Tag Archives: learn

#430761 How Robots Are Getting Better at Making ...

Posted on July 26, 2017 by Android

The multiverse of science fiction is populated by robots that are indistinguishable from humans. They are usually smarter, faster, and stronger than us. They seem capable of doing any job imaginable, from piloting a starship and battling alien invaders to taking out the trash and cooking a gourmet meal.
The reality, of course, is far from fantasy. Aside from industrial settings, robots have yet to meet The Jetsons. The robots the public are exposed to seem little more than over-sized plastic toys, pre-programmed to perform a set of tasks without the ability to interact meaningfully with their environment or their creators.
To paraphrase PayPal co-founder and tech entrepreneur Peter Thiel, we wanted cool robots, instead we got 140 characters and Flippy the burger bot. But scientists are making progress to empower robots with the ability to see and respond to their surroundings just like humans.
Some of the latest developments in that arena were presented this month at the annual Robotics: Science and Systems Conference in Cambridge, Massachusetts. The papers drilled down into topics that ranged from how to make robots more conversational and help them understand language ambiguities to helping them see and navigate through complex spaces.
Improved Vision
Ben Burchfiel, a graduate student at Duke University, and his thesis advisor George Konidaris, an assistant professor of computer science at Brown University, developed an algorithm to enable machines to see the world more like humans.
In the paper, Burchfiel and Konidaris demonstrate how they can teach robots to identify and possibly manipulate three-dimensional objects even when they might be obscured or sitting in unfamiliar positions, such as a teapot that has been tipped over.
The researchers trained their algorithm by feeding it 3D scans of about 4,000 common household items such as beds, chairs, tables, and even toilets. They then tested its ability to identify about 900 new 3D objects just from a bird’s eye view. The algorithm made the right guess 75 percent of the time versus a success rate of about 50 percent for other computer vision techniques.
In an email interview with Singularity Hub, Burchfiel notes his research is not the first to train machines on 3D object classification. How their approach differs is that they confine the space in which the robot learns to classify the objects.
“Imagine the space of all possible objects,” Burchfiel explains. “That is to say, imagine you had tiny Legos, and I told you [that] you could stick them together any way you wanted, just build me an object. You have a huge number of objects you could make!”
The infinite possibilities could result in an object no human or machine might recognize.
To address that problem, the researchers had their algorithm find a more restricted space that would host the objects it wants to classify. “By working in this restricted space—mathematically we call it a subspace—we greatly simplify our task of classification. It is the finding of this space that sets us apart from previous approaches.”
Following Directions
Meanwhile, a pair of undergraduate students at Brown University figured out a way to teach robots to understand directions better, even at varying degrees of abstraction.
The research, led by Dilip Arumugam and Siddharth Karamcheti, addressed how to train a robot to understand nuances of natural language and then follow instructions correctly and efficiently.
“The problem is that commands can have different levels of abstraction, and that can cause a robot to plan its actions inefficiently or fail to complete the task at all,” says Arumugam in a press release.
