Training a single AI model can emit as much carbon as five cars in their lifetimes - MIT Technology Review
▻https://www.technologyreview.com/s/613630/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in
In a new paper, researchers at the University of Massachusetts, Amherst, performed a life cycle assessment for training several common large AI models. They found that the process can emit more than 626,000 pounds of carbon dioxide equivalent—nearly five times the lifetime emissions of the average American car (and that includes manufacture of the car itself).
It’s a jarring quantification of something AI researchers have suspected for a long time. “While probably many of us have thought of this in an abstract, vague level, the figures really show the magnitude of the problem,” says Carlos Gómez-Rodríguez, a computer scientist at the University of A Coruña in Spain, who was not involved in the research. “Neither I nor other researchers I’ve discussed them with thought the environmental impact was that substantial.”
They found that the computational and environmental costs of training grew proportionally to model size and then exploded when additional tuning steps were used to increase the model’s final accuracy. In particular, they found that a tuning process known as neural architecture search, which tries to optimize a model by incrementally tweaking a neural network’s design through exhaustive trial and error, had extraordinarily high associated costs for little performance benefit. Without it, the most costly model, BERT, had a carbon footprint of roughly 1,400 pounds of carbon dioxide equivalent, close to a round-trip trans-American flight.
What’s more, the researchers note that the figures should only be considered as baselines. “Training a single model is the minimum amount of work you can do,” says Emma Strubell, a PhD candidate at the University of Massachusetts, Amherst, and the lead author of the paper. In practice, it’s much more likely that AI researchers would develop a new model from scratch or adapt an existing model to a new data set, either of which can require many more rounds of training and tuning.
The significance of those figures is colossal—especially when considering the current trends in AI research. “In general, much of the latest research in AI neglects efficiency, as very large neural networks have been found to be useful for a variety of tasks, and companies and institutions that have abundant access to computational resources can leverage this to obtain a competitive advantage,” Gómez-Rodríguez says. “This kind of analysis needed to be done to raise awareness about the resources being spent [...] and will spark a debate.”
“What probably many of us did not comprehend is the scale of it until we saw these comparisons,” echoed Siva Reddy, a postdoc at Stanford University who was not involved in the research.
The privatization of AI research
The results underscore another growing problem in AI, too: the sheer intensity of resources now required to produce paper-worthy results has made it increasingly challenging for people working in academia to continue contributing to research.
#Intelligence_artificielle #Consommation_énergie #Empreinte_carbone
]]>#Nextcloud 16 becomes smarter with #Machine_Learning for security and productivity – Nextcloud
▻https://nextcloud.com/blog/nextcloud-16-becomes-smarter-with-machine-learning-for-security-and-produ
The #Suspicious #Login Detection app tracks successful logins on the instance for a set period of time (default is 60 days) and then uses the generated data to train a neural network. As soon as the first model is trained, the app starts classifying logins. Should it detect a password login classified as suspicious by the trained model, it will add an entry to the suspicious_login table, including the timestamp, request id and URL. The user will get a notification and the system administrator will be able to find this information in the logs.
Plus de détail sur le blog de la personne qui a développé le bouzin :
▻https://blog.wuc.me/2019/04/25/nextcloud-suspicious-login-detection
Qui utilise ▻https://php-ml.org
Il y a peut-être des trucs à pomper pour #SPIP là dedans...
]]>10 Open Source #ai Project Ideas For Startups
▻https://hackernoon.com/10-open-source-ai-project-ideas-for-startups-1afda6fb0aa8?source=rss----
The open source AI projects particularly pay attention to deep learning, machine learning, neural network and other applications that are extending the use of AI.Those involved in deep researches have always had the goal of building machines capable of thinking like human beings.For the last few years, computer scientists have made unbelievable progress in Artificial Intelligence (AI) to this extent that the interest in AI project ideas keeps increasing among technology enthusiasts.As per Gartner’s prediction, Artificial Intelligence technologies going to be virtually prevalent in nearly all new software products and services.The contribution of open source software development to the rise of Artificial Intelligence is immeasurable. And, innumerable top machine learning, deep learning, (...)
]]>10 Top Open Source AI Technologies For Startups
▻https://hackernoon.com/10-top-open-source-ai-technologies-for-startups-7c5f10b82fb1?source=rss-
In the area of technology research, Artificial intelligence is one of the hottest trends. In fact, many startups have already made progress in areas like natural language, neural networks, AI, machine learning and image processing. Many other big companies like Google, Microsoft, IBM, Amazon and Facebook are heavily investing in their own R&D.Hence, it is no surprise now AI applications are increasingly useful for small as well as large businesses in 2019. In this blog, I have listed top 10 open source AI Technologies for small businesses and startups.1) Apache SystemMLIt is the machine learning technology created at IBM that has reached one of the top-level project levels in the Apache Software Foundation and is a flexible and scalable machine learning system. The important (...)
#machine-learning #artificial-intelligence #open-source #startup #open-source-ai
]]>Malicious Attacks to Neural Networks
▻https://hackernoon.com/malicious-attacks-to-neural-networks-8b966793dfe1?source=rss----3a8144ea
Adversarial Examples for Humans — An IntroductionThis article is based on a twenty-minute talk I gave for TrendMicro Philippines Decode Event 2018. It’s about how malicious people can attack deep neural networks. A trained neural network is a model; I’ll be using the terms network (short for neural network) and model interchangeably throughout this article.Deep learning in a nutshellThe basic building block of any neural network is an artificial neuron.Essentially, a neuron takes a bunch of inputs and outputs a value. A neuron gets the weighted sum of the inputs (plus a number called a bias) and feeds it to a non-linear activation function. Then, the function outputs a value that can be used as one of the inputs to other neurons.You can connect neurons in various different (usually (...)
#artificial-intelligence #neural-networks #deep-learning #machine-learning
]]>YouTube Executives Ignored Warnings, Let Toxic Videos Run Rampant - Bloomberg
▻https://www.bloomberg.com/news/features/2019-04-02/youtube-executives-ignored-warnings-letting-toxic-videos-run-rampant
Wojcicki’s media behemoth, bent on overtaking television, is estimated to rake in sales of more than $16 billion a year. But on that day, Wojcicki compared her video site to a different kind of institution. “We’re really more like a library,” she said, staking out a familiar position as a defender of free speech. “There have always been controversies, if you look back at libraries.”
