Tag Archives: framework

#433758 DeepMind’s New Research Plan to Make ...

Making sure artificial intelligence does what we want and behaves in predictable ways will be crucial as the technology becomes increasingly ubiquitous. It’s an area frequently neglected in the race to develop products, but DeepMind has now outlined its research agenda to tackle the problem.

AI safety, as the field is known, has been gaining prominence in recent years. That’s probably at least partly down to the overzealous warnings of a coming AI apocalypse from well-meaning, but underqualified pundits like Elon Musk and Stephen Hawking. But it’s also recognition of the fact that AI technology is quickly pervading all aspects of our lives, making decisions on everything from what movies we watch to whether we get a mortgage.

That’s why DeepMind hired a bevy of researchers who specialize in foreseeing the unforeseen consequences of the way we built AI back in 2016. And now the team has spelled out the three key domains they think require research if we’re going to build autonomous machines that do what we want.

In a new blog designed to provide updates on the team’s work, they introduce the ideas of specification, robustness, and assurance, which they say will act as the cornerstones of their future research. Specification involves making sure AI systems do what their operator intends; robustness means a system can cope with changes to its environment and attempts to throw it off course; and assurance involves our ability to understand what systems are doing and how to control them.

A classic thought experiment designed to illustrate how we could lose control of an AI system can help illustrate the problem of specification. Philosopher Nick Bostrom’s posited a hypothetical machine charged with making as many paperclips as possible. Because the creators fail to add what they might assume are obvious additional goals like not harming people, the AI wipes out humanity so we can’t switch it off before turning all matter in the universe into paperclips.

Obviously the example is extreme, but it shows how a poorly-specified goal can lead to unexpected and disastrous outcomes. Properly codifying the desires of the designer is no easy feat, though; often there are not neat ways to encompass both the explicit and implicit goals in ways that are understandable to the machine and don’t leave room for ambiguities, meaning we often rely on incomplete approximations.

The researchers note recent research by OpenAI in which an AI was trained to play a boat-racing game called CoastRunners. The game rewards players for hitting targets laid out along the race route. The AI worked out that it could get a higher score by repeatedly knocking over regenerating targets rather than actually completing the course. The blog post includes a link to a spreadsheet detailing scores of such examples.

Another key concern for AI designers is making their creation robust to the unpredictability of the real world. Despite their superhuman abilities on certain tasks, most cutting-edge AI systems are remarkably brittle. They tend to be trained on highly-curated datasets and so can fail when faced with unfamiliar input. This can happen by accident or by design—researchers have come up with numerous ways to trick image recognition algorithms into misclassifying things, including thinking a 3D printed tortoise was actually a gun.

Building systems that can deal with every possible encounter may not be feasible, so a big part of making AIs more robust may be getting them to avoid risks and ensuring they can recover from errors, or that they have failsafes to ensure errors don’t lead to catastrophic failure.

And finally, we need to have ways to make sure we can tell whether an AI is performing the way we expect it to. A key part of assurance is being able to effectively monitor systems and interpret what they’re doing—if we’re basing medical treatments or sentencing decisions on the output of an AI, we’d like to see the reasoning. That’s a major outstanding problem for popular deep learning approaches, which are largely indecipherable black boxes.

The other half of assurance is the ability to intervene if a machine isn’t behaving the way we’d like. But designing a reliable off switch is tough, because most learning systems have a strong incentive to prevent anyone from interfering with their goals.

The authors don’t pretend to have all the answers, but they hope the framework they’ve come up with can help guide others working on AI safety. While it may be some time before AI is truly in a position to do us harm, hopefully early efforts like these will mean it’s built on a solid foundation that ensures it is aligned with our goals.

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#433668 A Decade of Commercial Space ...

In many industries, a decade is barely enough time to cause dramatic change unless something disruptive comes along—a new technology, business model, or service design. The space industry has recently been enjoying all three.

But 10 years ago, none of those innovations were guaranteed. In fact, on Sept. 28, 2008, an entire company watched and hoped as their flagship product attempted a final launch after three failures. With cash running low, this was the last shot. Over 21,000 kilograms of kerosene and liquid oxygen ignited and powered two booster stages off the launchpad.

