Tag Archives: general

#434781 What Would It Mean for AI to Become ...

As artificial intelligence systems take on more tasks and solve more problems, it’s hard to say which is rising faster: our interest in them or our fear of them. Futurist Ray Kurzweil famously predicted that “By 2029, computers will have emotional intelligence and be convincing as people.”

We don’t know how accurate this prediction will turn out to be. Even if it takes more than 10 years, though, is it really possible for machines to become conscious? If the machines Kurzweil describes say they’re conscious, does that mean they actually are?

Perhaps a more relevant question at this juncture is: what is consciousness, and how do we replicate it if we don’t understand it?

In a panel discussion at South By Southwest titled “How AI Will Design the Human Future,” experts from academia and industry discussed these questions and more.

Wait, What Is AI?
Most of AI’s recent feats—diagnosing illnesses, participating in debate, writing realistic text—involve machine learning, which uses statistics to find patterns in large datasets then uses those patterns to make predictions. However, “AI” has been used to refer to everything from basic software automation and algorithms to advanced machine learning and deep learning.

“The term ‘artificial intelligence’ is thrown around constantly and often incorrectly,” said Jennifer Strong, a reporter at the Wall Street Journal and host of the podcast “The Future of Everything.” Indeed, one study found that 40 percent of European companies that claim to be working on or using AI don’t actually use it at all.

Dr. Peter Stone, associate chair of computer science at UT Austin, was the study panel chair on the 2016 One Hundred Year Study on Artificial Intelligence (or AI100) report. Based out of Stanford University, AI100 is studying and anticipating how AI will impact our work, our cities, and our lives.

“One of the first things we had to do was define AI,” Stone said. They defined it as a collection of different technologies inspired by the human brain to be able to perceive their surrounding environment and figure out what actions to take given these inputs.

Modeling on the Unknown
Here’s the crazy thing about that definition (and about AI itself): we’re essentially trying to re-create the abilities of the human brain without having anything close to a thorough understanding of how the human brain works.

“We’re starting to pair our brains with computers, but brains don’t understand computers and computers don’t understand brains,” Stone said. Dr. Heather Berlin, cognitive neuroscientist and professor of psychiatry at the Icahn School of Medicine at Mount Sinai, agreed. “It’s still one of the greatest mysteries how this three-pound piece of matter can give us all our subjective experiences, thoughts, and emotions,” she said.

This isn’t to say we’re not making progress; there have been significant neuroscience breakthroughs in recent years. “This has been the stuff of science fiction for a long time, but now there’s active work being done in this area,” said Amir Husain, CEO and founder of Austin-based AI company Spark Cognition.

Advances in brain-machine interfaces show just how much more we understand the brain now than we did even a few years ago. Neural implants are being used to restore communication or movement capabilities in people who’ve been impaired by injury or illness. Scientists have been able to transfer signals from the brain to prosthetic limbs and stimulate specific circuits in the brain to treat conditions like Parkinson’s, PTSD, and depression.

But much of the brain’s inner workings remain a deep, dark mystery—one that will have to be further solved if we’re ever to get from narrow AI, which refers to systems that can perform specific tasks and is where the technology stands today, to artificial general intelligence, or systems that possess the same intelligence level and learning capabilities as humans.

The biggest question that arises here, and one that’s become a popular theme across stories and films, is if machines achieve human-level general intelligence, does that also mean they’d be conscious?

Wait, What Is Consciousness?
As valuable as the knowledge we’ve accumulated about the brain is, it seems like nothing more than a collection of disparate facts when we try to put it all together to understand consciousness.

“If you can replace one neuron with a silicon chip that can do the same function, then replace another neuron, and another—at what point are you still you?” Berlin asked. “These systems will be able to pass the Turing test, so we’re going to need another concept of how to measure consciousness.”

Is consciousness a measurable phenomenon, though? Rather than progressing by degrees or moving through some gray area, isn’t it pretty black and white—a being is either conscious or it isn’t?

This may be an outmoded way of thinking, according to Berlin. “It used to be that only philosophers could study consciousness, but now we can study it from a scientific perspective,” she said. “We can measure changes in neural pathways. It’s subjective, but depends on reportability.”

She described three levels of consciousness: pure subjective experience (“Look, the sky is blue”), awareness of one’s own subjective experience (“Oh, it’s me that’s seeing the blue sky”), and relating one subjective experience to another (“The blue sky reminds me of a blue ocean”).

