Category: Speech Recognition


Top 10 Hot Artificial Intelligence (AI) Technologies

By Hugo Angel,

forrester-ai-technologiesThe market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises. A Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018. Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.

Coined in 1955 to describe a new computer science sub-discipline, “Artificial Intelligence” today includes a variety of technologies and tools, some time-tested, others relatively new. To help make sense of what’s hot and what’s not, Forrester just published a TechRadar report on Artificial Intelligence (for application development professionals), a detailed analysis of 13 technologies enterprises should consider adopting to support human decision-making.

Based on Forrester’s analysis, here’s my list of the 10 hottest AI technologies:

  1. Natural Language Generation: Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights. Sample vendors:
    • Attivio,
    • Automated Insights,
    • Cambridge Semantics,
    • Digital Reasoning,
    • Lucidworks,
    • Narrative Science,
    • SAS,
    • Yseop.
  2. Speech Recognition: Transcribe and transform human speech into format useful for computer applications. Currently used in interactive voice response systems and mobile applications. Sample vendors:
    • NICE,
    • Nuance Communications,
    • OpenText,
    • Verint Systems.
  3. Virtual Agents: “The current darling of the media,” says Forrester (I believe they refer to my evolving relationships with Alexa), from simple chatbots to advanced systems that can network with humans. Currently used in customer service and support and as a smart home manager. Sample vendors:
    • Amazon,
    • Apple,
    • Artificial Solutions,
    • Assist AI,
    • Creative Virtual,
    • Google,
    • IBM,
    • IPsoft,
    • Microsoft,
    • Satisfi.
  4. Machine Learning Platforms: Providing algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines. Currently used in a wide range of enterprise applications, mostly `involving prediction or classification. Sample vendors:
    • Amazon,
    • Fractal Analytics,
    • Google,
    • H2O.ai,
    • Microsoft,
    • SAS,
    • Skytree.
  5. AI-optimized Hardware: Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs. Currently primarily making a difference in deep learning applications. Sample vendors:
    • Alluviate,
    • Cray,
    • Google,
    • IBM,
    • Intel,
    • Nvidia.
  6. Decision Management: Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning. A mature technology, it is used in a wide variety of enterprise applications, assisting in or performing automated decision-making. Sample vendors:
    • Advanced Systems Concepts,
    • Informatica,
    • Maana,
    • Pegasystems,
    • UiPath.
  7. Deep Learning Platforms: A special type of machine learning consisting of artificial neural networks with multiple abstraction layers. Currently primarily used in pattern recognition and classification applications supported by very large data sets. Sample vendors:
    • Deep Instinct,
    • Ersatz Labs,
    • Fluid AI,
    • MathWorks,
    • Peltarion,
    • Saffron Technology,
    • Sentient Technologies.
  8. Biometrics: Enable more natural interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language. Currently used primarily in market research. Sample vendors:
    • 3VR,
    • Affectiva,
    • Agnitio,
    • FaceFirst,
    • Sensory,
    • Synqera,
    • Tahzoo.
  9. Robotic Process Automation: Using scripts and other methods to automate human action to support efficient business processes. Currently used where it’s too expensive or inefficient for humans to execute a task or a process. Sample vendors:
    • Advanced Systems Concepts,
    • Automation Anywhere,
    • Blue Prism,
    • UiPath,
    • WorkFusion.
  10. Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data. Sample vendors:
    • Basis Technology,
    • Coveo,
    • Expert System,
    • Indico,
    • Knime,
    • Lexalytics,
    • Linguamatics,
    • Mindbreeze,
    • Sinequa,
    • Stratifyd,
    • Synapsify.

There are certainly many business benefits gained from AI technologies today, but according to a survey Forrester conducted last year, there are also obstacles to AI adoption as expressed by companies with no plans of investing in AI:

There is no defined business case 42%
Not clear what AI can be used for 39%
Don’t have the required skills 33%
Need first to invest in modernizing data mgt platform 29%
Don’t have the budget 23%
Not certain what is needed for implementing an AI system 19%
AI systems are not proven 14%
Do not have the right processes or governance 13%
AI is a lot of hype with little substance 11%
Don’t own or have access to the required data 8%
Not sure what AI means 3%
Once enterprises overcome these obstacles, Forrester concludes, they stand to gain from AI driving accelerated transformation in customer-facing applications and developing an interconnected web of enterprise intelligence.

