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#437809 Q&A: The Masterminds Behind ...
Illustration: iStockphoto
Getting a car to drive itself is undoubtedly the most ambitious commercial application of artificial intelligence (AI). The research project was kicked into life by the 2004 DARPA Urban Challenge and then taken up as a business proposition, first by Alphabet, and later by the big automakers.
The industry-wide effort vacuumed up many of the world’s best roboticists and set rival companies on a multibillion-dollar acquisitions spree. It also launched a cycle of hype that paraded ever more ambitious deadlines—the most famous of which, made by Alphabet’s Sergei Brin in 2012, was that full self-driving technology would be ready by 2017. Those deadlines have all been missed.
Much of the exhilaration was inspired by the seeming miracles that a new kind of AI—deep learning—was achieving in playing games, recognizing faces, and transliterating voices. Deep learning excels at tasks involving pattern recognition—a particular challenge for older, rule-based AI techniques. However, it now seems that deep learning will not soon master the other intellectual challenges of driving, such as anticipating what human beings might do.
Among the roboticists who have been involved from the start are Gill Pratt, the chief executive officer of Toyota Research Institute (TRI) , formerly a program manager at the Defense Advanced Research Projects Agency (DARPA); and Wolfram Burgard, vice president of automated driving technology for TRI and president of the IEEE Robotics and Automation Society. The duo spoke with IEEE Spectrum’s Philip Ross at TRI’s offices in Palo Alto, Calif.
This interview has been condensed and edited for clarity.
IEEE Spectrum: How does AI handle the various parts of the self-driving problem?
Photo: Toyota
Gill Pratt
Gill Pratt: There are three different systems that you need in a self-driving car: It starts with perception, then goes to prediction, and then goes to planning.
The one that by far is the most problematic is prediction. It’s not prediction of other automated cars, because if all cars were automated, this problem would be much more simple. How do you predict what a human being is going to do? That’s difficult for deep learning to learn right now.
Spectrum: Can you offset the weakness in prediction with stupendous perception?
Photo: Toyota Research Institute for Burgard
Wolfram Burgard
Wolfram Burgard: Yes, that is what car companies basically do. A camera provides semantics, lidar provides distance, radar provides velocities. But all this comes with problems, because sometimes you look at the world from different positions—that’s called parallax. Sometimes you don’t know which range estimate that pixel belongs to. That might make the decision complicated as to whether that is a person painted onto the side of a truck or whether this is an actual person.
With deep learning there is this promise that if you throw enough data at these networks, it’s going to work—finally. But it turns out that the amount of data that you need for self-driving cars is far larger than we expected.
Spectrum: When do deep learning’s limitations become apparent?
Pratt: The way to think about deep learning is that it’s really high-performance pattern matching. You have input and output as training pairs; you say this image should lead to that result; and you just do that again and again, for hundreds of thousands, millions of times.
Here’s the logical fallacy that I think most people have fallen prey to with deep learning. A lot of what we do with our brains can be thought of as pattern matching: “Oh, I see this stop sign, so I should stop.” But it doesn’t mean all of intelligence can be done through pattern matching.
“I asked myself, if all of those cars had automated drive, how good would they have to be to tolerate the number of crashes that would still occur?”
—Gill Pratt, Toyota Research Institute
For instance, when I’m driving and I see a mother holding the hand of a child on a corner and trying to cross the street, I am pretty sure she’s not going to cross at a red light and jaywalk. I know from my experience being a human being that mothers and children don’t act that way. On the other hand, say there are two teenagers—with blue hair, skateboards, and a disaffected look. Are they going to jaywalk? I look at that, you look at that, and instantly the probability in your mind that they’ll jaywalk is much higher than for the mother holding the hand of the child. It’s not that you’ve seen 100,000 cases of young kids—it’s that you understand what it is to be either a teenager or a mother holding a child’s hand.
You can try to fake that kind of intelligence. If you specifically train a neural network on data like that, you could pattern-match that. But you’d have to know to do it.
Spectrum: So you’re saying that when you substitute pattern recognition for reasoning, the marginal return on the investment falls off pretty fast?
Pratt: That’s absolutely right. Unfortunately, we don’t have the ability to make an AI that thinks yet, so we don’t know what to do. We keep trying to use the deep-learning hammer to hammer more nails—we say, well, let’s just pour more data in, and more data.
