Mostrando entradas con la etiqueta speech recognition. Mostrar todas las entradas
Mostrando entradas con la etiqueta speech recognition. Mostrar todas las entradas

domingo, 31 de mayo de 2015

Google says its speech recognition technology now has only an 8% word error rate

Google's Sundar Pichai talks about its advancements in deep learning at the 2015 Google I/O conference in San Francisco on May 28.
Image Credit: Screenshot

Google today announced its advancements in deep learning, a type of artificial intelligence, for key processes like image recognition and speech recognition.

When it comes to accurately recognizing words in speech, Google now has just an 8 percent error rate. Compare that to 23 percent in 2013, Sundar Pichai, senior vice president of Android, Chrome, and Apps at Google, said at the company’s annual I/O developer conference in San Francisco.

Pichai boasted, “We have the best investments in machine learning over the past many years.” Indeed, Google has acquired several deep learning companies over the years, including DeepMind, DNNresearch, and Jetpac.

Deep learning involves ingesting lots of data to train systems called neural networks, and then feeding new data to those systems and receiving predictions in response.
The company’s current neural networks are now more than 30 layers deep, Pichai said.

Google uses deep learning across many types of services, including object recognition in YouTube videos and even optimization of its vast data centers.

Meanwhile, Baidu, Facebook, and Microsoft are also beefing up their deep learning capabilities. Earlier-stage companies like Flipboard, Pinterest, and Snapchat have also been doing research in the area — but none have the computing power that Google does. So Google’s achievements in real production apps are a pretty big deal.

To view all of VentureBeat’s Google I/O coverage, click here.


ORIGINAL: Venture Beat
May 28, 2015 10:40 AM

viernes, 6 de marzo de 2015

Steering a driving Android Phone over the Web via Speech Recognition in IBM Bluemix


Steering a driving Android Phone over the Web via Speech Recognition in IBM Bluemix



My colleagues Bryan Boyd and Mark VanderWiele have created a nice demo where you can drive smartphones using Sphero balls. I've modified the sample slightly so that it also works for Android phones. Watch the video to see how to steer a driving smartphone via IBM Bluemix, the Internet of Things and cognitive services from IBM Watson.





Here are the details:




The phone is carried by a Sphero ball and a chariot.


The phone communicates with the ball via bluetooth protocol. The native Android app uses the Sphero Android SDK.


The phone communicates with the Internet of Things service in Bluemix via a Java MQTT library.


A Node-RED flow is used to send commands to the native app either when certain URLs are invoked or certain Twitter tweets are sent.


For the speech recognition the sample of the Speech to Text service has been slightly modified to send the text to the Internet of Things service via MQTT from the client side JavaScript.



The Node-RED flow receives the spoken text via an Internet of Things input node. The first word of a text is used to interpret the command and then the same flow as previously is triggered.


This is a screenshot of the flow for incoming URL commands and the devices.




ORIGINAL: Niklas Heidloff
By Niklas Heidloff, posted on Mar 2, 2015

martes, 3 de marzo de 2015

What will happen when the internet of things becomes artificially intelligent?

From Stephen Hawking to Spike Jonze, the existential threat posed by the onset of the ‘conscious web’ is fuelling much debate – but should we be afraid?


Who’s afraid of artificial intelligence? Quite a few notable figures, it turns out. Photograph: Alamy

When Stephen Hawking, Bill Gates and Elon Musk all agree on something, it’s worth paying attention.

All three have warned of the potential dangers that artificial intelligence or AI can bring. The world’s foremost physicist, Hawking said that the full development of artificial intelligence (AI) could “spell the end of the human race”. Musk, the tech entrepreneur who brought us PayPal, Tesla and SpaceX described artificial intelligence as our “biggest existential threat” and said that playing around with AI was like “summoning the demon”. Gates, who knows a thing or two about tech, puts himself in the “concerned” camp when it comes to machines becoming too intelligent for us humans to control.