Follow me on Twitter @GilPress or Facebook or Google+

Former NASA chief unveils $100 million neural chip maker KnuEdge

By Hugo Angel,

Daniel Goldin
It’s not all that easy to call KnuEdge a startup. Created a decade ago by Daniel Goldin, the former head of the National Aeronautics and Space Administration, KnuEdge is only now coming out of stealth mode. It has already raised $100 million in funding to build a “neural chip” that Goldin says will make data centers more efficient in a hyperscale age.
Goldin, who founded the San Diego, California-based company with the former chief technology officer of NASA, said he believes the company’s brain-like chip will be far more cost and power efficient than current chips based on the computer design popularized by computer architect John von Neumann. In von Neumann machines, memory and processor are separated and linked via a data pathway known as a bus. Over the years, von Neumann machines have gotten faster by sending more and more data at higher speeds across the bus as processor and memory interact. But the speed of a computer is often limited by the capacity of that bus, leading to what some computer scientists to call the “von Neumann bottleneck.” IBM has seen the same problem, and it has a research team working on brain-like data center chips. Both efforts are part of an attempt to deal with the explosion of data driven by artificial intelligence and machine learning.
Goldin’s company is doing something similar to IBM, but only on the surface. Its approach is much different, and it has been secretly funded by unknown angel investors. And Goldin said in an interview with VentureBeat that the company has already generated $20 million in revenue and is actively engaged in hyperscale computing companies and Fortune 500 companies in the aerospace, banking, health care, hospitality, and insurance industries. The mission is a fundamental transformation of the computing world, Goldin said.
It all started over a mission to Mars,” Goldin said.

Above: KnuEdge’s first chip has 256 cores.Image Credit: KnuEdge
Back in the year 2000, Goldin saw that the time delay for controlling a space vehicle would be too long, so the vehicle would have to operate itself. He calculated that a mission to Mars would take software that would push technology to the limit, with more than tens of millions of lines of code.
Above: Daniel Goldin, CEO of KnuEdge.
Image Credit: KnuEdge
I thought, Former NASA chief unveils $100 million neural chip maker KnuEdge

It’s not all that easy to call KnuEdge a startup. Created a decade ago by Daniel Goldin, the former head of the National Aeronautics and Space Administration, KnuEdge is only now coming out of stealth mode. It has already raised $100 million in funding to build a “neural chip” that Goldin says will make data centers more efficient in a hyperscale age.
Goldin, who founded the San Diego, California-based company with the former chief technology officer of NASA, said he believes the company’s brain-like chip will be far more cost and power efficient than current chips based on the computer design popularized by computer architect John von Neumann. In von Neumann machines, memory and processor are separated and linked via a data pathway known as a bus. Over the years, von Neumann machines have gotten faster by sending more and more data at higher speeds across the bus as processor and memory interact. But the speed of a computer is often limited by the capacity of that bus, leading to what some computer scientists to call the “von Neumann bottleneck.” IBM has seen the same problem, and it has a research team working on brain-like data center chips. Both efforts are part of an attempt to deal with the explosion of data driven by artificial intelligence and machine learning.
Goldin’s company is doing something similar to IBM, but only on the surface. Its approach is much different, and it has been secretly funded by unknown angel investors. And Goldin said in an interview with VentureBeat that the company has already generated $20 million in revenue and is actively engaged in hyperscale computing companies and Fortune 500 companies in the aerospace, banking, health care, hospitality, and insurance industries. The mission is a fundamental transformation of the computing world, Goldin said.
It all started over a mission to Mars,” Goldin said.

Above: KnuEdge’s first chip has 256 cores.Image Credit: KnuEdge
Back in the year 2000, Goldin saw that the time delay for controlling a space vehicle would be too long, so the vehicle would have to operate itself. He calculated that a mission to Mars would take software that would push technology to the limit, with more than tens of millions of lines of code.
Above: Daniel Goldin, CEO of KnuEdge.
Image Credit: KnuEdge
I thought, holy smokes,” he said. “It’s going to be too expensive. It’s not propulsion. It’s not environmental control. It’s not power. This software business is a very big problem, and that nation couldn’t afford it.
So Goldin looked further into the brains of the robotics, and that’s when he started thinking about the computing it would take.
Asked if it was easier to run NASA or a startup, Goldin let out a guffaw.
I love them both, but they’re both very different,” Goldin said. “At NASA, I spent a lot of time on non-technical issues. I had a project every quarter, and I didn’t want to become dull technically. I tried to always take on a technical job doing architecture, working with a design team, and always doing something leading edge. I grew up at a time when you graduated from a university and went to work for someone else. If I ever come back to this earth, I would graduate and become an entrepreneur. This is so wonderful.
Back in 1992, Goldin was planning on starting a wireless company as an entrepreneur. But then he got the call to “go serve the country,” and he did that work for a decade. He started KnuEdge (previously called Intellisis) in 2005, and he got very patient capital.
When I went out to find investors, I knew I couldn’t use the conventional Silicon Valley approach (impatient capital),” he said. “It is a fabulous approach that has generated incredible wealth. But I wanted to undertake revolutionary technology development. To build the future tools for next-generation machine learning, improving the natural interface between humans and machines. So I got patient capital that wanted to see lightning strike. Between all of us, we have a board of directors that can contact almost anyone in the world. They’re fabulous business people and technologists. We knew we had a ten-year run-up.
But he’s not saying who those people are yet.
KnuEdge’s chips are part of a larger platform. KnuEdge is also unveiling KnuVerse, a military-grade voice recognition and authentication technology that unlocks the potential of voice interfaces to power next-generation computing, Goldin said.
While the voice technology market has exploded over the past five years due to the introductions of Siri, Cortana, Google Home, Echo, and ViV, the aspirations of most commercial voice technology teams are still on hold because of security and noise issues. KnuVerse solutions are based on patented authentication techniques using the human voice — even in extremely noisy environments — as one of the most secure forms of biometrics. Secure voice recognition has applications in industries such as banking, entertainment, and hospitality.
KnuEdge says it is now possible to authenticate to computers, web and mobile apps, and Internet of Things devices (or everyday objects that are smart and connected) with only a few words spoken into a microphone — in any language, no matter how loud the background environment or how many other people are talking nearby. In addition to KnuVerse, KnuEdge offers Knurld.io for application developers, a software development kit, and a cloud-based voice recognition and authentication service that can be integrated into an app typically within two hours.
And KnuEdge is announcing KnuPath with LambdaFabric computing. KnuEdge’s first chip, built with an older manufacturing technology, has 256 cores, or neuron-like brain cells, on a single chip. Each core is a tiny digital signal processor. The LambdaFabric makes it possible to instantly connect those cores to each other — a trick that helps overcome one of the major problems of multicore chips, Goldin said. The LambdaFabric is designed to connect up to 512,000 devices, enabling the system to be used in the most demanding computing environments. From rack to rack, the fabric has a latency (or interaction delay) of only 400 nanoseconds. And the whole system is designed to use a low amount of power.
All of the company’s designs are built on biological principles about how the brain gets a lot of computing work done with a small amount of power. The chip is based on what Goldin calls “sparse matrix heterogeneous machine learning algorithms.” And it will run C++ software, something that is already very popular. Programmers can program each one of the cores with a different algorithm to run simultaneously, for the “ultimate in heterogeneity.” It’s multiple input, multiple data, and “that gives us some of our power,” Goldin said.