Spectrum: Couldn’t you train the deep-learning system to recognize teenagers and to assign the category a high propensity for jaywalking?
Burgard: People have been doing that. But it turns out that these heuristics you come up with are extremely hard to tweak. Also, sometimes the heuristics are contradictory, which makes it extremely hard to design these expert systems based on rules. This is where the strength of the deep-learning methods lies, because somehow they encode a way to see a pattern where, for example, here’s a feature and over there is another feature; it’s about the sheer number of parameters you have available.
Our separation of the components of a self-driving AI eases the development and even the learning of the AI systems. Some companies even think about using deep learning to do the job fully, from end to end, not having any structure at all—basically, directly mapping perceptions to actions.
Pratt: There are companies that have tried it; Nvidia certainly tried it. In general, it’s been found not to work very well. So people divide the problem into blocks, where we understand what each block does, and we try to make each block work well. Some of the blocks end up more like the expert system we talked about, where we actually code things, and other blocks end up more like machine learning.
Spectrum: So, what’s next—what new technique is in the offing?
Pratt: If I knew the answer, we’d do it. [Laughter]
Spectrum: You said that if all cars on the road were automated, the problem would be easy. Why not “geofence” the heck out of the self-driving problem, and have areas where only self-driving cars are allowed?
Pratt: That means putting in constraints on the operational design domain. This includes the geography—where the car should be automated; it includes the weather, it includes the level of traffic, it includes speed. If the car is going slow enough to avoid colliding without risking a rear-end collision, that makes the problem much easier. Street trolleys operate with traffic still in some parts of the world, and that seems to work out just fine. People learn that this vehicle may stop at unexpected times. My suspicion is, that is where we’ll see Level 4 autonomy in cities. It’s going to be in the lower speeds.
“We are now in the age of deep learning, and we don’t know what will come after.”
—Wolfram Burgard, Toyota Research Institute
That’s a sweet spot in the operational design domain, without a doubt. There’s another one at high speed on a highway, because access to highways is so limited. But unfortunately there is still the occasional debris that suddenly crosses the road, and the weather gets bad. The classic example is when somebody irresponsibly ties a mattress to the top of a car and it falls off; what are you going to do? And the answer is that terrible things happen—even for humans.
Spectrum: Learning by doing worked for the first cars, the first planes, the first steam boilers, and even the first nuclear reactors. We ran risks then; why not now?
Pratt: It has to do with the times. During the era where cars took off, all kinds of accidents happened, women died in childbirth, all sorts of diseases ran rampant; the expected characteristic of life was that bad things happened. Expectations have changed. Now the chance of dying in some freak accident is quite low because of all the learning that’s gone on, the OSHA [Occupational Safety and Health Administration] rules, UL code for electrical appliances, all the building standards, medicine.
Furthermore—and we think this is very important—we believe that empathy for a human being at the wheel is a significant factor in public acceptance when there is a crash. We don’t know this for sure—it’s a speculation on our part. I’ve driven, I’ve had close calls; that could have been me that made that mistake and had that wreck. I think people are more tolerant when somebody else makes mistakes, and there’s an awful crash. In the case of an automated car, we worry that that empathy won’t be there.
Photo: Toyota
Toyota is using this
Platform 4 automated driving test vehicle, based on the Lexus LS, to develop Level-4 self-driving capabilities for its “Chauffeur” project.
Spectrum: Toyota is building a system called Guardian to back up the driver, and a more futuristic system called Chauffeur, to replace the driver. How can Chauffeur ever succeed? It has to be better than a human plus Guardian!
Pratt: In the discussions we’ve had with others in this field, we’ve talked about that a lot. What is the standard? Is it a person in a basic car? Or is it a person with a car that has active safety systems in it? And what will people think is good enough?
These systems will never be perfect—there will always be some accidents, and no matter how hard we try there will still be occasions where there will be some fatalities. At what threshold are people willing to say that’s okay?
Spectrum: You were among the first top researchers to warn against hyping self-driving technology. What did you see that so many other players did not?
Pratt: First, in my own case, during my time at DARPA I worked on robotics, not cars. So I was somewhat of an outsider. I was looking at it from a fresh perspective, and that helps a lot.
Second, [when I joined Toyota in 2015] I was joining a company that is very careful—even though we have made some giant leaps—with the Prius hybrid drive system as an example. Even so, in general, the philosophy at Toyota is kaizen—making the cars incrementally better every single day. That care meant that I was tasked with thinking very deeply about this thing before making prognostications.