Since Wojcicki took the stage, prominent conspiracy theories on the platform—including one on child vaccinations; another tying Hillary Clinton to a Satanic cult—have drawn the ire of lawmakers eager to regulate technology companies. And YouTube is, a year later, even more associated with the darker parts of the web.
The conundrum isn’t just that videos questioning the moon landing or the efficacy of vaccines are on YouTube. The massive “library,” generated by users with little editorial oversight, is bound to have untrue nonsense. Instead, YouTube’s problem is that it allows the nonsense to flourish. And, in some cases, through its powerful artificial intelligence system, it even provides the fuel that lets it spread.
Mais justement NON ! Ce ne peut être une “bibliothèque”, car une bibliothèque ne conserve que des documents qui ont été publiés, donc avec déjà une première instance de validation (ou en tout cas de responsabilité éditoriale... quelqu’un ira en procès le cas échéant).
YouTube est... YouTube, quelque chose de spécial à internet, qui remplit une fonction majeure... et également un danger pour la pensée en raison de “l’économie de l’attention”.
The company spent years chasing one business goal above others: “Engagement,” a measure of the views, time spent and interactions with online videos. Conversations with over twenty people who work at, or recently left, YouTube reveal a corporate leadership unable or unwilling to act on these internal alarms for fear of throttling engagement.
In response to criticism about prioritizing growth over safety, Facebook Inc. has proposed a dramatic shift in its core product. YouTube still has struggled to explain any new corporate vision to the public and investors – and sometimes, to its own staff. Five senior personnel who left YouTube and Google in the last two years privately cited the platform’s inability to tame extreme, disturbing videos as the reason for their departure. Within Google, YouTube’s inability to fix its problems has remained a major gripe. Google shares slipped in late morning trading in New York on Tuesday, leaving them up 15 percent so far this year. Facebook stock has jumped more than 30 percent in 2019, after getting hammered last year.
YouTube’s inertia was illuminated again after a deadly measles outbreak drew public attention to vaccinations conspiracies on social media several weeks ago. New data from Moonshot CVE, a London-based firm that studies extremism, found that fewer than twenty YouTube channels that have spread these lies reached over 170 million viewers, many who were then recommended other videos laden with conspiracy theories.
So YouTube, then run by Google veteran Salar Kamangar, set a company-wide objective to reach one billion hours of viewing a day, and rewrote its recommendation engine to maximize for that goal. When Wojcicki took over, in 2014, YouTube was a third of the way to the goal, she recalled in investor John Doerr’s 2018 book Measure What Matters.
“They thought it would break the internet! But it seemed to me that such a clear and measurable objective would energize people, and I cheered them on,” Wojcicki told Doerr. “The billion hours of daily watch time gave our tech people a North Star.” By October, 2016, YouTube hit its goal.
❞
YouTube doesn’t give an exact recipe for virality. But in the race to one billion hours, a formula emerged: Outrage equals attention. It’s one that people on the political fringes have easily exploited, said Brittan Heller, a fellow at Harvard University’s Carr Center. “They don’t know how the algorithm works,” she said. “But they do know that the more outrageous the content is, the more views.”
People inside YouTube knew about this dynamic. Over the years, there were many tortured debates about what to do with troublesome videos—those that don’t violate its content policies and so remain on the site. Some software engineers have nicknamed the problem “bad virality.”
Yonatan Zunger, a privacy engineer at Google, recalled a suggestion he made to YouTube staff before he left the company in 2016. He proposed a third tier: Videos that were allowed to stay on YouTube, but, because they were “close to the line” of the takedown policy, would be removed from recommendations. “Bad actors quickly get very good at understanding where the bright lines are and skating as close to those lines as possible,” Zunger said.
His proposal, which went to the head of YouTube policy, was turned down. “I can say with a lot of confidence that they were deeply wrong,” he said.
Rather than revamp its recommendation engine, YouTube doubled down. The neural network described in the 2016 research went into effect in YouTube recommendations starting in 2015. By the measures available, it has achieved its goal of keeping people on YouTube.
“It’s an addiction engine,” said Francis Irving, a computer scientist who has written critically about YouTube’s AI system.
Wojcicki and her lieutenants drew up a plan. YouTube called it Project Bean or, at times, “Boil The Ocean,” to indicate the enormity of the task. (Sometimes they called it BTO3 – a third dramatic overhaul for YouTube, after initiatives to boost mobile viewing and subscriptions.) The plan was to rewrite YouTube’s entire business model, according to three former senior staffers who worked on it.
It centered on a way to pay creators that isn’t based on the ads their videos hosted. Instead, YouTube would pay on engagement—how many viewers watched a video and how long they watched. A special algorithm would pool incoming cash, then divvy it out to creators, even if no ads ran on their videos. The idea was to reward video stars shorted by the system, such as those making sex education and music videos, which marquee advertisers found too risqué to endorse.
Coders at YouTube labored for at least a year to make the project workable. But company managers failed to appreciate how the project could backfire: paying based on engagement risked making its “bad virality” problem worse since it could have rewarded videos that achieved popularity achieved by outrage. One person involved said that the algorithms for doling out payments were tightly guarded. If it went into effect then, this person said, it’s likely that someone like Alex Jones—the Infowars creator and conspiracy theorist with a huge following on the site, before YouTube booted him last August—would have suddenly become one of the highest paid YouTube stars.
In February of 2018, the video calling the Parkland shooting victims “crisis actors” went viral on YouTube’s trending page. Policy staff suggested soon after limiting recommendations on the page to vetted news sources. YouTube management rejected the proposal, according to a person with knowledge of the event. The person didn’t know the reasoning behind the rejection, but noted that YouTube was then intent on accelerating its viewing time for videos related to news.
]]>Data is the New Oil
▻https://hackernoon.com/data-is-the-new-oil-1227197762b2?source=rss----3a8144eabfe3---4
“Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”— Clive HumbyDeep Learning is a revolutionary field, but for it to work as intended, it requires data. The area related to these big datasets is known as Big Data, which stands for the abundance of digital data. Data is as important for Deep Learning algorithms as the architecture of the network itself, i.e., the software. Acquiring and cleaning the data is one of the most valuable aspects of the work. Without data, the neural networks cannot learn.Most of the time, researchers can use the data given to them directly, but there are many (...)
