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#436065 From Mainframes to PCs: What Robot ...

Posted on October 8, 2019 by Android

This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE.

Autonomous robots are coming around slowly. We already got autonomous vacuum cleaners, autonomous lawn mowers, toys that bleep and blink, and (maybe) soon autonomous cars. Yet, generation after generation, we keep waiting for the robots that we all know from movies and TV shows. Instead, businesses seem to get farther and farther away from the robots that are able to do a large variety of tasks using general-purpose, human anatomy-inspired hardware.

Although these are the droids we have been looking for, anything that came close, such as Willow Garage’s PR2 or Rethink Robotics’ Baxter has bitten the dust. With building a robotic company being particularly hard, compounding business risk with technological risk, the trend goes from selling robots to selling actual services like mowing your lawn, provide taxi rides, fulfilling retail orders, or picking strawberries by the pound. Unfortunately for fans of R2-D2 and C-3PO, these kind of business models emphasize specialized, room- or fridge-sized hardware that is optimized for one very specific task, but does not contribute to a general-purpose robotic platform.

We have actually seen something very similar in the personal computer (PC) industry. In the 1950s, even though computers could be as big as an entire room and were only available to a selected few, the public already had a good idea of what computers would look like. A long list of fictional computers started to populate mainstream entertainment during that time. In a 1962 New York Times article titled “Pocket Computer to Replace Shopping List,” visionary scientist John Mauchly stated that “there is no reason to suppose the average boy or girl cannot be master of a personal computer.”

In 1968, Douglas Engelbart gave us the “mother of all demos,” browsing hypertext on a graphical screen and a mouse, and other ideas that have become standard only decades later. Now that we have finally seen all of this, it might be helpful to examine what actually enabled the computing revolution to learn where robotics is really at and what we need to do next.

The parallels between computers and robots

In the 1970s, mainframes were about to be replaced by the emerging class of mini-computers, fridge-sized devices that cost less than US $25,000 ($165,000 in 2019 dollars). These computers did not use punch-cards, but could be programmed in Fortran and BASIC, dramatically expanding the ease with which potential applications could be created. Yet it was still unclear whether mini-computers could ever replace big mainframes in applications that require fast and efficient processing of large amounts of data, let alone enter every living room. This is very similar to the robotics industry right now, where large-scale factory robots (mainframes) that have existed since the 1960s are seeing competition from a growing industry of collaborative robots that can safely work next to humans and can easily be installed and programmed (minicomputers). As in the ’70s, applications for these devices that reach system prices comparable to that of a luxury car are quite limited, and it is hard to see how they could ever become a consumer product.

Yet, as in the computer industry, successful architectures are quickly being cloned, driving prices down, and entirely new approaches on how to construct or program robotic arms are sprouting left and right. Arm makers are joined by manufacturers of autonomous carts, robotic grippers, and sensors. These components can be combined, paving the way for standard general purpose platforms that follow the model of the IBM PC, which built a capable, open architecture relying as much on commodity parts as possible.

General purpose robotic systems have not been successful for similar reasons that general purpose, also known as “personal,” computers took decades to emerge. Mainframes were custom-built for each application, while typewriters got smarter and smarter, not really leaving room for general purpose computers in between. Indeed, given the cost of hardware and the relatively little abilities of today’s autonomous robots, it is almost always smarter to build a special purpose machine than trying to make a collaborative mobile manipulator smart.

A current example is e-commerce grocery fulfillment. The current trend is to reserve underutilized parts of a brick-and-mortar store for a micro-fulfillment center that stores goods in little crates with an automated retrieval system and a (human) picker. A number of startups like Alert Innovation, Fabric, Ocado Technology, TakeOff Technologies, and Tompkins Robotics, to just name a few, have raised hundreds of millions of venture capital recently to build mainframe equivalents of robotic fulfillment centers. This is in contrast with a robotic picker, which would drive through the aisles to restock and pick from shelves. Such a robotic store clerk would come much closer to our vision of a general purpose robot, but would require many copies of itself that crowd the aisles to churn out hundreds of orders per hour as a microwarehouse could. Although eventually more efficient, the margins in retail are already low and make it unlikely that this industry will produce the technological jump that we need to get friendly C-3POs manning the aisles.

Startups have raised hundreds of millions of venture capital recently to build mainframe equivalents of robotic fulfillment centers. This is in contrast with a robotic picker, which would drive through the aisles to restock and pick from shelves, and would come much closer to our vision of a general purpose robot.

Mainframes were also attacked from the bottom. Fascination with the new digital technology has led to a hobbyist movement to create microcomputers that were sold via mail order or at RadioShack. Initially, a large number of small businesses was selling tens, at most hundreds, of devices, usually as a kit and with wooden enclosures. This trend culminated into the “1977 Trinity” in the form of the Apple II, the Commodore PET, and the Tandy TRS-80, complete computers that were sold for prices around $2500 (TRS) to $5000 (Apple) in today’s dollars. The main application of these computers was their programmability (in BASIC), which would enable consumers to “learn to chart your biorhythms, balance your checking account, or even control your home environment,” according to an original Apple advertisement. Similarly, there exists a myriad of gadgets that explore different aspects of robotics such as mobility, manipulation, and entertainment.

As in the fledgling personal computing industry, the advertised functionality was at best a model of the real deal. A now-famous milestone in entertainment robotics was the original Sony’s Aibo, a robotic dog that was advertised to have many properties that a real dog has such as develop its own personality, play with a toy, and interact with its owner. Released in 1999, and re-launched in 2018, the platform has a solid following among hobbyists and academics who like its programmability, but probably only very few users who accept the device as a pet stand-in.