What are these wise souls afraid of? AI is broadly described as the ability of computer systems to ape or mimic human intelligent behavior. This could be anything from recognizing speech, to visual perception, making decisions and translating languages. Examples run from Deep Blue who beat chess champion Garry Kasparov to supercomputer Watson who outguessed the world’s best Jeopardy player. Fictionally, we have Her, Spike Jonze’s movie that depicts the protagonist, played by Joaquin Phoenix, falling in love with his operating system, seductively voiced by Scarlet Johansson. And coming soon, Chappie stars a stolen police robot who is reprogrammed to make conscious choices and to feel emotions.

An important component of AI, and a key element in the fears it engenders, is the ability of machines to take action on their own without human intervention. This could take the form of a computer reprogramming itself in the face of an obstacle or restriction. In other words, to think for itself and to take action accordingly.

Needless to say, there are those in the tech world who have a more sanguine view of AI and what it could bring. Kevin Kelly, the founding editor of Wired magazine, does not see the future inhabited by HAL’s – the homicidal computer on board the spaceship in 2001: A Space Odyssey. Kelly sees a more prosaic world that looks more like Amazon Web Services: a cheap, smart, utility which is also exceedingly boring simply because it will run in the background of our lives. He says AI will enliven inert objects in the way that electricity did over 100 years ago. “Everything that we formerly electrified, we will now cognitize.” And he sees the business plans of the next 10,000 startups as easy to predict: “Take X and add AI.”

While he acknowledges the concerns about artificial intelligence, Kelly writes: “As AI develops, we might have to engineer ways to prevent consciousness in them – our most premium AI services will be advertised as consciousness-free.” (my emphasis).

Running parallel to the extraordinary advances in the field of AI is the even bigger development of what is loosely called, the internet of things (IoT). This can be broadly described as the emergence of countless objects, animals and even people with uniquely identifiable, embedded devices that are wirelessly connected to the internet. These ‘nodes’ can send or receive information without the need for human intervention. There are estimates that there will be 50 billion connected devices by 2020. Current examples of these smart devices include Nest thermostats, wifi-enabled washing machines and the increasingly connected cars with their built-in sensors that can avoid accidents and even park for you.

The US Federal Trade Commission is sufficiently concerned about the security and privacy implications of the Internet of Things, and has conducted a public workshop and released a report urging companies to adopt best practices and “bake in” procedures to minimise data collection and to ensure consumer trust in the new networked environment.

Tim O’Reilly
, coiner of the phrase “Web 2.0” sees the internet of things as the most important online development yet. He thinks the name is misleading – that IoT is “really about human augmentation”. O’Reilly believes that we should “expect our devices to anticipate us in all sorts of ways”. He uses the “intelligent personal assistant”, Google Now, to make his point.

So what happens when these millions of embedded devices connect to artificially intelligent machines? What does AI + IoT = ? Will it mean the end of civilisation as we know it? Will our self-programming computers send out hostile orders to the chips we’ve added to our everyday objects? Or is this just another disruptive moment, similar to the harnessing of steam or the splitting of the atom? An important step in our own evolution as a species, but nothing to be too concerned about?

The answer may lie in some new thinking about consciousness. As a concept, as well as an experience, consciousness has proved remarkably hard to pin down. We all know that we have it (or at least we think we do), but scientists are unable to prove that we have it or, indeed, exactly what it is and how it arises.

Dictionaries describe consciousness as the state of being awake and aware of our own existence. It is an “internal knowledge” characterized by sensation, emotions and thought.

Just over 20 years ago, an obscure Australian philosopher named David Chalmers created controversy in philosophical circles by raising what became known as the Hard Problem of Consciousness. He asked how the grey matter inside our heads gave rise to the mysterious experience of being. What makes us different to, say, a very efficient robot, one with, perhaps, artificial intelligence? And are we humans the only ones with consciousness?

  • Some scientists propose that consciousness is an illusion, a trick of the brain. 
  • Still others believe we will never solve the consciousness riddle. 
  • But a few neuroscientists think we may finally figure it out, provided we accept the remarkable idea that soon computers or the internet might one day become conscious.
In an extensive Guardian article, the author Oliver Burkeman wrote how Chalmers and others put forth a notion that all things in the universe might be (or potentially be) conscious, “providing the information it contains is sufficiently interconnected and organized.” So could an iPhone or a thermostat be conscious? And, if so, could we in the midst of a ‘Conscious Web’?

