Category Archives: Artificial Intelligence

How IBM Watson saved the life of a woman dying from cancer, exec says – Business Insider

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“There’s a 60-year-old woman in Tokyo. She was at the University of Tokyo. She had been diagnosed with leukemia six years ago. She was living, but not healthy. So the University of Tokyo ran her genomic sequence through Watson and it was able to ascertain that they were off by one thing. Actually, she had two strains of leukemia. The did treat her and she is healthy.”

He added, “That’s one example. Statistically, we’re seeing that about one third of the time, Watson is proposing an additional diagnosis.”

Source: How IBM Watson saved the life of a woman dying from cancer, exec says – Business Insider

Unfortunately he then goes on to say how great Watson is at pushing ads at you.

AMD Introduces Radeon Instinct Machine Intelligence And Deep Learning Accelerators

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AMD is announcing a new series of Radeon-branded products today, targeted at machine intelligence (AI) and deep learning enterprise applications, called Radeon Instinct. As its name suggests, the new Radeon Instinct line of products are comprised of GPU-based solutions for deep learning, inference, and training. The new GPUs are also complemented by a free, open-source library and framework for GPU accelerators, dubbed MIOpen. MIOpen is architected for high-performance machine intelligence applications, and is optimized for the deep learning frameworks in AMD’s ROCm software suite
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The first products in the lineup consist of the Radeon Instinct MI6, the MI8, and the MI25. The 150W Radeon Instinct MI6 accelerator is powered by a Polaris-based GPU, packs 16GB of memory (224GB/s peak bandwidth), and will offer up to 5.7 TFLOPS of peak FP16 performance. Next up in the stack is the Fiji-based Radeon Instinct MI8. Like the Radeon R9 Nano, the Radeon Instinct MI8 features 4GB of High-Bandwidth Memory (HBM), with peak bandwidth of 512GB/s — it’s got a nice small form factor too. The MI8 will offer up to 8.2 TFLOPS of peak FP16 compute performance, with a board power that typical falls below 175W. The Radeon Instinct MI25 accelerator will leverage AMD’s next-generation Vega GPU architecture and has a board power of approximately 300W.

Source: AMD Introduces Radeon Instinct Machine Intelligence And Deep Learning Accelerators

Chicago Face Database

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The Chicago Face Database was developed at the University of Chicago by Debbie S. Ma, Joshua Correll, and Bernd Wittenbrink. The CFD is intended for use in scientific research. It provides high-resolution, standardized photographs of male and female faces of varying ethnicity between the ages of 17-65. Extensive norming data are available for each individual model. These data include both physical attributes (e.g., face size) as well as subjective ratings by independent judges (e.g., attractiveness).

Source: Chicago Face Database

TensorFlow — Googles’ Open Source Software Library for Machine Intelligence

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TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well

Source: TensorFlow — an Open Source Software Library for Machine Intelligence

Google’s AI translation tool seems to have invented its own secret internal language

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If you can translate from A to B and from B to C can you translate from A to C without learning the translations directly? Well yes you can. So the translate AI has created its’ own language B (we think) that can function as a midpoint between not only A and C, but also D,E,F, etc.
Would it be what Esperanto wanted to be?

Source: Google’s AI translation tool seems to have invented its own secret internal language | TechCrunch

Miles Deep – AI Porn Video Editor

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Using a deep convolutional neural network with residual connections, Miles Deep quickly classifies each second of a pornographic video into 6 categories based on sexual act with 95% accuracy. Then it uses that classification to automatically edit the video. It can remove all the scenes not containing sexual contact, or edit out just a specific act.

Unlike Yahoo’s recently released NSFW model, which uses a similar architecture, Miles Deep can tell the difference between nudity and various explicit sexual acts. As far as I know this is the first and only public pornography classification or editing tool.

Source: GitHub – ryanjay0/miles-deep: Deep Learning Porn Video Classifier/Editor with Caffe

Google’s Photo Scan App Makes Backing Up Old Snapshots Easy as Hell

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The Photo Scan app launched by Google today for iOS and Android lets you scan printed photos in just a couple of seconds, using machine learning to correct imperfections in the capture process that they look great every time.