This first official picture of the Soviet satellite Sputnik I was issued in Moscow Oct. 9, 1957. The satellite measured 1 foot, 11 inches and weighed 184 pounds. The Space Age began as the Soviet Union launched Sputnik, the first man-made satellite, into orbit, on Oct. 4, 1957.AP Photo/TASS
When that Falcon 1 rocket successfully reached orbit and the company secured a subsequent contract with NASA, SpaceX had survived its ‘startup dip’. That milestone, the first privately developed liquid-fueled rocket to reach orbit, ignited a new space industry that is changing our world, on this planet and beyond. What has happened in the intervening years, and what does it mean going forward?

While scientists are busy developing new technologies that address the countless technical problems of space, there is another segment of researchers, including myself, studying the business angle and the operations issues facing this new industry. In a recent paper, my colleague Christopher Tang and I investigate the questions firms need to answer in order to create a sustainable space industry and make it possible for humans to establish extraterrestrial bases, mine asteroids and extend space travel—all while governments play an increasingly smaller role in funding space enterprises. We believe these business solutions may hold the less-glamorous key to unlocking the galaxy.

The New Global Space Industry
When the Soviet Union launched their Sputnik program, putting a satellite in orbit in 1957, they kicked off a race to space fueled by international competition and Cold War fears. The Soviet Union and the United States played the primary roles, stringing together a series of “firsts” for the record books. The first chapter of the space race culminated with Neil Armstrong and Buzz Aldrin’s historic Apollo 11 moon landing which required massive public investment, on the order of US$25.4 billion, almost $200 billion in today’s dollars.

Competition characterized this early portion of space history. Eventually, that evolved into collaboration, with the International Space Station being a stellar example, as governments worked toward shared goals. Now, we’ve entered a new phase—openness—with private, commercial companies leading the way.

The industry for spacecraft and satellite launches is becoming more commercialized, due, in part, to shrinking government budgets. According to a report from the investment firm Space Angels, a record 120 venture capital firms invested over $3.9 billion in private space enterprises last year. The space industry is also becoming global, no longer dominated by the Cold War rivals, the United States and USSR.

In 2018 to date, there have been 72 orbital launches, an average of two per week, from launch pads in China, Russia, India, Japan, French Guinea, New Zealand, and the US.

The uptick in orbital launches of actual rockets as well as spacecraft launches, which includes satellites and probes launched from space, coincides with this openness over the past decade.

More governments, firms and even amateurs engage in various spacecraft launches than ever before. With more entities involved, innovation has flourished. As Roberson notes in Digital Trends, “Private, commercial spaceflight. Even lunar exploration, mining, and colonization—it’s suddenly all on the table, making the race for space today more vital than it has felt in years.”

Worldwide launches into space. Orbital launches include manned and unmanned spaceships launched into orbital flight from Earth. Spacecraft launches include all vehicles such as spaceships, satellites and probes launched from Earth or space. Wooten, J. and C. Tang (2018) Operations in space, Decision Sciences; Space Launch Report (Kyle 2017); Spacecraft Encyclopedia (Lafleur 2017), CC BY-ND

One can see this vitality plainly in the news. On Sept. 21, Japan announced that two of its unmanned rovers, dubbed Minerva-II-1, had landed on a small, distant asteroid. For perspective, the scale of this landing is similar to hitting a 6-centimeter target from 20,000 kilometers away. And earlier this year, people around the world watched in awe as SpaceX’s Falcon Heavy rocket successfully launched and, more impressively, returned its two boosters to a landing pad in a synchronized ballet of epic proportions.

Challenges and Opportunities
Amidst the growth of capital, firms, and knowledge, both researchers and practitioners must figure out how entities should manage their daily operations, organize their supply chain, and develop sustainable operations in space. This is complicated by the hurdles space poses: distance, gravity, inhospitable environments, and information scarcity.