In this project, the young researchers crowdsourced instructions for moving a virtual robot through an online domain. The space consisted of several rooms and a chair, which the robot was told to manipulate from one place to another. The volunteers gave various commands to the robot, ranging from general (“take the chair to the blue room”) to step-by-step instructions.
The researchers then used the database of spoken instructions to teach their system to understand the kinds of words used in different levels of language. The machine learned to not only follow instructions but to recognize the level of abstraction. That was key to kickstart its problem-solving abilities to tackle the job in the most appropriate way.
The research eventually moved from virtual pixels to a real place, using a Roomba-like robot that was able to respond to instructions within one second 90 percent of the time. Conversely, when unable to identify the specificity of the task, it took the robot 20 or more seconds to plan a task about 50 percent of the time.
One application of this new machine-learning technique referenced in the paper is a robot worker in a warehouse setting, but there are many fields that could benefit from a more versatile machine capable of moving seamlessly between small-scale operations and generalized tasks.
“Other areas that could possibly benefit from such a system include things from autonomous vehicles… to assistive robotics, all the way to medical robotics,” says Karamcheti, responding to a question by email from Singularity Hub.
More to Come
These achievements are yet another step toward creating robots that see, listen, and act more like humans. But don’t expect Disney to build a real-life Westworld next to Toon Town anytime soon.
“I think we’re a long way off from human-level communication,” Karamcheti says. “There are so many problems preventing our learning models from getting to that point, from seemingly simple questions like how to deal with words never seen before, to harder, more complicated questions like how to resolve the ambiguities inherent in language, including idiomatic or metaphorical speech.”
Even relatively verbose chatbots can run out of things to say, Karamcheti notes, as the conversation becomes more complex.
The same goes for human vision, according to Burchfiel.
While deep learning techniques have dramatically improved pattern matching—Google can find just about any picture of a cat—there’s more to human eyesight than, well, meets the eye.
“There are two big areas where I think perception has a long way to go: inductive bias and formal reasoning,” Burchfiel says.
The former is essentially all of the contextual knowledge people use to help them reason, he explains. Burchfiel uses the example of a puddle in the street. People are conditioned or biased to assume it’s a puddle of water rather than a patch of glass, for instance.
“This sort of bias is why we see faces in clouds; we have strong inductive bias helping us identify faces,” he says. “While it sounds simple at first, it powers much of what we do. Humans have a very intuitive understanding of what they expect to see, [and] it makes perception much easier.”
Formal reasoning is equally important. A machine can use deep learning, in Burchfiel’s example, to figure out the direction any river flows once it understands that water runs downhill. But it’s not yet capable of applying the sort of human reasoning that would allow us to transfer that knowledge to an alien setting, such as figuring out how water moves through a plumbing system on Mars.
“Much work was done in decades past on this sort of formal reasoning… but we have yet to figure out how to merge it with standard machine-learning methods to create a seamless system that is useful in the actual physical world.”
Robots still have a lot to learn about being human, which should make us feel good that we’re still by far the most complex machines on the planet.
Image Credit: Alex Knight via Unsplash Continue reading →

