Category: Reasoning


Google’s AI can now learn from its own memory independently

By Hugo Angel,

An artist’s impression of the DNC. Credit: DeepMind
The DeepMind artificial intelligence (AI) being developed by Google‘s parent company, Alphabet, can now intelligently build on what’s already inside its memory, the system’s programmers have announced.
Their new hybrid system – called a Differential Neural Computer (DNC)pairs a neural network with the vast data storage of conventional computers, and the AI is smart enough to navigate and learn from this external data bank. 
What the DNC is doing is effectively combining external memory (like the external hard drive where all your photos get stored) with the neural network approach of AI, where a massive number of interconnected nodes work dynamically to simulate a brain.
These models… can learn from examples like neural networks, but they can also store complex data like computers,” write DeepMind researchers Alexander Graves and Greg Wayne in a blog post.
At the heart of the DNC is a controller that constantly optimises its responses, comparing its results with the desired and correct ones. Over time, it’s able to get more and more accurate, figuring out how to use its memory data banks at the same time.
Take a family tree: after being told about certain relationships, the DNC was able to figure out other family connections on its own – writing, rewriting, and optimising its memory along the way to pull out the correct information at the right time.
Another example the researchers give is a public transit system, like the London Underground. Once it’s learned the basics, the DNC can figure out more complex relationships and routes without any extra help, relying on what it’s already got in its memory banks.
In other words, it’s functioning like a human brain, taking data from memory (like tube station positions) and figuring out new information (like how many stops to stay on for).
Of course, any smartphone mapping app can tell you the quickest way from one tube station to another, but the difference is that the DNC isn’t pulling this information out of a pre-programmed timetable – it’s working out the information on its own, and juggling a lot of data in its memory all at once.
The approach means a DNC system could take what it learned about the London Underground and apply parts of its knowledge to another transport network, like the New York subway.
The system points to a future where artificial intelligence could answer questions on new topics, by deducing responses from prior experiences, without needing to have learned every possible answer beforehand.
Credit: DeepMind

Of course, that’s how DeepMind was able to beat human champions at Go – by studying millions of Go moves. But by adding external memory, DNCs are able to take on much more complex tasks and work out better overall strategies, its creators say.

Like a conventional computer, [a DNC] can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data,” the researchers explain in Nature.
In another test, the DNC was given two bits of information: “John is in the playground,” and “John picked up the football.” With those known facts, when asked “Where is the football?“, it was able to answer correctly by combining memory with deep learning. (The football is in the playground, if you’re stuck.)
Making those connections might seem like a simple task for our powerful human brains, but until now, it’s been a lot harder for virtual assistants, such as Siri, to figure out.
With the advances DeepMind is making, the researchers say we’re another step forward to producing a computer that can reason independently.
And then we can all start enjoying our robot-driven utopia – or technological dystopia – depending on your point of view.
ORIGINAL: ScienceAlert
By DAVID NIELD

14 OCT 2016

Show and Tell: image captioning open sourced in TensorFlow

By Hugo Angel,

 In 2014, research scientists on the Google Brain team trained a machine learning system to automatically produce captions that accurately describe images. Further development of that system led to its success in the Microsoft COCO 2015 image captioning challenge, a competition to compare the best algorithms for computing accurate image captions, where it tied for first place.
Today, we’re making the latest version of our image captioning system available as an open source model in TensorFlow.
This release contains significant improvements to the computer vision component of the captioning system, is much faster to train, and produces more detailed and accurate descriptions compared to the original system. These improvements are outlined and analyzed in the paper Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge, published in IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatically captioned by our system.
So what’s new? 
Our 2014 system used the Inception V1 image classification model to initialize the image encoder, which
produces the encodings that are useful for recognizing different objects in the images. This was the best image model available at the time, achieving 89.6% top-5 accuracy on the benchmark ImageNet 2012 image classification task. We replaced this in 2015 with the newer Inception V2 image classification model, which achieves 91.8% accuracy on the same task.The improved vision component gave our captioning system an accuracy boost of 2 points in the BLEU-4 metric (which is commonly used in machine translation to evaluate the quality of generated sentences) and was an important factor of its success in the captioning challenge.Today’s code release initializes the image encoder using the Inception V3 model, which achieves 93.9% accuracy on the ImageNet classification task. Initializing the image encoder with a better vision model gives the image captioning system a better ability to recognize different objects in the images, allowing it to generate more detailed and accurate descriptions. This gives an additional 2 points of improvement in the BLEU-4 metric over the system used in the captioning challenge.Another key improvement to the vision component comes from fine-tuning the image model. This step addresses the problem that the image encoder is initialized by a model trained to classify objects in images, whereas the goal of the captioning system is to describe the objects in images using the encodings produced by the image model.  For example, an image classification model will tell you that a dog, grass and a frisbee are in the image, but a natural description should also tell you the color of the grass and how the dog relates to the frisbee.  In the fine-tuning phase, the captioning system is improved by jointly training its vision and language components on human generated captions. This allows the captioning system to transfer information from the image that is specifically useful for generating descriptive captions, but which was not necessary for classifying objects. In particular,  after fine-tuning it becomes better at correctly describing the colors of objects. Importantly, the fine-tuning phase must occur after the language component has already learned to generate captions – otherwise, the noisiness of the randomly initialized language component causes irreversible corruption to the vision component. For more details, read the full paper here.
Left: the better image model allows the captioning model to generate more detailed and accurate descriptions. Right: after fine-tuning the image model, the image captioning system is more likely to describe the colors of objects correctly.
Until recently our image captioning system was implemented in the DistBelief software framework. The TensorFlow implementation released today achieves the same level of accuracy with significantly faster performance: time per training step
is just 0.7 seconds in TensorFlow compared to 3 seconds in DistBelief on an Nvidia K20 GPU, meaning that total training time is just 25% of the time previously required.
A natural question is whether our captioning system can generate novel descriptions of previously unseen contexts and interactions. The system is trained by showing it hundreds of thousands of images that were captioned manually by humans, and it often re-uses human captions when presented with scenes similar to what it’s seen before.
When the model is presented with scenes similar to what it’s seen before, it will often re-use human generated captions.
So does it really understand the objects and their interactions in each image? Or does it always regurgitate descriptions from the training data? Excitingly, our model does indeed develop the ability to generate accurate new captions when presented with completely new scenes, indicating a deeper understanding of the objects and context in the images. Moreover, it learns how to express that knowledge in natural-sounding English phrases despite receiving no additional language training other than reading the human captions.
 