“These subjective states exist all the way down the animal kingdom. As humans we have a sense of self that gives us another depth to that experience, but it’s not necessary for pure sensation,” Berlin said.

Husain took this definition a few steps farther. “It’s this self-awareness, this idea that I exist separate from everything else and that I can model myself,” he said. “Human brains have a wonderful simulator. They can propose a course of action virtually, in their minds, and see how things play out. The ability to include yourself as an actor means you’re running a computation on the idea of yourself.”

Most of the decisions we make involve envisioning different outcomes, thinking about how each outcome would affect us, and choosing which outcome we’d most prefer.

“Complex tasks you want to achieve in the world are tied to your ability to foresee the future, at least based on some mental model,” Husain said. “With that view, I as an AI practitioner don’t see a problem implementing that type of consciousness.”

Moving Forward Cautiously (But Not too Cautiously)
To be clear, we’re nowhere near machines achieving artificial general intelligence or consciousness, and whether a “conscious machine” is possible—not to mention necessary or desirable—is still very much up for debate.

As machine intelligence continues to advance, though, we’ll need to walk the line between progress and risk management carefully.

Improving the transparency and explainability of AI systems is one crucial goal AI developers and researchers are zeroing in on. Especially in applications that could mean the difference between life and death, AI shouldn’t advance without people being able to trace how it’s making decisions and reaching conclusions.

Medicine is a prime example. “There are already advances that could save lives, but they’re not being used because they’re not trusted by doctors and nurses,” said Stone. “We need to make sure there’s transparency.” Demanding too much transparency would also be a mistake, though, because it will hinder the development of systems that could at best save lives and at worst improve efficiency and free up doctors to have more face time with patients.

Similarly, self-driving cars have great potential to reduce deaths from traffic fatalities. But even though humans cause thousands of deadly crashes every day, we’re terrified by the idea of self-driving cars that are anything less than perfect. “If we only accept autonomous cars when there’s zero probability of an accident, then we will never accept them,” Stone said. “Yet we give 16-year-olds the chance to take a road test with no idea what’s going on in their brains.”

This brings us back to the fact that, in building tech modeled after the human brain—which has evolved over millions of years—we’re working towards an end whose means we don’t fully comprehend, be it something as basic as choosing when to brake or accelerate or something as complex as measuring consciousness.

“We shouldn’t charge ahead and do things just because we can,” Stone said. “The technology can be very powerful, which is exciting, but we have to consider its implications.”

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Posted in Human Robots

#434701 3 Practical Solutions to Offset ...

In recent years, the media has sounded the alarm about mass job loss to automation and robotics—some studies predict that up to 50 percent of current jobs or tasks could be automated in coming decades. While this topic has received significant attention, much of the press focuses on potential problems without proposing realistic solutions or considering new opportunities.

The economic impacts of AI, robotics, and automation are complex topics that require a more comprehensive perspective to understand. Is universal basic income, for example, the answer? Many believe so, and there are a number of experiments in progress. But it’s only one strategy, and without a sustainable funding source, universal basic income may not be practical.

As automation continues to accelerate, we’ll need a multi-pronged approach to ease the transition. In short, we need to update broad socioeconomic strategies for a new century of rapid progress. How, then, do we plan practical solutions to support these new strategies?

Take history as a rough guide to the future. Looking back, technology revolutions have three themes in common.

First, past revolutions each produced profound benefits to productivity, increasing human welfare. Second, technological innovation and technology diffusion have accelerated over time, each iteration placing more strain on the human ability to adapt. And third, machines have gradually replaced more elements of human work, with human societies adapting by moving into new forms of work—from agriculture to manufacturing to service, for example.

Public and private solutions, therefore, need to be developed to address each of these three components of change. Let’s explore some practical solutions for each in turn.

Figure 1. Technology’s structural impacts in the 21st century. Refer to Appendix I for quantitative charts and technological examples corresponding to the numbers (1-22) in each slice.
Solution 1: Capture New Opportunities Through Aggressive Investment
The rapid emergence of new technology promises a bounty of opportunity for the twenty-first century’s economic winners. This technological arms race is shaping up to be a global affair, and the winners will be determined in part by who is able to build the future economy fastest and most effectively. Both the private and public sectors have a role to play in stimulating growth.

