AI News, Deep Learning Weekly - 🤖 - Issue #65: Fully-Parallel Text Generation, Autonomous Cars Hit the Road, Feature Visualization, LSTMs more
Deep Learning Weekly - 🤖 - Issue #65: Fully-Parallel Text Generation, Autonomous Cars Hit the Road, Feature Visualization, LSTMs more
Andrej Karpathy’s post on software 2.0 made a splash earlier this week contending that “a large portion of programmers of tomorrow do not maintain complex software repositories, write intricate programs, or analyze their running times.
Why Deep Learning Is Suddenly Changing Your Life
Machine translation and other forms of language processing have also become far more convincing, with Google GOOGL 1.35% , Microsoft MSFT 2.50% , Facebook FB 0.89% , and Baidu BIDU 0.72% unveiling new tricks every month.
To gather up dog pictures, the app must identify anything from a Chihuahua to a German shepherd and not be tripped up if the pup is upside down or partially obscured, at the right of the frame or the left, in fog or snow, sun or shade.
Medical startups claim they’ll soon be able to use computers to read X-rays, MRIs, and CT scans more rapidly and accurately than radiologists, to diagnose cancer earlier and less invasively, and to accelerate the search for life-saving pharmaceuticals.
They’ve all been made possible by a family of artificial intelligence (AI) techniques popularly known as deep learning, though most scientists still prefer to call them by their original academic designation: deep neural networks.
Programmers have, rather, fed the computer a learning algorithm, exposed it to terabytes of data—hundreds of thousands of images or years’ worth of speech samples—to train it, and have then allowed the computer to figure out for itself how to recognize the desired objects, words, or sentences.
“You essentially have software writing software,” says Jen-Hsun Huang, CEO of graphics processing leader Nvidia NVDA 2.71% , which began placing a massive bet on deep learning about five years ago.
What’s changed is that today computer scientists have finally harnessed both the vast computational power and the enormous storehouses of data—images, video, audio, and text files strewn across the Internet—that, it turns out, are essential to making neural nets work well.
“We’re now living in an age,” Chen observes, “where it’s going to be mandatory for people building sophisticated software applications.” People will soon demand, he says, “ ‘Where’s your natural-language processing version?’ ‘How do I talk to your app?
The increased computational power that is making all this possible derives not only from Moore’s law but also from the realization in the late 2000s that graphics processing units (GPUs) made by Nvidia—the powerful chips that were first designed to give gamers rich, 3D visual experiences—were 20 to 50 times more efficient than traditional central processing units (CPUs) for deep-learning computations.
Its chief financial officer told investors that “the vast majority of the growth comes from deep learning by far.” The term “deep learning” came up 81 times during the 83-minute earnings call.
Evolution of machine learning Because of new computing technologies, machine learning today is not like machine learning of the past.
The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt.
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development.
He told Page, who had read an early draft, that he wanted to start a company to develop his ideas about how to build a truly intelligent computer: one that could understand language and then make inferences and decisions on its own.
The basic idea—that software can simulate the neocortex’s large array of neurons in an artificial “neural network”—is decades old, and it has led to as many disappointments as breakthroughs.
Last June, a Google deep-learning system that had been shown 10 million images from YouTube videos proved almost twice as good as any previous image recognition effort at identifying objects such as cats.
In October, Microsoft chief research officer Rick Rashid wowed attendees at a lecture in China with a demonstration of speech software that transcribed his spoken words into English text with an error rate of 7 percent, translated them into Chinese-language text, and then simulated his own voice uttering them in Mandarin.
Hinton, who will split his time between the university and Google, says he plans to “take ideas out of this field and apply them to real problems” such as image recognition, search, and natural-language understanding, he says.
Extending deep learning into applications beyond speech and image recognition will require more conceptual and software breakthroughs, not to mention many more advances in processing power.
One has been to feed computers with information and rules about the world, which required programmers to laboriously write software that is familiar with the attributes of, say, an edge or a sound.
Neural networks, developed in the 1950s not long after the dawn of AI research, looked promising because they attempted to simulate the way the brain worked, though in greatly simplified form.
These weights determine how each simulated neuron responds—with a mathematical output between 0 and 1—to a digitized feature such as an edge or a shade of blue in an image, or a particular energy level at one frequency in a phoneme, the individual unit of sound in spoken syllables.