Posted in Human Robots

#430668 Why Every Leader Needs to Be Obsessed ...

Posted on July 13, 2017 by Android

This article is part of a series exploring the skills leaders must learn to make the most of rapid change in an increasingly disruptive world. The first article in the series, “How the Most Successful Leaders Will Thrive in an Exponential World,” broadly outlines four critical leadership skills—futurist, technologist, innovator, and humanitarian—and how they work together.
Today’s post, part five in the series, takes a more detailed look at leaders as technologists. Be sure to check out part two of the series, “How Leaders Dream Boldly to Bring New Futures to Life,” part three of the series, “How All Leaders Can Make the World a Better Place,” and part four of the series, “How Leaders Can Make Innovation Everyone’s Day Job”.
In the 1990s, Tower Records was the place to get new music. Successful and popular, the California chain spread far and wide, and in 1998, they took on $110 million in debt to fund aggressive further expansion. This wasn’t, as it turns out, the best of timing.
The first portable digital music player went on sale the same year. The following year brought Napster, a file sharing service allowing users to freely share music online. By 2000, Napster hosted 20 million users swapping songs. Then in 2001, Apple’s iPod and iTunes arrived, and when the iTunes Music Store opened in 2003, Apple sold over a million songs the first week.
As music was digitized, hard copies began to go out of style, and sales and revenue declined.
Tower first filed for bankruptcy in 2004 and again (for the last time) in 2006. The internet wasn’t the only reason for Tower’s demise. Mismanagement and price competition from electronics retailers like Best Buy also played a part. Still, today, the vast majority of music is purchased or streamed entirely online, and record stores are for the most part a niche market.
The writing was on the wall, but those impacted most had trouble reading it.
Why is it difficult for leaders to see technological change coming and right the ship before it’s too late? Why did Tower go all out on expansion just as the next big thing took the stage?
This is one story of many. Digitization has moved beyond music and entertainment, and now many big retailers operating physical stores are struggling to stay relevant. Meanwhile, the pace of change is accelerating, and new potentially disruptive technologies are on the horizon.
More than ever, leaders need to develop a strong understanding of and perspective on technology. They need to survey new innovations, forecast their pace, gauge the implications, and adopt new tools and strategy to change course as an industry shifts, not after it’s shifted.
Simply, leaders need to adopt the mindset of a technologist. Here’s what that means.
Survey the Landscape
Nurturing curiosity is the first step to understanding technological change. To know how technology might disrupt your industry, you have to know what’s in the pipeline and identify which new inventions are directly or indirectly related to your industry.
Becoming more technologically minded takes discipline and focus as well as unstructured time to explore the non-obvious connections between what is right in front of us and what might be. It requires a commitment to ongoing learning and discovery.
Read outside your industry and comfort zone, not just Fast Company and Wired, but Science and Nature to expand your horizons. Identify experts with the ability to demystify specific technology areas—many have a solid following on Twitter or a frequently cited blog.
But it isn’t all about reading. Consider going where the change is happening too.
Visit one of the technology hubs around the world or a local university research lab in your own back yard. Or bring the innovation to you by building an internal exploration lab stocked with the latest technologies, creating a technology advisory board, hosting an internal innovation challenge, or a local pitch night where aspiring entrepreneurs can share their newest ideas.
You might even ask the crowd by inviting anyone to suggest what innovation is most likely to disrupt your product, service, or sector. And don’t hesitate to engage younger folks—the digital natives all around you—by asking questions about what technology they are using or excited about. Consider going on a field trip with them to see how they use technology in different aspects of their lives. Invite the seasoned executives on your team to explore long-term “reverse mentoring” with someone who can expose them to the latest technology and teach them to use it.
Whatever your strategy, the goal should be to develop a healthy obsession with technology.
By exploring fresh perspectives outside traditional work environments and then giving ourselves permission to see how these new ideas might influence existing products and strategies, we have a chance to be ready for what we’re not ready for—but is likely right around the corner.
Estimate the Pace of Progress
The next step is forecasting when a technology will mature.