One of the greatest challenges involves actually getting the things people want in space, into space. Manufacturing everything on Earth and then launching it with rockets is expensive and restrictive. A company called Made In Space is taking a different approach by maintaining an additive manufacturing facility on the International Space Station and 3D printing right in space. Tools, spare parts, and medical devices for the crew can all be created on demand. The benefits include more flexibility and better inventory management on the space station. In addition, certain products can be produced better in space than on Earth, such as pure optical fiber.

How should companies determine the value of manufacturing in space? Where should capacity be built and how should it be scaled up? The figure below breaks up the origin and destination of goods between Earth and space and arranges products into quadrants. Humans have mastered the lower left quadrant, made on Earth—for use on Earth. Moving clockwise from there, each quadrant introduces new challenges, for which we have less and less expertise.

A framework of Earth-space operations. Wooten, J. and C. Tang (2018) Operations in Space, Decision Sciences, CC BY-ND
I first became interested in this particular problem as I listened to a panel of robotics experts discuss building a colony on Mars (in our third quadrant). You can’t build the structures on Earth and easily send them to Mars, so you must manufacture there. But putting human builders in that extreme environment is equally problematic. Essentially, an entirely new mode of production using robots and automation in an advance envoy may be required.

Resources in Space
You might wonder where one gets the materials for manufacturing in space, but there is actually an abundance of resources: Metals for manufacturing can be found within asteroids, water for rocket fuel is frozen as ice on planets and moons, and rare elements like helium-3 for energy are embedded in the crust of the moon. If we brought that particular isotope back to Earth, we could eliminate our dependence on fossil fuels.

As demonstrated by the recent Minerva-II-1 asteroid landing, people are acquiring the technical know-how to locate and navigate to these materials. But extraction and transport are open questions.

How do these cases change the economics in the space industry? Already, companies like Planetary Resources, Moon Express, Deep Space Industries, and Asterank are organizing to address these opportunities. And scholars are beginning to outline how to navigate questions of property rights, exploitation and partnerships.

Threats From Space Junk
A computer-generated image of objects in Earth orbit that are currently being tracked. Approximately 95 percent of the objects in this illustration are orbital debris – not functional satellites. The dots represent the current location of each item. The orbital debris dots are scaled according to the image size of the graphic to optimize their visibility and are not scaled to Earth. NASA
The movie “Gravity” opens with a Russian satellite exploding, which sets off a chain reaction of destruction thanks to debris hitting a space shuttle, the Hubble telescope, and part of the International Space Station. The sequence, while not perfectly plausible as written, is a very real phenomenon. In fact, in 2013, a Russian satellite disintegrated when it was hit with fragments from a Chinese satellite that exploded in 2007. Known as the Kessler effect, the danger from the 500,000-plus pieces of space debris has already gotten some attention in public policy circles. How should one prevent, reduce or mitigate this risk? Quantifying the environmental impact of the space industry and addressing sustainable operations is still to come.

NASA scientist Mark Matney is seen through a fist-sized hole in a 3-inch thick piece of aluminum at Johnson Space Center’s orbital debris program lab. The hole was created by a thumb-size piece of material hitting the metal at very high speed simulating possible damage from space junk. AP Photo/Pat Sullivan
What’s Next?
It’s true that space is becoming just another place to do business. There are companies that will handle the logistics of getting your destined-for-space module on board a rocket; there are companies that will fly those rockets to the International Space Station; and there are others that can make a replacement part once there.

What comes next? In one sense, it’s anybody’s guess, but all signs point to this new industry forging ahead. A new breakthrough could alter the speed, but the course seems set: exploring farther away from home, whether that’s the moon, asteroids, or Mars. It’s hard to believe that 10 years ago, SpaceX launches were yet to be successful. Today, a vibrant private sector consists of scores of companies working on everything from commercial spacecraft and rocket propulsion to space mining and food production. The next step is working to solidify the business practices and mature the industry.

Standing in a large hall at the University of Pittsburgh as part of the White House Frontiers Conference, I see the future. Wrapped around my head are state-of-the-art virtual reality goggles. I’m looking at the surface of Mars. Every detail is immediate and crisp. This is not just a video game or an aimless exercise. The scientific community has poured resources into such efforts because exploration is preceded by information. And who knows, maybe 10 years from now, someone will be standing on the actual surface of Mars.

