatiques ou algorithmiques, par l’apprentissage technologique, l’étude des neuroscience, etc
Mais cette nouvelle est étonnante ( même si l’acupuncture, la culture Yogi, etc… parlaient de ce centre nerveux/conscient). En tout cas, j’espère que l’on en saura plus bientôt sur les informations échangées entre les deux cerveaux, et sur le rôle qu’ a put jouer ce « premier cerveau » (evolution-nairement parlant) dans la mise en place du deuxième cerveau "cranien". Mais aussi peut être sur l’état/l'existence de ce cerveau chez d’autres organismes vivant.
If the race to map the human genome was the last great competition in science, the challenge to reverse-engineer the brain is the most amazing race today. But experts wildly disagree on how we'll get there.
Will this summer be remembered as a turning point in the story of man versus machine? On June 23, with little fanfare, a computer program came within a hair’s breadth of passing the Turing test, a kind of parlour game for evaluating machine intelligence devised by mathematician Alan Turing more than 60 years ago. This wasn’t as dramatic as Skynet becoming self-aware in the Terminator films, or HAL killing off his human crew mates in 2001, A Space Odyssey. But it was still a sign that machines are getting better at the art of talking – something that comes naturally to humans, but has always been a formidable challenge for computers.
The term "cyborg" literally means "cybernetic organism" -- a being constructed of both mechanical and organic material. Although traditionally confined to the realms of science fiction, modern medicine and in particular prosthetics have made the term applicable to a number of human beings. Many people who could technically be labelled part-cybernetic, part-organic, have become so as the result of complex medical procedures, usually stemming from medical necessity. Some, however, chose to grant themselves cyborg status in the name of scientific advancement.
The constant advancements in computing power, machine learning algorithms and breakthroughs in relevant technologies is setting the interaction between humans and computers on a road where sometime in the near future advanced Artificial Intelligences (A.I.) will engage with people in many meaningful ways.
The possibility of a machine with consciousness raises many philosophical, psychological and sociological questions about the nature of consciousness itself and what it really means to be intelligent. The computational modelling of human cognitive abilities can play a significant role in the advancement of cognitive psychology, giving a better understanding of people’s own intelligence. Going from natural to Artificial Intelligence, there are many challenges and risks to be met, but also great opportunities.
Wired.co.uk seeks to navigate the thorny ethical, medical and social issues associated with using technology to enhance the human body and mind through a series of features, galleries and guest posts...
Sentient claims to have assembled machine-learning muscle to rival Google by rounding up idle computers.
Recent improvements in speech and image recognition have come as companies such as Google build bigger, more powerful systems of computers to run machine-learning software. Now a relative minnow, a private company called Sentient with only about 70 employees, says it can cheaply assemble even larger computing systems to power artificial-intelligence software. The company’s approach may not be suited to all types of machine learning, a technology that has uses as varied as facial recognition and financial trading. Sentient has not published details, but says it has shown that it can put together enough computing power to produce significant results in some cases.
Sentient’s power comes from linking up hundreds of thousands of computers over the Internet to work together as if they were a single machine. The company won’t say exactly where all the machines it taps into are. But many are idle inside data centers, the warehouse-like facilities that power Internet services such as websites and mobile apps, says Babak Hodjat, cofounder and chief scientist at Sentient. The company pays a data-center operator to make use of its spare machines.
Data centers often have significant numbers of idle machines because they are built to handle surges in demand, such as a rush of sales on Black Friday. Sentient has created software that connects machines in different places over the Internet and puts them to work running machine-learning software as if they were one very powerful computer. That software is designed to keep data encrypted as much as possible so that what Sentient is working on–perhaps for a client–is kept confidential.
Sentient can get up to one million processor cores working together on the same problem for months at a time, says Adam Beberg, principal architect for distributed computing at the company. Google’s biggest machine-learning systems don’t reach that scale, he says. A Google spokesman declined to share details of the company’s infrastructure and noted that results obtained using machine learning are more important than the scale of the computer system behind it. Google uses machine learning widely, in areas such as search, speech recognition and ad targeting.
Beberg helped pioneer the idea of linking up computers in different places to work together on a problem (see “Innovators Under 35: 1999”). He was a founder of Distributed.net, a project that was one of the first to demonstrate that idea at large scale. Its technology led to efforts such as Seti@Home andFolding@Home, in which millions of people installed software so their PCs could help search for alien life or contribute to molecular biology research.
Sentient was founded in 2007 and has received over $140 million in investment funding, with just over $100 million of that received late last year. The company has so far focused on using its technology to power a machine-learning technique known as evolutionary algorithms. That involves “breeding” a solution to a problem from an initial population of many slightly different algorithms. The best performers of the first generation are used to form the basis of the next, and over successive generations the solutions get better and better.