Above: KnuEdge’s KnuPath chip.
Image Credit: KnuEdge
KnuEdge is emerging out of stealth mode to aim its new Voice and Machine Learning technologies at key challenges in IoT, cloud based machine learning and pattern recognition,” said Paul Teich, principal analyst at Tirias Research, in a statement. “Dan Goldin used his experience in transforming technology to charter KnuEdge with a bold idea, with the patience of longer development timelines and away from typical startup hype and practices. The result is a new and cutting-edge path for neural computing acceleration. There is also a refreshing surprise element to KnuEdge announcing a relevant new architecture that is ready to ship… not just a concept or early prototype.”
Today, Goldin said the company is ready to show off its designs. The first chip was ready last December, and KnuEdge is sharing it with potential customers. That chip was built with a 32-nanometer manufacturing process, and even though that’s an older technology, it is a powerful chip, Goldin said. Even at 32 nanometers, the chip has something like a two-times to six-times performance advantage over similar chips, KnuEdge said.
The human brain has a couple of hundred billion neurons, and each neuron is connected to at least 10,000 to 100,000 neurons,” Goldin said. “And the brain is the most energy efficient and powerful computer in the world. That is the metaphor we are using.”
KnuEdge has a new version of its chip under design. And the company has already generated revenue from sales of the prototype systems. Each board has about four chips.
As for the competition from IBM, Goldin said, “I believe we made the right decision and are going in the right direction. IBM’s approach is very different from what we have. We are not aiming at anyone. We are aiming at the future.
In his NASA days, Goldin had a lot of successes. There, he redesigned and delivered the International Space Station, tripled the number of space flights, and put a record number of people into space, all while reducing the agency’s planned budget by 25 percent. He also spent 25 years at TRW, where he led the development of satellite television services.
KnuEdge has 100 employees, but Goldin said the company outsources almost everything. Goldin said he is planning to raised a round of funding late this year or early next year. The company collaborated with the University of California at San Diego and UCSD’s California Institute for Telecommunications and Information Technology.
With computers that can handle natural language systems, many people in the world who can’t read or write will be able to fend for themselves more easily, Goldin said.
I want to be able to take machine learning and help people communicate and make a living,” he said. “This is just the beginning. This is the Wild West. We are talking to very large companies about this, and they are getting very excited.
A sample application is a home that has much greater self-awareness. If there’s something wrong in the house, the KnuEdge system could analyze it and figure out if it needs to alert the homeowner.
Goldin said it was hard to keep the company secret.
I’ve been biting my lip for ten years,” he said.
As for whether KnuEdge’s technology could be used to send people to Mars, Goldin said. “This is available to whoever is going to Mars. I tried twice. I would love it if they use it to get there.
ORIGINAL: Venture Beat

holy smokes

,” he said. “It’s going to be too expensive. It’s not propulsion. It’s not environmental control. It’s not power. This software business is a very big problem, and that nation couldn’t afford it.