Image Credit: SpaceX

Joel Wooten, Assistant Professor of Management Science, University of South Carolina

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#433545 Six Degrees of Torque-controlled ...

ALMA (“Articulated Locomotion and Manipulation”), a quadrupedal robotic framework, allows a cool robotic arm six degrees of dynamic locomotion (“movement”) while doing something else. Possible motions include walking, trotting, pacing and torso-posturing, simultaneously with other complex tasks, as well as … Continue reading

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#433282 The 4 Waves of AI: Who Will Own the ...

Recently, I picked up Kai-Fu Lee’s newest book, AI Superpowers.

Kai-Fu Lee is one of the most plugged-in AI investors on the planet, managing over $2 billion between six funds and over 300 portfolio companies in the US and China.

Drawing from his pioneering work in AI, executive leadership at Microsoft, Apple, and Google (where he served as founding president of Google China), and his founding of VC fund Sinovation Ventures, Lee shares invaluable insights about:

The four factors driving today’s AI ecosystems;
China’s extraordinary inroads in AI implementation;
Where autonomous systems are headed;
How we’ll need to adapt.

With a foothold in both Beijing and Silicon Valley, Lee looks at the power balance between Chinese and US tech behemoths—each turbocharging new applications of deep learning and sweeping up global markets in the process.

In this post, I’ll be discussing Lee’s “Four Waves of AI,” an excellent framework for discussing where AI is today and where it’s going. I’ll also be featuring some of the hottest Chinese tech companies leading the charge, worth watching right now.

I’m super excited that this Tuesday, I’ve scored the opportunity to sit down with Kai-Fu Lee to discuss his book in detail via a webinar.

With Sino-US competition heating up, who will own the future of technology?

Let’s dive in.

The First Wave: Internet AI
In this first stage of AI deployment, we’re dealing primarily with recommendation engines—algorithmic systems that learn from masses of user data to curate online content personalized to each one of us.

Think Amazon’s spot-on product recommendations, or that “Up Next” YouTube video you just have to watch before getting back to work, or Facebook ads that seem to know what you’ll buy before you do.

Powered by the data flowing through our networks, internet AI leverages the fact that users automatically label data as we browse. Clicking versus not clicking; lingering on a web page longer than we did on another; hovering over a Facebook video to see what happens at the end.

These cascades of labeled data build a detailed picture of our personalities, habits, demands, and desires: the perfect recipe for more tailored content to keep us on a given platform.

Currently, Lee estimates that Chinese and American companies stand head-to-head when it comes to deployment of internet AI. But given China’s data advantage, he predicts that Chinese tech giants will have a slight lead (60-40) over their US counterparts in the next five years.

While you’ve most definitely heard of Alibaba and Baidu, you’ve probably never stumbled upon Toutiao.

Starting out as a copycat of America’s wildly popular Buzzfeed, Toutiao reached a valuation of $20 billion by 2017, dwarfing Buzzfeed’s valuation by more than a factor of 10. But with almost 120 million daily active users, Toutiao doesn’t just stop at creating viral content.

Equipped with natural-language processing and computer vision, Toutiao’s AI engines survey a vast network of different sites and contributors, rewriting headlines to optimize for user engagement, and processing each user’s online behavior—clicks, comments, engagement time—to curate individualized news feeds for millions of consumers.

And as users grow more engaged with Toutiao’s content, the company’s algorithms get better and better at recommending content, optimizing headlines, and delivering a truly personalized feed.

It’s this kind of positive feedback loop that fuels today’s AI giants surfing the wave of internet AI.

The Second Wave: Business AI
While internet AI takes advantage of the fact that netizens are constantly labeling data via clicks and other engagement metrics, business AI jumps on the data that traditional companies have already labeled in the past.

Think banks issuing loans and recording repayment rates; hospitals archiving diagnoses, imaging data, and subsequent health outcomes; or courts noting conviction history, recidivism, and flight.

While we humans make predictions based on obvious root causes (strong features), AI algorithms can process thousands of weakly correlated variables (weak features) that may have much more to do with a given outcome than the usual suspects.