]]>Dueling Neural Networks
▻https://hackernoon.com/dueling-neural-networks-a063af14f62e?source=rss----3a8144eabfe3---4
“What I cannot create, I do not understand.”— Richard FeynmanGANs generated by a computerThe above images look real, but more than that, they look familiar. They resemble a famous actress that you may have seen on television or in the movies. They are not real, however. A new type of neural network created them.Generative Adversarial Networks (GANs), sometimes called generative networks, generated these fake images. The NVIDIA research team used this new technique by feeding thousands of photos of celebrities to a neural network. The neural network produced thousands of pictures, like the ones above, that resembled the famous faces. They look real, but machines created them. #gans allow researchers to build images that look like the real ones that share many of the features the neural (...)
#birthday-paradox #deep-learning #generative-adversarial #machine-learning
]]>#perceptron — Deep Learning Basics
▻https://hackernoon.com/perceptron-deep-learning-basics-3a938c5f84b6?source=rss----3a8144eabfe3-
Perceptron — Deep Learning BasicsAn upgrade to McCulloch-Pitts Neuron.Perceptron is a fundamental unit of the neural network which takes weighted inputs, process it and capable of performing binary classifications. In this post, we will discuss the working of the Perceptron Model. This is a follow-up blog post to my previous post on McCulloch-Pitts Neuron.In 1958 Frank Rosenblatt proposed the perceptron, a more generalized computational model than the McCulloch-Pitts Neuron. The important feature in the Rosenblatt proposed perceptron was the introduction of weights for the inputs. Later in 1960s Rosenblatt’s Model was refined and perfected by Minsky and Papert. Rosenblatt’s model is called as classical perceptron and the model analyzed by Minsky and Papert is called perceptron.Disclaimer: (...)
#neurons #artificial-intelligence #deep-learning #deep-learning-basics
]]>Can #blockchain with Artificial Intelligence Fight Deep Fake?
▻https://hackernoon.com/can-blockchain-with-artificial-intelligence-fight-deep-fake-9b899b4d45e7
Truth has been the subject of discussion in its own rights, objectively and independently of the ways we think about it or describe it, for many ages. Philosophical theories about truth may have many relative grounds but in mathematics there exist absolute truth.Can truth shapeshift? In an emotion based market, truth is subjective to the intellectual spectrum of people’s belief and opinion. The deepfake video of Barack Obama’s speech created by BuzzFeed using power face swapping neural networking technology is one such example.▻https://medium.com/media/d1eb9049368b3a8bf7a4dd9b5a92a8c2/hrefSo what is a deep fake?“Deepfake, a portmanteau of “deep learning” and “fake”,[1] is an artificial intelligence-based human image synthesis technique. It is used to combine and superimpose existing images and (...)
#machine-learning #artificial-intelligence #deep-learning #venture-capital
]]>Building a Neural Network Only Using NumPy
▻https://hackernoon.com/building-a-neural-network-only-using-numpy-7ba75da60ec0?source=rss----3a
Using Andrew Ng’s Project Structure to Build a Neural Net in PythonIntroductionAfter having completed the deeplearning.ai Deep Learning specialization taught by Andrew Ng, I have decided to work through some of the assignments of the specialization and try to figure out the code myself without only filling in certain parts of it. Doing so, I want to deepen my understanding of neural networks and help others gain intuition by documenting my progress in articles. The complete notebook is available here.In this article, I’m going to build a neural network in #python only using NumPy based on the project structure proposed in the deeplearning.ai Deep Learning specialization:Define the structure of the neural network2. Initialize the parameters of the neural network defined in step one3. Loop (...)
#deep-learning #machine-learning #artificial-intelligence #data-science
]]>How we used #ai to hybridize humans with cartoon animals and made a business out of it.
▻https://hackernoon.com/how-we-used-ai-to-hybridize-humans-with-cartoon-animals-and-made-a-busin
Have you ever imagined yourself as a cartoon character? Well, now this is more than real.We are a team of 20 engineers and art designers who have developed a machine learning technology that morphs human faces with animated characters.The process starts by constructing a user’s 3D face model from just a single selfie shot. Importantly, our technology even works with older, regular smartphone cameras. With this single photo, our neural network builds a 3D mesh of the user’s head that looks like this:The neural network regresses a 3D model from a 2D photoNext, 3 other neural networks swing into action. The first draws eyebrows, the second detects and matches eye color, and the third detects and draws glasses if the user is wearing them. When these elements are ready, we morph the user with (...)
]]>GIPSA-lab invite Pablo JENSEN, directeur de recherche CNRS au Laboratoire de Physique de l’ENS de LYON pour un séminaire exceptionnel le 10 janvier 2019 à 10h30.
The unexpected link between neural nets and liberalism
Sixty years ago, Arthur Rosenblatt, a psychologist working for the army invented the perceptron, the first neural network capable of learning. Unexpectedly, Rosenblatt cites, as a major source of inspiration, an economist: Friedrich Hayek. He is well-known for his 1974 Nobel prize… and by his ultra-liberal stances, justifying the Pinochet coup in a Chilean newspaper: «Personally, I prefer a liberal dictator to a democratic government that lacks liberalism». This talk presents ongoing work on the link between Hayek’s ideology and neural networks.
After a PhD on experimental condensed-matter physics, Pablo JENSEN worked for 15 years on the modeling of nanostructure growth. This lead to major publications in top journals, including Nature, Phys Rev Lett and a widely cited review in Rev Mod Phys. After these achievements, he decided to follow an unconventional path and switch to the modeling of social systems. It takes time to become familiar with social science topics and literature, but it is mandatory to establish serious interdisciplinary connections. During that period, he also had national responsibilities at CNRS, to improve communication of physics. This investment has now started to pay, as shown by recent publications in major interdisciplinary or social science (geography, economics, sociology) journals, including PNAS, J Pub Eco and British J Sociology. His present work takes advantage of the avalanche of social data available on the Web to improve our understanding of society. To achieve this, he collaborate with hard scientists to develop appropriate analysis tools and with social scientists to find relevant questions and interpretations.
His last book : Pourquoi la société ne se laisse pas mettre en équations, Pablo Jensen, Seuil, coll. “Science ouverte”, mars 2018
Personal Web page : ▻http://perso.ens-lyon.fr/pablo.jensen
Lieu du séminaire : Laboratoire GIPSA-lab, 11 rue des Mathématiques, Campus de Saint Martin d’Hères, salle Mont-Blanc (bâtiment Ampère D, 1er étage)
]]>Joel Simon - Dimensions of dialogue
▻http://joelsimon.net/dimensions-of-dialogue.html
Here, new writing systems are created by challenging two neural networks to communicate information via images. Using the magic of machine learning, the networks attempt to create their own emergent language isolate that is robust to noise.