So Goldin looked further into the brains of the robotics, and that’s when he started thinking about the computing it would take.
Asked if it was easier to run NASA or a startup, Goldin let out a guffaw.
I love them both, but they’re both very different,” Goldin said. “At NASA, I spent a lot of time on non-technical issues. I had a project every quarter, and I didn’t want to become dull technically. I tried to always take on a technical job doing architecture, working with a design team, and always doing something leading edge. I grew up at a time when you graduated from a university and went to work for someone else. If I ever come back to this earth, I would graduate and become an entrepreneur. This is so wonderful.
Back in 1992, Goldin was planning on starting a wireless company as an entrepreneur. But then he got the call to “go serve the country,” and he did that work for a decade. He started KnuEdge (previously called Intellisis) in 2005, and he got very patient capital.
When I went out to find investors, I knew I couldn’t use the conventional Silicon Valley approach (impatient capital),” he said. “It is a fabulous approach that has generated incredible wealth. But I wanted to undertake revolutionary technology development. To build the future tools for next-generation machine learning, improving the natural interface between humans and machines. So I got patient capital that wanted to see lightning strike. Between all of us, we have a board of directors that can contact almost anyone in the world. They’re fabulous business people and technologists. We knew we had a ten-year run-up.
But he’s not saying who those people are yet.
KnuEdge’s chips are part of a larger platform. KnuEdge is also unveiling KnuVerse, a military-grade voice recognition and authentication technology that unlocks the potential of voice interfaces to power next-generation computing, Goldin said.
While the voice technology market has exploded over the past five years due to the introductions of Siri, Cortana, Google Home, Echo, and ViV, the aspirations of most commercial voice technology teams are still on hold because of security and noise issues. KnuVerse solutions are based on patented authentication techniques using the human voice — even in extremely noisy environments — as one of the most secure forms of biometrics. Secure voice recognition has applications in industries such as banking, entertainment, and hospitality.
KnuEdge says it is now possible to authenticate to computers, web and mobile apps, and Internet of Things devices (or everyday objects that are smart and connected) with only a few words spoken into a microphone — in any language, no matter how loud the background environment or how many other people are talking nearby. In addition to KnuVerse, KnuEdge offers Knurld.io for application developers, a software development kit, and a cloud-based voice recognition and authentication service that can be integrated into an app typically within two hours.
And KnuEdge is announcing KnuPath with LambdaFabric computing. KnuEdge’s first chip, built with an older manufacturing technology, has 256 cores, or neuron-like brain cells, on a single chip. Each core is a tiny digital signal processor. The LambdaFabric makes it possible to instantly connect those cores to each other — a trick that helps overcome one of the major problems of multicore chips, Goldin said. The LambdaFabric is designed to connect up to 512,000 devices, enabling the system to be used in the most demanding computing environments. From rack to rack, the fabric has a latency (or interaction delay) of only 400 nanoseconds. And the whole system is designed to use a low amount of power.
All of the company’s designs are built on biological principles about how the brain gets a lot of computing work done with a small amount of power. The chip is based on what Goldin calls “sparse matrix heterogeneous machine learning algorithms.” And it will run C++ software, something that is already very popular. Programmers can program each one of the cores with a different algorithm to run simultaneously, for the “ultimate in heterogeneity.” It’s multiple input, multiple data, and “that gives us some of our power,” Goldin said.

Above: KnuEdge’s KnuPath chip.
Image Credit: KnuEdge
KnuEdge is emerging out of stealth mode to aim its new Voice and Machine Learning technologies at key challenges in IoT, cloud based machine learning and pattern recognition,” said Paul Teich, principal analyst at Tirias Research, in a statement. “Dan Goldin used his experience in transforming technology to charter KnuEdge with a bold idea, with the patience of longer development timelines and away from typical startup hype and practices. The result is a new and cutting-edge path for neural computing acceleration. There is also a refreshing surprise element to KnuEdge announcing a relevant new architecture that is ready to ship… not just a concept or early prototype.”
Today, Goldin said the company is ready to show off its designs. The first chip was ready last December, and KnuEdge is sharing it with potential customers. That chip was built with a 32-nanometer manufacturing process, and even though that’s an older technology, it is a powerful chip, Goldin said. Even at 32 nanometers, the chip has something like a two-times to six-times performance advantage over similar chips, KnuEdge said.
The human brain has a couple of hundred billion neurons, and each neuron is connected to at least 10,000 to 100,000 neurons,” Goldin said. “And the brain is the most energy efficient and powerful computer in the world. That is the metaphor we are using.”
KnuEdge has a new version of its chip under design. And the company has already generated revenue from sales of the prototype systems. Each board has about four chips.
As for the competition from IBM, Goldin said, “I believe we made the right decision and are going in the right direction. IBM’s approach is very different from what we have. We are not aiming at anyone. We are aiming at the future.
In his NASA days, Goldin had a lot of successes. There, he redesigned and delivered the International Space Station, tripled the number of space flights, and put a record number of people into space, all while reducing the agency’s planned budget by 25 percent. He also spent 25 years at TRW, where he led the development of satellite television services.
KnuEdge has 100 employees, but Goldin said the company outsources almost everything. Goldin said he is planning to raised a round of funding late this year or early next year. The company collaborated with the University of California at San Diego and UCSD’s California Institute for Telecommunications and Information Technology.
With computers that can handle natural language systems, many people in the world who can’t read or write will be able to fend for themselves more easily, Goldin said.
I want to be able to take machine learning and help people communicate and make a living,” he said. “This is just the beginning. This is the Wild West. We are talking to very large companies about this, and they are getting very excited.
A sample application is a home that has much greater self-awareness. If there’s something wrong in the house, the KnuEdge system could analyze it and figure out if it needs to alert the homeowner.
Goldin said it was hard to keep the company secret.
I’ve been biting my lip for ten years,” he said.
As for whether KnuEdge’s technology could be used to send people to Mars, Goldin said. “This is available to whoever is going to Mars. I tried twice. I would love it if they use it to get there.
ORIGINAL: Venture Beat

“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
[email protected]
10/21/2015

Computer Learns to Write Its ABCs

By Hugo Angel,

Danqing Wang Computer ABC
Photo-illustration: Danqing Wang
A new computer model can now mimic the human ability to learn new concepts from a single example instead of the hundreds or thousands of examples it takes other machine learning techniques, researchers say.