Back in the mid-1990s, the author Jennifer Cobb Kreisberg wrote an influential piece for Wired, A Globe, Clothing Itself with a Brain. In it she described the work of a little known Jesuit priest and paleontologist, Teilhard de Chardin, who 50 years earlier described a global sphere of thought, the “living unity of a single tissue” containing our collective thoughts, experiences and consciousness.

Teilhard called it the “nooshphere” (noo is Greek for mind). He saw it as the evolutionary step beyond our geosphere (physical world) and biosphere (biological world). The informational wiring of a being, whether it is made up of neurons or electronics, gives birth to consciousness. As the diversification of nervous connections increase, de Chardin argued, evolution is led towards greater consciousness. Or as John Perry Barlow, Grateful Dead lyricist, cyber advocate and Teilhard de Chardin fan said: “With cyberspace, we are, in effect, hard-wiring the collective consciousness.”

So, perhaps we shouldn’t be so alarmed. Maybe we are on the cusp of a breakthrough not just in the field of artificial intelligence and the emerging internet of things, but also in our understanding of consciousness itself. If we can resolve the privacy, security and trust issues that both AI and the IoT present, we might make an evolutionary leap of historic proportions. And it’s just possible Teilhard’s remarkable vision of an interconnected “thinking layer” is what the web has been all along.

• Stephen Balkam is CEO of the Family Online Safety Institute in the US

ORIGINAL: The Guardian

Stephen Balkam

Friday 20 February 2015

miércoles, 4 de febrero de 2015

BrainCard, pattern recognition for ALL

BrainCard, pattern recognition for ALL Patern Recognition, Brain Card, Image Recognition, AI, Sound Recognition, Biosensors, speech recognition, Intel Edison, Neuromorphic Computing, General Vision, NeuroMem CM1K,
ORIGINAL: IndieGogo

Embedded recognition for images, speech, sound, biosensors or any signal with zero programming.

Petaluma, California, United States Technology

Text and Numbers
 
Pattern & image recognition module with neuromorphic learning for all your maker projects.
Robotics fans, drone pilots, hackers & data-miners - rejoice!

The BrainCard is an open source hardware platform featuring the worlds only fully functional and field-tested Neuromorphic Chip containing 1024 silicon neurons. It is able to learn and recognize patterns within any dataset generated by any source, from the physical (sensors), to the virtual (data).  

Offered here, for the first time, to makers in a format compatible with nearly all other popular electronics platforms — from Raspberry Pi to Arduino and Intel Edison —  we aim to help you add cognitive perception to any electronics project.

Add a brain to: Robots, toys or an old GoPro. Give them the ability to recognize and recall almost anything... You can also add a brain to any digital cameras including dash cams. Vision not your thing? The same technology can recognize patterns in data like that packet of code you're looking for in a sea of C++, a phrase in an eBook (regardless of the books length), even real time data: Build your own biosensors!  Make any appliance you like “smart”, like a coffee pot that recognizes you and starts making your coffee the way you like best.
Simply put; make it think.

The BrainCard is an open source hardware platform featuring the worlds only fully functional and field-tested Neuromorphic Chip containing 1024 silicon neurons. It is able to learn and recognize patterns within any dataset generated by any source, from the physical (sensors), to the virtual (data). Offered here, for the first time, to makers in a format compatible with nearly all other popular electronics platforms — from Raspberry Pi to Arduino and Intel Edison —  we aim to help you add cognitive perception to any electronics project.

Add a brain to: Robots, toys or an old GoPro. Give them the ability to recognize and recall almost anything... You can also add a brain to any digital cameras including dash cams. Vision not your thing? The same technology can recognize patterns in data like that packet of code you're looking for in a sea of C++, a phrase in an eBook (regardless of the books length), even real time data: Build your own biosensors!  Make any appliance you like “smart”, like a coffee pot that recognizes you and starts making your coffee the way you like best.
Simply put; make it think.


Cannot wait for technical details?
Before we carry on, for those of you that are quick studies and/or already know everything, we thought you might like to skip straight to the specs so here you go:

BrainCard Specifications (Hardware and API)

For everyone else - please read on...