]]>Interview. How Neural Networks And Machine Learning Are Making #games More Interesting
▻https://hackernoon.com/interview-how-neural-networks-and-machine-learning-are-making-games-more
[Interview] How Neural Networks And Machine Learning Are Making Games More InterestingImage credit: UnsplashMachine learning and neural networks are hot topics in many tech areas, and the game dev is one of them. There such new technologies are used to make games more interesting.How this is achieved, what companies are now leaders in new tech adoption and research when we as users will see any notable results of this research and lots more to be discussed today. We will talk to Vladimir Ivanov, the leading ML in gaming expert.The first question is: what do you mean when talking that games are “not interesting” and the new tech could fix this?Well, the thing is pretty simple: if we are talking not about human vs. human game mode, you need to compete with bots. Often this is not that (...)
#gamedev #machine-learning #game-development #artificial-intelligence
]]>Preprocess Keras Model for TensorSpace
▻https://hackernoon.com/preprocess-keras-model-for-tensorspace-ed5e4db9a2a1?source=rss----3a8144
How to preprocess Keras model to be TensorSpace compatible for neural network 3D visualizationTensorSpace & Keras“TensorSpace is a neural network 3D visualization framework. — TensorSpace.org”“Keras is a high-level neural network API. — keras.io ”IntroductionYou may learn about TensorSpace can be used to 3D visualize the neural networks. You might have read my previous introduction about TensorSpace. Maybe you find it is a little complicated on the model preprocess.Hence today, I want to talk about the model preprocess of TensorSpace for more details. To be more specific, how to preprocess the deep learning model built by Keras to be TensorSpace compatible.Fig. 1 — Use TensorSpace to visualize an LeNet built by KerasWhat we should have?To make a model built by Keras to be TensorSpace compatible, (...)
#python #data-visualization #machine-learning #technology #javascript
]]>How Artists Can Set Up Their Own Neural Network — Part 3 — Image Generation
▻https://hackernoon.com/how-artists-can-set-up-their-own-neural-network-part-3-image-generation-
How Artists Can Set Up Their Own Neural Network — Part 3 — Image GenerationAlright, so we’ve installed linux and the neural network now it’s time to actually run it!First though I want to apologize for the delay in getting these last two parts of the #tutorial series out. As I explained in my Skonk Works post, I’ve been learning so fast that it’s actually been kind of hard to catch time to digest and write any of it down.For instance, this tutorial series began with teaching you how to install Ubuntu 16.04, but support for Ubuntu 16.04 has just ended and you really should install Ubuntu 18.04, which is what I did after wiping my desktop and turning it into a fulltime personal cloud server! This is good because now I have a completely dedicated Linux machine to run neural network batch jobs on (...)
#artist #artist-neural-network #neural-networks #setup-neural-network
]]>How to optimize C and C++ code in 2018—Iurii Krasnoshchok
▻http://isocpp.org/feeder/?FeederAction=clicked&feed=All+Posts&seed=http%3A%2F%2Fisocpp.org%2Fblog%2F2
Are you aware?
How to optimize C and C++ code in 2018 by Iurii Krasnoshchok
From the article:
We are still limited by our current hardware. There are numerous areas where it just not good enough: neural networks and virtual reality to name a few. There are plenty of devices where battery life is crucial, and we must count every single CPU tick. Even when we’re talking about clouds and microservices and lambdas, there are enormous data centers that consume vast amounts of electricity. Even boring tests routine may quietly start to take 5 hours to run. And this is tricky. Program performance doesn‘t matter, only until it does. A modern way to squeeze performance out of silicon is to make hardware more and more (...)
#News,Articles&_Books,
]]>Fake fingerprints can imitate real ones in biometric systems – research
▻https://www.theguardian.com/technology/2018/nov/15/fake-fingerprints-can-imitate-real-fingerprints-in-biometric-systems-re
DeepMasterPrints created by a machine learning technique have error rate of only one in five Researchers have used a neural network to generate artificial fingerprints that work as a “master key” for biometric identification systems and prove fake fingerprints can be created. According to a paper presented at a security conference in Los Angeles, the artificially generated fingerprints, dubbed “DeepMasterPrints” by the researchers from New York University, were able to imitate more than one (...)
▻https://i.guim.co.uk/img/media/132ddbcc93e3444767f5a1d170ca1b8273f9d665/0_0_1079_647/master/1079.png
]]>In the Age of A.I., Is Seeing Still Believing ? | The New Yorker
▻https://www.newyorker.com/magazine/2018/11/12/in-the-age-of-ai-is-seeing-still-believing
In a media environment saturated with fake news, such technology has disturbing implications. Last fall, an anonymous Redditor with the username Deepfakes released a software tool kit that allows anyone to make synthetic videos in which a neural network substitutes one person’s face for another’s, while keeping their expressions consistent. Along with the kit, the user posted pornographic videos, now known as “deepfakes,” that appear to feature various Hollywood actresses. (The software is complex but comprehensible: “Let’s say for example we’re perving on some innocent girl named Jessica,” one tutorial reads. “The folders you create would be: ‘jessica; jessica_faces; porn; porn_faces; model; output.’ ”) Around the same time, “Synthesizing Obama,” a paper published by a research group at the University of Washington, showed that a neural network could create believable videos in which the former President appeared to be saying words that were really spoken by someone else. In a video voiced by Jordan Peele, Obama seems to say that “President Trump is a total and complete dipshit,” and warns that “how we move forward in the age of information” will determine “whether we become some kind of fucked-up dystopia.”
“People have been doing synthesis for a long time, with different tools,” he said. He rattled off various milestones in the history of image manipulation: the transposition, in a famous photograph from the eighteen-sixties, of Abraham Lincoln’s head onto the body of the slavery advocate John C. Calhoun; the mass alteration of photographs in Stalin’s Russia, designed to purge his enemies from the history books; the convenient realignment of the pyramids on the cover of National Geographic, in 1982; the composite photograph of John Kerry and Jane Fonda standing together at an anti-Vietnam demonstration, which incensed many voters after the Times credulously reprinted it, in 2004, above a story about Kerry’s antiwar activities.