And the final part: It was a new job for me. The first night after I signed the contract I felt this incredible responsibility. I couldn’t sleep that whole night, so I started to multiply out the numbers, all using a factor of 10. How many cars do we have on the road? Cars on average last 10 years, though ours last 20, but let’s call it 10. They travel on an order of 10,000 miles per year. Multiply all that out and you get 10 to the 10th miles per year for our fleet on Planet Earth, a really big number. I asked myself, if all of those cars had automated drive, how good would they have to be to tolerate the number of crashes that would still occur? And the answer was so incredibly good that I knew it would take a long time. That was five years ago.
Burgard: We are now in the age of deep learning, and we don’t know what will come after. We are still making progress with existing techniques, and they look very promising. But the gradient is not as steep as it was a few years ago.
Pratt: There isn’t anything that’s telling us that it can’t be done; I should be very clear on that. Just because we don’t know how to do it doesn’t mean it can’t be done. Continue reading
Posted in Human Robots
#437667 17 Teams to Take Part in DARPA’s ...
Among all of the other in-person events that have been totally wrecked by COVID-19 is the Cave Circuit of the DARPA Subterranean Challenge. DARPA has already hosted the in-person events for the Tunnel and Urban SubT circuits (see our previous coverage here), and the plan had always been for a trio of events representing three uniquely different underground environments in advance of the SubT Finals, which will somehow combine everything into one bonkers course.
While the SubT Urban Circuit event snuck in just under the lockdown wire in late February, DARPA made the difficult (but prudent) decision to cancel the in-person Cave Circuit event. What this means is that there will be no Systems Track Cave competition, which is a serious disappointment—we were very much looking forward to watching teams of robots navigating through an entirely unpredictable natural environment with a lot of verticality. Fortunately, DARPA is still running a Virtual Cave Circuit, and 17 teams will be taking part in this competition featuring a simulated cave environment that’s as dynamic and detailed as DARPA can make it.
From DARPA’s press releases:
DARPA’s Subterranean (SubT) Challenge will host its Cave Circuit Virtual Competition, which focuses on innovative solutions to map, navigate, and search complex, simulated cave environments November 17. Qualified teams have until Oct. 15 to develop and submit software-based solutions for the Cave Circuit via the SubT Virtual Portal, where their technologies will face unknown cave environments in the cloud-based SubT Simulator. Until then, teams can refine their roster of selected virtual robot models, choose sensor payloads, and continue to test autonomy approaches to maximize their score.
The Cave Circuit also introduces new simulation capabilities, including digital twins of Systems Competition robots to choose from, marsupial-style platforms combining air and ground robots, and breadcrumb nodes that can be dropped by robots to serve as communications relays. Each robot configuration has an associated cost, measured in SubT Credits – an in-simulation currency – based on performance characteristics such as speed, mobility, sensing, and battery life.
Each team’s simulated robots must navigate realistic caves, with features including natural terrain and dynamic rock falls, while they search for and locate various artifacts on the course within five meters of accuracy to score points during a 60-minute timed run. A correct report is worth one point. Each course contains 20 artifacts, which means each team has the potential for a maximum score of 20 points. Teams can leverage numerous practice worlds and even build their own worlds using the cave tiles found in the SubT Tech Repo to perfect their approach before they submit one official solution for scoring. The DARPA team will then evaluate the solution on a set of hidden competition scenarios.
Of the 17 qualified teams (you can see all of them here), there are a handful that we’ll quickly point out. Team BARCS, from Michigan Tech, was the winner of the SubT Virtual Urban Circuit, meaning that they may be the team to beat on Cave as well, although the course is likely to be unique enough that things will get interesting. Some Systems Track teams to watch include Coordinated Robotics, CTU-CRAS-NORLAB, MARBLE, NUS SEDS, and Robotika, and there are also a handful of brand new teams as well.
Now, just because there’s no dedicated Cave Circuit for the Systems Track teams, it doesn’t mean that there won’t be a Cave component (perhaps even a significant one) in the final event, which as far as we know is still scheduled to happen in fall of next year. We’ve heard that many of the Systems Track teams have been testing out their robots in caves anyway, and as the virtual event gets closer, we’ll be doing a sort of Virtual Systems Track series that highlights how different teams are doing mock Cave Circuits in caves they’ve found for themselves.
