Tag Archives: cars

#431165 Intel Jumps Into Brain-Like Computing ...

Posted on September 29, 2017 by Android

The brain has long inspired the design of computers and their software. Now Intel has become the latest tech company to decide that mimicking the brain’s hardware could be the next stage in the evolution of computing.
On Monday the company unveiled an experimental “neuromorphic” chip called Loihi. Neuromorphic chips are microprocessors whose architecture is configured to mimic the biological brain’s network of neurons and the connections between them called synapses.
While neural networks—the in vogue approach to artificial intelligence and machine learning—are also inspired by the brain and use layers of virtual neurons, they are still implemented on conventional silicon hardware such as CPUs and GPUs.
The main benefit of mimicking the architecture of the brain on a physical chip, say neuromorphic computing’s proponents, is energy efficiency—the human brain runs on roughly 20 watts. The “neurons” in neuromorphic chips carry out the role of both processor and memory which removes the need to shuttle data back and forth between separate units, which is how traditional chips work. Each neuron also only needs to be powered while it’s firing.

At present, most machine learning is done in data centers due to the massive energy and computing requirements. Creating chips that capture some of nature’s efficiency could allow AI to be run directly on devices like smartphones, cars, and robots.
This is exactly the kind of application Michael Mayberry, managing director of Intel’s research arm, touts in a blog post announcing Loihi. He talks about CCTV cameras that can run image recognition to identify missing persons or traffic lights that can track traffic flow to optimize timing and keep vehicles moving.
There’s still a long way to go before that happens though. According to Wired, so far Intel has only been working with prototypes, and the first full-size version of the chip won’t be built until November.
Once complete, it will feature 130,000 neurons and 130 million synaptic connections split between 128 computing cores. The device will be 1,000 times more energy-efficient than standard approaches, according to Mayberry, but more impressive are claims the chip will be capable of continuous learning.
Intel’s newly launched self-learning neuromorphic chip.
Normally deep learning works by training a neural network on giant datasets to create a model that can then be applied to new data. The Loihi chip will combine training and inference on the same chip, which will allow it to learn on the fly, constantly updating its models and adapting to changing circumstances without having to be deliberately re-trained.
A select group of universities and research institutions will be the first to get their hands on the new chip in the first half of 2018, but Mayberry said it could be years before it’s commercially available. Whether commercialization happens at all may largely depend on whether early adopters can get the hardware to solve any practically useful problems.
So far neuromorphic computing has struggled to gain traction outside the research community. IBM released a neuromorphic chip called TrueNorth in 2014, but the device has yet to showcase any commercially useful applications.
Lee Gomes summarizes the hurdles facing neuromorphic computing excellently in IEEE Spectrum. One is that deep learning can run on very simple, low-precision hardware that can be optimized to use very little power, which suggests complicated new architectures may struggle to find purchase.
It’s also not easy to transfer deep learning approaches developed on conventional chips over to neuromorphic hardware, and even Intel Labs chief scientist Narayan Srinivasa admitted to Forbes Loihi wouldn’t work well with some deep learning models.
Finally, there’s considerable competition in the quest to develop new computer architectures specialized for machine learning. GPU vendors Nvidia and AMD have pivoted to take advantage of this newfound market and companies like Google and Microsoft are developing their own in-house solutions.
Intel, for its part, isn’t putting all its eggs in one basket. Last year it bought two companies building chips for specialized machine learning—Movidius and Nervana—and this was followed up with the $15 billion purchase of self-driving car chip- and camera-maker Mobileye.
And while the jury is still out on neuromorphic computing, it makes sense for a company eager to position itself as the AI chipmaker of the future to have its fingers in as many pies as possible. There are a growing number of voices suggesting that despite its undoubted power, deep learning alone will not allow us to imbue machines with the kind of adaptable, general intelligence humans possess.
What new approaches will get us there are hard to predict, but it’s entirely possible they will only work on hardware that closely mimics the one device we already know is capable of supporting this kind of intelligence—the human brain.
Image Credit: Intel 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

#430656 Code @ TACC robotics camp delivers on ...

Posted on July 12, 2017 by Android

On a hot and breezy June day in Austin, parents, friends, brothers and sisters navigated through main campus at The University of Texas at Austin and helped carry luggage for the new arrivals to their dorm rooms. Thirty-four high school students from mostly low-income Title I schools in Central Texas, some from as far away as Houston, said good-bye to their families. Continue reading →

Posted in Human Robots

#430556 Forget Flying Cars, the Future Is ...