There also exist countless “build-your-own-robotic-arm” kits. One of the more successful examples is the uArm, which sells for around $800, and is advertised to perform pick and place, assembly, 3D printing, laser engraving, and many other things that sound like high value applications. Using compelling videos of the robot actually doing these things in a constrained environment has led to two successful crowd-funding campaigns, and have established the robot as a successful educational tool.

Finally, there exist platforms that allow hobbyist programmers to explore mobility to construct robots that patrol your house, deliver items, or provide their users with telepresence abilities. An example of that is the Misty II. Much like with the original Apple II, there remains a disconnect between the price of the hardware and the fidelity of the applications that were available.

For computers, this disconnect began to disappear with the invention of the first electronic spreadsheet software VisiCalc that spun out of Harvard in 1979 and prompted many people to buy an entire microcomputer just to run the program. VisiCalc was soon joined by WordStar, a word processing application, that sold for close to $2000 in today’s dollars. WordStar, too, would entice many people to buy the entire hardware just to use the software. The two programs are early examples of what became known as “killer application.”

With factory automation being mature, and robots with the price tag of a minicomputer being capable of driving around and autonomously carrying out many manipulation tasks, the robotics industry is somewhere where the PC industry was between 1973—the release of the Xerox Alto, the first computer with a graphical user interface, mouse, and special software—and 1979—when microcomputers in the under $5000 category began to take off.

Killer apps for robots
So what would it take for robotics to continue to advance like computers did? The market itself already has done a good job distilling what the possible killer apps are. VCs and customers alike push companies who have set out with lofty goals to reduce their offering to a simple value proposition. As a result, companies that started at opposite ends often converge to mirror images of each other that offer very similar autonomous carts, (bin) picking, palletizing, depalletizing, or sorting solutions. Each of these companies usually serves a single application to a single vertical—for example bin-picking clothes, transporting warehouse goods, or picking strawberries by the pound. They are trying to prove that their specific technology works without spreading themselves too thin.

Very few of these companies have really taken off. One example is Kiva Systems, which turned into the logistic robotics division of Amazon. Kiva and others are structured around sound value propositions that are grounded in well-known user needs. As these solutions are very specialized, however, it is unlikely that they result into any economies of scale of the same magnitude that early computer users who bought both a spreadsheet and a word processor application for their expensive minicomputer could enjoy. What would make these robotic solutions more interesting is when functionality becomes stackable. Instead of just being able to do bin picking, palletizing, and transportation with the same hardware, these three skills could be combined to model entire processes.

A skill that is yet little addressed by startups and is historically owned by the mainframe equivalent of robotics is assembly of simple mechatronic devices. The ability to assemble mechatronic parts is equivalent to other tasks such as changing a light bulb, changing the batteries in a remote control, or tending machines like a lever-based espresso machine. These tasks would involve the autonomous execution of complete workflows possible using a single machine, eventually leading to an explosion of industrial productivity across all sectors. For example, picking up an item from a bin, arranging it on the robot, moving it elsewhere, and placing it into a shelf or a machine is a process that equally applies to a manufacturing environment, a retail store, or someone’s kitchen.

Image: Robotic Materials Inc.

Autonomous, vision and force-based assembly of the
Siemens robot learning challenge.

Even though many of the above applications are becoming possible, it is still very hard to get a platform off the ground without added components that provide “killer app” value of their own. Interesting examples are Rethink Robotics or the Robot Operating System (ROS). Rethink Robotics’ Baxter and Sawyer robots pioneered a great user experience (like the 1973 Xerox Alto, really the first PC), but its applications were difficult to extend beyond simple pick-and-place and palletizing and depalletizing items.

ROS pioneered interprocess communication software that was adapted to robotic needs (multiple computers, different programming languages) and the idea of software modularity in robotics, but—in the absence of a common hardware platform—hasn’t yet delivered a single application, e.g. for navigation, path planning, or grasping, that performs beyond research-grade demonstration level and won’t get discarded once developers turn to production systems. At the same time, an increasing number of robotic devices, such as robot arms or 3D perception systems that offer intelligent functionality, provide other ways to wire them together that do not require an intermediary computer, while keeping close control over the real-time aspects of their hardware.

Image: Robotic Materials Inc.

Robotic Materials GPR-1 combines a MIR-100 autonomous cart with an UR-5 collaborative robotic arm, an onRobot force/torque sensor and Robotic Materials’ SmartHand to perform out-of-the-box mobile assembly, bin picking, palletizing, and depalletizing tasks.

At my company, Robotic Materials Inc., we have made strides to identify a few applications such as bin picking and assembly, making them configurable with a single click by combining machine learning and optimization with an intuitive user interface. Here, users can define object classes and how to grasp them using a web browser, which then appear as first-class objects in a robot-specific graphical programming language. We have also done this for assembly, allowing users to stack perception-based picking and force-based assembly primitives by simply dragging and dropping appropriate commands together.

While such an approach might answer the question of a killer app for robots priced in the “minicomputer” range, it is unclear how killer app-type value can be generated with robots in the less-than-$5000 category. A possible answer is two-fold: First, with low-cost arms, mobility platforms, and entertainment devices continuously improving, a confluence of technology readiness and user innovation, like with the Apple II and VisiCalc, will eventually happen. For example, there is not much innovation needed to turn Misty into a home security system; the uArm into a low-cost bin-picking system; or an Aibo-like device into a therapeutic system for the elderly or children with autism.