By scouting out hidden correlations that escape our linear cause-and-effect logic, business AI leverages labeled data to train algorithms that outperform even the most veteran of experts.

Apply these data-trained AI engines to banking, insurance, and legal sentencing, and you get minimized default rates, optimized premiums, and plummeting recidivism rates.

While Lee confidently places America in the lead (90-10) for business AI, China’s substantial lag in structured industry data could actually work in its favor going forward.

In industries where Chinese startups can leapfrog over legacy systems, China has a major advantage.

Take Chinese app Smart Finance, for instance.

While Americans embraced credit and debit cards in the 1970s, China was still in the throes of its Cultural Revolution, largely missing the bus on this technology.

Fast forward to 2017, and China’s mobile payment spending outnumbered that of Americans’ by a ratio of 50 to 1. Without the competition of deeply entrenched credit cards, mobile payments were an obvious upgrade to China’s cash-heavy economy, embraced by 70 percent of China’s 753 million smartphone users by the end of 2017.

But by leapfrogging over credit cards and into mobile payments, China largely left behind the notion of credit.

And here’s where Smart Finance comes in.

An AI-powered app for microfinance, Smart Finance depends almost exclusively on its algorithms to make millions of microloans. For each potential borrower, the app simply requests access to a portion of the user’s phone data.

On the basis of variables as subtle as your typing speed and battery percentage, Smart Finance can predict with astounding accuracy your likelihood of repaying a $300 loan.

Such deployments of business AI and internet AI are already revolutionizing our industries and individual lifestyles. But still on the horizon lie two even more monumental waves— perception AI and autonomous AI.

The Third Wave: Perception AI
In this wave, AI gets an upgrade with eyes, ears, and myriad other senses, merging the digital world with our physical environments.

As sensors and smart devices proliferate through our homes and cities, we are on the verge of entering a trillion-sensor economy.

Companies like China’s Xiaomi are putting out millions of IoT-connected devices, and teams of researchers have already begun prototyping smart dust—solar cell- and sensor-geared particulates that can store and communicate troves of data anywhere, anytime.

As Kai-Fu explains, perception AI “will bring the convenience and abundance of the online world into our offline reality.” Sensor-enabled hardware devices will turn everything from hospitals to cars to schools into online-merge-offline (OMO) environments.

Imagine walking into a grocery store, scanning your face to pull up your most common purchases, and then picking up a virtual assistant (VA) shopping cart. Having pre-loaded your data, the cart adjusts your usual grocery list with voice input, reminds you to get your spouse’s favorite wine for an upcoming anniversary, and guides you through a personalized store route.

While we haven’t yet leveraged the full potential of perception AI, China and the US are already making incredible strides. Given China’s hardware advantage, Lee predicts China currently has a 60-40 edge over its American tech counterparts.

Now the go-to city for startups building robots, drones, wearable technology, and IoT infrastructure, Shenzhen has turned into a powerhouse for intelligent hardware, as I discussed last week. Turbocharging output of sensors and electronic parts via thousands of factories, Shenzhen’s skilled engineers can prototype and iterate new products at unprecedented scale and speed.

With the added fuel of Chinese government support and a relaxed Chinese attitude toward data privacy, China’s lead may even reach 80-20 in the next five years.

Jumping on this wave are companies like Xiaomi, which aims to turn bathrooms, kitchens, and living rooms into smart OMO environments. Having invested in 220 companies and incubated 29 startups that produce its products, Xiaomi surpassed 85 million intelligent home devices by the end of 2017, making it the world’s largest network of these connected products.

One KFC restaurant in China has even teamed up with Alipay (Alibaba’s mobile payments platform) to pioneer a ‘pay-with-your-face’ feature. Forget cash, cards, and cell phones, and let OMO do the work.

The Fourth Wave: Autonomous AI
But the most monumental—and unpredictable—wave is the fourth and final: autonomous AI.

Integrating all previous waves, autonomous AI gives machines the ability to sense and respond to the world around them, enabling AI to move and act productively.