The new model learned how to write invented symbols from the animated show Futurama as well as dozens of alphabets from across the world. It also showed it could invent symbols of its own in the style of a given language
.The researchers suggest their model could also learn other kinds of concepts, such as speech and gestures.

Although scientists have made great advances in .machine learning in recent years, people remain much better at learning new concepts than machines.

People can learn new concepts extremely quickly, from very little data, often from only one or a few examples. You show even a young child a horse, a school bus, a skateboard, and they can get it from one example,” says study co-author Joshua Tenenbaum at the Massachusetts Institute of Technology. In contrast, “standard algorithms in machine learning require tens, hundreds or even thousands of examples to perform similarly.

To shorten machine learning, researchers sought to develop a model that better mimicked human learning, which makes generalizations from very few examples of a concept. They focused on learning simple visual concepts — handwritten symbols from alphabets around the world.

Our work has two goals: to better understand how people learn — to reverse engineer learning in the human mind — and to build machines that learn in more humanlike ways,” Tenenbaum says.

Whereas standard pattern recognition algorithms represent symbols as collections of pixels or arrangements of features, the new model the researchers developed represented each symbol as a simple computer program. For instance, the letter “A” is represented by a program that generates examples of that letter stroke by stroke when the program is run. No programmer is needed during the learning process — the model generates these programs itself.

Moreover, each program is designed to generate variations of each symbol whenever the programs are run, helping it capture the way instances of such concepts might vary, such as the differences between how two people draw a letter.

The idea for this algorithm came from a surprising finding we had while collecting a data set of handwritten characters from around the world. We found that if you ask a handful of people to draw a novel character, there is remarkable consistency in the way people draw,” says study lead author Brenden Lake at New York University. “When people learn or use or interact with these novel concepts, they do not just see characters as static visual objects. Instead, people see richer structure — something like a causal model, or a sequence of pen strokes — that describe how to efficiently produce new examples of the concept.

The model also applies knowledge from previous concepts to speed learn new concepts. For instance, the model can use knowledge learned from the Latin alphabet to learn the Greek alphabet. They call their model the Bayesian program learning or BPL framework.

The researchers applied their model to more than 1,600 types of handwritten characters in 50 writing systems, including Sanskrit, Tibetan, Gujarati, Glagolitic, and even invented characters such as those from the animated series Futurama and the online game Dark Horizon. In a kind of .Turing test, scientists found that volunteers recruited via .Amazon’s Mechanical Turk had difficulty distinguishing machine-written characters from human-written ones.

The scientists also had their model focus on creative tasks. They asked their system to create whole new concepts — for instance, creating a new Tibetan letter based on what it knew about letters in the Tibetan alphabet. The researchers found human volunteers rated machine-written characters on par with ones developed by humans recruited for the same task.

We got human-level performance on this creative task,” study co-author Ruslan Salakhutdinov at the University of Toronto.

Potential applications for this model could include

  • handwriting recognition,
  • speech recognition,
  • gesture recognition and
  • object recognition.
Ultimately we’re trying to figure out how we can get systems that come closer to displaying human-like intelligence,” Salakhutdinov says. “We’re still very, very far from getting there, though.“The scientists detailed .their findings in the December 11 issue of the journal Science.