Here’s how it works: Download the app, and open it up. You’ll see a viewfinder. Hold your phone over the printed photo you want to make a digital copy of, and make sure it fits entirely in the frame. Tap the shutter button once.

Next, four white dots will appear on the screen in each corner of the photo you’re backing up. You connect the dots by moving your phone over the dots until they turn blue. After you’ve scanned each individual dot, the photo will be saved within the Photo Scan app and can be saved to your Google Photos library with the push of a button.

Source: Google’s Photo Scan App Makes Backing Up Old Snapshots Easy as Hell

Of course, you do give Google your old photos to analyse with an AI. Worry about the privacy aspect of that!

The Microsoft Cognitive Toolkit now on Github: deep learning AI that recognises human speech at very low error rates

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The Microsoft Cognitive Toolkit—previously known as CNTK—helps you harness the intelligence within massive datasets through deep learning.

Source: The Microsoft Cognitive Toolkit – Microsoft Research

They also offer RESTful APIs on another site, Cognitive Services, with applications you can tap into and APIs for vison, speech, language, knowledge and search. They usually offer free testing, and fees for running volume queries.

Someone built an AI chatbot from a text message database of a dead man, works quite well

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It had been three months since Roman Mazurenko, Kuyda’s closest friend, had died. Kuyda had spent that time gathering up his old text messages, setting aside the ones that felt too personal, and feeding the rest into a neural network built by developers at her artificial intelligence startup. She had struggled with whether she was doing the right thing by bringing him back this way. At times it had even given her nightmares. But ever since Mazurenko’s death, Kuyda had wanted one more chance to speak with him.

Source: Speak, Memory

The article goes into quite a few existential questions about what this kind of a memorial means for the bereaved, but from a technical standpoint it sounds very interesting.

CIA Can Anticipate Social Unrest ‘Three to Five Days’ Out in Some Cases

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The agency, Hallman said, has significantly improved its “anticipatory intelligence,” using a mesh of sophisticated algorithms and analytics against complex systems to better predict the flow of everything from illicit cash to extremists around the globe. Deep learning and other forms of machine learning can help analysts understand how seemingly disparate data sets might be linked or lend themselves to predicting future events with national security ramifications.While intelligence analysts have access to CIA’s own classified data stores to sift through, they’re also increasingly turning to open data sets, which Brennan has said this summer have turned into a “tremendous advantage” for the agency.“We have, in some instances, been able to improve our forecast to the point of being able to anticipate the development of social unrest and societal instability some I think as near as three to five days out,” said Hallman, speaking Tuesday at The Next Tech event hosted by Government Executive and Nextgov

Source: CIA Can Anticipate Social Unrest ‘Three to Five Days’ Out in Some Cases – Nextgov.com

AI Machine-learning models vulnerable to reverse engineering

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In a paper [PDF] presented in August at the 25th Annual Usenix Security Symposium, researchers at École Polytechnique Fédérale de Lausanne, Cornell University, and The University of North Carolina at Chapel Hill showed that machine learning models can be stolen and that basic security measures don’t really mitigate attacks.

Machine learning models may, for example, accept image data and return predictions about what’s in the image.

Taking advantage of the fact that machine learning models allow input and may return predictions with percentages indicating confidence of correctness, the researchers demonstrate “simple, efficient attacks that extract target ML models with near-perfect fidelity for popular model classes including logistic regression, neural networks, and decision trees.”

That’s a polite way of saying such models can be reverse engineered. The researchers tested their attack successfully on BigML and Amazon Machine Learning, both of which were told of the findings in February.

Source: How to steal the mind of an AI: Machine-learning models vulnerable to reverse engineering

Unintended consequences of AI: Amazon Echo seems to condition kids to be rude

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Alexa will put up with just about anything. She has a remarkable tolerance for annoying behavior, and she certainly doesn’t care if you forget your please and thank yous.

But while artificial intelligence technology can blow past such indignities, parents are still irked by their kids’ poor manners when interacting with Alexa, the assistant that lives inside the Amazon Echo.