For more, we checked in with DARPA SubT program manager Dr. Timothy H. Chung.
IEEE Spectrum: Was it a difficult decision to cancel the Systems Track for Cave?
Tim Chung: The decision to go virtual only was heart wrenching, because I think DARPA’s role is to offer up opportunities that may be unimaginable for some of our competitors, like opening up a cave-type site for this competition. We crawled and climbed through a number of these sites, and I share the sense of disappointment that both our team and the competitors have that we won’t be able to share all the advances that have been made since the Urban Circuit. But what we’ve been able to do is pour a lot of our energy and the insights that we got from crawling around in those caves into what’s going to be a really great opportunity on the Virtual Competition side. And whether it’s a global pandemic, or just lack of access to physical sites like caves, virtual environments are an opportunity that we want to develop.
“The simulator offers us a chance to look at where things could be … it really allows for us to find where some of those limits are in the technology based only on our imagination.”
—Timothy H. Chung, DARPA
What kind of new features will be included in the Virtual Cave Circuit for this competition?
I’m really excited about these particular features because we’re seeing an opportunity for increased synergy between the physical and the virtual. The first I’d say is that we scanned some of the Systems Track robots using photogrammetry and combined that with some additional models that we got from the systems competitors themselves to turn their systems robots into virtual models. We often talk about the sim to real transfer and how successful we can get a simulation to transfer over to the physical world, but now we’ve taken something from the physical world and made it virtual. We’ve validated the controllers as well as the kinematics of the robots, we’ve iterated with the systems competitors themselves, and now we have these 13 robots (air and ground) in the SubT Tech Repo that now all virtual competitors can take advantage of.
We also have additional robot capability. Those comms bread crumbs are common among many of the competitors, so we’ve adopted that in the virtual world, and now you have comms relay nodes that are baked in to the SubT Simulator—you can have either six or twelve comms nodes that you can drop from a variety of our ground robot platforms. We have the marsupial deployment capability now, so now we have parent ground robots that can be mixed and matched with different child drones to become marsupial pairs.
And this is something I’ve been planning for for a while: we now have the ability to trigger things like rock falls. They still don’t quite look like Indiana Jones with the boulder coming down the corridor, but this comes really close. In addition to it just being an interesting and realistic consideration, we get to really dynamically test and stress the robots’ ability to navigate and recognize that something has changed in the environment and respond to it.
Image: DARPA
DARPA is still running a Virtual Cave Circuit, and 17 teams will be taking part in this competition featuring a simulated cave environment.
No simulation is perfect, so can you talk to us about what kinds of things aren’t being simulated right now? Where does the simulator not match up to reality?
I think that question is foundational to any conversation about simulation. I’ll give you a couple of examples:
We have the ability to represent wholesale damage to a robot, but it’s not at the actuator or component level. So there’s not a reliability model, although I think that would be really interesting to incorporate so that you could do assessments on things like mean time to failure. But if a robot falls off a ledge, it can be disabled by virtue of being too damaged to continue.
With communications, and this is one that’s near and dear not only to my heart but also to all of those that have lived through developing communication systems and robotic systems, we’ve gone through and conducted RF surveys of underground environments to get a better handle on what propagation effects are. There’s a lot of research that has gone into this, and trying to carry through some of that realism, we do have path loss models for RF communications baked into the SubT Simulator. For example, when you drop a bread crumb node, it’s using a path loss model so that it can represent the degradation of signal as you go farther into a cave. Now, we’re not modeling it at the Maxwell equations level, which I think would be awesome, but we’re not quite there yet.
We do have things like battery depletion, sensor degradation to the extent that simulators can degrade sensor inputs, and things like that. It’s just amazing how close we can get in some places, and how far away we still are in others, and I think showing where the limits are of how far you can get simulation is all part and parcel of why SubT Challenge wants to have both System and Virtual tracks. Simulation can be an accelerant, but it’s not going to be the panacea for development and innovation, and I think all the competitors are cognizant those limitations.
One of the most amazing things about the SubT Virtual Track is that all of the robots operate fully autonomously, without the human(s) in the loop that the System Track teams have when they compete. Why make the Virtual Track even more challenging in that way?