Posted on July 3, 2017 by Android

Flying car concepts have been around nearly as long as their earthbound cousins, but no one has yet made them a commercial success. MIT engineers think we’ve been coming at the problem from the wrong direction; rather than putting wings on cars, we should be helping drones to drive.
The team from the university’s Computer Science and Artificial Intelligence Laboratory (CSAIL) added wheels to a fleet of eight mini-quadcopters and tested driving and flying them around a tiny toy town made out of cardboard and fabric.
Adding the ability to drive reduced the distance the drone could fly by 14 percent compared to a wheel-less version. But while driving was slower, the drone could travel 150 percent further than when flying. The result is a vehicle that combines the speed and mobility of flying with the energy-efficiency of driving.

CSAIL director Daniela Rus told MIT News their work suggested that when looking to create flying cars, it might make more sense to build on years of research into drones rather than trying to simply “put wings on cars.”
Historically, flying car concepts have looked like someone took apart a Cessna light aircraft and a family sedan, mixed all the parts up, and bolted them back together again. Not everyone has abandoned this approach—two of the most developed flying car designs from Terrafugia and AeroMobil are cars with folding wings that need an airstrip to take off.
But flying car concepts are looking increasingly drone-like these days, with multiple small rotors, electric propulsion and vertical take-off abilities. Take the eHang 184 autonomous aerial vehicle being developed in China, the Kitty Hawk all-electric aircraft backed by Google founder Larry Page, which is little more than a quadcopter with a seat, the AirQuadOne designed by UK consortium Neva Aerospace, or Lilium Aviation’s Jet.
The attraction is obvious. Electric-powered drones are more compact, maneuverable, and environmentally friendly, making them suitable for urban environments.
Most of these vehicles are not quite the same as those proposed by the MIT engineers, as they’re pure flying machines. But a recent Airbus concept builds on the same principle that the future of urban mobility is vehicles that can both fly and drive. Its Pop.Up design is a two-passenger pod that can either be clipped to a set of wheels or hang under a quadcopter.
Importantly, they envisage their creation being autonomous in both flight and driving modes. And they’re not the only ones who think the future of flying cars is driverless. Uber has committed to developing a network of autonomous air taxis within a decade. This spring, Dubai announced it would launch a pilotless passenger drone service using the Ehang 184 as early as next month (July).
While integrating fully-fledged autonomous flying cars into urban environments will be far more complex, the study by Rus and her colleagues provides a good starting point for the kind of 3D route-planning and collision avoidance capabilities this would require.
The team developed multi-robot path planning algorithms that were able to control all eight drones as they flew and drove around their mock up city, while also making sure they didn’t crash into each other and avoided no-fly zones.
“This work provides an algorithmic solution for large-scale, mixed-mode transportation and shows its applicability to real-world problems,” Jingjin Yu, a computer science professor at Rutgers University who was not involved in the research, told MIT News.
This vision of a driverless future for flying cars might be a bit of a disappointment for those who’d envisaged themselves one day piloting their own hover car just like George Jetson. But autonomy and Uber-like ride-hailing business models are likely to be attractive, as they offer potential solutions to three of the biggest hurdles drone-like passenger vehicles face.
Firstly, it makes the vehicles accessible to anyone by removing the need to learn how to safely pilot an aircraft. Secondly, battery life still limits most electric vehicles to flight times measured in minutes. For personal vehicles this could be frustrating, but if you’re just hopping in a driverless air taxi for a five minute trip across town it’s unlikely to become apparent to you.
Operators of the service simply need to make sure they have a big enough fleet to ensure a charged vehicle is never too far away, or they’ll need a way to swap out batteries easily, such as the one suggested by the makers of the Volocopter electric helicopter.
Finally, there has already been significant progress in developing technology and regulations needed to integrate autonomous drones into our airspace that future driverless flying cars can most likely piggyback off of.
Safety requirements will inevitably be more stringent, but adding more predictable and controllable autonomous drones to the skies is likely to be more attractive to regulators than trying to license and police thousands of new amateur pilots.
Image Credit: Lilium Continue reading →

Posted in Human Robots