Our model generates a completely new caption using concepts learned from similar scenes in the training set
We hope that sharing this model in TensorFlow will help push forward image captioning research and applications, and will also
allow interested people to learn and have fun. To get started training your own image captioning system, and for more details on the neural network architecture, navigate to the model’s home-page here. While our system uses the Inception V3 image classification model, you could even try training our system with the recently released Inception-ResNet-v2 model to see if it can do even better!

ORIGINAL: Google Blog

by Chris Shallue, Software Engineer, Google Brain Team
September 22, 2016

IBM, Local Motors debut Olli, the first Watson-powered self-driving vehicle

By Hugo Angel,

Olli hits the road in the Washington, D.C. area and later this year in Miami-Dade County and Las Vegas.
Local Motors CEO and co-founder John B. Rogers, Jr. with “Olli” & IBM, June 15, 2016.Rich Riggins/Feature Photo Service for IBM

IBM, along with the Arizona-based manufacturer Local Motors, debuted the first-ever driverless vehicle to use the Watson cognitive computing platform. Dubbed “Olli,” the electric vehicle was unveiled at Local Motors’ new facility in National Harbor, Maryland, just outside of Washington, D.C.

Olli, which can carry up to 12 passengers, taps into four Watson APIs (

  • Speech to Text, 
  • Natural Language Classifier, 
  • Entity Extraction and 
  • Text to Speech

) to interact with its riders. It can answer questions like “Can I bring my children on board?” and respond to basic operational commands like, “Take me to the closest Mexican restaurant.” Olli can also give vehicle diagnostics, answering questions like, “Why are you stopping?

Olli learns from data produced by more than 30 sensors embedded throughout the vehicle, which will added and adjusted to meet passenger needs and local preferences.
While Olli is the first self-driving vehicle to use IBM Watson Internet of Things (IoT), this isn’t Watson’s first foray into the automotive industry. IBM launched its IoT for Automotive unit in September of last year, and in March, IBM and Honda announced a deal for Watson technology and analytics to be used in the automaker’s Formula One (F1) cars and pits.
IBM demonstrated its commitment to IoT in March of last year, when it announced it was spending $3B over four years to establish a separate IoT business unit, whch later became the Watson IoT business unit.
IBM says that starting Thursday, Olli will be used on public roads locally in Washington, D.C. and will be used in Miami-Dade County and Las Vegas later this year. Miami-Dade County is exploring a pilot program that would deploy several autonomous vehicles to shuttle people around Miami.
ORIGINAL: ZDnet
By Stephanie Condon for Between the Lines
June 16, 2016