Second, robots and their components have to become dramatically cheaper. Indeed, computers have seen an exponential reduction in price accompanied by an exponential increase in computational power, thanks in great part to Moore’s Law. This development has helped robotics too, allowing us to reach breakthroughs in mobility and manipulation due to the ability to process massive amounts of image and depth data in real-time, and we can expect it to continue to do so.

Is there a Moore’s Law for robots?
One might ask, however, how a similar dynamics might be possible for robots as a whole, including all their motors and gears, and what a “Moore’s Law” would look like for the robotics industry. Here, it helps to remember that the perpetuation of Moore’s Law is not the reason, but the result of the PC revolution. Indeed, the first killer apps for bookkeeping, editing, and gaming were so good that they unleashed tremendous consumer demand, beating the benchmark on what was thought to be physically possible over and over again. (I vividly remember 56 kbps to be the absolute maximum data rate for copper phone lines until DSL appeared.)

That these economies of scale are also applicable to mechatronics is impressively demonstrated by the car industry. A good example is the 2020 Prius Prime, a highly computerized plug-in hybrid, that is available for one third of the cost of my company’s GPR-1 mobile manipulator while being orders of magnitude more complex, sporting an electrical motor, a combustion engine, and a myriad of sensors and computers. It is therefore very well conceivable to produce a mobile manipulator that retails at one tenth of the cost of a modern car, once robotics enjoy similar mass-market appeal. Given that these robots are part of the equation, actively lowering cost of production, this might happen as fast as never before in the history of industrialization.

It is therefore very well conceivable to produce a mobile manipulator that retails at one tenth of the cost of a modern car, once robotics enjoy similar mass-market appeal.

There is one more driver that might make robots exponentially more capable: the cloud. Once a general purpose robot has learned or was programmed with a new skill, it could share it with every other robot. At some point, a grocer who buys a robot could assume that it already knows how to recognize and handle 99 percent of the retail items in the store. Likewise, a manufacturer can assume that the robot can handle and assemble every item available from McMaster-Carr and Misumi. Finally, families could expect a robot to know every kitchen item that Ikea and Pottery Barn is selling. Sounds like a labor intense problem, but probably more manageable than collecting footage for Google’s Street View using cars, tricycles, and snowmobiles, among other vehicles.

Strategies for robot startups
While we are waiting for these two trends—better and better applications and hardware with decreasing cost—to converge, we as a community have to keep exploring what the canonical robotic applications beyond mobility, bin picking, palletizing, depalletizing, and assembly are. We must also continue to solve the fundamental challenges that stand in the way of making these solutions truly general and robust.

For both questions, it might help to look at the strategies that have been critical in the development of the personal computer, which might equally well apply to robotics:

Start with a solution to a problem your customers have. Unfortunately, their problem is almost never that they need your sensor, widget, or piece of code, but something that already costs them money or negatively affects them in some other way. Example: There are many more people who had a problem calculating their taxes (and wanted to buy VisiCalc) than writing their own solution in BASIC.

Build as little of your own hardware as necessary. Your business model should be stronger than the margin you can make on the hardware. Why taking the risk? Example: Why build your own typewriter if you can write the best typewriting application that makes it worth buying a computer just for that?

If your goal is a platform, make sure it comes with a killer application, which alone justifies the platform cost. Example: Microcomputer companies came and went until the “1977 Trinity” intersected with the killer apps spreadsheet and word processors. Corollary: You can also get lucky.

Use an open architecture, which creates an ecosystem where others compete on creating better components and peripherals, while allowing others to integrate your solution into their vertical and stack it with other devices. Example: Both the Apple II and the IBM PC were completely open architectures, enabling many clones, thereby growing the user and developer base.

It’s worthwhile pursuing this. With most business processes already being digitized, general purpose robots will allow us to fill in gaps in mobility and manipulation, increasing productivity at levels only limited by the amount of resources and energy that are available, possibly creating a utopia in which creativity becomes the ultimate currency. Maybe we’ll even get R2-D2.

Nikolaus Correll is an associate professor of computer science at the University of Colorado at Boulder where he works on mobile manipulation and other robotics applications. He’s co-founder and CTO of Robotic Materials Inc., which is supported by the National Science Foundation and the National Institute of Standards and Technology via their Small Business Innovative Research (SBIR) programs. Continue reading →

Posted in Human Robots

#435528 The Time for AI Is Now. Here’s Why

Posted on August 21, 2019 by Android

You hear a lot these days about the sheer transformative power of AI.

There’s pure intelligence: DeepMind’s algorithms readily beat humans at Go and StarCraft, and DeepStack triumphs over humans at no-limit hold’em poker. Often, these silicon brains generate gameplay strategies that don’t resemble anything from a human mind.

There’s astonishing speed: algorithms routinely surpass radiologists in diagnosing breast cancer, eye disease, and other ailments visible from medical imaging, essentially collapsing decades of expert training down to a few months.

Although AI’s silent touch is mainly felt today in the technological, financial, and health sectors, its impact across industries is rapidly spreading. At the Singularity University Global Summit in San Francisco this week Neil Jacobstein, Chair of AI and Robotics, painted a picture of a better AI-powered future for humanity that is already here.

Thanks to cloud-based cognitive platforms, sophisticated AI tools like deep learning are no longer relegated to academic labs. For startups looking to tackle humanity’s grand challenges, the tools to efficiently integrate AI into their missions are readily available. The progress of AI is massively accelerating—to the point you need help from AI to track its progress, joked Jacobstein.

Now is the time to consider how AI can impact your industry, and in the process, begin to envision a beneficial relationship with our machine coworkers. As Jacobstein stressed in his talk, the future of a brain-machine mindmeld is a collaborative intelligence that augments our own. “AI is reinventing the way we invent,” he said.