At the country level, several nations have created competitive strategies to promote research and development investments as automation technologies become more mature.

Germany and China have two of the most notable growth strategies. Germany’s Industrie 4.0 plan targets a 50 percent increase in manufacturing productivity via digital initiatives, while halving the resources required. China’s Made in China 2025 national strategy sets ambitious targets and provides subsidies for domestic innovation and production. It also includes building new concept cities, investing in robotics capabilities, and subsidizing high-tech acquisitions abroad to become the leader in certain high-tech industries. For China, specifically, tech innovation is driven partially by a fear that technology will disrupt social structures and government control.

Such opportunities are not limited to existing economic powers. Estonia’s progress after the breakup of the Soviet Union is a good case study in transitioning to a digital economy. The nation rapidly implemented capitalistic reforms and transformed itself into a technology-centric economy in preparation for a massive tech disruption. Internet access was declared a right in 2000, and the country’s classrooms were outfitted for a digital economy, with coding as a core educational requirement starting at kindergarten. Internet broadband speeds in Estonia are among the fastest in the world. Accordingly, the World Bank now ranks Estonia as a high-income country.

Solution 2: Address Increased Rate of Change With More Nimble Education Systems
Education and training are currently not set for the speed of change in the modern economy. Schools are still based on a one-time education model, with school providing the foundation for a single lifelong career. With content becoming obsolete faster and rapidly escalating costs, this system may be unsustainable in the future. To help workers more smoothly transition from one job into another, for example, we need to make education a more nimble, lifelong endeavor.

Primary and university education may still have a role in training foundational thinking and general education, but it will be necessary to curtail rising price of tuition and increase accessibility. Massive open online courses (MooCs) and open-enrollment platforms are early demonstrations of what the future of general education may look like: cheap, effective, and flexible.

Georgia Tech’s online Engineering Master’s program (a fraction of the cost of residential tuition) is an early example in making university education more broadly available. Similarly, nanodegrees or microcredentials provided by online education platforms such as Udacity and Coursera can be used for mid-career adjustments at low cost. AI itself may be deployed to supplement the learning process, with applications such as AI-enhanced tutorials or personalized content recommendations backed by machine learning. Recent developments in neuroscience research could optimize this experience by perfectly tailoring content and delivery to the learner’s brain to maximize retention.

Finally, companies looking for more customized skills may take a larger role in education, providing on-the-job training for specific capabilities. One potential model involves partnering with community colleges to create apprenticeship-style learning, where students work part-time in parallel with their education. Siemens has pioneered such a model in four states and is developing a playbook for other companies to do the same.

Solution 3: Enhance Social Safety Nets to Smooth Automation Impacts
If predicted job losses to automation come to fruition, modernizing existing social safety nets will increasingly become a priority. While the issue of safety nets can become quickly politicized, it is worth noting that each prior technological revolution has come with corresponding changes to the social contract (see below).

The evolving social contract (U.S. examples)
– 1842 | Right to strike
– 1924 | Abolish child labor
– 1935 | Right to unionize
– 1938 | 40-hour work week
– 1962, 1974 | Trade adjustment assistance
– 1964 | Pay discrimination prohibited
– 1970 | Health and safety laws
– 21st century | AI and automation adjustment assistance?

Figure 2. Labor laws have historically adjusted as technology and society progressed

Solutions like universal basic income (no-strings-attached monthly payout to all citizens) are appealing in concept, but somewhat difficult to implement as a first measure in countries such as the US or Japan that already have high debt. Additionally, universal basic income may create dis-incentives to stay in the labor force. A similar cautionary tale in program design was the Trade Adjustment Assistance (TAA), which was designed to protect industries and workers from import competition shocks from globalization, but is viewed as a missed opportunity due to insufficient coverage.

A near-term solution could come in the form of graduated wage insurance (compensation for those forced to take a lower-paying job), including health insurance subsidies to individuals directly impacted by automation, with incentives to return to the workforce quickly. Another topic to tackle is geographic mismatch between workers and jobs, which can be addressed by mobility assistance. Lastly, a training stipend can be issued to individuals as means to upskill.