While today’s machines can outperform us on repetitive tasks in structured and even unstructured environments (think Boston Dynamics’ humanoid Atlas or oncoming autonomous vehicles), machines with the power to see, hear, touch and optimize data will be a whole new ballgame.

Think: swarms of drones that can selectively spray and harvest entire farms with computer vision and remarkable dexterity, heat-resistant drones that can put out forest fires 100X more efficiently, or Level 5 autonomous vehicles that navigate smart roads and traffic systems all on their own.

While autonomous AI will first involve robots that create direct economic value—automating tasks on a one-to-one replacement basis—these intelligent machines will ultimately revamp entire industries from the ground up.

Kai-Fu Lee currently puts America in a commanding lead of 90-10 in autonomous AI, especially when it comes to self-driving vehicles. But Chinese government efforts are quickly ramping up the competition.

Already in China’s Zhejiang province, highway regulators and government officials have plans to build China’s first intelligent superhighway, outfitted with sensors, road-embedded solar panels and wireless communication between cars, roads and drivers.

Aimed at increasing transit efficiency by up to 30 percent while minimizing fatalities, the project may one day allow autonomous electric vehicles to continuously charge as they drive.

A similar government-fueled project involves Beijing’s new neighbor Xiong’an. Projected to take in over $580 billion in infrastructure spending over the next 20 years, Xiong’an New Area could one day become the world’s first city built around autonomous vehicles.

Baidu is already working with Xiong’an’s local government to build out this AI city with an environmental focus. Possibilities include sensor-geared cement, computer vision-enabled traffic lights, intersections with facial recognition, and parking lots-turned parks.

Lastly, Lee predicts China will almost certainly lead the charge in autonomous drones. Already, Shenzhen is home to premier drone maker DJI—a company I’ll be visiting with 24 top executives later this month as part of my annual China Platinum Trip.

Named “the best company I have ever encountered” by Chris Anderson, DJI owns an estimated 50 percent of the North American drone market, supercharged by Shenzhen’s extraordinary maker movement.

While the long-term Sino-US competitive balance in fourth wave AI remains to be seen, one thing is certain: in a matter of decades, we will witness the rise of AI-embedded cityscapes and autonomous machines that can interact with the real world and help solve today’s most pressing grand challenges.

Join Me
Webinar with Dr. Kai-Fu Lee: Dr. Kai-Fu Lee — one of the world’s most respected experts on AI — and I will discuss his latest book AI Superpowers: China, Silicon Valley, and the New World Order. Artificial Intelligence is reshaping the world as we know it. With U.S.-Sino competition heating up, who will own the future of technology? Register here for the free webinar on September 4th, 2018 from 11:00am–12:30pm PST.

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#432262 How We Can ‘Robot-Proof’ Education ...

Like millions of other individuals in the workforce, you’re probably wondering if you will one day be replaced by a machine. If you’re a student, you’re probably wondering if your chosen profession will even exist by the time you’ve graduated. From driving to legal research, there isn’t much that technology hasn’t already automated (or begun to automate). Many of us will need to adapt to this disruption in the workforce.

But it’s not enough for students and workers to adapt, become lifelong learners, and re-skill themselves. We also need to see innovation and initiative at an institutional and governmental level. According to research by The Economist, almost half of all jobs could be automated by computers within the next two decades, and no government in the world is prepared for it.

While many see the current trend in automation as a terrifying threat, others see it as an opportunity. In Robot-Proof: Higher Education in the Age of Artificial Intelligence, Northeastern University president Joseph Aoun proposes educating students in a way that will allow them to do the things that machines can’t. He calls for a new paradigm that teaches young minds “to invent, to create, and to discover”—filling the relevant needs of our world that robots simply can’t fill. Aoun proposes a much-needed novel framework that will allow us to “robot-proof” education.

Literacies and Core Cognitive Capacities of the Future
Aoun lays a framework for a new discipline, humanics, which discusses the important capacities and literacies for emerging education systems. At its core, the framework emphasizes our uniquely human abilities and strengths.

The three key literacies include data literacy (being able to manage and analyze big data), technological literacy (being able to understand exponential technologies and conduct computational thinking), and human literacy (being able to communicate and evaluate social, ethical, and existential impact).