AI’s Rapid Revolution
Machine learning and other AI-based methods may seem academic and abstruse. But Jacobstein pointed out that there are already plenty of real-world AI application frameworks.

Their secret? Rather than coding from scratch, smaller companies—with big visions—are tapping into cloud-based solutions such as Google’s TensorFlow, Microsoft’s Azure, or Amazon’s AWS to kick off their AI journey. These platforms act as all-in-one solutions that not only clean and organize data, but also contain built-in security and drag-and-drop coding that allow anyone to experiment with complicated machine learning algorithms.

Google Cloud’s Anthos, for example, lets anyone migrate data from other servers—IBM Watson or AWS, for example—so users can leverage different computing platforms and algorithms to transform data into insights and solutions.

Rather than coding from scratch, it’s already possible to hop onto a platform and play around with it, said Jacobstein. That’s key: this democratization of AI is how anyone can begin exploring solutions to problems we didn’t even know we had, or those long thought improbable.

The acceleration is only continuing. Much of AI’s mind-bending pace is thanks to a massive infusion of funding. Microsoft recently injected $1 billion into OpenAI, the Elon Musk venture that engineers socially responsible artificial general intelligence (AGI).

The other revolution is in hardware, and Google, IBM, and NVIDIA—among others—are racing to manufacture computing chips tailored to machine learning.

Democratizing AI is like the birth of the printing press. Mechanical printing allowed anyone to become an author; today, an iPhone lets anyone film a movie masterpiece.

However, this diffusion of AI into the fabric of our lives means tech explorers need to bring skepticism to their AI solutions, giving them a dose of empathy, nuance, and humanity.

A Path Towards Ethical AI
The democratization of AI is a double-edged sword: as more people wield the technology’s power in real-world applications, problems embedded in deep learning threaten to disrupt those very judgment calls.

Much of the press on the dangers of AI focuses on superintelligence—AI that’s more adept at learning than humans—taking over the world, said Jacobstein. But the near-term threat, and far more insidious, is in humans misusing the technology.

Deepfakes, for example, allow AI rookies to paste one person’s head on a different body or put words into a person’s mouth. As the panel said, it pays to think of AI as a cybersecurity problem, one with currently shaky accountability and complexity, and one that fails at diversity and bias.

Take bias. Thanks to progress in natural language processing, Google Translate works nearly perfectly today, so much so that many consider the translation problem solved. Not true, the panel said. One famous example is how the algorithm translates gender-neutral terms like “doctor” into “he” and “nurse” into “she.”

These biases reflect our own, and it’s not just a data problem. To truly engineer objective AI systems, ones stripped of our society’s biases, we need to ask who is developing these systems, and consult those who will be impacted by the products. In addition to gender, racial bias is also rampant. For example, one recent report found that a supposedly objective crime-predicting system was trained on falsified data, resulting in outputs that further perpetuate corrupt police practices. Another study from Google just this month found that their hate speech detector more often labeled innocuous tweets from African-Americans as “obscene” compared to tweets from people of other ethnicities.

We often think of building AI as purely an engineering job, the panelists agreed. But similar to gene drives, germ-line genome editing, and other transformative—but dangerous—tools, AI needs to grow under the consultation of policymakers and other stakeholders. It pays to start young: educating newer generations on AI biases will mold malleable minds early, alerting them to the problem of bias and potentially mitigating risks.

As panelist Tess Posner from AI4ALL said, AI is rocket fuel for ambition. If young minds set out using the tools of AI to tackle their chosen problems, while fully aware of its inherent weaknesses, we can begin to build an AI-embedded future that is widely accessible and inclusive.

The bottom line: people who will be impacted by AI need to be in the room at the conception of an AI solution. People will be displaced by the new technology, and ethical AI has to consider how to mitigate human suffering during the transition. Just because AI looks like “magic fairy dust doesn’t mean that you’re home free,” the panelists said. You, the sentient human, bear the burden of being responsible for how you decide to approach the technology.

The time for AI is now. Let’s make it ethical.

Image Credit: GrAI / Shutterstock.com Continue reading →

Posted in Human Robots

#435520 These Are the Meta-Trends Shaping the ...

Posted on August 20, 2019 by Android

Life is pretty different now than it was 20 years ago, or even 10 years ago. It’s sort of exciting, and sort of scary. And hold onto your hat, because it’s going to keep changing—even faster than it already has been.

The good news is, maybe there won’t be too many big surprises, because the future will be shaped by trends that have already been set in motion. According to Singularity University co-founder and XPRIZE founder Peter Diamandis, a lot of these trends are unstoppable—but they’re also pretty predictable.

At SU’s Global Summit, taking place this week in San Francisco, Diamandis outlined some of the meta-trends he believes are key to how we’ll live our lives and do business in the (not too distant) future.

Increasing Global Abundance
Resources are becoming more abundant all over the world, and fewer people are seeing their lives limited by scarcity. “It’s hard for us to realize this as we see crisis news, but what people have access to is more abundant than ever before,” Diamandis said. Products and services are becoming cheaper and thus available to more people, and having more resources then enables people to create more, thus producing even more resources—and so on.

Need evidence? The proportion of the world’s population living in extreme poverty is currently lower than it’s ever been. The average human life expectancy is longer than it’s ever been. The costs of day-to-day needs like food, energy, transportation, and communications are on a downward trend.