“In the past, anybody could buy Photoshop. But to really use it well you had to be highly skilled,” Farid said. “Now the technology is democratizing.” It used to be safe to assume that ordinary people were incapable of complex image manipulations. Farid recalled a case—a bitter divorce—in which a wife had presented the court with a video of her husband at a café table, his hand reaching out to caress another woman’s. The husband insisted it was fake. “I noticed that there was a reflection of his hand in the surface of the table,” Farid said, “and getting the geometry exactly right would’ve been really hard.” Now convincing synthetic images and videos were becoming easier to make.
The acceleration of home computing has converged with another trend: the mass uploading of photographs and videos to the Web. Later, when I sat down with Efros in his office, he explained that, even in the early two-thousands, computer graphics had been “data-starved”: although 3-D modellers were capable of creating photorealistic scenes, their cities, interiors, and mountainscapes felt empty and lifeless. True realism, Efros said, requires “data, data, data” about “the gunk, the dirt, the complexity of the world,” which is best gathered by accident, through the recording of ordinary life.
Today, researchers have access to systems like ImageNet, a site run by computer scientists at Stanford and Princeton which brings together fourteen million photographs of ordinary places and objects, most of them casual snapshots posted to Flickr, eBay, and other Web sites. Initially, these images were sorted into categories (carrousels, subwoofers, paper clips, parking meters, chests of drawers) by tens of thousands of workers hired through Amazon Mechanical Turk. Then, in 2012, researchers at the University of Toronto succeeded in building neural networks capable of categorizing ImageNet’s images automatically; their dramatic success helped set off today’s neural-networking boom. In recent years, YouTube has become an unofficial ImageNet for video. Efros’s lab has overcome the site’s “platform bias”—its preference for cats and pop stars—by developing a neural network that mines, from “life style” videos such as “My Spring Morning Routine” and “My Rustic, Cozy Living Room,” clips of people opening packages, peering into fridges, drying off with towels, brushing their teeth. This vast archive of the uninteresting has made a new level of synthetic realism possible.
In 2016, the Defense Advanced Research Projects Agency (DARPA) launched a program in Media Forensics, or MediFor, focussed on the threat that synthetic media poses to national security. Matt Turek, the program’s manager, ticked off possible manipulations when we spoke: “Objects that are cut and pasted into images. The removal of objects from a scene. Faces that might be swapped. Audio that is inconsistent with the video. Images that appear to be taken at a certain time and place but weren’t.” He went on, “What I think we’ll see, in a couple of years, is the synthesis of events that didn’t happen. Multiple images and videos taken from different perspectives will be constructed in such a way that they look like they come from different cameras. It could be something nation-state driven, trying to sway political or military action. It could come from a small, low-resource group. Potentially, it could come from an individual.”
As with today’s text-based fake news, the problem is double-edged. Having been deceived by a fake video, one begins to wonder whether many real videos are fake. Eventually, skepticism becomes a strategy in itself. In 2016, when the “Access Hollywood” tape surfaced, Donald Trump acknowledged its accuracy while dismissing his statements as “locker-room talk.” Now Trump suggests to associates that “we don’t think that was my voice.”
“The larger danger is plausible deniability,” Farid told me. It’s here that the comparison with counterfeiting breaks down. No cashier opens up the register hoping to find counterfeit bills. In politics, however, it’s often in our interest not to believe what we are seeing.
As alarming as synthetic media may be, it may be more alarming that we arrived at our current crises of misinformation—Russian election hacking; genocidal propaganda in Myanmar; instant-message-driven mob violence in India—without it. Social media was enough to do the job, by turning ordinary people into media manipulators who will say (or share) anything to win an argument. The main effect of synthetic media may be to close off an escape route from the social-media bubble. In 2014, video of the deaths of Michael Brown and Eric Garner helped start the Black Lives Matter movement; footage of the football player Ray Rice assaulting his fiancée catalyzed a reckoning with domestic violence in the National Football League. It seemed as though video evidence, by turning us all into eyewitnesses, might provide a path out of polarization and toward reality. With the advent of synthetic media, all that changes. Body cameras may still capture what really happened, but the aesthetic of the body camera—its claim to authenticity—is also a vector for misinformation. “Eyewitness video” becomes an oxymoron. The path toward reality begins to wash away.
]]>Deep learning of aftershock patterns following large earthquakes | Nature
▻https://www.nature.com/articles/s41586-018-0438-y
we use a deep-learning approach to identify a static-stress-based criterion that forecasts aftershock locations without prior assumptions about fault orientation. We show that a neural network trained on more than 131,000 mainshock–aftershock pairs can predict the locations of aftershocks in an independent test dataset of more than 30,000 mainshock–aftershock pairs more accurately (area under curve of 0.849) than can classic Coulomb failure stress change (area under curve of 0.583). We find that the learned aftershock pattern is physically interpretable
]]>Forecasting Market Movements Using #tensorflow
▻https://hackernoon.com/forecasting-market-movements-using-tensorflow-fb73e614cd06?source=rss---
Photo by jesse orrico on UnsplashMulti-Layer Perceptron for ClassificationIs it possible to create a neural network for predicting daily market movements from a set of standard trading indicators?In this post we’ll be looking at a simple model using Tensorflow to create a framework for testing and development, along with some preliminary results and suggested improvements.The ML Task and Input FeaturesTo keep the basic design simple, it’s setup for a binary classification task, predicting whether the next day’s close is going to be higher or lower than the current, corresponding to a prediction to either go long or short for the next time period. In reality, this could be applied to a bot which calculates and executes a set of positions at the start of a trading day to capture the day’s (...)
]]>OpenAI teaching neural networks to compete with Dota 2 professionals
▻https://www.gamesindustry.biz/articles/2018-07-02-openai-teaching-neural-networks-to-compete-with-dota-2-pro
Team of five neural networks has already beaten multiple amateur human teams
]]> “[they] train a neural network to interpret the way radio WiFi signals bounce off a person’s body and translate it into the movement of 14 different key points on the body.” "Let’s say the police want to use such a device to see behind a wall"
▻https://motherboard.vice.com/amp/en_us/article/a3aaqp/mit-device-uses-wifi-to-see-through-walls-and-track-your-movements
How to Initialize weights in a neural net so it performs well?
▻https://hackernoon.com/how-to-initialize-weights-in-a-neural-net-so-it-performs-well-3e9302d449
How to Initialize weights in a neural net so it performs well? — Super fast explanation for Xavier’s Random Weight Initialization▻http://www.mdpi.com/1099-4300/19/3/101We know that in a neural network, weights are initialized usually randomly and that kind of initialization takes fair / significant amount of repetitions to converge to the least loss and reach to the ideal weight matrix. The problem is, this kind of initialization is prone to vanishing or exploding gradient problems.One way to reduce this problem is carefully choosing the random weight initialization. Xavier’s random weight initialization aka Xavier’s algorithm factors into the equation the size of the network (number of input and output neurons) and addresses these problems.Xavier Glorot and Yoshua Bengio are the (...)