Policymakers can intervene to reverse recent historical trends that have shifted incomes from labor to capital owners. The balance could be shifted back to labor by placing higher taxes on capital—an example is the recently proposed “robot tax” where the taxation would be on the work rather than the individual executing it. That is, if a self-driving car performs the task that formerly was done by a human, the rideshare company will still pay the tax as if a human was driving.

Other solutions may involve distribution of work. Some countries, such as France and Sweden, have experimented with redistributing working hours. The idea is to cap weekly hours, with the goal of having more people employed and work more evenly spread. So far these programs have had mixed results, with lower unemployment but high costs to taxpayers, but are potential models that can continue to be tested.

We cannot stop growth, nor should we. With the roles in response to this evolution shifting, so should the social contract between the stakeholders. Government will continue to play a critical role as a stabilizing “thumb” in the invisible hand of capitalism, regulating and cushioning against extreme volatility, particularly in labor markets.

However, we already see business leaders taking on some of the role traditionally played by government—thinking about measures to remedy risks of climate change or economic proposals to combat unemployment—in part because of greater agility in adapting to change. Cross-disciplinary collaboration and creative solutions from all parties will be critical in crafting the future economy.

Note: The full paper this article is based on is available here.

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Posted in Human Robots

#434685 How Tech Will Let You Learn Anything, ...

Today, over 77 percent of Americans own a smartphone with access to the world’s information and near-limitless learning resources.

Yet nearly 36 million adults in the US are constrained by low literacy skills, excluding them from professional opportunities, prospects of upward mobility, and full engagement with their children’s education.

And beyond its direct impact, low literacy rates affect us all. Improving literacy among adults is predicted to save $230 billion in national healthcare costs and could result in US labor productivity increases of up to 2.5 percent.

Across the board, exponential technologies are making demonetized learning tools, digital training platforms, and literacy solutions more accessible than ever before.

With rising automation and major paradigm shifts underway in the job market, these tools not only promise to make today’s workforce more versatile, but could play an invaluable role in breaking the poverty cycles often associated with low literacy.

Just three years ago, the Barbara Bush Foundation for Family Literacy and the Dollar General Literacy Foundation joined forces to tackle this intractable problem, launching a $7 million Adult Literacy XPRIZE.

Challenging teams to develop smartphone apps that significantly increase literacy skills among adult learners in just 12 months, the competition brought five prize teams to the fore, each targeting multiple demographics across the nation.

Now, after four years of research, prototyping, testing, and evaluation, XPRIZE has just this week announced two grand prize winners: Learning Upgrade and People ForWords.

In this blog, I’ll be exploring the nuts and bolts of our two winning teams and how exponential technologies are beginning to address rapidly shifting workforce demands.

We’ll discuss:

Meeting 100 percent adult literacy rates
Retooling today’s workforce for tomorrow’s job market
Granting the gift of lifelong learning

Let’s dive in.

Adult Literacy XPRIZE
Emphasizing the importance of accessible mediums and scalability, the Adult Literacy XPRIZE called for teams to create mobile solutions that lower the barrier to entry, encourage persistence, develop relevant learning content, and can scale nationally.

Outperforming the competition in two key demographic groups in aggregate—native English speakers and English language learners—teams Learning Upgrade and People ForWords together claimed the prize.

To win, both organizations successfully generated the greatest gains between a pre- and post-test, administered one year apart to learners in a 12-month field test across Los Angeles, Dallas, and Philadelphia.

Prize money in hand, Learning Upgrade and People ForWords are now scaling up their solutions, each targeting a key demographic in America’s pursuit of adult literacy.

Based in San Diego, Learning Upgrade has developed an Android and iOS app that helps students learn English and math through video, songs, and gamification. Offering a total of 21 courses from kindergarten through adult education, Learning Upgrade touts a growing platform of over 900 lessons spanning English, reading, math, and even GED prep.

To further personalize each student’s learning, Learning Upgrade measures time-on-task and builds out formative performance assessments, granting teachers a quantified, real-time view of each student’s progress across both lessons and criteria.

Specialized in English reading skills, Dallas-based People ForWords offers a similarly delocalized model with its mobile game “Codex: Lost Words of Atlantis.” Based on an archaeological adventure storyline, the app features an immersive virtual environment.

Set in the Atlantis Library (now with a 3D rendering underway), Codex takes its students through narrative-peppered lessons covering everything from letter-sound practice to vocabulary reinforcement in a hidden object game.