Programmers would train a neural network to detect an object or phoneme by blitzing the network with digitized versions of images containing those objects or sound waves containing those phonemes.
The eventual goal of this training was to get the network to consistently recognize the patterns in speech or sets of images that we humans know as, say, the phoneme “d” or the image of a dog.
This is much the same way a child learns what a dog is by noticing the details of head shape, behavior, and the like in furry, barking animals that other people call dogs.
Once that layer accurately recognizes those features, they’re fed to the next layer, which trains itself to recognize more complex features, like a corner or a combination of speech sounds.
Because the multiple layers of neurons allow for more precise training on the many variants of a sound, the system can recognize scraps of sound more reliably, especially in noisy environments such as subway platforms.
Hawkins, author of On Intelligence, a 2004 book on how the brain works and how it might provide a guide to building intelligent machines, says deep learning fails to account for the concept of time.
Brains process streams of sensory data, he says, and human learning depends on our ability to recall sequences of patterns: when you watch a video of a cat doing something funny, it’s the motion that matters, not a series of still images like those Google used in its experiment.
In high school, he wrote software that enabled a computer to create original music in various classical styles, which he demonstrated in a 1965 appearance on the TV show I’ve Got a Secret.
Since then, his inventions have included several firsts—a print-to-speech reading machine, software that could scan and digitize printed text in any font, music synthesizers that could re-create the sound of orchestral instruments, and a speech recognition system with a large vocabulary.
This isn’t his immediate goal at Google, but it matches that of Google cofounder Sergey Brin, who said in the company’s early days that he wanted to build the equivalent of the sentient computer HAL in 2001: A Space Odyssey—except one that wouldn’t kill people.
“My mandate is to give computers enough understanding of natural language to do useful things—do a better job of search, do a better job of answering questions,” he says.
queries as quirky as “a long, tiresome speech delivered by a frothy pie topping.” (Watson’s correct answer: “What is a meringue harangue?”) Kurzweil isn’t focused solely on deep learning, though he says his approach to speech recognition is based on similar theories about how the brain works.
“That’s not a project I think I’ll ever finish.” Though Kurzweil’s vision is still years from reality, deep learning is likely to spur other applications beyond speech and image recognition in the nearer term.
Microsoft’s Peter Lee says there’s promising early research on potential uses of deep learning in machine vision—technologies that use imaging for applications such as industrial inspection and robot guidance.
- On Sunday, December 17, 2017
- By TOM SIMONITE
Soon We Won't Program Computers. We'll Train Them Like Dogs
BEFORE THE INVENTION of the computer, most experimental psychologists thought the brain was an unknowable black box.
The so-called cognitive revolution started small, but as computers became standard equipment in psychology labs across the country, it gained broader acceptance.
By the late 1970s, cognitive psychology had overthrown behaviorism, and with the new regime came a whole new language for talking about mental life.
Psychologists began describing thoughts as programs, ordinary people talked about storing facts away in their memory banks, and business gurus fretted about the limits of mental bandwidth and processing power in the modern workplace.
As software has eaten the world, to paraphrase venture capitalist Marc Andreessen, we have surrounded ourselves with machines that convert our actions, thoughts, and emotions into data—raw material for armies of code-wielding engineers to manipulate.
Facebook's Mark Zuckerberg has gone so far as to suggest there might be a “fundamental mathematical law underlying human relationships that governs the balance of who and what we all care about.” In 2013, Craig Venter announced that, a decade after the decoding of the human genome, he had begun to write code that would allow him to create synthetic organisms.
“It is becoming clear,” he said, “that all living cells that we know of on this planet are DNA-software-driven biological machines.” Even self-help literature insists that you can hack your own source code, reprogramming your love life, your sleep routine, and your spending habits.
(In Bloomberg Businessweek, Paul Ford was slightly more circumspect: “If coders don't run the world, they run the things that run the world.” Tomato, tomahto.) But whether you like this state of affairs or hate it—whether you're a member of the coding elite or someone who barely feels competent to futz with the settings on your phone—don't get used to it.
This approach is not new—it's been around for decades—but it has recently become immensely more powerful, thanks in part to the rise of deep neural networks, massively distributed computational systems that mimic the multilayered connections of neurons in the brain.