#andrew-ng #deep-learning #deep-neural-networks #machine-learning #neural-networks
]]>DeepLearning 101 : #coursera Vs #udemy Vs #udacity
▻https://hackernoon.com/deeplearning-101-coursera-vs-udemy-vs-udacity-b4eb3de06dbe?source=rss---
The era of self-learningDeep Learning has taken the world by storm and the juggernaut has kept rolling since early 2017.As far as the core methodology goes, neural networks have been around since decades and convolutional neural networks and recurrent neural networks have been around since 15 odd years.What has changed suddenly you ask? GPUs and breakthroughs in automated systems like self driving cars. As Andrew Ng himself says, “I think the other reasons the term deep learning has taken off is just branding. These things are just neural networks with more hidden layers, but the phrase deep learning is just a great brand, it’s just so deep.”Developers all around the globe are heavily motivated seeing the innovation DL is driving in each and every sector. Every company is either pitching (...)
]]>Tutorial for Artists on how to use a Neural Network — Part 2
▻https://hackernoon.com/tutorial-for-artists-on-how-to-use-a-neural-network-part-2-4e94e1d2cbe9?
Originally published at www.jackalope.tech on April 30, 2018.Okay so we’ve installed the Ubuntu partition last week, and now we’re going to install the neural network Deep Style. This is where stuff is probably the most difficult. I’m going to equip you with the tools to solve those problems.What is a CLI?When you use a program such as your internet browser, or Photoshop you are using a Graphical User Interface. GUI. Before GUI there were CLI . Command Line Interface. A GUI allows you to control a program using buttons. A command line interface allows you to control it using written commands. When you tell Siri to navigate you to your friends house, you are in a way using a modern version of a CLI. Siri is much more sophisticated though. It can listen to your voice and translate that into (...)
#artisits #deep-learning #neural-networks #neural-network-deep-style #artist-neural-network
]]>Understanding YOLO
▻https://hackernoon.com/understanding-yolo-f5a74bbc7967?source=rss----3a8144eabfe3---4
This article explains the YOLO object detection architecture, from the point of view of someone who wants to implement it from scratch. It will not describe the advantages/disadvantages of the network or the reasons for each design choice. Instead, it focus on how it works. You should have a basic understanding of neural networks, specially CNNS, before you read this.All the descriptions in this post are related to the original YOLO paper: You Only Look Once: Unified, Real-Time Object Detection by Joseph Redmon, Santosh Divvala, Ross Girshick and Ali Farhadi (2015). There have been many improvements proposed since then, that were combined in the newer YOLOv2 version which I might write about another time. It is easier to understand this original version first, and then check what (...)
#object-detection #deep-learning #machine-learning #computer-vision #neural-networks
]]>Trends and Challenges in Cloud Computing with Deep Learning
▻https://hackernoon.com/trends-and-challenges-in-cloud-computing-with-deep-learning-33c23e9201a9
Artificial intelligence is ubiquitous. From daily transactional tasks like online shopping to bank transactions to robotics every field is affected by it. Deep learning a part of machine learning has made its presence felt in the machine learning world. Major players like Facebook, Microsoft and Google are all using it. However, for deep learning to be effective it requires huge amounts of data. Deep learning architecture ensures many layers of the neural network. “Deep” will be useful when the depth i.e. number of layers are more in number. This requires more storage for this large amount of data needed for training.The power requirements also increase as the tasks become computationally intensive. So the traditional computers may not work very effectively. Also this leads to more (...)
#deep-learning #cloud-computing #machine-learning #deep-learning-cloud #big-data-analytics
]]>5 advantages of the top-down approach in the creation of AI
▻https://hackernoon.com/five-advantages-of-the-top-down-approach-in-the-creation-of-ai-3e4166a74
Nowadays, the progress in the development of the neural networks has shifted the focus in the creation of AI towards the «top-down» approach. At the same time, we may notice a certain decrease of the advancement pace towards this approach.Our team, mainly, consists of the psychologists and the psychoanalysts. For many years we have been modeling the mental processes in various IT-products and this predetermined our choice of strategy in the creation of AI. We have chosen the «top-down» approach. Similar views on this issue of such authorities in AI technologies as Marvin Minsky and Seymour Papert strengthen our confidence.In the article I will constantly compare these two approaches. We assume that among the solutions which must form the basis of a strong AI have to be such that founded (...)
#artificial-intelligence #neural-networks #machine-learning #marvin-minsky #psychology
]]>This Neural Net Hallucinates Sheep - Facts So Romantic
▻http://nautil.us/blog/this-neural-net-hallucinates-sheep
If you’ve been on the internet today, you’ve probably interacted with a neural network. They’re a type of machine learning algorithm that’s used for everything from language translation to finance modeling. One of their specialties is image recognition. Several companies—including Google, Microsoft, IBM, and Facebook—have their own algorithms for labeling photos. But image recognition algorithms can make really bizarre mistakes.Janelle ShaneMicrosoft Azure’s computer vision API added the above caption and tags. But there are no sheep in the image of above. None. I zoomed all the way in and inspected every speck. Janelle ShaneIt also tagged sheep in this image. I happen to know there were sheep nearby. But none actually present.It hasn’t realized that “sheep” means the actual animal, not just a (...)
]]>The Shallowness of Google Translate - The Atlantic
▻https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570
Un excellent papier par Douglas Hofstadter (ah, D.H., Godel, Escher et Bach... !!!)
As a language lover and an impassioned translator, as a cognitive scientist and a lifelong admirer of the human mind’s subtlety, I have followed the attempts to mechanize translation for decades. When I first got interested in the subject, in the mid-1970s, I ran across a letter written in 1947 by the mathematician Warren Weaver, an early machine-translation advocate, to Norbert Wiener, a key figure in cybernetics, in which Weaver made this curious claim, today quite famous:
When I look at an article in Russian, I say, “This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode.”
Some years later he offered a different viewpoint: “No reasonable person thinks that a machine translation can ever achieve elegance and style. Pushkin need not shudder.” Whew! Having devoted one unforgettably intense year of my life to translating Alexander Pushkin’s sparkling novel in verse Eugene Onegin into my native tongue (that is, having radically reworked that great Russian work into an English-language novel in verse), I find this remark of Weaver’s far more congenial than his earlier remark, which reveals a strangely simplistic view of language. Nonetheless, his 1947 view of translation-as-decoding became a credo that has long driven the field of machine translation.