Unfamiliar with Neural Networks or Neuromorphic Chips? Watch this:



(If you want some more background info, click here)

Now back to you project...
The BrainCard™ is a small electronics board with a NeuroMem® CM1K device plus a FPGA (Field Programmable Gate Array) chip to connect to platform buses and sensor inputs. There is even an optional image sensor featured on the BrainCard 1KIS (Image Sensor) version. It can be connected to almost any popular electronics platform including Arduino/Raspberry Pi/Intel Edison and enables users to massively boost any devices capability by creating a brain-like system architecture – hence the name.

The CM1K chip(s) on the BrainCard essentially acts as a right-brain hemisphere ready to learn, recognize and recall patterns/images/sounds/inputs from any incoming data stream. This allows the accompanying MPU device to concentrate on what it’s good at — left-brain functions such as logic, procedural computing and as a communications and I/O interface.

The BrainCard is an open source hardware platform featuring the world's only fully functional and field-tested Neuromorphic Chip containing 1024 silicon neurons. It is able to learn and recognize patterns within any dataset generated by any source, from the physical (sensors), to the virtual (data). Offered here, for the first time, to makers in a format compatible with nearly all other popular electronics platforms — from Raspberry Pi to Arduino and Intel Edison —  we aim to help you add cognitive perception to any electronics project.

Add a brain to: Robots, toys or an old GoPro. Give them the ability to recognize and recall almost anything... You can also add a brain to any digital cameras including dash cams. Vision not your thing? The same technology can recognize patterns in data like that packet of code you're looking for in a sea of C++, a phrase in an eBook (regardless of the books length), even real time data: Build your own biosensors!  Make any appliance you like “smart”, like a coffee pot that recognizes you and starts making your coffee the way you like best.
Simply put; make it think.


The key to success is teaching BrainCard as you would a child: Teach it too conservatively and it will not generalize enough; too moderately and it could get confused. It is not like traditional programming and we have found that part of the fun in building projects with the BrainCard is in this new learning parameter.
It’s really quite simple: Show the BrainCard what it must recognize and assign the example a category. So: This face is John, that voice is Emma, this vibration is made by your cat purring and so on.
Getting started:
The BrainCard is delivered with a default configuration which can communicate with either one of the proposed controllers (Arduino, Raspberry PI or Edison) through a same communication protocol over their SPI lines.  Access to generic pattern learning and recognition functions using the CM1K chip are made through a simple API delivered for the different IDE (Arduino and Eclipse). More specific function libraries will be released shortly after and we hope to start a repository of your libraries too! 
  1. Install and connect the BrainCard to the MPU/Device of your choice. View the hardware datasheet
  2. Install the API in the IDE of your choice (Arduino, Eclipse). View the BrainCard API preliminary datasheet
  3. Now, you can program to teach the BrainCard using examples previously collected and saved to disk (waveforms, images, movies). Or you can program some GPIOs to trigger teaching (bush buttons, keyboard inputs and even voice control! As illustrated in the following video, teaching amounts to selecting examples and sending one of more signatures of this example to the neurons of the BrainCard. The neurons will decide if the example is worth learning based on what they already know. If applicable, some neurons will autonomosuly correct themselfves if they contradict the teacher and never repeat this mistake again.
  4. Recognition is the same as learning except that this time, your program monitors the response of the neurons to the incoming signatures instead of sending them learning commands. Your program can then act based on what is recognized using the wealth of GPIOs available through Arduino Shields, as well as  DeviceToDevice or DeviceToCloud communications, and more. 

So what can it do?
This is a great question, as even we have not fully explored the full range of the BrainCard/CM1K’s capabilities. Almost every day we are coming up with new applications for the technology, which is one of our quandaries, and is where YOU come in. It’s also why we are choosing to announce ourselves to the world via Indiegogo.

A simple list of known capabilities 

Object recognition
Using the KIS vesion or an off-the-shelf image sensor of your own and teach your BrainCard to recognize shapes, colors, objects, signs, people and animals.



Stereoscopic vision
With two image sensors attached, along with a CPU, your project can work in stereoscopic vision! The processor can triangulate distance and the CM1K can recognize what it’s looking at. Add some motors to the image sensors and it can track things too.