In February the company replaced its longtime head of search with machine-learning expert John Giannandrea, and it has initiated a major program to retrain its engineers in these new techniques.
“By building learning systems,” Giannandrea told reporters this fall, “we don't have to write these rules anymore.” But here's the thing: With machine learning, the engineer never knows precisely how the computer accomplishes its tasks.
And as these black boxes assume responsibility for more and more of our daily digital tasks, they are not only going to change our relationship to technology—they are going to change how we think about ourselves, our world, and our place within it.
Rubin is excited about the rise of machine learning—his new company, Playground Global, invests in machine-learning startups and is positioning itself to lead the spread of intelligent devices—but it saddens him a little too.
You can't cut your head off and see what you're thinking.” When engineers do peer into a deep neural network, what they see is an ocean of math: a massive, multilayer set of calculus problems that—by constantly deriving the relationship between billions of data points—generate guesses about the world.
They largely ignored, even vilified, early proponents of machine learning, who argued in favor of plying machines with data until they reached their own conclusions.
For the past two decades, learning to code has been one of the surest routes to reliable employment—a fact not lost on all those parents enrolling their kids in after-school code academies.
“I was pointing out how different programming jobs would be by the time all these STEM-educated kids grow up.” Traditional coding won't disappear completely—indeed, O'Reilly predicts that we'll still need coders for a long time yet—but there will likely be less of it, and it will become a meta skill, a way of creating what Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, calls the “scaffolding” within which machine learning can operate.
If the rise of human-written software led to the cult of the engineer, and to the notion that human experience can ultimately be reduced to a series of comprehensible instructions, machine learning kicks the pendulum in the opposite direction.
It's not news that even simple algorithms can create unpredictable emergent behavior—an insight that goes back to chaos theory and random number generators.
Over the past few years, as networks have grown more intertwined and their functions more complex, code has come to seem more like an alien force, the ghosts in the machine ever more elusive and ungovernable.
“One can imagine such technology outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand,” wrote Stephen Hawking—sentiments echoed by Elon Musk and Bill Gates, among others.
But discoveries in the field of epigenetics suggest that genetic material is not in fact an immutable set of instructions but rather a dynamic set of switches that adjusts depending on the environment and experiences of its host.
Venter may believe cells are DNA-software-driven machines, but epigeneticist Steve Cole suggests a different formulation: “A cell is a machine for turning experience into biology.” And now, 80 years after Alan Turing first sketched his designs for a problem-solving machine, computers are becoming devices for turning experience into technology.
- On Monday, December 9, 2019
Hello World - Machine Learning Recipes #1
Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's important. Then, we'll follow...
4 Tips for Computer Programming Beginners – Software Developer Guide
Enroll for exercises, tutorials, courses, and projects... Let's talk about the top 4 best tips you must learn to excel in the software developer world! Use..
CppCon 2017: Hartmut Kaiser “The Asynchronous C++ Parallel Programming Model”
— Presentation Slides, PDFs, Source Code and other presenter materials are available at: — With the advent of modern computer architecture.
CppCon 2017: Bjarne Stroustrup “Learning and Teaching Modern C++”
— Presentation Slides, PDFs, Source Code and other presenter materials are available at: — We – attendees at CppCon – are all.
Using Deep Learning to Extract Feature Data from Imagery
Vector data collection is the most tedious task in a GIS workflow. Digitizing features from imagery or scanned maps is a manual process that is costly, requiring significant human resources...
The Future of Deep Learning Research
Back-propagation is fundamental to deep learning. Hinton (the inventor) recently said we should "throw it all away and start over". What should we do? I'll describe how back-propagation works,...
Deep Learning Frameworks Compared
In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of each, as well as some code samples,...
CppCon 2017: Peter Goldsborough “A Tour of Deep Learning With C++”
— Presentation Slides, PDFs, Source Code and other presenter materials are available at: — Deep Learning is a subfield of artificial.
How we teach computers to understand pictures | Fei Fei Li
When a very young child looks at a picture, she can identify simple elements: "cat," "book," "chair." Now, computers are getting smart enough to do that too. What's next? In a thrilling talk,...
Monitoring and improving your big data applications (Google Cloud Next '17)
In this video, Andrea Foegler shares an overview of Stackdriver support specific to each component of the Google Cloud Platform (GCP) data and machine learning stack. She also discusses additional...