Before showing my findings, though, I should point out that an ambiguity in the adjective “deep” is being exploited here. When one hears that Google bought a company called DeepMind whose products have “deep neural networks” enhanced by “deep learning,” one cannot help taking the word “deep” to mean “profound,” and thus “powerful,” “insightful,” “wise.” And yet, the meaning of “deep” in this context comes simply from the fact that these neural networks have more layers (12, say) than do older networks, which might have only two or three. But does that sort of depth imply that whatever such a network does must be profound? Hardly. This is verbal spinmeistery .
I began my explorations very humbly, using the following short remark, which, in a human mind, evokes a clear scenario:
In their house, everything comes in pairs. There’s his car and her car, his towels and her towels, and his library and hers.
The translation challenge seems straightforward, but in French (and other Romance languages), the words for “his” and “her” don’t agree in gender with the possessor, but with the item possessed. So here’s what Google Translate gave me:
Dans leur maison, tout vient en paires. Il y a sa voiture et sa voiture, ses serviettes et ses serviettes, sa bibliothèque et les siennes.
We humans know all sorts of things about couples, houses, personal possessions, pride, rivalry, jealousy, privacy, and many other intangibles that lead to such quirks as a married couple having towels embroidered “his” and “hers.” Google Translate isn’t familiar with such situations. Google Translate isn’t familiar with situations, period. It’s familiar solely with strings composed of words composed of letters. It’s all about ultrarapid processing of pieces of text, not about thinking or imagining or remembering or understanding. It doesn’t even know that words stand for things. Let me hasten to say that a computer program certainly could, in principle, know what language is for, and could have ideas and memories and experiences, and could put them to use, but that’s not what Google Translate was designed to do. Such an ambition wasn’t even on its designers’ radar screens.
It’s hard for a human, with a lifetime of experience and understanding and of using words in a meaningful way, to realize how devoid of content all the words thrown onto the screen by Google Translate are. It’s almost irresistible for people to presume that a piece of software that deals so fluently with words must surely know what they mean. This classic illusion associated with artificial-intelligence programs is called the “Eliza effect,” since one of the first programs to pull the wool over people’s eyes with its seeming understanding of English, back in the 1960s, was a vacuous phrase manipulator called Eliza, which pretended to be a psychotherapist, and as such, it gave many people who interacted with it the eerie sensation that it deeply understood their innermost feelings.
To me, the word “translation” exudes a mysterious and evocative aura. It denotes a profoundly human art form that graciously carries clear ideas in Language A into clear ideas in Language B, and the bridging act not only should maintain clarity, but also should give a sense for the flavor, quirks, and idiosyncrasies of the writing style of the original author. Whenever I translate, I first read the original text carefully and internalize the ideas as clearly as I can, letting them slosh back and forth in my mind. It’s not that the words of the original are sloshing back and forth; it’s the ideas that are triggering all sorts of related ideas, creating a rich halo of related scenarios in my mind. Needless to say, most of this halo is unconscious. Only when the halo has been evoked sufficiently in my mind do I start to try to express it—to “press it out”—in the second language. I try to say in Language B what strikes me as a natural B-ish way to talk about the kinds of situations that constitute the halo of meaning in question.
This process, mediated via meaning, may sound sluggish, and indeed, in comparison with Google Translate’s two or three seconds per page, it certainly is—but it is what any serious human translator does. This is the kind of thing I imagine when I hear an evocative phrase like “deep mind.”
A friend asked me whether Google Translate’s level of skill isn’t merely a function of the program’s database. He figured that if you multiplied the database by a factor of, say, a million or a billion, eventually it would be able to translate anything thrown at it, and essentially perfectly. I don’t think so. Having ever more “big data” won’t bring you any closer to understanding, since understanding involves having ideas, and lack of ideas is the root of all the problems for machine translation today. So I would venture that bigger databases—even vastly bigger ones—won’t turn the trick.
Another natural question is whether Google Translate’s use of neural networks—a gesture toward imitating brains—is bringing us closer to genuine understanding of language by machines. This sounds plausible at first, but there’s still no attempt being made to go beyond the surface level of words and phrases. All sorts of statistical facts about the huge databases are embodied in the neural nets, but these statistics merely relate words to other words, not to ideas. There’s no attempt to create internal structures that could be thought of as ideas, images, memories, or experiences. Such mental etherea are still far too elusive to deal with computationally, and so, as a substitute, fast and sophisticated statistical word-clustering algorithms are used. But the results of such techniques are no match for actually having ideas involved as one reads, understands, creates, modifies, and judges a piece of writing.
Let me return to that sad image of human translators, soon outdone and outmoded, gradually turning into nothing but quality controllers and text tweakers. That’s a recipe for mediocrity at best. A serious artist doesn’t start with a kitschy piece of error-ridden bilgewater and then patch it up here and there to produce a work of high art. That’s not the nature of art. And translation is an art.
In my writings over the years, I’ve always maintained that the human brain is a machine—a very complicated kind of machine—and I’ve vigorously opposed those who say that machines are intrinsically incapable of dealing with meaning. There is even a school of philosophers who claim computers could never “have semantics” because they’re made of “the wrong stuff” (silicon). To me, that’s facile nonsense. I won’t touch that debate here, but I wouldn’t want to leave readers with the impression that I believe intelligence and understanding to be forever inaccessible to computers. If in this essay I seem to come across sounding that way, it’s because the technology I’ve been discussing makes no attempt to reproduce human intelligence. Quite the contrary: It attempts to make an end run around human intelligence, and the output passages exhibited above clearly reveal its giant lacunas.
From my point of view, there is no fundamental reason that machines could not, in principle, someday think, be creative, funny, nostalgic, excited, frightened, ecstatic, resigned, hopeful, and, as a corollary, able to translate admirably between languages. There’s no fundamental reason that machines might not someday succeed smashingly in translating jokes, puns, screenplays, novels, poems, and, of course, essays like this one. But all that will come about only when machines are as filled with ideas, emotions, and experiences as human beings are. And that’s not around the corner. Indeed, I believe it is still extremely far away. At least that is what this lifelong admirer of the human mind’s profundity fervently hopes.