One of the most challenging aspects of the changes underway is that in many technology arenas, we are quickly moving from a linear to an exponential pace. It is hard enough to envision what is needed in an industry buffeted by progress that is changing 10% per year, but what happens when technological progress doubles annually? That is another world altogether.
This kind of change can be deceiving. For example, machine learning and big data are finally reaching critical momentum after more than twenty years of being right around the corner. The advances in applications like speech and image recognition that we’ve seen in recent years dwarf what came before and many believe we’ve just begun to understand the implications.
Even as we begin to embrace disruptive change in one technology arena, far more exciting possibilities unfold when we explore how multiple arenas are converging.
Artificial intelligence and big data are great examples. As Hod Lipson, professor of Mechanical Engineering and Data Science at Columbia University and co-author of Driverless: Intelligent Cars and the Road Ahead, says, “AI is the engine, but big data is the fuel. They need each other.”
This convergence paired with an accelerating pace makes for surprising applications.
To keep his research lab agile and open to new uses of advancing technologies, Lipson routinely asks his PhD students, “How might AI disrupt this industry?” to prompt development of applications across a wide spectrum of sectors from healthcare to agriculture to food delivery.
Explore the Consequences
New technology inevitably gives rise to new ethical, social, and moral questions that we have never faced before. Rather than bury our heads in the sand, as leaders we must explore the full range of potential consequences of whatever is underway or still to come.
We can add AI to kids’ toys, like Mattel’s Hello Barbie or use cutting-edge gene editing technology like CRISPR-Cas9 to select for preferred gene sequences beyond basic health. But just because we can do something doesn’t mean we should.
Take time to listen to skeptics and understand the risks posed by technology.
Elon Musk, Stephen Hawking, Steve Wozniak, Bill Gates, and other well-known names in science and technology have expressed concern in the media and via open letters about the risks posed by AI. Microsoft’s CEO, Satya Nadella, has even argued tech companies shouldn’t build artificial intelligence systems that will replace people rather than making them more productive.
Exploring unintended consequences goes beyond having a Plan B for when something goes wrong. It requires broadening our view of what we’re responsible for. Beyond customers, shareholders, and the bottom line, we should understand how our decisions may impact employees, communities, the environment, our broader industry, and even our competitors.
The minor inconvenience of mitigating these risks now is far better than the alternative. Create forums to listen to and value voices outside of the board room and C-Suite. Seek out naysayers, ethicists, community leaders, wise elders, and even neophytes—those who may not share our preconceived notions of right and wrong or our narrow view of our role in the larger world.
The question isn’t: If we build it, will they come? It’s now: If we can build it, should we?
Adopt New Technologies and Shift Course
The last step is hardest. Once you’ve identified a technology (or technologies) as a potential disruptor and understand the implications, you need to figure out how to evolve your organization to make the most of the opportunity. Simply recognizing disruption isn’t enough.
Take today’s struggling brick-and-mortar retail business. Online shopping isn’t new. Amazon isn’t a plucky startup. Both have been changing how we buy stuff for years. And yet many who still own and operate physical stores—perhaps most prominently, Sears—are now on the brink of bankruptcy.
There’s hope though. Netflix began as a DVD delivery service in the 90s, but quickly realized its core business didn’t have staying power. It would have been laughable to stream movies when Netflix was founded. Still, computers and bandwidth were advancing fast. In 2007, the company added streaming to its subscription. Even then it wasn’t a totally compelling product.
But Netflix clearly saw a streaming future would likely end their DVD business.
In recent years, faster connection speeds, a growing content library, and the company’s entrance into original programming have given Netflix streaming the upper hand over DVDs. Since 2011, DVD subscriptions have steadily declined. Yet the company itself is doing fine. Why? It anticipated the shift to streaming and acted on it.
Never Stop Looking for the Next Big Thing
Technology is and will increasingly be a driver of disruption, destabilizing entrenched businesses and entire industries while also creating new markets and value not yet imagined.
When faced with the rapidly accelerating pace of change, many companies still default to old models and established practices. Leading like a technologist requires vigilant understanding of potential sources of disruption—what might make your company’s offering obsolete? The answers may not always be perfectly clear. What’s most important is relentlessly seeking them.
Stock Media provided by MJTierney / Pond5 Continue reading →