Take energy. In most of the world, though its costs are decreasing, it’s still a fairly precious commodity; we turn off our lights and our air conditioners when we don’t need them (ideally, both to save money and to avoid wastefulness). But the cost of solar energy has plummeted, and the storage capacity of batteries is improving, and solar technology is steadily getting more efficient. Bids for new solar power plants in the past few years have broken each other’s records for lowest cost per kilowatt hour.

“We’re not far from a penny per kilowatt hour for energy from the sun,” Diamandis said. “And if you’ve got energy, you’ve got water.” Desalination, for one, will be much more widely feasible once the cost of the energy needed for it drops.

Knowledge is perhaps the most crucial resource that’s going from scarce to abundant. All the world’s knowledge is now at the fingertips of anyone who has a mobile phone and an internet connection—and the number of people connected is only going to grow. “Everyone is being connected at gigabit connection speeds, and this will be transformative,” Diamandis said. “We’re heading towards a world where anyone can know anything at any time.”

Increasing Capital Abundance
It’s not just goods, services, and knowledge that are becoming more plentiful. Money is, too—particularly money for business. “There’s more and more capital available to invest in companies,” Diamandis said. As a result, more people are getting the chance to bring their world-changing ideas to life.

Venture capital investments reached a new record of $130 billion in 2018, up from $84 billion in 2017—and that’s just in the US. Globally, VC funding grew 21 percent from 2017 to a total of $207 billion in 2018.

Through crowdfunding, any person in any part of the world can present their idea and ask for funding. That funding can come in the form of a loan, an equity investment, a reward, or an advanced purchase of the proposed product or service. “Crowdfunding means it doesn’t matter where you live, if you have a great idea you can get it funded by people from all over the world,” Diamandis said.

All this is making a difference; the number of unicorns—privately-held startups valued at over $1 billion—currently stands at an astounding 360.

One of the reasons why the world is getting better, Diamandis believes, is because entrepreneurs are trying more crazy ideas—not ideas that are reasonable or predictable or linear, but ideas that seem absurd at first, then eventually end up changing the world.

Everyone and Everything, Connected
As already noted, knowledge is becoming abundant thanks to the proliferation of mobile phones and wireless internet; everyone’s getting connected. In the next decade or sooner, connectivity will reach every person in the world. 5G is being tested and offered for the first time this year, and companies like Google, SpaceX, OneWeb, and Amazon are racing to develop global satellite internet constellations, whether by launching 12,000 satellites, as SpaceX’s Starlink is doing, or by floating giant balloons into the stratosphere like Google’s Project Loon.

“We’re about to reach a period of time in the next four to six years where we’re going from half the world’s people being connected to the whole world being connected,” Diamandis said. “What happens when 4.2 billion new minds come online? They’re all going to want to create, discover, consume, and invent.”

And it doesn’t stop at connecting people. Things are becoming more connected too. “By 2020 there will be over 20 billion connected devices and more than one trillion sensors,” Diamandis said. By 2030, those projections go up to 500 billion and 100 trillion. Think about it: there’s home devices like refrigerators, TVs, dishwashers, digital assistants, and even toasters. There’s city infrastructure, from stoplights to cameras to public transportation like buses or bike sharing. It’s all getting smart and connected.

Soon we’ll be adding autonomous cars to the mix, and an unimaginable glut of data to go with them. Every turn, every stop, every acceleration will be a data point. Some cars already collect over 25 gigabytes of data per hour, Diamandis said, and car data is projected to generate $750 billion of revenue by 2030.

“You’re going to start asking questions that were never askable before, because the data is now there to be mined,” he said.

Increasing Human Intelligence
Indeed, we’ll have data on everything we could possibly want data on. We’ll also soon have what Diamandis calls just-in-time education, where 5G combined with artificial intelligence and augmented reality will allow you to learn something in the moment you need it. “It’s not going and studying, it’s where your AR glasses show you how to do an emergency surgery, or fix something, or program something,” he said.

We’re also at the beginning of massive investments in research working towards connecting our brains to the cloud. “Right now, everything we think, feel, hear, or learn is confined in our synaptic connections,” Diamandis said. What will it look like when that’s no longer the case? Companies like Kernel, Neuralink, Open Water, Facebook, Google, and IBM are all investing billions of dollars into brain-machine interface research.

Increasing Human Longevity
One of the most important problems we’ll use our newfound intelligence to solve is that of our own health and mortality, making 100 years old the new 60—then eventually, 120 or 150.

“Our bodies were never evolved to live past age 30,” Diamandis said. “You’d go into puberty at age 13 and have a baby, and by the time you were 26 your baby was having a baby.”

Seeing how drastically our lifespans have changed over time makes you wonder what aging even is; is it natural, or is it a disease? Many companies are treating it as one, and using technologies like senolytics, CRISPR, and stem cell therapy to try to cure it. Scaffolds of human organs can now be 3D printed then populated with the recipient’s own stem cells so that their bodies won’t reject the transplant. Companies are testing small-molecule pharmaceuticals that can stop various forms of cancer.

“We don’t truly know what’s going on inside our bodies—but we can,” Diamandis said. “We’re going to be able to track our bodies and find disease at stage zero.”

Chins Up
The world is far from perfect—that’s not hard to see. What’s less obvious but just as true is that we’re living in an amazing time. More people are coming together, and they have more access to information, and that information moves faster, than ever before.

“I don’t think any of us understand how fast the world is changing,” Diamandis said. “Most people are fearful about the future. But we should be excited about the tools we now have to solve the world’s problems.”

Image Credit: spainter_vfx / Shutterstock.com Continue reading →

Posted in Human Robots

#435474 Watch China’s New Hybrid AI Chip Power ...