But while both mobile apps have recruited initial piloting populations, the key to success is scale.

Using a similar incentive prize competition structure to drive recruitment, the second phase of the XPRIZE is a $1 million Barbara Bush Foundation Adult Literacy XPRIZE Communities Competition. For 15 months, the competition will challenge organizations, communities, and individuals alike to onboard adult learners onto both prize-winning platforms and fellow finalist team apps, AmritaCREATE and Cell-Ed.

Each awarded $125,000 for participation in the Communities Competition, AmritaCREATE and Cell-Ed bring yet other nuanced advantages to the table.

While AmritaCREATE curates culturally appropriate e-content relevant to given life skills, Cell-Ed takes a learn-on-the-go approach, offering micro-lessons, on-demand essential skills training, and individualized coaching on any mobile device, no internet required.

Although all these cases target slightly different demographics and problem niches, they converge upon common phenomena: mobility, efficiency, life skill relevance, personalized learning, and practicability.

And what better to scale these benefits than AI and immersive virtual environments?

In the case of education’s growing mobility, 5G and the explosion of connectivity speeds will continue to drive a learn-anytime-anywhere education model, whereby adult users learn on the fly, untethered to web access or rigid time strictures.

As I’ve explored in a previous blog on AI-crowd collaboration, we might also see the rise of AI learning consultants responsible for processing data on how you learn.

Quantifying and analyzing your interaction with course modules, where you get stuck, where you thrive, and what tools cause you ease or frustration, each user’s AI trainer might then issue personalized recommendations based on crowd feedback.

Adding a human touch, each app’s hired teaching consultants would thereby be freed to track many more students’ progress at once, vetting AI-generated tips and adjustments, and offering life coaching along the way.

Lastly, virtual learning environments—and, one day, immersive VR—will facilitate both speed and retention, two of the most critical constraints as learners age.

As I often reference, people generally remember only 10 percent of what we see, 20 percent of what we hear, and 30 percent of what we read…. But over a staggering 90 percent of what we do or experience.

By introducing gamification, immersive testing activities, and visually rich sensory environments, adult literacy platforms have a winning chance at scalability, retention, and user persistence.

Exponential Tools: Training and Retooling a Dynamic Workforce
Beyond literacy, however, virtual and augmented reality have already begun disrupting the professional training market.

As projected by ABI Research, the enterprise VR training market is on track to exceed $6.3 billion in value by 2022.

Leading the charge, Walmart has already implemented VR across 200 Academy training centers, running over 45 modules and simulating everything from unusual customer requests to a Black Friday shopping rush.

Then in September of last year, Walmart committed to a 17,000-headset order of the Oculus Go to equip every US Supercenter, neighborhood market, and discount store with VR-based employee training.

In the engineering world, Bell Helicopter is using VR to massively expedite development and testing of its latest aircraft, FCX-001. Partnering with Sector 5 Digital and HTC VIVE, Bell found it could concentrate a typical six-year aircraft design process into the course of six months, turning physical mockups into CAD-designed virtual replicas.

But beyond the design process itself, Bell is now one of a slew of companies pioneering VR pilot tests and simulations with real-world accuracy. Seated in a true-to-life virtual cockpit, pilots have now tested countless iterations of the FCX-001 in virtual flight, drawing directly onto the 3D model and enacting aircraft modifications in real time.

And in an expansion of our virtual senses, several key players are already working on haptic feedback. In the case of VR flight, French company Go Touch VR is now partnering with software developer FlyInside on fingertip-mounted haptic tech for aviation.

Dramatically reducing time and trouble required for VR-testing pilots, they aim to give touch-based confirmation of every switch and dial activated on virtual flights, just as one would experience in a full-sized cockpit mockup. Replicating texture, stiffness, and even the sensation of holding an object, these piloted devices contain a suite of actuators to simulate everything from a light touch to higher-pressured contact, all controlled by gaze and finger movements.

When it comes to other high-risk simulations, virtual and augmented reality have barely scratched the surface.
Firefighters can now combat virtual wildfires with new platforms like FLAIM Trainer or TargetSolutions. And thanks to the expansion of medical AR/VR services like 3D4Medical or Echopixel, surgeons might soon perform operations on annotated organs and magnified incision sites, speeding up reaction times and vastly improving precision.

But perhaps most urgently, virtual reality will offer an immediate solution to today’s constant industry turnover and large-scale re-education demands.