Posted in Human Robots

#430658 Why Every Leader Needs a Healthy ...

Posted on July 12, 2017 by Android

This article is part of a series exploring the skills leaders must learn to make the most of rapid change in an increasingly disruptive world. The first article in the series, “How the Most Successful Leaders Will Thrive in an Exponential World,” broadly outlines four critical leadership skills—futurist, technologist, innovator, and humanitarian—and how they work together.
Today’s post, part five in the series, takes a more detailed look at leaders as technologists. Be sure to check out part two of the series, “How Leaders Dream Boldly to Bring New Futures to Life,” part three of the series, “How All Leaders Can Make the World a Better Place,” and part four of the series, “How Leaders Can Make Innovation Everyone’s Day Job”.
In the 1990s, Tower Records was the place to get new music. Successful and popular, the California chain spread far and wide, and in 1998, they took on $110 million in debt to fund aggressive further expansion. This wasn’t, as it turns out, the best of timing.
The first portable digital music player went on sale the same year. The following year brought Napster, a file sharing service allowing users to freely share music online. By 2000, Napster hosted 20 million users swapping songs. Then in 2001, Apple’s iPod and iTunes arrived, and when the iTunes Music Store opened in 2003, Apple sold over a million songs the first week.
As music was digitized, hard copies began to go out of style, and sales and revenue declined.
Tower first filed for bankruptcy in 2004 and again (for the last time) in 2006. The internet wasn’t the only reason for Tower’s demise. Mismanagement and price competition from electronics retailers like Best Buy also played a part. Still, today, the vast majority of music is purchased or streamed entirely online, and record stores are for the most part a niche market.
The writing was on the wall, but those impacted most had trouble reading it.
Why is it difficult for leaders to see technological change coming and right the ship before it’s too late? Why did Tower go all out on expansion just as the next big thing took the stage?
This is one story of many. Digitization has moved beyond music and entertainment, and now many big retailers operating physical stores are struggling to stay relevant. Meanwhile, the pace of change is accelerating, and new potentially disruptive technologies are on the horizon.
More than ever, leaders need to develop a strong understanding of and perspective on technology. They need to survey new innovations, forecast their pace, gauge the implications, and adopt new tools and strategy to change course as an industry shifts, not after it’s shifted.
Simply, leaders need to adopt the mindset of a technologist. Here’s what that means.
Survey the Landscape
Nurturing curiosity is the first step to understanding technological change. To know how technology might disrupt your industry, you have to know what’s in the pipeline and identify which new inventions are directly or indirectly related to your industry.
Becoming more technologically minded takes discipline and focus as well as unstructured time to explore the non-obvious connections between what is right in front of us and what might be. It requires a commitment to ongoing learning and discovery.
Read outside your industry and comfort zone, not just Fast Company and Wired, but Science and Nature to expand your horizons. Identify experts with the ability to demystify specific technology areas—many have a solid following on Twitter or a frequently cited blog.
But it isn’t all about reading. Consider going where the change is happening too.
Visit one of the technology hubs around the world or a local university research lab in your own back yard. Or bring the innovation to you by building an internal exploration lab stocked with the latest technologies, creating a technology advisory board, hosting an internal innovation challenge, or a local pitch night where aspiring entrepreneurs can share their newest ideas.
You might even ask the crowd by inviting anyone to suggest what innovation is most likely to disrupt your product, service, or sector. And don’t hesitate to engage younger folks—the digital natives all around you—by asking questions about what technology they are using or excited about. Consider going on a field trip with them to see how they use technology in different aspects of their lives. Invite the seasoned executives on your team to explore long-term “reverse mentoring” with someone who can expose them to the latest technology and teach them to use it.
Whatever your strategy, the goal should be to develop a healthy obsession with technology.
By exploring fresh perspectives outside traditional work environments and then giving ourselves permission to see how these new ideas might influence existing products and strategies, we have a chance to be ready for what we’re not ready for—but is likely right around the corner.
Estimate the Pace of Progress
The next step is forecasting when a technology will mature.