Posted on August 7, 2019 by Android

When I lived in Beijing back in the 90s, a man walking his bike was nothing to look at. But today, I did a serious double-take at a video of a bike walking his man.

No kidding.

The bike itself looks overloaded but otherwise completely normal. Underneath its simplicity, however, is a hybrid computer chip that combines brain-inspired circuits with machine learning processes into a computing behemoth. Thanks to its smart chip, the bike self-balances as it gingerly rolls down a paved track before smoothly gaining speed into a jogging pace while navigating dexterously around obstacles. It can even respond to simple voice commands such as “speed up,” “left,” or “straight.”

Far from a circus trick, the bike is a real-world demo of the AI community’s latest attempt at fashioning specialized hardware to keep up with the challenges of machine learning algorithms. The Tianjic (天机*) chip isn’t just your standard neuromorphic chip. Rather, it has the architecture of a brain-like chip, but can also run deep learning algorithms—a match made in heaven that basically mashes together neuro-inspired hardware and software.

The study shows that China is readily nipping at the heels of Google, Facebook, NVIDIA, and other tech behemoths investing in developing new AI chip designs—hell, with billions in government investment it may have already had a head start. A sweeping AI plan from 2017 looks to catch up with the US on AI technology and application by 2020. By 2030, China’s aiming to be the global leader—and a champion for building general AI that matches humans in intellectual competence.

The country’s ambition is reflected in the team’s parting words.

“Our study is expected to stimulate AGI [artificial general intelligence] development by paving the way to more generalized hardware platforms,” said the authors, led by Dr. Luping Shi at Tsinghua University.

A Hardware Conundrum
Shi’s autonomous bike isn’t the first robotic two-wheeler. Back in 2015, the famed research nonprofit SRI International in Menlo Park, California teamed up with Yamaha to engineer MOTOBOT, a humanoid robot capable of driving a motorcycle. Powered by state-of-the-art robotic hardware and machine learning, MOTOBOT eventually raced MotoGPTM world champion Valentino Rossi in a nail-biting match-off.

However, the technological core of MOTOBOT and Shi’s bike vastly differ, and that difference reflects two pathways towards more powerful AI. One, exemplified by MOTOBOT, is software—developing brain-like algorithms with increasingly efficient architecture, efficacy, and speed. That sounds great, but deep neural nets demand so many computational resources that general-purpose chips can’t keep up.

As Shi told China Science Daily: “CPUs and other chips are driven by miniaturization technologies based on physics. Transistors might shrink to nanoscale-level in 10, 20 years. But what then?” As more transistors are squeezed onto these chips, efficient cooling becomes a limiting factor in computational speed. Tax them too much, and they melt.

For AI processes to continue, we need better hardware. An increasingly popular idea is to build neuromorphic chips, which resemble the brain from the ground up. IBM’s TrueNorth, for example, contains a massively parallel architecture nothing like the traditional Von Neumann structure of classic CPUs and GPUs. Similar to biological brains, TrueNorth’s memory is stored within “synapses” between physical “neurons” etched onto the chip, which dramatically cuts down on energy consumption.

But even these chips are limited. Because computation is tethered to hardware architecture, most chips resemble just one specific type of brain-inspired network called spiking neural networks (SNNs). Without doubt, neuromorphic chips are highly efficient setups with dynamics similar to biological networks. They also don’t play nicely with deep learning and other software-based AI.

Brain-AI Hybrid Core
Shi’s new Tianjic chip brought the two incompatibilities together onto a single piece of brainy hardware.

First was to bridge the deep learning and SNN divide. The two have very different computation philosophies and memory organizations, the team said. The biggest difference, however, is that artificial neural networks transform multidimensional data—image pixels, for example—into a single, continuous, multi-bit 0 and 1 stream. In contrast, neurons in SNNs activate using something called “binary spikes” that code for specific activation events in time.

Confused? Yeah, it’s hard to wrap my head around it too. That’s because SNNs act very similarly to our neural networks and nothing like computers. A particular neuron needs to generate an electrical signal (a “spike”) large enough to transfer down to the next one; little blips in signals don’t count. The way they transmit data also heavily depends on how they’re connected, or the network topology. The takeaway: SNNs work pretty differently than deep learning.

Shi’s team first recreated this firing quirk in the language of computers—0s and 1s—so that the coding mechanism would become compatible with deep learning algorithms. They then carefully aligned the step-by-step building blocks of the two models, which allowed them to tease out similarities into a common ground to further build on. “On the basis of this unified abstraction, we built a cross-paradigm neuron scheme,” they said.

In general, the design allowed both computational approaches to share the synapses, where neurons connect and store data, and the dendrites, the outgoing branches of the neurons. In contrast, the neuron body, where signals integrate, was left reconfigurable for each type of computation, as were the input branches. Each building block was combined into a single unified functional core (FCore), which acts like a deep learning/SNN converter depending on its specific setup. Translation: the chip can do both types of previously incompatible computation.

The Chip
Using nanoscale fabrication, the team arranged 156 FCores, containing roughly 40,000 neurons and 10 million synapses, onto a chip less than a fifth of an inch in length and width. Initial tests showcased the chip’s versatility, in that it can run both SNNs and deep learning algorithms such as the popular convolutional neural network (CNNs) often used in machine vision.

Compared to IBM TrueNorth, the density of Tianjic’s cores increased by 20 percent, speeding up performance ten times and increasing bandwidth at least 100-fold, the team said. When pitted against GPUs, the current hardware darling of machine learning, the chip increased processing throughput up to 100 times, while using just a sliver (1/10,000) of energy.