Inside Vicarious, the Secretive AI Startup Bringing Imagination to Computers

By Hugo Angel,

By reinventing the neural network, the company hopes to help computers make the leap from processing words and symbols to comprehending the real world.
Life would be pretty dull without imagination. In fact, maybe the biggest problem for computers is that they don’t have any.
That’s the belief motivating the founders of Vicarious, an enigmatic AI company backed by some of the most famous and successful names in Silicon Valley. Vicarious is developing a new way of processing data, inspired by the way information seems to flow through the brain. The company’s leaders say this gives computers something akin to imagination, which they hope will help make the machines a lot smarter.
Vicarious is also, essentially, betting against the current boom in AI. Companies including Google, Facebook, Amazon, and Microsoft have made stunning progress in the past few years by feeding huge quantities of data into large neural networks in a process called “deep learning.” When trained on enough examples, for instance, deep-learning systems can learn to recognize a particular face or type of animal with very high accuracy (see “10 Breakthrough Technologies 2013: Deep Learning”). But those neural networks are only very crude approximations of what’s found inside a real brain.
Illustration by Sophia Foster-Dimino
Vicarious has introduced a new kind of neural-network algorithm designed to take into account more of the features that appear in biology. An important one is the ability to picture what the information it’s learned should look like in different scenarios—a kind of artificial imagination. The company’s founders believe a fundamentally different design will be essential if machines are to demonstrate more human like intelligence. Computers will have to be able to learn from less data, and to recognize stimuli or concepts more easily.
Despite generating plenty of early excitement, Vicarious has been quiet over the past couple of years. But this year, the company says, it will publish details of its research, and it promises some eye-popping demos that will show just how useful a computer with an imagination could be.
The company’s headquarters don’t exactly seem like the epicenter of a revolution in artificial intelligence. Located in Union City, a short drive across the San Francisco Bay from Palo Alto, the offices are plain—a stone’s throw from a McDonald’s and a couple of floors up from a dentist. Inside, though, are all the trappings of a vibrant high-tech startup. A dozen or so engineers were hard at work when I visited, several using impressive treadmill desks. Microsoft Kinect 3-D sensors sat on top of some of the engineers’ desks.
D. Scott Phoenix, the company’s 33-year-old CEO, speaks in suitably grandiose terms. “We are really rapidly approaching the amount of computational power we need to be able to do some interesting things in AI,” he told me shortly after I walked through the door. “In 15 years, the fastest computer will do more operations per second than all the neurons in all the brains of all the people who are alive. So we are really close.
Vicarious is about more than just harnessing more computer power, though. Its mathematical innovations, Phoenix says, will more faithfully mimic the information processing found in the human brain. It’s true enough that the relationship between the neural networks currently used in AI and the neurons, dendrites, and synapses found in a real brain is tenuous at best.
One of the most glaring shortcomings of artificial neural networks, Phoenix says, is that information flows only one way. “If you look at the information flow in a classic neural network, it’s a feed-forward architecture,” he says. “There are actually more feedback connections in the brain than feed-forward connections—so you’re missing more than half of the information flow.
It’s undeniably alluring to think that imagination—a capability so fundamentally human it sounds almost mystical in a computer—could be the key to the next big advance in AI.
Vicarious has so far shown that its approach can create a visual system capable of surprisingly deft interpretation. In 2013 it showed that the system could solve any captcha (the visual puzzles that are used to prevent spam-bots from signing up for e-mail accounts and the like). As Phoenix explains it, the feedback mechanism built into Vicarious’s system allows it to imagine what a character would look like if it weren’t distorted or partly obscured (see “AI Startup Says It Has Defeated Captchas”).
Phoenix sketched out some of the details of the system at the heart of this approach on a whiteboard. But he is keeping further details quiet until a scientific paper outlining the captcha approach is published later this year.
In principle, this visual system could be put to many other practical uses, like recognizing objects on shelves more accurately or interpreting real-world scenes more intelligently. The founders of Vicarious also say that their approach extends to other, much more complex areas of intelligence, including language and logical reasoning.
Phoenix says his company may give a demo later this year involving robots. And indeed, the job listings on the company’s website include several postings for robotics experts. Currently robots are bad at picking up unfamiliar, oddly arranged, or partly obscured objects, because they have trouble recognizing what they are. “If you look at people who are picking up objects in an Amazon facility, most of the time they aren’t even looking at what they’re doing,” he explains. “And they’re imagining—using their sensory motor simulator—where the object is, and they’re imagining at what point their finger will touch it.
While Phoenix is the company’s leader, his cofounder, Dileep George, might be considered its technical visionary. George was born in India and received a PhD in electrical engineering from Stanford University, where he turned his attention to neuroscience toward the end of his doctoral studies. In 2005 he cofounded Numenta with Jeff Hawkins, the creator of Palm Computing. But in 2010 George left to pursue his own ideas about the mathematical principles behind information processing in the brain, founding Vicarious with Phoenix the same year.
I bumped into George in the elevator when I first arrived. He is unassuming and speaks quietly, with a thick accent. But he’s also quite matter-of-fact about what seem like very grand objectives.
George explained that imagination could help computers process language by tying words, or symbols, to low-level physical representations of real-world things. In theory, such a system might automatically understand the physical properties of something like water, for example, which would make it better able to discuss the weather. “When I utter a word, you know what it means because you can simulate the concept,” he says.
This ambitious vision for the future of AI has helped Vicarious raise an impressive $72 million so far. Its list of investors also reads like a who’s who of the tech world. Early cash came from Dustin Moskovitz, ex-CTO of Facebook, and Adam D’Angelo, cofounder of Quora. Further funding came from Peter Thiel, Mark Zuckerberg, Jeff Bezos, and Elon Musk.
Many people are itching to see what Vicarious has done beyond beating captchas. “I would love it if they showed us something new this year,” says Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence in Seattle.
In contrast to the likes of Google, Facebook, or Baidu, Vicarious hasn’t published any papers or released any tools that researchers can play with. “The people [involved] are great, and the problems [they are working on] are great,” says Etzioni. “But it’s time to deliver.
For those who’ve put their money behind Vicarious, the company’s remarkable goals should make the wait well worth it. Even if progress takes a while, the potential payoffs seem so huge that the bet makes sense, says Matt Ocko, a partner at Data Collective, a venture firm that has backed Vicarious. A better machine-learning approach could be applied in just about any industry that handles large amounts of data, he says. “Vicarious sat us down and demonstrated the most credible pathway to reasoning machines that I have ever seen.
Ocko adds that Vicarious has demonstrated clear evidence it can commercialize what it’s working on. “We approached it with a crapload of intellectual rigor,” he says.
It will certainly be interesting to see if Vicarious can inspire this kind of confidence among other AI researchers and technologists with its papers and demos this year. If it does, then the company could quickly go from one of the hottest prospects in the Valley to one of its fastest-growing businesses.
That’s something the company’s founders would certainly like to imagine.
ORIGINAL: MIT Tech Review
by Will Knight. Senior Editor, AI
May 19, 2016