One of the most challenging aspects of the changes underway is that in many technology arenas, we are quickly moving from a linear to an exponential pace. It is hard enough to envision what is needed in an industry buffeted by progress that is changing 10% per year, but what happens when technological progress doubles annually? That is another world altogether.
This kind of change can be deceiving. For example, machine learning and big data are finally reaching critical momentum after more than twenty years of being right around the corner. The advances in applications like speech and image recognition that we’ve seen in recent years dwarf what came before and many believe we’ve just begun to understand the implications.
Even as we begin to embrace disruptive change in one technology arena, far more exciting possibilities unfold when we explore how multiple arenas are converging.
Artificial intelligence and big data are great examples. As Hod Lipson, professor of Mechanical Engineering and Data Science at Columbia University and co-author of Driverless: Intelligent Cars and the Road Ahead, says, “AI is the engine, but big data is the fuel. They need each other.”
This convergence paired with an accelerating pace makes for surprising applications.
To keep his research lab agile and open to new uses of advancing technologies, Lipson routinely asks his PhD students, “How might AI disrupt this industry?” to prompt development of applications across a wide spectrum of sectors from healthcare to agriculture to food delivery.
Explore the Consequences
New technology inevitably gives rise to new ethical, social, and moral questions that we have never faced before. Rather than bury our heads in the sand, as leaders we must explore the full range of potential consequences of whatever is underway or still to come.
We can add AI to kids’ toys, like Mattel’s Hello Barbie or use cutting-edge gene editing technology like CRISPR-Cas9 to select for preferred gene sequences beyond basic health. But just because we can do something doesn’t mean we should.
Take time to listen to skeptics and understand the risks posed by technology.
Elon Musk, Stephen Hawking, Steve Wozniak, Bill Gates, and other well-known names in science and technology have expressed concern in the media and via open letters about the risks posed by AI. Microsoft’s CEO, Satya Nadella, has even argued tech companies shouldn’t build artificial intelligence systems that will replace people rather than making them more productive.
Exploring unintended consequences goes beyond having a Plan B for when something goes wrong. It requires broadening our view of what we’re responsible for. Beyond customers, shareholders, and the bottom line, we should understand how our decisions may impact employees, communities, the environment, our broader industry, and even our competitors.
The minor inconvenience of mitigating these risks now is far better than the alternative. Create forums to listen to and value voices outside of the board room and C-Suite. Seek out naysayers, ethicists, community leaders, wise elders, and even neophytes—those who may not share our preconceived notions of right and wrong or our narrow view of our role in the larger world.
The question isn’t: If we build it, will they come? It’s now: If we can build it, should we?
Adopt New Technologies and Shift Course
The last step is hardest. Once you’ve identified a technology (or technologies) as a potential disruptor and understand the implications, you need to figure out how to evolve your organization to make the most of the opportunity. Simply recognizing disruption isn’t enough.
Take today’s struggling brick-and-mortar retail business. Online shopping isn’t new. Amazon isn’t a plucky startup. Both have been changing how we buy stuff for years. And yet many who still own and operate physical stores—perhaps most prominently, Sears—are now on the brink of bankruptcy.
There’s hope though. Netflix began as a DVD delivery service in the 90s, but quickly realized its core business didn’t have staying power. It would have been laughable to stream movies when Netflix was founded. Still, computers and bandwidth were advancing fast. In 2007, the company added streaming to its subscription. Even then it wasn’t a totally compelling product.
But Netflix clearly saw a streaming future would likely end their DVD business.
In recent years, faster connection speeds, a growing content library, and the company’s entrance into original programming have given Netflix streaming the upper hand over DVDs. Since 2011, DVD subscriptions have steadily declined. Yet the company itself is doing fine. Why? It anticipated the shift to streaming and acted on it.
Never Stop Looking for the Next Big Thing
Technology is and will increasingly be a driver of disruption, destabilizing entrenched businesses and entire industries while also creating new markets and value not yet imagined.
When faced with the rapidly accelerating pace of change, many companies still default to old models and established practices. Leading like a technologist requires vigilant understanding of potential sources of disruption—what might make your company’s offering obsolete? The answers may not always be perfectly clear. What’s most important is relentlessly seeking them.
Stock Media provided by MJTierney / Pond5 Continue reading →