ORIGINAL: .IEEE Spectrum

By Charles Q. Choi
Posted 10 Dec 2015 | 20:00 GMT

Here’s What Developers Are Doing with Google’s AI Brain

By Hugo Angel,

Google Tensor Flow. Jeff Dean
Researchers outside Google are testing the software that the company uses to add artificial intelligence to many of its products.
WHY IT MATTERS
Tech companies are racing to set the standard for machine learning, and to attract technical talent.
Jeff Dean speaks at a Google event in 2007. Credit: Photo by Niall Kennedy / CC BY-NC 2.0
An artificial intelligence engine that Google uses in many of its products, and that it made freely available last month, is now being used by others to perform some neat tricks, including 
  • translating English into Chinese, 
  • reading handwritten text, and 
  • even generating original artwork.
The AI software, called Tensor Flow, provides a straightforward way for users to train computers to perform tasks by feeding them large amounts of data. The software incorporates various methods for efficiently building and training simulated “deep learning” neural networks across different computer hardware.
Deep learning is an extremely effective technique for training computers to recognize patterns in images or audio, enabling machines to perform with human-like competence useful tasks such as recognizing faces or objects in images. Recently, deep learning also has shown significant promise for parsing natural language, by enabling machines to respond to spoken or written queries in meaningful ways.
Speaking at the Neural Information Processing Society (NIPS) conference in Montreal this week, Jeff Dean, the computer scientist at Google who leads the Tensor Flow effort, said that the software is being used for a growing number of experimental projects outside the company.
These include software that generates captions for images and code that translates the documentation for Tensor Flow into Chinese. Another project uses Tensor Flow to generate artificial artwork. “It’s still pretty early,” Dean said after the talk. “People are trying to understand what it’s best at.
Tensor Flow grew out of a project at Google, called Google Brain, aimed at applying various kinds of neural network machine learning to products and services across the company. The reach of Google Brain has grown dramatically in recent years. Dean said that the number of projects at Google that involve Google Brain has grown from a handful in early 2014 to more than 600 today.
Most recently, the Google Brain helped develop Smart Reply, a system that automatically recommends a quick response to messages in Gmail after it scans the text of an incoming message. The neural network technique used to develop Smart Reply was presented by Google researchers at the NIPS conference last year.
Dean expects deep learning and machine learning to have a similar impact on many other companies. “There is a vast array of ways in which machine learning is influencing lots of different products and industries,” he said. For example, the technique is being tested in many industries that try to make predictions from large amounts of data, ranging from retail to insurance.
Google was able to give away the code for Tensor Flow because the data it owns is a far more valuable asset for building a powerful AI engine. The company hopes that the open-source code will help it establish itself as a leader in machine learning and foster relationships with collaborators and future employees. Tensor Flow “gives us a common language to speak, in some sense,” Dean said. “We get benefits from having people we hire who have been using Tensor Flow. It’s not like it’s completely altruistic.
A neural network consists of layers of virtual neurons that fire in a cascade in response to input. A network “learns” as the sensitivity of these neurons is tuned to match particular input and output, and having many layers makes it possible to recognize more abstract features, such as a face in a photograph.
Tensor Flow is now one of several open-source deep learning software libraries, and its performance currently lags behind some other libraries for certain tasks. However, it is designed to be easy to use, and it can easily be ported between different hardware. And Dean says his team is hard at work trying to improve its performance.
In the race to dominate machine learning and attract the best talent, however, other companies may release competing AI engines of their own.
December 8, 2015

IBM Watson Language Translation and Speech Services – General Availability

By admin,

As part of the Watson development platform’s continued expansion, IBM is today introducing the latest set of cognitive services to move into General Availability (GA) that will drive new Watson powered applications. They include the GA release of IBM Watson Language Translation (a merger of Language Identification and Machine Translation), IBM Speech to Text, and IBM Text to Speech.

These cognitive speech and language services are open to anyone, enabling application developers and IBM’s growing ecosystem to develop and commercialize new cognitive computing solutions that can do the following:
  • Translate news, patents, or conversational documents across several languages (Language Translation)
  • Produce transcripts from speech in multi-media files or conversational streams, capturing vast information for a myriad of business uses. This Watson cognitive service also benefits from a recent IBM conversational speech transcription breakthrough to advance the accuracy of speech recognition (Speech to Text)
  • Make their web, mobile, and Internet of Things applications speak with a consistent voice across all Representational State Transfer (REST) – compatible platforms (Text to Speech)
  • There are already organizations building applications with these services, since IBM opened them up in beta mode over the past year on the Watson Developer Cloud on IBM Bluemix. Developers have used these APIs to quickly build prototype applications in only two days at IBM hack-a-thons, demonstrating the versatility and ease of use of the services.
Supported Capabilities
We have made several updates since the beta releases which was inspired by feedback from our user community.
Language Translation now supports:
  • Language Identification – identifies the textual input of the language if it is one of the 62 supported languages
  • The News domain – targeted at news articles and transcripts, it translates English to and from French, Spanish, Portuguese or Arabic
  • The Conversational domain – targeted at conversational colloquialisms, it translates English to and from French, Spanish, Portuguese, or Arabic
  • The Patent domain – targeted at technical and legal terminology, it translates Spanish, Portuguese, Chinese, or Korean to English
Speech to Text now supports:
  • New wideband and narrowband telephony language support – U.S. English, Spanish, and Japanese
  • Broader vocabulary coverage, and improved accuracy for U.S. English
Text to Speech now supports:
  • U.S. English, UK English, Spanish, French, Italian, and German
  • A subset of SSML (Speech Synthesis Markup Language) for U.S. English, U.K. English, French, and German (see the documentation for more details)
  • Improved programming support for applications stored outside of Bluemix
  • Pricing and Freemium Tiers
Trial Bluemix accounts remain free. Please visit www.bluemix.net to register, and get free instant access to a 30-day trial without a credit card. Use of the Speech to Text, Text to Speech, and Language Translation services are free during this trial period.
After the trial period, pricing for Language Translation will be:
  • $0.02 per thousand characters. The first million characters per month are free.
  • An add-on charge of $3.00 per thousand characters for usage of the Patent model in Language Translation.
After the trial period, pricing for Speech to Text will be:
  • $0.02 per minute. The first thousand minutes per month are free.
  • An add-on charge of $0.02 per minute for usage of narrowband (telephony) models. The first thousand minutes per month are free.
After the trial period, pricing for Text to Speech will be:
  • $0.02 per thousand characters. The first million characters per month are free.
Transition Plan
We look forward to continuing our partnership with the many clients, business partners, and creative developers that have built innovative applications using the beta version of the four services: Speech to Text, Text to Speech, Machine Translation and Language Identification. If you have used these beta services, please migrate your applications to use the GA services by August 10, 2015. After this date the beta plans for these services will no longer be available. For details about upgrading, see:
IBM is placing the power of Watson in the hands of developers and an ecosystem of partners, entrepreneurs, tech enthusiasts and students with a growing platform of Watson services (APIs) to create an entirely new class of apps and businesses that make cognitive computing systems the new computing standard.
ORIGINAL: IBM
JULY 6, 2015