I think it’s one of the defining, delineating attributes of the Virtual Track. Our continued vision for the simulation side is that the simulator offers us a chance to look at where things could be, and allows for us to explore things like larger scales, or increased complexity, or types of environments that we can’t physically gain access to—it really allows for us to find where some of those limits are in the technology based only on our imagination, and this is one of the intrinsic values of simulation.
But I think finding a way to incorporate human input, or more generally human factors like teleoperation interfaces and the in-situ stress that you might not be able to recreate in the context of a virtual competition provided a good reason for us to delineate the two competitions, with the Virtual Competition really being about the role of fully autonomous or self-sufficient systems going off and doing their solution without human guidance, while also acknowledging that the real world has conditions that would not necessarily be represented by a fully simulated version. Having said that, I think cognitive engineering still has an incredibly important role to play in human robot interaction.
What do we have to look forward to during the Virtual Competition Showcase?
We have a number of additional features and capabilities that we’ve baked into the simulator that will allow for us to derive some additional insights into our competition runs. Those insights might involve things like the performance of one or more robots in a given scenario, or the impact of the environment on different types of robots, and what I can tease is that this will be an opportunity for us to showcase both the technology and also the excitement of the robots competing in the virtual environment. I’m trying not to give too many spoilers, but we’ll have an opportunity to really get into the details.
Check back as we get closer to the 17 November event for more on the DARPA SubT Challenge. Continue reading
Posted in Human Robots
#436946 Coronavirus May Mean Automation Is ...
We’re in the midst of a public health emergency, and life as we know it has ground to a halt. The places we usually go are closed, the events we were looking forward to are canceled, and some of us have lost our jobs or fear losing them soon.
But although it may not seem like it, there are some silver linings; this crisis is bringing out the worst in some (I’m looking at you, toilet paper hoarders), but the best in many. Italians on lockdown are singing together, Spaniards on lockdown are exercising together, this entrepreneur made a DIY ventilator and put it on YouTube, and volunteers in Italy 3D printed medical valves for virus treatment at a fraction of their usual cost.
Indeed, if you want to feel like there’s still hope for humanity instead of feeling like we’re about to snowball into terribleness as a species, just look at these examples—and I’m sure there are many more out there. There’s plenty of hope and opportunity to be found in this crisis.
Peter Xing, a keynote speaker and writer on emerging technologies and associate director in technology and growth initiatives at KPMG, would agree. Xing believes the coronavirus epidemic is presenting us with ample opportunities for increased automation and remote delivery of goods and services. “The upside right now is the burgeoning platform of the digital transformation ecosystem,” he said.
In a thought-provoking talk at Singularity University’s COVID-19 virtual summit this week, Xing explained how the outbreak is accelerating our transition to a highly-automated society—and painted a picture of what the future may look like.
Confronting Scarcity
You’ve probably seen them by now—the barren shelves at your local grocery store. Whether you were in the paper goods aisle, the frozen food section, or the fresh produce area, it was clear something was amiss; the shelves were empty. One of the most inexplicable items people have been panic-bulk-buying is toilet paper.
Xing described this toilet paper scarcity as a prisoner’s dilemma, pointing out that we have a scarcity problem right now in terms of our mindset, not in terms of actual supply shortages. “It’s a prisoner’s dilemma in that we’re all prisoners in our homes right now, and we can either hoard or not hoard, and the outcomes depend on how we collaborate with each other,” he said. “But it’s not a zero-sum game.”
Xing referenced a CNN article about why toilet paper, of all things, is one of the items people have been panic-buying most (I, too, have been utterly baffled by this phenomenon). But maybe there’d be less panic if we knew more about the production methods and supply chain involved in manufacturing toilet paper. It turns out it’s a highly automated process (you can learn more about it in this documentary by National Geographic) and requires very few people (though it does require about 27,000 trees a day—so stop bulk-buying it! Just stop!).
The supply chain limitation here is in the raw material; we certainly can’t keep cutting down this many trees a day forever. But—somewhat ironically, given the Costco cartloads of TP people have been stuffing into their trunks and backseats—thanks to automation, toilet paper isn’t something stores are going to stop receiving anytime soon.
Automation For All
Now we have a reason to apply this level of automation to, well, pretty much everything.
Though our current situation may force us into using more robots and automated systems sooner than we’d planned, it will end up saving us money and creating opportunity, Xing believes. He cited “fast-casual” restaurants (Chipotle, Panera, etc.) as a prime example.