Posted in Human Robots

#430652 The Jobs AI Will Take Over First

Posted on July 11, 2017 by Android

11th July 2017: The robotic revolution is set to cause the biggest transformation in the world’s workforce since the industrial revolution. In fact, research suggests that over 30% of jobs in Britain are under threat from breakthroughs in artificial intelligence (AI) technology.

With pioneering advances in technology many jobs that weren’t considered ripe for automation suddenly are. RS Components have used PWC Data to reveal how many jobs per sector are at risk of being taken by robots by 2030, a mere 13 years away. Did you think you were exempt from the robot revolution?

The top three sectors who are most exposed to the threats of robots are Transport and Storage, Manufacturing and Wholesale and Retail with 56%, 46% and 44% risk of automation respectively. The PWC report states that the differentiating factor between losing jobs to automation probability is education; those with a GCSE-level education or lower face a 46% risk, whilst those with undergraduate degrees or higher face a 12% risk. If a job is repetitive, physical and requires minimum effort to train for, this will have a higher likelihood to become automated by machines.

The manufacturing industry has the 3rd highest likelihood potential at 46.6%, shortly behind Transportation and Storage (56.4%) and Water, Sewage and Waste Management (62.6%). Although the manufacturing sector has the 3rd highest likelihood, it has the second largest number of jobs at risk of being taken by robots; an astonishing 1.22 million jobs are at risk in the near future. Repetitive manual labour and routine tasks can be taught to fixed machines and mimicked easily, saving employers both time and money.

The three sectors least at risk are Education, Health and Social and Agriculture, Forestry and Fishing with 9%, 17% and 19% risk of automation respectively. These operations are non-repetitive and consist of characteristics that cannot be taught and are harder to replicate with AI and robotics.

These are not the only fields where the introduction of AI will have an impact on employment prospects; Administrative and Support Services, Accommodation and Food Services, Finance and Insurance, Construction, Real Estate, Public Administration and Defence, and Arts and Entertainment are not out of the woods either.

The future is not all doom and gloom. Automation is set to boost productivity to enable workers to focus on higher value, more rewarding jobs; leaving repetitive and uncomplicated ones to the robots. An increase in sectors that are less easy to automate is also expected due to lower running costs. Wealth and spending will also be boosted by the initiation of AI seizing work. Also, there are just some things AI cannot learn so these jobs will be safe.

In some sectors half of the jobs could be taken by a fully automated system. Is your job next?

The post The Jobs AI Will Take Over First appeared first on Roboticmagazine. Continue reading →

Posted in Human Robots

#430579 What These Lifelike Androids Can Teach ...

Posted on July 5, 2017 by Android

For Dr. Hiroshi Ishiguro, one of the most interesting things about androids is the changing questions they pose us, their creators, as they evolve. Does it, for example, do something to the concept of being human if a human-made creation starts telling you about what kind of boys ‘she’ likes?
If you want to know the answer to the boys question, you need to ask ERICA, one of Dr. Ishiguro’s advanced androids. Beneath her plastic skull and silicone skin, wires connect to AI software systems that bring her to life. Her ability to respond goes far beyond standard inquiries. Spend a little time with her, and the feeling of a distinct personality starts to emerge. From time to time, she works as a receptionist at Dr. Ishiguro and his team’s Osaka University labs. One of her android sisters is an actor who has starred in plays and a film.