Although these stats are great, real-life performance is even better as a demo. Here’s where the authors gave their Tianjic brain a body. The team combined one chip with multiple specialized networks to process vision, balance, voice commands, and decision-making in real time. Object detection and target tracking, for example, relied on a deep neural net CNN, whereas voice commands and balance data were recognized using an SNN. The inputs were then integrated inside a neural state machine, which churned out decisions to downstream output modules—for example, controlling the handle bar to turn left.

Thanks to the chip’s brain-like architecture and bilingual ability, Tianjic “allowed all of the neural network models to operate in parallel and realized seamless communication across the models,” the team said. The result is an autonomous bike that rolls after its human, balances across speed bumps, avoids crashing into roadblocks, and answers to voice commands.

General AI?
“It’s a wonderful demonstration and quite impressive,” said the editorial team at Nature, which published the study on its cover last week.

However, they cautioned, when comparing Tianjic with state-of-the-art chips designed for a single problem toe-to-toe on that particular problem, Tianjic falls behind. But building these jack-of-all-trades hybrid chips is definitely worth the effort. Compared to today’s limited AI, what people really want is artificial general intelligence, which will require new architectures that aren’t designed to solve one particular problem.

Until people start to explore, innovate, and play around with different designs, it’s not clear how we can further progress in the pursuit of general AI. A self-driving bike might not be much to look at, but its hybrid brain is a pretty neat place to start.

*The name, in Chinese, means “heavenly machine,” “unknowable mystery of nature,” or “confidentiality.” Go figure.

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#434753 Top Takeaways From The Economist ...

Posted on March 14, 2019 by Android

Over the past few years, the word ‘innovation’ has degenerated into something of a buzzword. In fact, according to Vijay Vaitheeswaran, US business editor at The Economist, it’s one of the most abused words in the English language.

The word is over-used precisely because we’re living in a great age of invention. But the pace at which those inventions are changing our lives is fast, new, and scary.

So what strategies do companies need to adopt to make sure technology leads to growth that’s not only profitable, but positive? How can business and government best collaborate? Can policymakers regulate the market without suppressing innovation? Which technologies will impact us most, and how soon?

At The Economist Innovation Summit in Chicago last week, entrepreneurs, thought leaders, policymakers, and academics shared their insights on the current state of exponential technologies, and the steps companies and individuals should be taking to ensure a tech-positive future. Here’s their expert take on the tech and trends shaping the future.

Blockchain
There’s been a lot of hype around blockchain; apparently it can be used for everything from distributing aid to refugees to voting. However, it’s too often conflated with cryptocurrencies like Bitcoin, and we haven’t heard of many use cases. Where does the technology currently stand?

Julie Sweet, chief executive of Accenture North America, emphasized that the technology is still in its infancy. “Everything we see today are pilots,” she said. The most promising of these pilots are taking place across three different areas: supply chain, identity, and financial services.

When you buy something from outside the US, Sweet explained, it goes through about 80 different parties. 70 percent of the relevant data is replicated and is prone to error, with paper-based documents often to blame. Blockchain is providing a secure way to eliminate paper in supply chains, upping accuracy and cutting costs in the process.

One of the most prominent use cases in the US is Walmart—the company has mandated that all suppliers in its leafy greens segment be on a blockchain, and its food safety has improved as a result.

Beth Devin, head of Citi Ventures’ innovation network, added “Blockchain is an infrastructure technology. It can be leveraged in a lot of ways. There’s so much opportunity to create new types of assets and securities that aren’t accessible to people today. But there’s a lot to figure out around governance.”

Open Source Technology
Are the days of proprietary technology numbered? More and more companies and individuals are making their source code publicly available, and its benefits are thus more widespread than ever before. But what are the limitations and challenges of open source tech, and where might it go in the near future?

Bob Lord, senior VP of cognitive applications at IBM, is a believer. “Open-sourcing technology helps innovation occur, and it’s a fundamental basis for creating great technology solutions for the world,” he said. However, the biggest challenge for open source right now is that companies are taking out more than they’re contributing back to the open-source world. Lord pointed out that IBM has a rule about how many lines of code employees take out relative to how many lines they put in.

Another challenge area is open governance; blockchain by its very nature should be transparent and decentralized, with multiple parties making decisions and being held accountable. “We have to embrace open governance at the same time that we’re contributing,” Lord said. He advocated for a hybrid-cloud environment where people can access public and private data and bring it together.

Augmented and Virtual Reality
Augmented and virtual reality aren’t just for fun and games anymore, and they’ll be even less so in the near future. According to Pearly Chen, vice president at HTC, they’ll also go from being two different things to being one and the same. “AR overlays digital information on top of the real world, and VR transports you to a different world,” she said. “In the near future we will not need to delineate between these two activities; AR and VR will come together naturally, and will change everything we do as we know it today.”

For that to happen, we’ll need a more ergonomically friendly device than we have today for interacting with this technology. “Whenever we use tech today, we’re multitasking,” said product designer and futurist Jody Medich. “When you’re using GPS, you’re trying to navigate in the real world and also manage this screen. Constant task-switching is killing our brain’s ability to think.” Augmented and virtual reality, she believes, will allow us to adapt technology to match our brain’s functionality.

This all sounds like a lot of fun for uses like gaming and entertainment, but what about practical applications? “Ultimately what we care about is how this technology will improve lives,” Chen said.

A few ways that could happen? Extended reality will be used to simulate hazardous real-life scenarios, reduce the time and resources needed to bring a product to market, train healthcare professionals (such as surgeons), or provide therapies for patients—not to mention education. “Think about the possibilities for children to learn about history, science, or math in ways they can’t today,” Chen said.

