AI News, What is machine learning?

What is machine learning?

However, traditional computer science [2] faces a major limitation regarding the task of extending human intelligence: we first need to explain to the computer how to perform the task we want to accomplish.

Unlike the example of the oven, you cannot start describing the characteristics of every animal like: “If the animal is within this range of colors and has black vertical stripes with a slightly elliptical shape and has a nose like… then it is a tiger”.

Well, it turns out that if you want to program a computer to identify animals, the only way to do so is to use a programming language to manually describe each animal so that the computer can tell the difference between one animal and another.

The need to make a program that explains to computers how to perform each task is the great limitation faced by traditional computer science programming.

In other words, a Machine Learning algorithm is a computer program that teaches computers how to program themselves so that we don’t have to explicitly describe how to perform the task we want to achieve.

Now you can see how enabling computers to learn and enabling computers to write their own code are the same thing (if you want to read a brief explanation about how Machine Learning algorithms work, take a look at [4]).

Therefore, Machine Learning is the way to make computers learn how to perform complex tasks whose processes cannot be easily described by humans, or even tasks that we don’t know how to accomplish (e.g.

We usually deem “prediction” to be the action of computing the most likely outcome of a very complex process that humans can hardly compute, and that is why we usually say that Machine Learning models are used to make predictions.

Machine Learning is the way to make computers learn how to perform complex tasks whose processes cannot be easily described by humans, or even tasks that we don’t know how accomplish.

Many useful Machine Learning algorithms were already known twenty years ago, but just recently we obtained enough computing power combined with lots of data to make them work.

Imagine the following series: 1: [0, 0] 2: [2, 3] 3: [4, 6] 4: [6, 9] …

This is the reason why Machine Learning is already extremely useful in helping humans perform complex tasks, such as predicting diseases, predicting stock market evolution, self-driving cars, and an infinite number of other applications.

[4] As you already know, instead of making programs that explain to computers how to perform specific tasks, in Machine Learning we make programs that explain computers how to learn by themselves to perform tasks.

“I predicted a lion but I see that the label of this image says this is a tiger”) and it will follow a set of rules given by the Machine Learning scientists in order to modify the weight of each synaptic connection so that the error of the prediction is reduced (e.g.

Note that we do not have provide the computer with an update rule for each individual synaptic connection, but we provide the computer with some general rules that it applies millions of times (for those of you that still remember some high school calculus, what we do is to compute the derivative of the error with respect each synaptic connection and then move the value of each connection towards the direction than reduces the prediction error).

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.

Just as Newtonian physics wasn't obviated by the discovery of quantum mechanics, code will remain a powerful, if incomplete, tool set to explore the world.

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.

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.

Deep Learning

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.

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.

Artificial intelligence: computer says YES (but is it right?)

Of course many people die in car crashes every day – in the USA there is one fatality every 94 million miles, and according to Tesla this was the first known fatality in over 130 million miles of driving with activated autopilot.

It happens every time your digital camera detects a face and throws a box around it to focus, or the personal assistant on your smartphone answers a question, or the adverts match your interests when you search online.

Dr Adrian Weller, who works with Ghahramani, highlights the difficulty: “A particular issue with new artificial intelligence (AI) systems that learn or evolve is that their processes do not clearly map to rational decision-making pathways that are easy for humans to understand.” His research aims both at making these pathways more transparent, sometimes through visualisation, and at looking at what happens when systems are used in real-world scenarios that extend beyond their training environments – an increasingly common occurrence.

“We would like AI systems to monitor their situation dynamically, detect whether there has been a change in their environment and – if they can no longer work reliably – then provide an alert and perhaps shift to a safety mode.” A driverless car, for instance, might decide that a foggy night in heavy traffic requires a human driver to take control.

Over the coming years, philosophers, social scientists, cognitive scientists and computer scientists will help guide the future of the technology and study its implications – both the concerns and the benefits to society.” CFI brings together four of the world’s leading universities (Cambridge, Oxford, Berkeley and Imperial College, London) to explore the implications of AI for human civilisation.

And as we see an escalation in what machines can do, they will challenge our notions of intelligence and make it all the more important that we have the means to trust what they tell us.” Artificial intelligence has the power to eradicate poverty and disease or hasten the end of human civilisation as we know it – according to a speech delivered by Professor Stephen Hawking 19 October 2016 at the launch of the Centre for the Future of Intelligence.

Machine Learning | Artificial Intelligence Research Department at Carnegie Mellon University