Next Rembrandt

By Hugo Angel,

01 GATHERING THE DATA
To distill the artistic DNA of Rembrandt, an extensive database of his paintings was built and analyzed, pixel by pixel.
FUN FACT:
150 Gigabytes of digitally rendered graphics

BUILDING AN EXTENSIVE POOL OF DATA
t’s been almost four centuries since the world lost the talent of one its most influential classical painters, Rembrandt van Rijn. To bring him back, we distilled the artistic DNA from his work and used it to create The Next Rembrandt.
We examined the entire collection of Rembrandt’s work, studying the contents of his paintings pixel by pixel. To get this data, we analyzed a broad range of materials like high resolution 3D scans and digital files, which were upscaled by deep learning algorithms to maximize resolution and quality. This extensive database was then used as the foundation for creating The Next Rembrandt.
Data is used by many people today to help them be more efficient and knowledgeable about their daily work, and about the decisions they need to make. But in this project it’s also used to make life itself more beautiful. It really touches the human soul.
– Ron Augustus, Microsoft
02 DETERMINING THE SUBJECT
Data from Rembrandt’s body of work showed the way to the subject of the new painting.
FUN FACT:
346 Paintings were studied


DELVING INTO REMBRANDT VAN RIJN
  • 49% FEMALE
  • 51% MALE
Throughout his life, Rembrandt painted a great number of self-portraits, commissioned portraits and group shots, Biblical scenes, and even a few landscapes. He’s known for painting brutally honest and unforgiving portrayals of his subjects, utilizing a limited color palette for facial emphasis, and innovating the use of light and shadows.
“There’s a lot of Rembrandt data available — you have this enormous amount of technical data from all these paintings from various collections. And can we actually create something out of it that looks like Rembrandt? That’s an appealing question.”
– Joris Dik, Technical University Delft
BREAKING DOWN THE DEMOGRAPHICS IN REMBRANDT’S WORK
To create new artwork using data from Rembrandt’s paintings, we had to maximize the data pool from which to pull information. Because he painted more portraits than any other subject, we narrowed down our exploration to these paintings.
Then we found the period in which the majority of these paintings were created: between 1632 and 1642. Next, we defined the demographic segmentation of the people in these works and saw which elements occurred in the largest sample of paintings. We funneled down that selection starting with gender and then went on to analyze everything from age and head direction, to the amount of facial hair present.
After studying the demographics, the data lead us to a conclusive subject: a portrait of a Caucasian male with facial hair, between the ages of thirty and forty, wearing black clothes with a white collar and a hat, facing to the right.
03 GENERATING THE FEATURES
A software system was designed to understand Rembrandt’s style and generate new features.
FUN FACT:
500+ Hours of rendering
MASTERING THE STYLE OF REMBRANDT
In creating the new painting, it was imperative to stay accurate to Rembrandt’s unique style. As “The Master of Light and Shadow,” Rembrandt relied on his innovative use of lighting to shape the features in his paintings. By using very concentrated light sources, he essentially created a “spotlight effect” that gave great attention to the lit elements and left the rest of the painting shrouded in shadows. This resulted in some of the features being very sharp and in focus and others becoming soft and almost blurry, an effect that had to be replicated in the new artwork.
When you want to make a new painting you have some idea of how it’s going to look. But in our case we started from basically nothing — we had to create a whole painting using just data from Rembrandt’s paintings.
– Ben Haanstra, Developer
GENERATING FEATURES BASED ON DATA
To master his style, we designed a software system that could understand Rembrandt based on his use of geometry, composition, and painting materials. A facial recognition algorithm identified and classified the most typical geometric patterns used by Rembrandt to paint human features. It then used the learned principles to replicate the style and generate new facial features for our painting.
CONSTRUCTING A FACE OUT OF THE NEW FEATURES
Once we generated the individual features, we had to assemble them into a fully formed face and bust according to Rembrandt’s use of proportions. An algorithm measured the distances between the facial features in Rembrandt’s paintings and calculated them based on percentages. Next, the features were transformed, rotated, and scaled, then accurately placed within the frame of the face. Finally, we rendered the light based on gathered data in order to cast authentic shadows on each feature.
04 BRINGING IT TO LIFE
CREATING ACCURATE DEPTH AND TEXTURE
Analyses
We now had a digital file true to Rembrandt’s style in content, shapes, and lighting. But paintings aren’t just 2D — they have a remarkable three-dimensionality that comes from brushstrokes and layers of paint. To recreate this texture, we had to study 3D scans of Rembrandt’s paintings and analyze the intricate layers on top of the canvas.
“We looked at a number of Rembrandt paintings, and we scanned their surface texture, their elemental composition, and what kinds of pigments were used. That’s the kind of information you need if you want to generate a painting by Rembrandt virtually.”
– Joris Dik, Technical University Delft
USING A HEIGHT MAP TO PRINT IN 3D
We created a height map using two different algorithms that found texture patterns of canvas surfaces and layers of paint. That information was transformed into height data, allowing us to mimic the brushstrokes used by Rembrandt.
We then used an elevated printing technique on a 3D printer that output multiple layers of paint-based UV ink. The final height map determined how much ink was released onto the canvas during each layer of the printing process. In the end, we printed thirteen layers of ink, one on top of the other, to create a painting texture true to Rembrandt’s style.