VR educational facilities with exact replicas of anything from large industrial equipment to minute circuitry will soon give anyone a second chance at the 21st-century job market.

Want to become an electric, autonomous vehicle mechanic at age 44? Throw on a demonetized VR module and learn by doing, testing your prototype iterations at almost zero cost and with no risk of harming others.

Want to be a plasma physicist and play around with a virtual nuclear fusion reactor? Now you’ll be able to simulate results and test out different tweaks, logging Smart Educational Record credits in the process.

As tomorrow’s career model shifts from a “one-and-done graduate degree” to continuous lifelong education, professional VR-based re-education will allow for a continuous education loop, reducing the barrier to entry for anyone wanting to try their hand at a new industry.

Learn Anything, Anytime, at Any Age
As VR and artificial intelligence converge with demonetized mobile connectivity, we are finally witnessing an era in which no one will be left behind.

Whether in pursuit of fundamental life skills, professional training, linguistic competence, or specialized retooling, users of all ages, career paths, income brackets, and goals are now encouraged to be students, no longer condemned to stagnancy.

Traditional constraints need no longer prevent non-native speakers from gaining an equal foothold, or specialists from pivoting into new professions, or low-income parents from staking new career paths.

As exponential technologies drive democratized access, bolstering initiatives such as the Barbara Bush Foundation Adult Literacy XPRIZE are blazing the trail to make education a scalable priority for all.

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Posted in Human Robots

#434616 What Games Are Humans Still Better at ...

Artificial intelligence (AI) systems’ rapid advances are continually crossing rows off the list of things humans do better than our computer compatriots.

AI has bested us at board games like chess and Go, and set astronomically high scores in classic computer games like Ms. Pacman. More complex games form part of AI’s next frontier.

While a team of AI bots developed by OpenAI, known as the OpenAI Five, ultimately lost to a team of professional players last year, they have since been running rampant against human opponents in Dota 2. Not to be outdone, Google’s DeepMind AI recently took on—and beat—several professional players at StarCraft II.

These victories beg the questions: what games are humans still better at than AI? And for how long?

The Making Of AlphaStar
DeepMind’s results provide a good starting point in a search for answers. The version of its AI for StarCraft II, dubbed AlphaStar, learned to play the games through supervised learning and reinforcement learning.

First, AI agents were trained by analyzing and copying human players, learning basic strategies. The initial agents then played each other in a sort of virtual death match where the strongest agents stayed on. New iterations of the agents were developed and entered the competition. Over time, the agents became better and better at the game, learning new strategies and tactics along the way.

One of the advantages of AI is that it can go through this kind of process at superspeed and quickly develop better agents. DeepMind researchers estimate that the AlphaStar agents went through the equivalent of roughly 200 years of game time in about 14 days.

Cheating or One Hand Behind the Back?
The AlphaStar AI agents faced off against human professional players in a series of games streamed on YouTube and Twitch. The AIs trounced their human opponents, winning ten games on the trot, before pro player Grzegorz “MaNa” Komincz managed to salvage some pride for humanity by winning the final game. Experts commenting on AlphaStar’s performance used words like “phenomenal” and “superhuman”—which was, to a degree, where things got a bit problematic.

AlphaStar proved particularly skilled at controlling and directing units in battle, known as micromanagement. One reason was that it viewed the whole game map at once—something a human player is not able to do—which made it seemingly able to control units in different areas at the same time. DeepMind researchers said the AIs only focused on a single part of the map at any given time, but interestingly, AlphaStar’s AI agent was limited to a more restricted camera view during the match “MaNA” won.

Potentially offsetting some of this advantage was the fact that AlphaStar was also restricted in certain ways. For example, it was prevented from performing more clicks per minute than a human player would be able to.

Where AIs Struggle
Games like StarCraft II and Dota 2 throw a lot of challenges at AIs. Complex game theory/ strategies, operating with imperfect/incomplete information, undertaking multi-variable and long-term planning, real-time decision-making, navigating a large action space, and making a multitude of possible decisions at every point in time are just the tip of the iceberg. The AIs’ performance in both games was impressive, but also highlighted some of the areas where they could be said to struggle.