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machine learning ] [ microsoft machine learning ] [ svm machine learning ] [ machine learning for dummies ] [ machine learning in finance ] [ spark machine learning ] [ tensorflow ] [ deep learning ] [ scikit learn] [ sklearn ] [ python machine learning ] [ support vector machine ] [ unsupervised learning ] [ azure machine learning ] [ automl ] [ sagemaker ] [ machine learning with r ] [ aws machine learning ] [ google tensorflow ] [ deep learning with python ] [ aws sagemaker ] [ supervised learning ] [ amazon sagemaker ] [ deep reinforcement learning ] [ amazon machine learning ] [ deeplens aws ] [ deeplens ] [ machine learning examples ] [ support vector machines ] [ python sklearn ] [ boltzmann machine ] [ semi supervised learning ] [ bigml ] [ svm python ] [ mlib ] [ kubeflow ] [ ensemble learning ] [ deep learning with r ] [ svm sklearn ] [ types of machine learning ] [ microsoft machine learning ] [ svm machine learning ] [ machine learning for dummies ] [ machine learning in finance ] [ spark machine learning ] [ tensorflow ] [ deep learning ] [ scikit learn] [ sklearn ] [ python machine learning ] [ support vector machine ] [ unsupervised learning ] [ azure machine learning ] [ automl ] [ sagemaker ] [ machine learning with r ] [ aws machine learning ] [ google tensorflow ] [ deep learning with python ] [ aws sagemaker ] [ supervised learning ] [ amazon sagemaker ] [ deep reinforcement learning ] [ amazon machine learning ] [ deeplens aws ] [ deeplens ] [ machine learning examples ] [ support vector machines ] [ python sklearn ] [ boltzmann machine ] [ semi supervised learning ] [ bigml ] [ svm python ] [ mlib ] [ kubeflow ] [ ensemble learning ] [ deep learning with r ] [ svm sklearn ] [ types of machine learning ] [ microsoft machine learning ] [ svm machine learning ] [ machine learning for dummies ] [ machine learning in finance ] [ spark machine learning ] [ tensorflow ] [ deep learning ] [ scikit learn] [ sklearn ] [ python machine learning ] [ support vector machine ] [ unsupervised learning ] [ azure machine learning ] [ automl ] [ sagemaker ] [ machine learning with r ] [ aws machine learning ] [ google tensorflow ] [ deep learning with python ] [ aws sagemaker ] [ supervised learning ] [ amazon sagemaker ] [ deep reinforcement learning ] [ amazon machine learning ] [ deeplens aws ] [ deeplens ] [ machine learning examples ] [ support vector machines ] [ python sklearn ] [ boltzmann machine ] [ semi supervised learning ] [ bigml ] [ svm python ] [ mlib ] [ kubeflow ] [ ensemble learning ] [ deep learning with r ] [ svm sklearn ] [ types of machine learning ] [ microsoft machine learning ] [ svm machine learning ] [ machine learning for dummies ] [ machine learning in finance ] [ spark machine learning ] [ tensorflow ] [ deep learning ] [ scikit learn] [ sklearn ] [ python machine learning ] [ support vector machine ] [ unsupervised learning ] [ azure machine learning ] [ automl ] [ sagemaker ] [ machine learning with r ] [ aws machine learning ] [ google tensorflow ] [ deep learning with python ] [ aws sagemaker ] [ supervised learning ] [ amazon sagemaker ] [ deep reinforcement learning ] [ amazon machine learning ] [ deeplens aws ] [ deeplens ] [ machine learning examples ] [ support vector machines ] [ python sklearn ] [ boltzmann machine ] [ semi supervised learning ] [ bigml ] [ svm python ] [ mlib ] [ kubeflow ] [ ensemble learning ] [ deep learning with r ] [ svm sklearn ] [ types of machine learning ] [ microsoft machine learning ] [ svm machine learning ] [ machine learning for dummies ] [ machine learning in finance ] [ spark machine learning ] [ tensorflow ] [ deep learning ] [ scikit learn] [ sklearn ] [ python machine learning ] [ support vector machine ] [ unsupervised learning ] [ azure machine learning ] [ automl ] [ sagemaker ] [ machine learning with r ] [ aws machine learning ] [ google tensorflow ] [ deep learning with python ] [ aws sagemaker ] [ supervised learning ] [ amazon sagemaker ] [ deep reinforcement learning ] [ amazon machine learning ] [ deeplens aws ] [ deeplens ] [ machine learning examples ] [ support vector machines ] [ python sklearn ] [ boltzmann machine ] [ semi supervised learning ] [ bigml ] [ svm python ] [ mlib ] [ kubeflow ] [ ensemble learning ] [ deep learning with r ] [ svm sklearn ] [ types of machine learning ] [ microsoft machine learning ] [ svm machine learning ] [ machine learning for dummies ] [ machine learning in finance ] [ spark machine learning ]

Early Computing: Crash Course Computer Science #1

Hello, world! Welcome to Crash Course Computer Science! So today, we're going to take a look at computing's origins, because even though our digital ...

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.

Chimp vs Human! | Memory Test | BBC Earth

New David Attenborough series Dynasties coming soon! Watch the first trailer here: --~-- Ayumu the ..

Animator vs. Animation IV (original)

Help me make more of these! The struggle between a stick figure and its creator, a computer animator. In this fourth ..

Karl Sims - Evolving Virtual Creatures With Genetic Algorithms

1994.

Differentiable Neural Computer (LIVE)

The Differentiable Neural Computer is an awesome model that DeepMind recently released. It's a memory augmented network that can perform meta-learning ...

The Race to Quantum AI

AI is changing the world around us, making its way into businesses, health care, science and many other fields. In fact, most of us are happy to work daily with ...

How computers are learning to be creative | Blaise Agüera y Arcas

We're on the edge of a new frontier in art and creativity — and it's not human. Blaise Agüera y Arcas, principal scientist at Google, works with deep neural ...

Evolved Electrophysiological Soft Robots

The research field of evolutionary robotics abstracts some of the major themes in biological evolution (heritable traits, genetic variation, and competition for ...

How to Make an Image Classifier - Intro to Deep Learning #6

We're going to make our own Image Classifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive into the ...