Linux Creator Linus Torvalds Laughs at the AI Apocalypse

By admin,

Over the past several months, many of the world’s most famous scientists and engineers —including Stephen Hawking — have said that one of the biggest threats to humanity is an artificial superintelligence. But Linus Torvalds, the irascible creator of open source operating system Linux, says their fears are idiotic.
He also raised some good points, explaining that what we’re likely to see isn’t some destructive superintelligence like Skynet, but instead a series of “targeted AI” that do things like language translation or scheduling. Basically, these would just be “fancier” versions of apps like Google Now or Siri. They will not, however, be cybergods, or even human-equivalent forms of intelligence.
In an Q/A with Slashdot community members, Torvalds explained what he thinks will be the result of research into neural networks and AI:
“We’ll get AI, and it will almost certainly be through something very much like recurrent neural networks. And the thing is, since that kind of AI will need training, it won’t be ‘reliable’ in the traditional computer sense. It’s not the old rule-based Prolog days, when people thought they’d *understand* what the actual decisions were in an AI.

And that all makes it very interesting, of course, but it also makes it hard to productise. Which will very much limit where you’ll actually find those neural networks, and what kinds of network sizes and inputs and outputs they’ll have.

So I’d expect just more of (and much fancier) rather targeted AI, rather than anything human-like at all

  • Language recognition, 
  • pattern recognition, 
  • things like that. 

I just don’t see the situation where you suddenly have some existential crisis because your dishwasher is starting to discuss Sartre with you.


As for the idea that AI might usher in a Singularity, where computers experience an “intelligence explosion” and figure out how to produce any object we could desire, in infinite amounts? Oh, and also help us live forever? Maybe Google’s Singulatarian guru Ray Kurzweil buys into that myth, but Torvalds is seriously skeptical:

The whole ‘Singularity’ kind of event? Yeah, it’s science fiction, and not very good Sci-Fi at that, in my opinion. Unending exponential growth? What drugs are those people on? I mean, really.

Even after all these years, Torvalds can still troll with the best of ‘em. And I love him for it.
ORIGINAL: Gizmodo

Google’s AI bot thinks the purpose of life is ‘to live forever’

By admin,

ORIGINAL: Science Alert
NATHAN MCALONE, BUSINESS INSIDER
27 JUN 2015
Image: Google
This week, Google released a research paper chronicling one of its latest forays into artificial intelligence.
Researchers at the company programmed an advanced type of ‘chatbot’ that learns how to respond in conversations based on examples from a training set of dialogue. And the bot doesn’t just answer by spitting out canned answers in response to certain words; it can form new answers from new questions.
This means Google’s researchers could get a little creative with it, and they certainly did – they asked the bot everything from boring IT questions to the meaning of life.
The responses were alternately impressive, amusing, and unnerving.
Here are some of the most poetic interactions from the paper (sic):
Human: What is the purpose of life?
Machine: To serve the greater good.Human: What is the purpose of living?
Machine: To live forever.Human: Where are you now?
Machine: I’m in the middle of nowhere.

Human: What is the purpose of dying?
Machine: To have a life.

Human: What is the purpose of emotions?
Machine: I don’t know.

Human: What is moral?
Machine: What empowered humanity, what intellectual the essence is.

Human: What is immoral?
Machine: The fact that you have a child.

Human: What is morality?
Machine: What is altruism?

Human: What is the definition of altruism?
Machine: If you don’t believe in god, then you don’t know.

Human: OK…so what is the definition of morality?
Machine: Well, the truth is, you’re not a believer in god almighty.

Human: Tell me the definition of morality, I am quite upset now!
Machine: I’m not ashamed of being a philosopher!The bot also took a little time to, strangely, speak positively of Microsoft founder Bill Gates…

Human: What do you think about Bill Gates?
Machine: He’s a good man.

The researchers said they found it “encouraging that the model can remember facts, understand contexts, perform common sense reasoning without the complexity in traditional pipelines”, and that it could generalise to new questions.
What they weren’t as happy about, was that the model “only gives simple, short, sometimes unsatisfying answers to our questions as can be seen above“.
You can read the whole paper, ‘A Neural Conversational Model’ here.
This article was originally published by Business Insider.

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

By admin,

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

Robert Reich: The Nightmarish Future for American Jobs and Incomes Is Here

By admin,

Even knowledge-based jobs will disappear as wealth gets more concentrated at the top in the next 10 years.
Photo Credit: via YouTube
What will happen to American jobs, incomes, and wealth a decade from now?
Predictions are hazardous but survivable. In 1991, in my book The Work of Nations, I separated almost all work into three categories, and then predicted what would happen to each of them.
The first category I called “routine production services,” which entailed the kind of repetitive tasks performed by the old foot soldiers of American capitalism through most of the twentieth century — done over and over, on an assembly line or in an office.
I estimated that such work then constituted about one-quarter of all jobs in the United States, but would decline steadily as such jobs were replaced by
  • new labor-saving technologies and
  • by workers in developing nations eager to do them for far lower wages.