ORIGINAL: Next Rembrandt

“AI & The Future Of Civilization” A Conversation With Stephen Wolfram

By Hugo Angel,

“AI & The Future Of Civilization” A Conversation With Stephen Wolfram [3.1.16]
Stephen Wolfram
What makes us different from all these things? What makes us different is the particulars of our history, which gives us our notions of purpose and goals. That’s a long way of saying when we have the box on the desk that thinks as well as any brain does, the thing it doesn’t have, intrinsically, is the goals and purposes that we have. Those are defined by our particulars—our particular biology, our particular psychology, our particular cultural history.

The thing we have to think about as we think about the future of these things is the goals. That’s what humans contribute, that’s what our civilization contributes—execution of those goals; that’s what we can increasingly automate. We’ve been automating it for thousands of years. We will succeed in having very good automation of those goals. I’ve spent some significant part of my life building technology to essentially go from a human concept of a goal to something that gets done in the world.

There are many questions that come from this. For example, we’ve got these great AIs and they’re able to execute goals, how do we tell them what to do?…


STEPHEN WOLFRAM, distinguished scientist, inventor, author, and business leader, is Founder & CEO, Wolfram Research; Creator, Mathematica, Wolfram|Alpha & the Wolfram Language; Author, A New Kind of Science. Stephen Wolfram’s EdgeBio Page

THE REALITY CLUB: Nicholas Carr

AI & THE FUTURE OF CIVILIZATION
Some tough questions. One of them is about the future of the human condition. That’s a big question. I’ve spent some part of my life figuring out how to make machines automate stuff. It’s pretty obvious that we can automate many of the things that we humans have been proud of for a long time. What’s the future of the human condition in that situation?


More particularly, I see technology as taking human goals and making them able to be automatically executed by machines. The human goals that we’ve had in the past have been things like moving objects from here to there and using a forklift rather than our own hands. Now, the things that we can do automatically are more intellectual kinds of things that have traditionally been the professions’ work, so to speak. These are things that we are going to be able to do by machine. The machine is able to execute things, but something or someone has to define what its goals should be and what it’s trying to execute.

People talk about the future of the intelligent machines, and whether intelligent machines are going to take over and decide what to do for themselves. What one has to figure out, while given a goal, how to execute it into something that can meaningfully be automated, the actual inventing of the goal is not something that in some sense has a path to automation.

How do we figure out goals for ourselves? How are goals defined? They tend to be defined for a given human by their own personal history, their cultural environment, the history of our civilization. Goals are something that are uniquely human. It’s something that almost doesn’t make any sense. We ask, what’s the goal of our machine? We might have given it a goal when we built the machine.

The thing that makes this more poignant for me is that I’ve spent a lot of time studying basic science about computation, and I’ve realized something from that. It’s a little bit of a longer story, but basically, if we think about intelligence and things that might have goals, things that might have purposes, what kinds of things can have intelligence or purpose? Right now, we know one great example of things with intelligence and purpose and that’s us, and our brains, and our own human intelligence. What else is like that? The answer, I had at first assumed, is that there are the systems of nature. They do what they do, but human intelligence is far beyond anything that exists naturally in the world. It’s something that’s the result of all of this elaborate process of evolution. It’s a thing that stands apart from the rest of what exists in the universe. What I realized, as a result of a whole bunch of science that I did, was that is not the case.

Robots are learning from YouTube tutorials

By Hugo Angel,

Do it yourself, robot. (Reuters/Kim Kyung-Hoon)
For better or worse, we’ve taught robots to mimic human behavior in countless ways. They can perform tasks as rudimentary as picking up objects, or as creative as dreaming their own dreams. They can identify bullying, and even play jazz. Now, we’ve taught robots the most human task of all: how to teach themselves to make Jell-O shots from watching YouTube videos.
Ever go to YouTube and type in something like, “How to make pancakes,” or, “How to mount a TV”? Sure you have. While many such tutorials are awful—and some are just deliberately misleading—the sheer number of instructional videos offers strong odds of finding one that’s genuinely helpful. And when all those videos are aggregated and analyzed simultaneously, it’s not hard for a robot to figure out what the correct steps are.

Researchers at Cornell University have taught robots to do just that with a system called RoboWatch. By watching and scanning multiple videos of the same “how-to” activity (with subtitles enabled), bots can 

  • identify common steps, 
  • put them in order, and 
  • learn how to do whatever the tutorials are teaching.
Robot learning is not new, but what’s unusual here is that these robots can learn without human supervision, as Phys.Org points out.
Similar research usually requires human overseers to introduce and explain words, or captions, for the robots to parse. RoboWatch, however, needs no human help, save that someone ensure all the videos analyzed fall into a single category (pdf). The idea is that a human could one day tell a robot to perform a task and then the robot would independently research and learn how to carry out that task.
So next time you getting frustrated watching a video on how to change a tire, don’t fret. Soon, a robot will do all that for you. We just have to make sure it doesn’t watch any videos about “how to take over the world.
ORIGINAL: QZ
December 22, 2015