In Dota 2 and StarCraft II, AI bots have seemed more vulnerable in longer games, or when confronted with surprising, unfamiliar strategies. They seem to struggle with complexity over time and improvisation/adapting to quick changes. This could be tied to how AIs learn. Even within the first few hours of performing a task, humans tend to gain a sense of familiarity and skill that takes an AI much longer. We are also better at transferring skill from one area to another. In other words, experience playing Dota 2 can help us become good at StarCraft II relatively quickly. This is not the case for AI—yet.

Dwindling Superiority
While the battle between AI and humans for absolute superiority is still on in Dota 2 and StarCraft II, it looks likely that AI will soon reign supreme. Similar things are happening to other types of games.

In 2017, a team from Carnegie Mellon University pitted its Libratus AI against four professionals. After 20 days of No Limit Texas Hold’em, Libratus was up by $1.7 million. Another likely candidate is the destroyer of family harmony at Christmas: Monopoly.

Poker involves bluffing, while Monopoly involves negotiation—skills you might not think AI would be particularly suited to handle. However, an AI experiment at Facebook showed that AI bots are more than capable of undertaking such tasks. The bots proved skilled negotiators, and developed negotiating strategies like pretending interest in one object while they were interested in another altogether—bluffing.

So, what games are we still better at than AI? There is no precise answer, but the list is getting shorter at a rapid pace.

The Aim Of the Game
While AI’s mastery of games might at first glance seem an odd area to focus research on, the belief is that the way AI learn to master a game is transferrable to other areas.

For example, the Libratus poker-playing AI employed strategies that could work in financial trading or political negotiations. The same applies to AlphaStar. As Oriol Vinyals, co-leader of the AlphaStar project, told The Verge:

“First and foremost, the mission at DeepMind is to build an artificial general intelligence. […] To do so, it’s important to benchmark how our agents perform on a wide variety of tasks.”

A 2017 survey of more than 350 AI researchers predicts AI could be a better driver than humans within ten years. By the middle of the century, AI will be able to write a best-selling novel, and a few years later, it will be better than humans at surgery. By the year 2060, AI may do everything better than us.

Whether you think this is a good or a bad thing, it’s worth noting that AI has an often overlooked ability to help us see things differently. When DeepMind’s AlphaGo beat human Go champion Lee Sedol, the Go community learned from it, too. Lee himself went on a win streak after the match with AlphaGo. The same is now happening within the Dota 2 and StarCraft II communities that are studying the human vs. AI games intensely.

More than anything, AI’s recent gaming triumphs illustrate how quickly artificial intelligence is developing. In 1997, Dr. Piet Hut, an astrophysicist at the Institute for Advanced Study at Princeton and a GO enthusiast, told the New York Times that:

”It may be a hundred years before a computer beats humans at Go—maybe even longer.”

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Posted in Human Robots

#434569 From Parkour to Surgery, Here Are the ...

The robot revolution may not be here quite yet, but our mechanical cousins have made some serious strides. And now some of the leading experts in the field have provided a rundown of what they see as the 10 most exciting recent developments.

Compiled by the editors of the journal Science Robotics, the list includes some of the most impressive original research and innovative commercial products to make a splash in 2018, as well as a couple from 2017 that really changed the game.

1. Boston Dynamics’ Atlas doing parkour

It seems like barely a few months go by without Boston Dynamics rewriting the book on what a robot can and can’t do. Last year they really outdid themselves when they got their Atlas humanoid robot to do parkour, leaping over logs and jumping between wooden crates.

Atlas’s creators have admitted that the videos we see are cherry-picked from multiple attempts, many of which don’t go so well. But they say they’re meant to be inspirational and aspirational rather than an accurate picture of where robotics is today. And combined with the company’s dog-like Spot robot, they are certainly pushing boundaries.

2. Intuitive Surgical’s da Vinci SP platform
Robotic surgery isn’t new, but the technology is improving rapidly. Market leader Intuitive’s da Vinci surgical robot was first cleared by the FDA in 2000, but since then it’s come a long way, with the company now producing three separate systems.

The latest addition is the da Vinci SP (single port) system, which is able to insert three instruments into the body through a single 2.5cm cannula (tube) bringing a whole new meaning to minimally invasive surgery. The system was granted FDA clearance for urological procedures last year, and the company has now started shipping the new system to customers.

3. Soft robot that navigates through growth

Roboticists have long borrowed principles from the animal kingdom, but a new robot design that mimics the way plant tendrils and fungi mycelium move by growing at the tip has really broken the mold on robot navigation.