Quantum Computing
If there’s one technology that’s truly baffling, it’s quantum computing. Qubits, entanglement, quantum states—it’s hard to wrap our heads around these concepts, but they hold great promise. Where is the tech right now?

Mandy Birch, head of engineering strategy at Rigetti Computing, thinks quantum development is starting slowly but will accelerate quickly. “We’re at the innovation stage right now, trying to match this capability to useful applications,” she said. “Can we solve problems cheaper, better, and faster than classical computers can do?” She believes quantum’s first breakthrough will happen in two to five years, and that is highest potential is in applications like routing, supply chain, and risk optimization, followed by quantum chemistry (for materials science and medicine) and machine learning.

David Awschalom, director of the Chicago Quantum Exchange and senior scientist at Argonne National Laboratory, believes quantum communication and quantum sensing will become a reality in three to seven years. “We’ll use states of matter to encrypt information in ways that are completely secure,” he said. A quantum voting system, currently being prototyped, is one application.

Who should be driving quantum tech development? The panelists emphasized that no one entity will get very far alone. “Advancing quantum tech will require collaboration not only between business, academia, and government, but between nations,” said Linda Sapochak, division director of materials research at the National Science Foundation. She added that this doesn’t just go for the technology itself—setting up the infrastructure for quantum will be a big challenge as well.

Space
Space has always been the final frontier, and it still is—but it’s not quite as far-removed from our daily lives now as it was when Neil Armstrong walked on the moon in 1969.

The space industry has always been funded by governments and private defense contractors. But in 2009, SpaceX launched its first commercial satellite, and in subsequent years have drastically cut the cost of spaceflight. More importantly, they published their pricing, which brought transparency to a market that hadn’t seen it before.

Entrepreneurs around the world started putting together business plans, and there are now over 400 privately-funded space companies, many with consumer applications.

Chad Anderson, CEO of Space Angels and managing partner of Space Capital, pointed out that the technology floating around in space was, until recently, archaic. “A few NASA engineers saw they had more computing power in their phone than there was in satellites,” he said. “So they thought, ‘why don’t we just fly an iPhone?’” They did—and it worked.

Now companies have networks of satellites monitoring the whole planet, producing a huge amount of data that’s valuable for countless applications like agriculture, shipping, and observation. “A lot of people underestimate space,” Anderson said. “It’s already enabling our modern global marketplace.”

Next up in the space realm, he predicts, are mining and tourism.

Artificial Intelligence and the Future of Work
From the US to Europe to Asia, alarms are sounding about AI taking our jobs. What will be left for humans to do once machines can do everything—and do it better?

These fears may be unfounded, though, and are certainly exaggerated. It’s undeniable that AI and automation are changing the employment landscape (not to mention the way companies do business and the way we live our lives), but if we build these tools the right way, they’ll bring more good than harm, and more productivity than obsolescence.

Accenture’s Julie Sweet emphasized that AI alone is not what’s disrupting business and employment. Rather, it’s what she called the “triple A”: automation, analytics, and artificial intelligence. But even this fear-inducing trifecta of terms doesn’t spell doom, for workers or for companies. Accenture has automated 40,000 jobs—and hasn’t fired anyone in the process. Instead, they’ve trained and up-skilled people. The most important drivers to scale this, Sweet said, are a commitment by companies and government support (such as tax credits).

Imbuing AI with the best of human values will also be critical to its impact on our future. Tracy Frey, Google Cloud AI’s director of strategy, cited the company’s set of seven AI principles. “What’s important is the governance process that’s put in place to support those principles,” she said. “You can’t make macro decisions when you have technology that can be applied in many different ways.”

High Risks, High Stakes
This year, Vaitheeswaran said, 50 percent of the world’s population will have internet access (he added that he’s disappointed that percentage isn’t higher given the proliferation of smartphones). As technology becomes more widely available to people around the world and its influence grows even more, what are the biggest risks we should be monitoring and controlling?

Information integrity—being able to tell what’s real from what’s fake—is a crucial one. “We’re increasingly operating in siloed realities,” said Renee DiResta, director of research at New Knowledge and head of policy at Data for Democracy. “Inadvertent algorithmic amplification on social media elevates certain perspectives—what does that do to us as a society?”

Algorithms have also already been proven to perpetuate the bias of the people who create it—and those people are often wealthy, white, and male. Ensuring that technology doesn’t propagate unfair bias will be crucial to its ability to serve a diverse population, and to keep societies from becoming further polarized and inequitable. The polarization of experience that results from pronounced inequalities within countries, Vaitheeswaran pointed out, can end up undermining democracy.

We’ll also need to walk the line between privacy and utility very carefully. As Dan Wagner, founder of Civis Analytics put it, “We want to ensure privacy as much as possible, but open access to information helps us achieve important social good.” Medicine in the US has been hampered by privacy laws; if, for example, we had more data about biomarkers around cancer, we could provide more accurate predictions and ultimately better healthcare.

But going the Chinese way—a total lack of privacy—is likely not the answer, either. “We have to be very careful about the way we bake rights and freedom into our technology,” said Alex Gladstein, chief strategy officer at Human Rights Foundation.

Technology’s risks are clearly as fraught as its potential is promising. As Gary Shapiro, chief executive of the Consumer Technology Association, put it, “Everything we’ve talked about today is simply a tool, and can be used for good or bad.”

The decisions we’re making now, at every level—from the engineers writing algorithms, to the legislators writing laws, to the teenagers writing clever Instagram captions—will determine where on the spectrum we end up.

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