Forward to the Future: Visions of 2045

By Hugo Angel,

DARPA asked the world and our own researchers what technologies they expect to see 30 years from now—and received insightful, sometimes funny predictions
Today—October 21, 2015—is famous in popular culture as the date 30 years in the future when Marty McFly and Doc Brown arrive in their time-traveling DeLorean in the movie “Back to the Future Part II.” The film got some things right about 2015, including in-home videoconferencing and devices that recognize people by their voices and fingerprints. But it also predicted trunk-sized fusion reactors, hoverboards and flying cars—game-changing technologies that, despite the advances we’ve seen in so many fields over the past three decades, still exist only in our imaginations.
A big part of DARPA’s mission is to envision the future and make the impossible possible. So ten days ago, as the “Back to the Future” day approached, we turned to social media and asked the world to predict: What technologies might actually surround us 30 years from now? We pointed people to presentations from DARPA’s Future Technologies Forum, held last month in St. Louis, for inspiration and a reality check before submitting their predictions.
Well, you rose to the challenge and the results are in. So in honor of Marty and Doc (little known fact: he is a DARPA alum) and all of the world’s innovators past and future, we present here some highlights from your responses, in roughly descending order by number of mentions for each class of futuristic capability:
  • Space: Interplanetary and interstellar travel, including faster-than-light travel; missions and permanent settlements on the Moon, Mars and the asteroid belt; space elevators
  • Transportation & Energy: Self-driving and electric vehicles; improved mass transit systems and intercontinental travel; flying cars and hoverboards; high-efficiency solar and other sustainable energy sources
  • Medicine & Health: Neurological devices for memory augmentation, storage and transfer, and perhaps to read people’s thoughts; life extension, including virtual immortality via uploading brains into computers; artificial cells and organs; “Star Trek”-style tricorder for home diagnostics and treatment; wearable technology, such as exoskeletons and augmented-reality glasses and contact lenses
  • Materials & Robotics: Ubiquitous nanotechnology, 3-D printing and robotics; invisibility and cloaking devices; energy shields; anti-gravity devices
  • Cyber & Big Data: Improved artificial intelligence; optical and quantum computing; faster, more secure Internet; better use of data analytics to improve use of resources
A few predictions inspired us to respond directly:
  • Pizza delivery via teleportation”—DARPA took a close look at this a few years ago and decided there is plenty of incentive for the private sector to handle this challenge.
  • Time travel technology will be close, but will be closely guarded by the military as a matter of national security”—We already did this tomorrow.
  • Systems for controlling the weather”—Meteorologists told us it would be a job killer and we didn’t want to rain on their parade.
  • Space colonies…and unlimited cellular data plans that won’t be slowed by your carrier when you go over a limit”—We appreciate the idea that these are equally difficult, but they are not. We think likable cell-phone data plans are beyond even DARPA and a total non-starter.
So seriously, as an adjunct to this crowd-sourced view of the future, we asked three DARPA researchers from various fields to share their visions of 2045, and why getting there will require a group effort with players not only from academia and industry but from forward-looking government laboratories and agencies:

Pam Melroy, an aerospace engineer, former astronaut and current deputy director of DARPA’s Tactical Technologies Office (TTO), foresees technologies that would enable machines to collaborate with humans as partners on tasks far more complex than those we can tackle today:
Justin Sanchez, a neuroscientist and program manager in DARPA’s Biological Technologies Office (BTO), imagines a world where neurotechnologies could enable users to interact with their environment and other people by thought alone:
Stefanie Tompkins, a geologist and director of DARPA’s Defense Sciences Office, envisions building substances from the atomic or molecular level up to create “impossible” materials with previously unattainable capabilities.
Check back with us in 2045—or sooner, if that time machine stuff works out—for an assessment of how things really turned out in 30 years.
# # #
Associated images posted on www.darpa.mil and video posted at www.youtube.com/darpatv may be reused according to the terms of the DARPA User Agreement, available here:http://www.darpa.mil/policy/usage-policy.
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ORIGINAL: DARPA
10/21/2015

Automating big-data analysis. System that replaces human intuition with algorithms outperforms 615 of 906 human teams.

By Hugo Angel,

COMMENT
Big-data analysis consists of searching for buried patterns that have some kind of predictive power. But choosing which “features” of the data to analyze usually requires some human intuition. In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.
MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too. To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets. Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.
In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions. In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.
We view the Data Science Machine as a natural complement to human intelligence,” says Max Kanter, whose MIT master’s thesis in computer science is the basis of the Data Science Machine. “There’s so much data out there to be analyzed. And right now it’s just sitting there not doing anything. So maybe we can come up with a solution that will at least get us started on it, at least get us moving.
Between the lines
Kanter and his thesis advisor, Kalyan Veeramachaneni, a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), describe the Data Science Machine in a paper that Kanter will present next week at the IEEE International Conference on Data Science and Advanced Analytics.
Veeramachaneni co-leads the Anyscale Learning for All group at CSAIL, which applies machine-learning techniques to practical problems in big-data analysis, such as determining the power-generation capacity of wind-farm sites or predicting which students are at risk for dropping out of online courses.
What we observed from our experience solving a number of data science problems for industry is that one of the very critical steps is called feature engineering,” Veeramachaneni says. “The first thing you have to do is identify what variables to extract from the database or compose, and for that, you have to come up with a lot of ideas.
In predicting dropout, for instance, two crucial indicators proved to be how long before a deadline a student begins working on a problem set and how much time the student spends on the course website relative to his or her classmates. MIT’s online-learning platform MITx doesn’t record either of those statistics, but it does collect data from which they can be inferred.
Featured composition
Kanter and Veeramachaneni use a couple of tricks to manufacture candidate features for data analyses.