Audio RecognitionAttach a microphone and teach the BrainCard to recognize a noise, a voice, YOUR voice or other audio signals like a bird song or a dog.

Vibration and motionAttach a MEMS (Micro Electrical Mechanical Systems) device and teach the BrainCard to recognize vibrations or physical motion.

Bio signals
BrainCard can recognize data from any Bio-signal source – such as:

Electroencephalogram (EEG), Electrocardiogram (ECG), Electromyogram (EMG), Mechanomyogram (MMG), Electrooculography (EOG), Galvanic skin response (GSR), Magnetoencephalogram (MEG).



Text and Numbers
You can run your data through the BrainCard in any form — from text to binary to DNA sequences — and teach it to recognize patterns, which will allow it to detect anomalies, identify clusters and make predictions.

There are MANY MORE applications we just haven't tried yet...

Flexibility
If you go crazy while teaching and fill all 1024 neurons on a chip, don’t panic. BrainCard provides an expansion bus to stack more CM1K chips in boards of two, thereby increasing the number of modules (subject to availability) you can teach by increments of 2048 (1x CM1K equals 1,024 neurons). This expansion can be done at any time to its maximum of 8,192 (plus the original 1024 on the BrainCard), and will not impact your teaching allowing you to experiment to your heart’s content.


Maturity
The NeuroMem CM1K technology has already found many applications in industry and has been working in the real world since 2007 – so we know everything we’re claiming above is 100% true, because most of these applications have been built somewhere.

What we need, and what you getThis Indiegogo campaign has been launched with one aim: To generate the volume and revenue we need to manufacture the maker version of the CM1K technology — the BrainCard.

By supporting this Indiegogo project you will be a part of the first chapter of a much bigger story: We aim to change the way the world computes with neural network technology. We're looking to raise at least $200k to start manufacturing in volume, which will make the BrainCard as cheap as possible.

We're beginning with 1000 chips that we already have in inventory which were originally ordered by an industrial client. After that, we will aim to start manufacturing on a mass production line, and this will take approximately six months. So, those first 1000 purchasers will be the only ones able to experience the unique capabilities of the BrainCard until mid-2015.

The first 1000 BrainCard's will cost $199 and are what we call IWIN (I Want It Now), or $219 for a version including an image sensor (the IS version) - so 500 of each version.

If we don't reach the goal, all the money raised will be aimed at manufacturing as many BrainCards as we can, so that it can be more affordable for the masses.

This is why we're turning to the maker community — we'd like to crowdsource our research and development through YOU!
The impact Neural networks should be everywhere by now, in your phone, in wearable technology. The NeuroMem technology is mature and the market needs exist. This project has the ability to propel neuromorphic technology into the mainstream consciousness by showing electronics manufacturers what can be done with it.

This is why we're turning to the maker community — we'd like to crowdsource our research and development through YOU!

Risks and challengesThe core of the NeuroMem/NeuromorThings team has been in place for 16 years and has plenty of research and industrial customers already using the CM1K chip, so this is not a typical “prototype” project.

We have a full supply chain already in place for both the board and for mounting the chips. We also have a wealth of knowledge in developing board-level and semiconductor technologies — all of which makes the risks to you a bare minimum.

We just need your support to complete prototyping/testing and to begin volume manufacturing. The first 1000 IWIN BrainCards will have exclusive access to the technology for the three months it takes us to make the new batch of chips.

Once we begin mass manufacturing the BrainCard, we will begin our long development roadmap on its successors and other neuromorthings.

After the first run of IWIN devices, the rest of the time will be dedicated to mounting the chips to the boards and testing them. With enough support we can get production runs up to very large numbers per month very quickly.

Shipping
Shipping a technology product is fraught with issues like export restrictions. We've tried to make it as simple as possible and built shipping as a perk.

In the US, Mexico and Canada? included

Rest of World? $30 Shipping & Packing

Due to the technical nature of the BrainCard it can be liable to Export Restrictions in certain countries under United States Law. If you are unsure if you are effected - please contact us at: info@neuromorthings.com and put "Export" in the subject line and we'll do everything we can to help.

Other Ways You Can HelpCan't buy a BrainCard? How about giving us a High $5? High 5'ers will all feature on the website and be written into NeuromorThings lore... it's a program for those interested in the technology and who want to help but who can't spring for their own BrainCard.