Sentient currently earns some revenue from operating financial-trading algorithms created by running its evolutionary process for months at a time on hundreds of thousands of processors. But the company now plans to use its infrastructure to offer services targeted at industries such as health care or online commerce, says Hodjat.
University of Washington researchers have successfully replicated a direct brain-to-brain connection between pairs of people as part of a scientific study following the team’s initial demonstration a year ago. In the newly published study, which involved six people, researchers were able to transmit the signals from one person’s brain over the Internet and use these signals to control the hand motions of another person within a split second of sending that signal.
At the time of the first experiment in August 2013, the UW team was the first to demonstrate two human brains communicating in this way. The researchers then tested their brain-to-brain interface in a more comprehensive study, published Nov. 5 in the journal PLOS ONE ("A Direct Brain-to-Brain Interface in Humans").
“The new study brings our brain-to-brain interfacing paradigm from an initial demonstration to something that is closer to a deliverable technology,” said co-author Andrea Stocco, a research assistant professor of psychology and a researcher at UW’s Institute for Learning & Brain Sciences. “Now we have replicated our methods and know that they can work reliably with walk-in participants.”
In this photo, UW students Darby Losey, left, and Jose Ceballos are positioned in two different buildings on campus as they would be during a brain-to-brain interface demonstration. The sender, left, thinks about firing a cannon at various points throughout a computer game. That signal is sent over the Web directly to the brain of the receiver, right, whose hand hits a touchpad to fire the cannon.
Read more: Study shows direct brain interface between humans (w/video)
Ce n'est pas la première fois que je parle de neuromarketing (et comme beaucoup d'autres point qui flirtent avec l'éthique, dur d'avoir une position tranchée). cette fois-ci, un article plus orienté marketing.
Les sensations dans les membres amputés (membres fantomes) viennent d'une région du cerveau particulière reliés à différents nerfs. Il est désormais possible de relier ces régions du cerveau à des prothèses plus ou moins évoluées pour "sentir" et donc, grâce au feedback, mieux manipuler) les objets.
What happens when we link films and music to devices that capture small changes in our emotions? Welcome to the world of reactive media, says Alexis Kirke
Lucile Debethune's insight:
Neuroscience et marketing sont liés (pour le meilleur et pour le pire) depuis longtemps, l’analyse des sentiments et le ciblage du public sont aussi des méthodes utilisées par le marketing (la publicité, par exemple Adword ou les Ad Facebbok) pour s’approcher de ce que veux le consommateur et créer des produits adaptés (que ce soit des biens de consommations ou des biens culturels, par exemple… lesl ivres, les films, etc…). Mais pour l’instant il s’agit plus de culture de masse créée pour répondre aux envies du plus grande nombre, ou alors de système de recommandation par similarité (longue traine, etc…) que d’un produit complètement lié à un individu particulier, ou à ses émotions (plus difficile à évaluer que son comportement conscient). C’est ce à quoi s’attaque les technologies de détection des émotions (mood sensing technologies)… progrès étonnant ou nouvelle erreur dérive de la technologie ? difficile de le dire, mais la frontière peut être mince
In 2002 Stephen Wolfram released A New Kind of Science and immediately unleashed a firestorm of wonder, controversy, and criticism as the British-born scientist, programmer, and entrepreneur overturned conventional ideas on how to pursue knowledge.
Earlier this month, he teased something with the capacity to create as much passion — and, likely, much more actual change — in the world of programming, computation, and applications.
Inventor and entrepreneur Ray Kurzweil is a pioneer in artificial intelligence—the principal developer of the first print-to-speech reading machine for the blind, and the first text-to-speech synthesizer, among other breakthroughs. He is also a writer who explores the future of information technology and how it is changing our word.
In a wide-ranging interview, Mr. Kurzweil and The Wall Street Journal's Alan Murray discussed advances in artificial intelligence, nanotechnology, and what it means to be human. Here are edited excerpts of their conversation.
If you were to ask a random person what the best example of Artificial Intelligence is out there, what do you think it would be?
Most likely, it would be IBM’s Watson.
In a stunning display of knowledge and accuracy, Watson blew away the world Jeopardy champions Ken Jennings and Brad Rutter without blowing a fuse, and ended with Jennings proclaiming, “I for one welcome our new computer overlords.”
IBM’s Watson represents the current popular approach to AI: that is, spending hundreds of hours hand-coding and fine-tuning a program to perform exceedingly well on a single task. Most people in the field of AI call machines like Watson an expert system because they are designed to be experts at a single task. This approach has been wildly successful lately, producing machines that drive cars and fly UAVs by themselves, beat world chess and Jeopardy champions, and even fool some people into thinking they’re human.
However, imagine how hard it would be to hand-code a system that could do everything the human brain is capable of. Do you think that sounds impossible? That’s the reason why the field of neuroevolution was born: scientists wanted to harness the creative power of evolution to design the programs that could achieve human-level intelligence.
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