When, one day, a translation engine crafts an artistic novel in verse in English, using precise rhyming iambic tetrameter rich in wit, pathos, and sonic verve, then I’ll know it’s time for me to tip my hat and bow out.
]]>Turning Design Mockups Into Code With Deep Learning - FloydHub Blog
▻https://blog.floydhub.com/turning-design-mockups-into-code-with-deep-learning
Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software.
In this post, we’ll teach a neural network how to code a basic a HTML and CSS website based on a picture of a design mockup.
]]>How an A.I. ‘Cat-and-Mouse Game’ Generates Believable Fake Photos - The New York Times
▻https://www.nytimes.com/interactive/2018/01/02/technology/ai-generated-photos.html
At a lab in Finland, a small team of Nvidia researchers recently built a system that can analyze thousands of (real) celebrity snapshots, recognize common patterns, and create new images that look much the same — but are still a little different. The system can also generate realistic images of horses, buses, bicycles, plants and many other common objects.
The project is part of a vast and varied effort to build technology that can automatically generate convincing images — or alter existing images in equally convincing ways. The hope is that this technology can significantly accelerate and improve the creation of computer interfaces, games, movies and other media, eventually allowing software to create realistic imagery in moments rather than the hours — if not days — it can now take human developers.
In recent years, thanks to a breed of algorithm that can learn tasks by analyzing vast amounts of data, companies like Google and Facebook have built systems that can recognize faces and common objects with an accuracy that rivals the human eye. Now, these and other companies, alongside many of the world’s top academic A.I. labs, are using similar methods to both recognize and create.
As it built a system that generates new celebrity faces, the Nvidia team went a step further in an effort to make them far more believable. It set up two neural networks — one that generated the images and another that tried to determine whether those images were real or fake. These are called generative adversarial networks, or GANs. In essence, one system does its best to fool the other — and the other does its best not to be fooled.
“The computer learns to generate these images by playing a cat-and-mouse game against itself,” said Mr. Lehtinen.
A second team of Nvidia researchers recently built a system that can automatically alter a street photo taken on a summer’s day so that it looks like a snowy winter scene. Researchers at the University of California, Berkeley, have designed another that learns to convert horses into zebras and Monets into Van Goghs. DeepMind, a London-based A.I. lab owned by Google, is exploring technology that can generate its own videos. And Adobe is fashioning similar machine learning techniques with an eye toward pushing them into products like Photoshop, its popular image design tool.
Trained designers and engineers have long used technology like Photoshop and other programs to build realistic images from scratch. This is what movie effects houses do. But it is becoming easier for machines to learn how to generate these images on their own, said Durk Kingma, a researcher at OpenAI, the artificial intelligence lab founded by Tesla chief executive Elon Musk and others, who specializes in this kind of machine learning.
“We now have a model that can generate faces that are more diverse and in some ways more realistic than what we could program by hand,” he said, referring to Nvidia’s work in Finland.
But new concerns come with the power to create this kind of imagery.
With so much attention on fake media these days, we could soon face an even wider range of fabricated images than we do today.
“The concern is that these techniques will rise to the point where it becomes very difficult to discern truth from falsity,” said Tim Hwang, who previously oversaw A.I. policy at Google and is now director of the Ethics and Governance of Artificial Intelligence Fund, an effort to fund ethical A.I. research. “You might believe that accelerates problems we already have.”
But many of us still put a certain amount of trust in photos and videos that we don’t necessarily put in text or word of mouth. Mr. Hwang believes the technology will evolve into a kind of A.I. arms race pitting those trying to deceive against those trying to identify the deception.
Mr. Lehtinen downplays the effect his research will have on the spread of misinformation online. But he does say that, as a time goes on, we may have to rethink the very nature of imagery. “We are approaching some fundamental questions,” he said.
#Image #Fake_news #Post_truth #Intelligence_artificielle #AI_war #Désinformation
]]>[1710.10777] Understanding Hidden Memories of Recurrent Neural Networks
▻https://arxiv.org/abs/1710.10777
Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs’ hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN’s hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.
]]>Visualizing neural networks as large directed graphs [OC] : dataisbeautiful
▻https://www.reddit.com/r/dataisbeautiful/comments/78vo65/visualizing_neural_networks_as_large_directed
Been a while since I posted on here regarding the large directed graph visualization that I have been doing whilst working at www.graphcore.ai. I am continually moving these forwards as I understand how to push the size of the graph and get good results. The image here is the first time I have been able to generate a full layout of the ResNet-50 training graph which is a neural network that came out of Microsoft research. It has ~3 million nodes and ~10 million edges and uses Gephi for the graph layout.
▻https://www.graphcore.ai/posts/graph-computing-for-machine-intelligence-with-poplar
#beau (j’ai juste admiré cette #visualisation mais pas lu #machine_learning :))
]]>Your Data is Being Manipulated - danah boyd – Data & Society: Points
►https://points.datasociety.net/your-data-is-being-manipulated-a7e31a83577b
Practical Black-Box Attacks against Machine, March 19, 2017. The images in the top row are altered to disrupt the neural network leading to the misinterpretation on the bottom row. The alterations are not visible to the human eye.
#IA #machine_learning #manipulation #pirates #données via @amtpl
Only when journalists shame us by finding ways to trick our systems into advertising to neo-Nazis do we pay attention. Yet, far more maliciously intended actors are starting to play the long game in messing with our data. Why aren’t we trying to get ahead of this?
un peu de #théorie_des_jeux ?
Algoliterary Encounter
▻http://constantvzw.org/site/Algoliterary-Encounter.html
In the framework of Saison Numérique the Maison du Livre opens its space for #Algolit during three days in a row. The group presents lectures, workshops and a small #Exhibition about the narrative perspective of neural networks. Neural networks are selflearning algorithms based on statistics. They often function as opaque ’blackbox’ algorithms, while they shape applications that are daily used on a worldwide scale, like search engines on the web, translation machines, advertising profiling, (...)
Algolit / #Workshop, #Lecture, Exhibition, #Hybrid_languages, #Literature, #Algorithm
]]>Dreams of imaginary people, by Mike Tyka, 2017
▻http://www.miketyka.com/projects/dreams
Neural Network - CodeProject
▻https://www.codeproject.com/Articles/1200392/Neural-Network
This article provides simple and complete explanation for neural network with practical example. You will read here that what happens exactly in human brain and also in artificial implementation.
Trop,de biologisme, mais une approche pédagogique de l’implémentation informatique.
#Réseaux_neurones #connexionisme