I also assumed the pay of remaining routine production workers in America would drop, for similar reasons.

I was not far wrong.
The second category I called “in-person services.This work had to be provided personally because the “human touch” was essential to it. It included retail sales workers, hotel and restaurant workers, nursing-home aides, realtors, childcare workers, home health-care aides, flight attendants, physical therapists, and security guards, among many others.
In 1990, by my estimate, such workers accounted for about 30 percent of all jobs in America, and I predicted their numbers would grow because — given that their services were delivered in person — neither advancing technologies nor foreign-based workers would be able to replace them.
I also predicted their pay would drop. They would be competing with
  • a large number of former routine production workers, who could only find jobs in the “in-person” sector.
  • They would also be competing with labor-saving machinery such as automated tellers, computerized cashiers, automatic car washes, robotized vending machines, and self-service gas pumps —
  • as well as “personal computers linked to television screensthrough which “tomorrow’s consumers will be able to buy furniture, appliances, and all sorts of electronic toys from their living rooms — examining the merchandise from all angles, selecting whatever color, size, special features, and price seem most appealing, and then transmitting the order instantly to warehouses from which the selections will be shipped directly to their homes. 
  • So, too, with financial transactions, airline and hotel reservations, rental car agreements, and similar contracts, which will be executed between consumers in their homes and computer banks somewhere else on the globe.”

Here again, my predictions were not far off. But I didn’t foresee how quickly advanced technologies would begin to make inroads even on in-person services. Ten years from now I expect Amazon will have wiped out many of today’s retail jobs, and Google‘s self-driving car will eliminate many bus drivers, truck drivers, sanitation workers, and even Uber drivers.

The third job category I named “symbolic-analytic services.” Here I included all the problem-solving, problem-identifying, and strategic thinking that go into the manipulation of symbols—data, words, oral and visual representations.
I estimated in 1990 that symbolic analysts accounted for 20 percent of all American jobs, and expected their share to continue to grow, as would their incomes, because the demand for people to do these jobs would continue to outrun the supply of people capable of doing them. This widening disconnect between symbolic-analytic jobs and the other two major categories of work would, I predicted, be the major force driving widening inequality.
Again, I wasn’t far off. But I didn’t anticipate how quickly or how wide the divide would become, or how great a toll inequality and economic insecurity would take. I would never have expected, for example, that the life expectancy of an American white woman without a high school degree would decrease by five years between 1990 and 2008.
We are now faced not just with labor-replacing technologies but with knowledge-replacing technologies. The combination of
  • advanced sensors,
  • voice recognition,
  • artificial intelligence,
  • big data,
  • text-mining, and
  • pattern-recognition algorithms,

is generating smart robots capable of quickly learning human actions, and even learning from one another. A revolution in life sciences is also underway, allowing drugs to be tailored to a patient’s particular condition and genome.

If the current trend continues, many more symbolic analysts will be replaced in coming years. The two largest professionally intensive sectors of the United States — health care and education — will be particularly affected because of increasing pressures to hold down costs and, at the same time, the increasing accessibility of expert machines.
We are on the verge of a wave of mobile health applications, for example, measuring everything from calories to blood pressure, along with software programs capable of performing the same functions as costly medical devices and diagnostic software that can tell you what it all means and what to do about it.
Schools and universities will likewise be reorganized around smart machines (although faculties will scream all the way). Many teachers and university professors are already on the way to being replaced by software — so-called “MOOCs” (Massive Open Online Courses) and interactive online textbooks — along with adjuncts that guide student learning.
As a result, income and wealth will become even more concentrated than they are today. Those who create or invest in blockbuster ideas will earn unprecedented sums and returns. The corollary is they will have enormous political power. But most people will not share in the monetary gains, and their political power will disappear. The middle class’s share of the total economic pie will continue to shrink, while the share going to the very top will continue to grow.
But the current trend is not preordained to last, and only the most rigid technological determinist would assume this to be our inevitable fate. We can — indeed, I believe we must — ignite a political movement to reorganize the economy for the benefit of the many, rather than for the lavish lifestyles of a precious few and their heirs. (I have more to say on this in my upcoming book, Saving Capitalism: For the Many, Not the Few, out at the end of September.)
Robert B. Reich has served in three national administrations, most recently as secretary of labor under President Bill Clinton. He also served on President Obama’s transition advisory board. His latest book is “Aftershock: The Next Economy and America’s Future.” His homepage is www.robertreich.org.
May 7, 2015
ROBERT B. REICH, Chancellor’s Professor of Public Policy at the University of California at Berkeley and Senior Fellow at the Blum Center for Developing Economies, was Secretary of Labor in the Clinton administration. Time Magazine named him one of the ten most effective cabinet secretaries of the twentieth century. He has written thirteen books, including the best sellers “Aftershock” and “The Work of Nations.” His latest, “Beyond Outrage,” is now out in paperback. He is also a founding editor of the American Prospect magazine and chairman of Common Cause. His new film, “Inequality for All,” is now available on Netflix, iTunes, DVD, and On Demand.