ERICA’s ‘brother’ is an android version of Dr. Ishiguro himself, which has represented its creator at various events while the biological Ishiguro can remain in his offices in Japan. Microphones and cameras capture Ishiguro’s voice and face movements, which are relayed to the android. Apart from mimicking its creator, the Geminoid™ android is also capable of lifelike blinking, fidgeting, and breathing movements.
Say hello to relaxation
As technological development continues to accelerate, so do the possibilities for androids. From a position as receptionist, ERICA may well branch out into many other professions in the coming years. Companion for the elderly, comic book storyteller (an ancient profession in Japan), pop star, conversational foreign language partner, and newscaster are some of the roles and responsibilities Dr. Ishiguro sees androids taking on in the near future.
“Androids are not uncanny anymore. Most people adapt to interacting with Erica very quickly. Actually, I think that in interacting with androids, which are still different from us, we get a better appreciation of interacting with other cultures. In both cases, we are talking with someone who is different from us and learn to overcome those differences,” he says.
A lot has been written about how robots will take our jobs. Dr. Ishiguro believes these fears are blown somewhat out of proportion.
“Robots and androids will take over many simple jobs. Initially there might be some job-related issues, but new schemes, like for example a robot tax similar to the one described by Bill Gates, should help,” he says.
“Androids will make it possible for humans to relax and keep evolving. If we compare the time we spend studying now compared to 100 years ago, it has grown a lot. I think it needs to keep growing if we are to keep expanding our scientific and technological knowledge. In the future, we might end up spending 20 percent of our lifetime on work and 80 percent of the time on education and growing our skills.”
Android asks who you are
For Dr. Ishiguro, another aspect of robotics in general, and androids in particular, is how they question what it means to be human.
“Identity is a very difficult concept for humans sometimes. For example, I think clothes are part of our identity, in a way that is similar to our faces and bodies. We don’t change those from one day to the next, and that is why I have ten matching black outfits,” he says.
This link between physical appearance and perceived identity is one of the aspects Dr. Ishiguro is exploring. Another closely linked concept is the connection between body and feeling of self. The Ishiguro avatar was once giving a presentation in Austria. Its creator recalls how he felt distinctly like he was in Austria, even capable of feeling sensation of touch on his own body when people laid their hands on the android. If he was distracted, he felt almost ‘sucked’ back into his body in Japan.
“I am constantly thinking about my life in this way, and I believe that androids are a unique mirror that helps us formulate questions about why we are here and why we have been so successful. I do not necessarily think I have found the answers to these questions, so if you have, please share,” he says with a laugh.
His work and these questions, while extremely interesting on their own, become extra poignant when considering the predicted melding of mind and machine in the near future.
The ability to be present in several locations through avatars—virtual or robotic—raises many questions of both philosophical and practical nature. Then add the hypotheticals, like why send a human out onto the hostile surface of Mars if you could send a remote-controlled android, capable of relaying everything it sees, hears and feels?
The two ways of robotics will meet
Dr. Ishiguro sees the world of AI-human interaction as currently roughly split into two. One is the chat-bot approach that companies like Amazon, Microsoft, Google, and recently Apple, employ using stationary objects like speakers. Androids like ERICA represent another approach.
“It is about more than the form factor. I think that the android approach is generally more story-based. We are integrating new conversation features based on assumptions about the situation and running different scenarios that expand the android’s vocabulary and interactions. Another aspect we are working on is giving androids desire and intention. Like with people, androids should have desires and intentions in order for you to want to interact with them over time,” Dr. Ishiguro explains.
This could be said to be part of a wider trend for Japan, where many companies are developing human-like robots that often have some Internet of Things capabilities, making them able to handle some of the same tasks as an Amazon Echo. The difference in approach could be summed up in the words ‘assistant’ (Apple, Amazon, etc.) and ‘companion’ (Japan).
Dr. Ishiguro sees this as partly linked to how Japanese as a language—and market—is somewhat limited. This has a direct impact on viability and practicality of ‘pure’ voice recognition systems. At the same time, Japanese people have had greater exposure to positive images of robots, and have a different cultural / religious view of objects having a ‘soul’. However, it may also mean Japanese companies and android scientists are both stealing a lap on their western counterparts.
“If you speak to an Amazon Echo, that is not a natural way to interact for humans. This is part of why we are making human-like robot systems. The human brain is set up to recognize and interact with humans. So, it makes sense to focus on developing the body for the AI mind, as well as the AI. I believe that the final goal for both Japanese and other companies and scientists is to create human-like interaction. Technology has to adapt to us, because we cannot adapt fast enough to it, as it develops so quickly,” he says.
Banner image courtesy of Hiroshi Ishiguro Laboratories, ATR all rights reserved.
Dr. Ishiguro’s team has collaborated with partners and developed a number of android systems:
Geminoid™ HI-2 has been developed by Hiroshi Ishiguro Laboratories and Advanced Telecommunications Research Institute International (ATR).
Geminoid™ F has been developed by Osaka University and Hiroshi Ishiguro Laboratories, Advanced Telecommunications Research Institute International (ATR).
ERICA has been developed by ERATO ISHIGURO Symbiotic Human-Robot Interaction Project Continue reading →

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