Beyond the literacies, at the heart of Aoun’s framework are four cognitive capacities that are crucial to develop in our students if they are to be resistant to automation: critical thinking, systems thinking, entrepreneurship, and cultural agility.

“These capacities are mindsets rather than bodies of knowledge—mental architecture rather than mental furniture,” he writes. “Going forward, people will still need to know specific bodies of knowledge to be effective in the workplace, but that alone will not be enough when intelligent machines are doing much of the heavy lifting of information. To succeed, tomorrow’s employees will have to demonstrate a higher order of thought.”

Like many other experts in education, Joseph Aoun emphasizes the importance of critical thinking. This is important not just when it comes to taking a skeptical approach to information, but also being able to logically break down a claim or problem into multiple layers of analysis. We spend so much time teaching students how to answer questions that we often neglect to teach them how to ask questions. Asking questions—and asking good ones—is a foundation of critical thinking. Before you can solve a problem, you must be able to critically analyze and question what is causing it. This is why critical thinking and problem solving are coupled together.

The second capacity, systems thinking, involves being able to think holistically about a problem. The most creative problem-solvers and thinkers are able to take a multidisciplinary perspective and connect the dots between many different fields. According to Aoun, it “involves seeing across areas that machines might be able to comprehend individually but that they cannot analyze in an integrated way, as a whole.” It represents the absolute opposite of how most traditional curricula is structured with emphasis on isolated subjects and content knowledge.

Among the most difficult-to-automate tasks or professions is entrepreneurship.

In fact, some have gone so far as to claim that in the future, everyone will be an entrepreneur. Yet traditionally, initiative has been something students show in spite of or in addition to their schoolwork. For most students, developing a sense of initiative and entrepreneurial skills has often been part of their extracurricular activities. It needs to be at the core of our curricula, not a supplement to it. At its core, teaching entrepreneurship is about teaching our youth to solve complex problems with resilience, to become global leaders, and to solve grand challenges facing our species.

Finally, with an increasingly globalized world, there is a need for more workers with cultural agility, the ability to build amongst different cultural contexts and norms.

One of the major trends today is the rise of the contingent workforce. We are seeing an increasing percentage of full-time employees working on the cloud. Multinational corporations have teams of employees collaborating at different offices across the planet. Collaboration across online networks requires a skillset of its own. As education expert Tony Wagner points out, within these digital contexts, leadership is no longer about commanding with top-down authority, but rather about leading by influence.

An Emphasis on Creativity
The framework also puts an emphasis on experiential or project-based learning, wherein the heart of the student experience is not lectures or exams but solving real-life problems and learning by doing, creating, and executing. Unsurprisingly, humans continue to outdo machines when it comes to innovating and pushing intellectual, imaginative, and creative boundaries, making jobs involving these skills the hardest to automate.

In fact, technological trends are giving rise to what many thought leaders refer to as the imagination economy. This is defined as “an economy where intuitive and creative thinking create economic value, after logical and rational thinking have been outsourced to other economies.” Consequently, we need to develop our students’ creative abilities to ensure their success against machines.

In its simplest form, creativity represents the ability to imagine radical ideas and then go about executing them in reality.

In many ways, we are already living in our creative imaginations. Consider this: every invention or human construct—whether it be the spaceship, an architectural wonder, or a device like an iPhone—once existed as a mere idea, imagined in someone’s mind. The world we have designed and built around us is an extension of our imaginations and is only possible because of our creativity. Creativity has played a powerful role in human progress—now imagine what the outcomes would be if we tapped into every young mind’s creative potential.

The Need for a Radical Overhaul
What is clear from the recommendations of Aoun and many other leading thinkers in this space is that an effective 21st-century education system is radically different from the traditional systems we currently have in place. There is a dramatic contrast between these future-oriented frameworks and the way we’ve structured our traditional, industrial-era and cookie-cutter-style education systems.

It’s time for a change, and incremental changes or subtle improvements are no longer enough. What we need to see are more moonshots and disruption in the education sector. In a world of exponential growth and accelerating change, it is never too soon for a much-needed dramatic overhaul.

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