Got no cash at all? No problem - simply SPREAD THE WORD! Tell everyone you know about us and help us that way instead, on Facebook, on Twitter - wherever.

Every little bit helps!

Export regulations:
It might occurs, in certain rare cases that your country is under export embargo and we cannot ship because of the nature of the technology included in the BrainCard.If this exceptional situation occurs your money will be fully refunded.
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jueves, 28 de agosto de 2014

It's Time to Take Artificial Intelligence Seriously

No Longer an Academic Curiosity, It Now Has Measurable Impact on Our Lives

A still from "2001: A Space Odyssey" with Keir Dullea reflected in the lens of HAL's "eye." MGM / POLARIS / STANLEY KUBRICK

The age of intelligent machines has arrived—only they don't look at all like we expected. Forget what you've seen in movies; this is no HAL from "2001: A Space Odyssey," and it's certainly not Scarlett Johansson's disembodied voice in "Her." It's more akin to what happens when insects, or even fungi, do when they "think." (What, you didn't know that slime molds can solve mazes?)

Artificial intelligence has lately been transformed from an academic curiosity to something that has measurable impact on our lives. Google Inc. used it to increase the accuracy of voice recognition in Android by 25%. The Associated Press is printing business stories written by it. Facebook Inc. is toying with it as a way to improve the relevance of the posts it shows you.

What is especially interesting about this point in the history of AI is that it's no longer just for technology companies. Startups are beginning to adapt it to problems where, at least to me, its applicability is genuinely surprising.

Take advertising copywriting. Could the "Mad Men" of Don Draper's day have predicted that by the beginning of the next century, they would be replaced by machines? Yet a company called Persado aims to do just that.

Persado does one thing, and judging by its client list, which includes Citigroup Inc. and Motorola Mobility, it does it well. It writes advertising emails and "landing pages" (where you end up if you click on a link in one of those emails, or an ad).

Here's an example: Persado's engine is being used across all of the types of emails a top U.S. wireless carrier sends out when it wants to convince its customers to renew their contracts, upgrade to a better plan or otherwise spend money.

Traditionally, an advertising copywriter would pen these emails; perhaps the company would test a few variants on a subset of its customers, to see which is best.

But Persado's software deconstructs advertisements into five components, including emotion words, characteristics of the product, the "call to action" and even the position of text and the images accompanying it. By recombining them in millions of ways and then distilling their essential characteristics into eight or more test emails that are sent to some customers, Persado says it can effectively determine the best possible come-on.

"A creative person is good but random," says Lawrence Whittle, head of sales at Persado. "We've taken the randomness out by building an ontology of language."

The results speak for themselves: In the case of emails intended to convince mobile subscribers to renew their plans, initial trials with Persado increased click-through rates by 195%, the company says.

Here's another example of AI becoming genuinely useful: X.ai is a startup aimed, like Persado, at doing one thing exceptionally well. In this case, it's scheduling meetings. X.ai's virtual assistant, Amy, isn't a website or an app; she's simply a "person" whom you cc: on emails to anyone with whom you'd like to schedule a meeting. Her sole "interface" is emails she sends and receives—just like a real assistant. Thus, you don't have to bother with back-and-forth emails trying to find a convenient time and available place for lunch. Amy can correspond fluidly with anyone, but only on the subject of his or her calendar. This sounds like a simple problem to crack, but it isn't, because Amy must communicate with a human being who might not even know she's an AI, and she must do it flawlessly, says X.ai founder Dennis Mortensen.

E-mail conversations with Amy are already quite smooth. Mr. Mortensen used her to schedule our meeting, naturally, and it worked even though I purposely threw in some ambiguous language about the times I was available. But that is in part because Amy is still in the "training" stage, where anything she doesn't understand gets handed to humans employed by X.ai.

It sounds like cheating, but every artificially intelligent system needs a body of data on which to "train" initially. For Persado, that body of data was text messages sent to prepaid cellphone customers in Europe, urging them to re-up their minutes or opt into special plans. For Amy, it's a race to get a body of 100,000 email meeting requests. Amusingly, engineers at X.ai thought about using one of the biggest public database of emails available, the Enron emails, but there is too much scheming in them to be a good sample.