  • One is to exploit structural relationships inherent in database design. Databases typically store different types of data in different tables, indicating the correlations between them using numerical identifiers. The Data Science Machine tracks these correlations, using them as a cue to feature constructionFor instance, one table might list retail items and their costs; another might list items included in individual customers’ purchases. The Data Science Machine would begin by importing costs from the first table into the second. Then, taking its cue from the association of several different items in the second table with the same purchase number, it would execute a suite of operations to generate candidate features
  • total cost per order, 
  • average cost per order, 
  • minimum cost per order, and 
  • so on. As numerical identifiers proliferate across tables, the Data Science Machine layers operations on top of each other, finding minima of averages, averages of sums, and so on.
  • It also looks for so-called categorical data, which appear to be restricted to a limited range of values, such as days of the week or brand names. It then generates further feature candidates by dividing up existing features across categories. Once it’s produced an array of candidates, it reduces their number by identifying those whose values seem to be correlated. Then it starts testing its reduced set of features on sample data, recombining them in different ways to optimize the accuracy of the predictions they yield.
  • The Data Science Machine is one of those unbelievable projects where applying cutting-edge research to solve practical problems opens an entirely new way of looking at the problem,” says Margo Seltzer, a professor of computer science at Harvard University who was not involved in the work. “I think what they’ve done is going to become the standard quickly — very quickly.
    ORIGINAL: MIT News
    Larry Hardesty | MIT News Office 
    October 16, 2015

    Robot Demonstrates Self-Awareness

    By admin,

    photo credit: The robot on the right was able to pass a self-awareness test. RAIR Lab/YouTube
    A king is seeking a new advisor, and to do so he invites three wise men to his castle. He tells them he will place a hat on each of their heads that will be either white or blue, and at least one of the hats will be blue. The wise men must work out the color of their own hat they are wearing without talking to each other to become the advisor. After a few minutes of sitting in silence, one of the wise men stands up and guesses correctly.
    This riddle (you can read the solution here) is a famous test of logic and self-awareness, and a group of researchers have now recreated a similar test in robots to prove the ability of artificial intelligence to be self-aware – within, of course, limitations.
    Three humanoid Nao robots were programmed to think that two of them had been given a “dumbing pill” that prevented them from speaking. All of them were asked “which pill did you receive?” but as two of them were mute, only one was able to answer, saying: “I don’t know.” It then works out that, as it can talk, it must not have been given the pill, so it changes its answer to: “Sorry, I know now. I was able to prove that I was not given a dumbing pill.

    Results of the test, carried out by the Rensselaer Artificial Intelligence and Reasoning (RAIR) Laboratory, will be presented in a paper at RO-MAN 2015 later this year. Selmer Bringsjor from the Rensselaer Polytechnic Institute, one of the test’s administrators, told Vice that it showed that a “logical and a mathematical correlate to self-consciousness” was possible, suggesting that robots can be designed in such a way that their actions and decisions resemble a degree of self-awareness.

    Before you start preparing for an onslaught of Terminator-style killer robots, though, it should be noted that this test was obviously rather limited. Nonetheless, it suggests that self-awareness is something that can be programmed, and may open up new avenues for artificial intelligence. Just being able to understand the question and hear their own voice to solve the puzzle is an important skill for robots to demonstrate.

    There are myriad additional steps that need to ultimately be taken,” the researchers write in their paper, “but one step at a time is the only way forward.

    ORIGINAL: IFLScience

    by Jonathan O’Callaghan
    July 17, 2015

    It’s No Myth: Robots and Artificial Intelligence Will Erase Jobs in Nearly Every Industry

    By admin,

    With the unemployment rate falling to 5.3 percent, the lowest in seven years, policy makers are heaving a sigh of relief. Indeed, with the technology boom in progress, there is a lot to be optimistic about.

    • Manufacturing will be returning to U.S. shores with robots doing the job of Chinese workers; 
    • American carmakers will be mass-producing self-driving electric vehicles; 
    • technology companies will develop medical devices that greatly improve health and longevity; 
    • we will have unlimited clean energy and 3D print our daily needs. 

    The cost of all of these things will plummet and make it possible to provide for the basic needs of every human being.

    I am talking about technology advances that are happening now, which will bear fruit in the 2020s.
    But policy makers will have a big new problem to deal with: the disappearance of human jobs. Not only will there be fewer jobs for people doing manual work, the jobs of knowledge workers will also be replaced by computers. Almost every industry and profession will be impacted and this will create a new set of social problems — because most people can’t adapt to such dramatic change.
    If we can develop the economic structures necessary to distribute the prosperity we are creating, most people will no longer have to work to sustain themselves. They will be free to pursue other creative endeavors. The problem, however, is that without jobs, they will not have the dignity, social engagement, and sense of fulfillment that comes from work. The life, liberty and pursuit of happiness that the constitution entitles us to won’t be through labor, it will have to be through other means.
    It is imperative that we understand the changes that are happening and find ways to cushion the impacts.
    The technology elite who are leading this revolution will reassure you that there is nothing to worry about because we will create new jobs just as we did in previous centuries when the economy transitioned from agrarian to industrial to knowledge-based. Tech mogul Marc Andreessen has called the notion of a jobless future a “Luddite fallacy,” referring to past fears that machines would take human jobs away. Those fears turned out to be unfounded because we created newer and better jobs and were much better off.
    True, we are living better lives. But what is missing from these arguments is the timeframe over which the transitions occurred. The industrial revolution unfolded over centuries. Today’s technology revolutions are happening within years. We will surely create a few intellectually-challenging jobs, but we won’t be able to retrain the workers who lose today’s jobs. They will experience the same unemployment and despair that their forefathers did. It is they who we need to worry about.
    The first large wave of unemployment will be caused by self-driving cars. These will provide tremendous benefit by eliminating traffic accidents and congestion, making commuting time more productive, and reducing energy usage. But they will eliminate the jobs of millions of taxi and truck drivers and delivery people. Fully-automated robotic cars are no longer in the realm of science fiction; you can see Google’s cars on the streets of Mountain View, Calif. There are also self-driving trucks on our highways and self-driving tractors on farms. Uber just hired away dozens of engineers from Carnegie Mellon University to build its own robotic cars. It will surely start replacing its human drivers as soon as its technology is ready — later in this decade. As Uber CEO Travis Kalanick reportedly said in an interview, “The reason Uber could be expensive is you’re paying for the other dude in the car. When there is no other dude in the car, the cost of taking an Uber anywhere is cheaper. Even on a road trip.
    The dude in the driver’s seat will go away.