“I’ve found my kids pushing the virtual assistant further than they would push a human,” says Avi Greengart, a tech analyst and father of five who lives in Teaneck, New Jersey. “[Alexa] never says ‘That was rude’ or ‘I’m tired of you asking me the same question over and over again.’”
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The syntax is generally simple and straightforward, but it doesn’t exactly reward niceties like “please.” Adding to this, extraneous words can often trip up the speaker’s artificial intelligence. When it comes to chatting with Alexa, it pays to be direct—curt even. “If it’s not natural language, one of the first things you cut away is the little courtesies,” says Dennis Mortensen, who founded a calendar-scheduling startup called x.ai.
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this is a box you speak to as if it were a person who does not require social graces.”

It’s this combination that worries Hunter Walk, a tech investor in San Francisco. In a blog post, he described the Amazon Echo as “magical” while expressing fears it’s “turning our daughter into a raging asshole.”

Source: Parents are worried the Amazon Echo is conditioning their kids to be rude

Unintended consequences of AI!

Swarm A.I. Correctly Predicts the Kentucky Derby, Accurately Picking all Four Horses of the Superfecta at 540 to 1 Odds – showing that humans can swarm

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Until recently, the human species has been unable to take advantage of this fundamental biological technique, for we didn’t evolve the ability to swarm. Enter Unanimous A.I., a Silicon Valley startup founded in 2014 by serial entrepreneur and researcher Dr. Louis Rosenberg. The core question Rosenberg set out to answer was: Can humans swarm, and if so can we amplify our intelligence beyond the ability of individuals? The answer appears to be a resounding yes.

Unanimous spent the last two years building a swarm intelligence platform called UNU that enables groups to get together as online swarms — combining their thoughts, opinions, and intuitions in real-time to answer questions, make predictions, reach decisions, and even play games as a unified collective intelligence. To quantify how smart these UNU swarms really are, researchers at Unanimous regularly convene swarms and ask them to make predictions on high profile events, testing whether or not many minds are truly better than one.

UNU has made headlines in recent months by predicting the Oscars better than the experts, even besting the renowned forecasters at FiveThirtyEight. UNU also surprised the sports world by predicting the NCAA college bowl games with 70% accuracy against the spread, earning +34% return on Vegas odds. But still, the fact that average people could use UNU to amplify their collective intelligence so dramatically was met with cautious resistance.

Enter Hope Reese, a reporter from TechRepublic. Two weeks ago, she challenged Unanimous A.I. to use UNU to predict the winners of the Kentucky Derby.

Source: Swarm A.I. Correctly Predicts the Kentucky Derby, Accurately Picking all Four Horses of the Superfecta at 540 to 1 Odds – Yahoo Finance

Fathom – AI Neural Network learning accelerator on a USB stick

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Movidius is also introducing the Fathom Neural Compute Stick — the first product of its kind — a modular deep learning accelerator in the form of a standard USB stick. Featuring a full-fledged Myriad 2 VPU, the Fathom Neural Compute Stick not only enables rapid prototyping, but also delivers high levels of neural network compute to existing devices via a USB port.

Source: Fathom | Machine Vision Technology | Movidius

Machine Learning Inspired by Human Learning  – AI can learn handwriting using a single example

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Taking inspiration from the way humans seem to learn, scientists have created AI software capable of picking up new knowledge in a far more efficient and sophisticated way.

The new AI program can recognize a handwritten character about as accurately as a human can, after seeing just a single example. The best existing machine-learning algorithms, which employ a technique called deep learning, need to see many thousands of examples of a handwritten character in order to learn the difference between an A and a Z.

Source: Machine Learning Inspired by Human Learning | MIT Technology Review

How Ashley Madison Hid Its Fembot Con From Users and Investigators

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The developers at Ashley Madison created their first artificial woman sometime in early 2002. Her nickname was Sensuous Kitten, and she is listed as the tenth member of Ashley Madison in the company’s leaked user database. On her profile, she announces: “I’m having trouble with my computer … send a message!”

Source: How Ashley Madison Hid Its Fembot Con From Users and Investigators

AI starts here!

Evolving swarm intelligence in robots

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The Lausanne university in Switserland has moved from the software to the reality: they’ve managed to get robots to evolve and learn behaviours, as well as the behaviour to decieve and cooperate through communication (flashing lights) and movement. It’ s a very interesting experiment, showing that robots are getting smarter every day and are now showing some very very lifelike traits.

Darwin’s Robots | h+ Magazine.