Both of these systems, and others like them, work precisely because their makers have decided to tackle problems that are as narrowly defined as possible. Amy doesn't have to have a conversation about the weather—just when and where you'd like to schedule a meeting. And Persado's system isn't going to come up with the next "Just Do It" campaign.

This is where some might object that the commercialized vision for AI isn't intelligent at all. But academics can't even agree on where the cutoff for "intelligence" is in living things, so the fact that these first steps toward economically useful artificial intelligence lie somewhere near the bottom of the spectrum of things that think shouldn't bother us.

We're also at a time when it seems that advances in the sheer power of computers will lead to AI that becomes progressively smarter. So-called deep-learning algorithms allow machines to learn unsupervised, whereas both Persado and X.ai's systems require training guided by humans.

Last year Google showed that its own deep-learning systems could learn to recognize a cat from millions of images scraped from the Internet, without ever being told what a cat was in the first place. It's a parlor trick, but it isn't hard to see where this is going—the enhancement of the effectiveness of knowledge workers. Mr. Mortensen estimates there are 87 million of them in the world already, and they schedule 10 billion meetings a year. As more tools tackling specific portions of their job become available, their days could be filled with the things that only humans can do, like creativity.

"I think the next Siri is not Siri; it's 100 companies like ours mashed into one," says Mr. Mortensen.

—Follow Christopher Mims on Twitter @Mims or write to him atchristopher.mims@wsj.com.

By CHRISTOPHER MIMS
Aug. 24, 2014

lunes, 25 de noviembre de 2013

Neural Networks and Deep Learning Book Project


A book that will teach you the core concepts of neural networks and deep learning

About the book
This book will teach you the core concepts of neural networks and deep learning. These are powerful machine learning techniques which have achieved outstanding results for problems in image recognition, speech recognition, and natural language processing. Neural networks and deep learning are now being adopted by many companies, including Google, Microsoft, and Facebook.

I'm writing this book to bridge the gap between popular accounts and the many technical papers on neural networks and deep learning. The book will make it easy and fun for people with programming and basic mathematical skills to come up to speed.

I love explaining complex technical subjects. I've written two previous books. The first book, "Quantum Computation and Quantum Information" (joint with Ike Chuang), is the standard text on quantum computing, and one of the ten most cited books in the history of physics. The second book, "Reinventing Discovery: The New Era of Networked Science", is a book for a general audience about networked science. It was named one of the best books of 2011 by The Financial Times and the Boston Globe.

In addition to my books I've written many technical articles, including "Lisp as the Maxwell's equations of software", "How to crawl a quarter billion webpages in 40 hours", and "Why Bloom filters work the way they do", all of which made the top five posts on Hacker News.

You can see a draft of chapter 1 of the book at neuralnetworksanddeeplearning.com.

The book will be made freely available online, under a Creative Commons Attribution-Non-Commercial license.

As an independent writer and scientist, the reason I'm undertaking this Indiegogo campaign is to give me some partial support while I complete the book.

Draft table of contents
  • Using neural nets to recognize handwritten digits: We get off to a flying start, creating a neural network that can solve a hard problem - recognizing handwritten digits.
  • Using backpropagation to speed up learning: We'll master the ins-and-outs of the backpropagation algorithm, which is the fundamental algorithm used to learn in neural nets, and the basis for deep learning.
  • Neural nets: the big picture: How do artificial neural nets compare to biological brains? Is there a simple universal algorithm for thinking? How can we use neural nets to solve problems in speech recognition and natural language processing? Can we use neural nets to compute an arbitrary function?
  • Deep learning: What makes deep neural networks hard to train with conventional approaches? How can we overcome those challenges? We'll see how deep neural nets can be pre-trained, and how they can learn high-level representations of knowledge from complex data.
  • Recent progress in image recognition: We'll dive into exciting recent work using deep learning to solve difficult problems in image recognition, including recognizing the images in ImageNet, and the Stanford-Google "cat neuron" paper.
  • The future of neural nets: Will neural nets help lead to artificial intelligence? Can they be used to simulate a human brain?
ORIGINAL: Indiegogo