The editors point out that this is the perfect example of bio-inspired design; the researchers didn’t simply copy nature, they took a general principle and expanded on it. The tube-like robot unfolds from the front as pneumatic pressure is applied, but unlike a plant, it can grow at the speed of an animal walking and can navigate using visual feedback from a camera.

4. 3D printed liquid crystal elastomers for soft robotics
Soft robotics is one of the fastest-growing sub-disciplines in the field, but powering these devices without rigid motors or pumps is an ongoing challenge. A variety of shape-shifting materials have been proposed as potential artificial muscles, including liquid crystal elastomeric actuators.

Harvard engineers have now demonstrated that these materials can be 3D printed using a special ink that allows the designer to easily program in all kinds of unusual shape-shifting abilities. What’s more, their technique produces actuators capable of lifting significantly more weight than previous approaches.

5. Muscle-mimetic, self-healing, and hydraulically amplified actuators
In another effort to find a way to power soft robots, last year researchers at the University of Colorado Boulder designed a series of super low-cost artificial muscles that can lift 200 times their own weight and even heal themselves.

The devices rely on pouches filled with a liquid that makes them contract with the force and speed of mammalian skeletal muscles when a voltage is applied. The most promising for robotics applications is the so-called Peano-HASEL, which features multiple rectangular pouches connected in series that contract linearly, just like real muscle.

6. Self-assembled nanoscale robot from DNA

While you may think of robots as hulking metallic machines, a substantial number of scientists are working on making nanoscale robots out of DNA. And last year German researchers built the first remote-controlled DNA robotic arm.

They created a length of tightly-bound DNA molecules to act as the arm and attached it to a DNA base plate via a flexible joint. Because DNA carries a charge, they were able to get the arm to swivel around like the hand of a clock by applying a voltage and switch direction by reversing that voltage. The hope is that this arm could eventually be used to build materials piece by piece at the nanoscale.

7. DelFly nimble bioinspired robotic flapper

Robotics doesn’t only borrow from biology—sometimes it gives back to it, too. And a new flapping-winged robot designed by Dutch engineers that mimics the humble fruit fly has done just that, by revealing how the animals that inspired it carry out predator-dodging maneuvers.

The lab has been building flapping robots for years, but this time they ditched the airplane-like tail used to control previous incarnations. Instead, they used insect-inspired adjustments to the motions of its twin pairs of flapping wings to hover, pitch, and roll with the agility of a fruit fly. That has provided a useful platform for investigating insect flight dynamics, as well as more practical applications.

8. Soft exosuit wearable robot

Exoskeletons could prevent workplace injuries, help people walk again, and even boost soldiers’ endurance. Strapping on bulky equipment isn’t ideal, though, so researchers at Harvard are working on a soft exoskeleton that combines specially-designed textiles, sensors, and lightweight actuators.

And last year the team made an important breakthrough by combining their novel exoskeleton with a machine-learning algorithm that automatically tunes the device to the user’s particular walking style. Using physiological data, it is able to adjust when and where the device needs to deliver a boost to the user’s natural movements to improve walking efficiency.

9. Universal Robots (UR) e-Series Cobots
Robots in factories are nothing new. The enormous mechanical arms you see in car factories normally have to be kept in cages to prevent them from accidentally crushing people. In recent years there’s been growing interest in “co-bots,” collaborative robots designed to work side-by-side with their human colleagues and even learn from them.

Earlier this year saw the demise of ReThink robotics, the pioneer of the approach. But the simple single arm devices made by Danish firm Universal Robotics are becoming ubiquitous in workshops and warehouses around the world, accounting for about half of global co-bot sales. Last year they released their latest e-Series, with enhanced safety features and force/torque sensing.

10. Sony’s aibo
After a nearly 20-year hiatus, Sony’s robotic dog aibo is back, and it’s had some serious upgrades. As well as a revamp to its appearance, the new robotic pet takes advantage of advances in AI, with improved environmental and command awareness and the ability to develop a unique character based on interactions with its owner.

The editors note that this new context awareness mark the device out as a significant evolution in social robots, which many hope could aid in childhood learning or provide companionship for the elderly.

Image Credit: DelFly Nimble / CC BY – SA 4.0 Continue reading

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