    Manufacturing will be the next industry to be transformed. Robots have, for many years, been able to perform surgery, milk cows, do military reconnaissance and combat, and assemble goods. But they weren’t dexterous enough to do the type of work that humans do in installing circuit boards. The latest generation of industrial robots by ABB of Switzerland and Rethink Robotics of Boston can do this however. ABB’s robot, Yumi, can even thread a needle. It costs only $40,000.

    China, fearing the demise of its industry, is setting up fully-automated robotic factories in the hope that by becoming more price-competitive, it can continue to be the manufacturing capital of the world. But its advantage only holds up as long as the supply chains are in China and shipping raw materials and finished goods over the oceans remains cost-effective. Don’t forget that our robots are as productive as theirs are; they too don’t join labor unions (yet) and will work around the clock without complaining. Supply chains will surely shift and the trickle of returning manufacturing will become a flood.

    But there will be few jobs for humans once the new, local factories are built.
    With advances in artificial intelligence, any job that requires the analysis of information can be done better by computers. This includes the jobs of physicians, lawyers, accountants, and stock brokers. We will still need some humans to interact with the ones who prefer human contact, but the grunt work will disappear. The machines will need very few humans to help them.
    This jobless future will surely create social problems — but it may be an opportunity for humanity to uplift itself. Why do we need to work 40, 50, or 60 hours a week, after all? Just as we were better off leaving the long and hard agrarian and factory jobs behind, we may be better off without the mindless work at the office. What if we could be working 10 or 15 hours per week from anywhere we want and have the remaining time for leisure, social work, or attainment of knowledge?
    Yes, there will be a booming tourism and recreation industry and new jobs will be created in these — for some people.
    There are as many things to be excited about as to fear. If we are smart enough to develop technologies that solve the problems of disease, hunger, energy, and education, we can — and surely will — develop solutions to our social problems. But we need to start by understanding where we are headed and prepare for the changes. We need to get beyond the claims of a Luddite fallacy — to a discussion about the new future.
    ORIGINAL: Singularity Hub

    ON JUL 07, 2015

    Vivek
    Wadhwa is a fellow at Rock Center for Corporate Governance at Stanford
    University, director of research at Center for Entrepreneurship and
    Research Commercialization at Duke, and distinguished fellow at
    Singularity University.
    His
    past appointments include Harvard Law School, University of California
    Berkeley, and Emory University. Follow him on Twitter @wadhwa.

    Biggest Neural Network Ever Pushes AI Deep Learning

    By admin,

    Illustration: Getty Images
    Silicon Valley giants such as Google and Facebook have been trying to harness artificial intelligence by training brain-inspired neural networks to better represent the real world. Digital Reasoning, a cognitive computing company based in Franklin, Tenn., recently announced that it has trained a neural network consisting of 160 billion parameters—more than 10 times larger than previous neural networks.
    The Digital Reasoning neural network easily surpassed previous records held by Google’s 11.2-billion parameter system and Lawrence Livermore National Laboratory’s 15-billion parameter system. But it also showed improved accuracy over previous neural networks in tackling an “industry-standard dataset” consisting of 20,000 word analogies. Digital Reasoning’s model achieved an accuracy of almost 86 percent; significantly higher than Google’s previous record of just over 76 percent and Stanford University’s 75 percent.
    We are extremely proud of the results we have achieved, and the contribution we are making daily to the field of deep learning,” said Matthew Russell, chief technology officer for Digital Reasoning, in a press release.
    Deep learninginvolves the building of learning machines from five or more layers of artificial neural networks. (“Deep” refers to the depth of the layers, rather than any depth of knowledge.) Yann LeCun, head of the Artificial Intelligence Research Lab at Facebook, has described the idea of deep learning as “machines that learn to represent the world.” (For a more detailed description—complete with knobs and lights—see IEEE Spectrum’s previous interview with LeCun on deep learning.)
    Digital Reasoning’s neural network was trained on three multi-core computers overnight in order to achieve its accuracy in tackling the word analogies dataset. But the company’s researchers plan to test the system on larger datasets and vocabularies in the near future. Their results so far have been detailed in a paper on the preprint server arXiv and in the Journal of Machine Learning.
    Deep learning neural networks have received a growing amount of attention lately. For example, Google has been training its deep learning AI to figure out classic arcade games from scratch. The tech giant also recently unveiled its “DeepDream” tool for visualizing neural networks; a tool that also happened to produce beautiful, sometimes surreal images.
    ORIGINAL: Spectrum
    By Jeremy Hsu
    8 Jul 2015