Currently, people in the US spend much more to eat at home than we do to eat in fast-casual restaurants if you take into account the cost of the food we’re preparing plus the value of the time we’re spending on cooking, grocery shopping, and cleaning up after meals. According to research from investment management firm ARK Invest, taking all these costs into account makes for about $12 per meal for food cooked at home.
That’s the same as or more than the cost of grabbing a burrito or a sandwich at the joint around the corner. As more of the repetitive, low-skill tasks involved in preparing fast casual meals are automated, their cost will drop even more, giving us more incentive to forego home cooking. (But, it’s worth noting that these figures don’t take into account that eating at home is, in most cases, better for you since you’re less likely to fill your food with sugar, oil, or various other taste-enhancing but health-destroying ingredients—plus, there are those of us who get a nearly incomparable amount of joy from laboring over then savoring a homemade meal).
Now that we’re not supposed to be touching each other or touching anything anyone else has touched, but we still need to eat, automating food preparation sounds appealing (and maybe necessary). Multiple food delivery services have already implemented a contactless delivery option, where customers can choose to have their food left on their doorstep.
Besides the opportunities for in-restaurant automation, “This is an opportunity for automation to happen at the last mile,” said Xing. Delivery drones, robots, and autonomous trucks and vans could all play a part. In fact, use of delivery drones has ramped up in China since the outbreak.
Speaking of deliveries, service robots have steadily increased in numbers at Amazon; as of late 2019, the company employed around 650,000 humans and 200,000 robots—and costs have gone down as robots have gone up.
ARK Invest’s research predicts automation could add $800 billion to US GDP over the next 5 years and $12 trillion during the next 15 years. On this trajectory, GDP would end up being 40 percent higher with automation than without it.
Automating Ourselves?
This is all well and good, but what do these numbers and percentages mean for the average consumer, worker, or citizen?
“The benefits of automation aren’t being passed on to the average citizen,” said Xing. “They’re going to the shareholders of the companies creating the automation.” This is where policies like universal basic income and universal healthcare come in; in the not-too-distant future, we may see more movement toward measures like these (depending how the election goes) that spread the benefit of automation out rather than concentrating it in a few wealthy hands.
In the meantime, though, some people are benefiting from automation in ways that maybe weren’t expected. We’re in the midst of what’s probably the biggest remote-work experiment in US history, not to mention remote learning. Tools that let us digitally communicate and collaborate, like Slack, Zoom, Dropbox, and Gsuite, are enabling remote work in a way that wouldn’t have been possible 20 or even 10 years ago.
In addition, Xing said, tools like DataRobot and H2O.ai are democratizing artificial intelligence by allowing almost anyone, not just data scientists or computer engineers, to run machine learning algorithms. People are codifying the steps in their own repetitive work processes and having their computers take over tasks for them.
As 3D printing gets cheaper and more accessible, it’s also being more widely adopted, and people are finding more applications (case in point: the Italians mentioned above who figured out how to cheaply print a medical valve for coronavirus treatment).
The Mother of Invention
This movement towards a more automated society has some positives: it will help us stay healthy during times like the present, it will drive down the cost of goods and services, and it will grow our GDP in the long run. But by leaning into automation, will we be enabling a future that keeps us more physically, psychologically, and emotionally distant from each other?
We’re in a crisis, and desperate times call for desperate measures. We’re sheltering in place, practicing social distancing, and trying not to touch each other. And for most of us, this is really unpleasant and difficult. We can’t wait for it to be over.
For better or worse, this pandemic will likely make us pick up the pace on our path to automation, across many sectors and processes. The solutions people implement during this crisis won’t disappear when things go back to normal (and, depending who you talk to, they may never really do so).
But let’s make sure to remember something. Even once robots are making our food and drones are delivering it, and our computers are doing data entry and email replies on our behalf, and we all have 3D printers to make anything we want at home—we’re still going to be human. And humans like being around each other. We like seeing one another’s faces, hearing one another’s voices, and feeling one another’s touch—in person, not on a screen or in an app.
No amount of automation is going to change that, and beyond lowering costs or increasing GDP, our greatest and most crucial responsibility will always be to take care of each other.
Image Credit: Gritt Zheng on Unsplash Continue reading
Posted in Human Robots
#436944 Is Digital Learning Still Second Best?
As Covid-19 continues to spread, the world has gone digital on an unprecedented scale. Tens of thousands of employees are working from home, and huge conferences, like the Google I/O and Apple WWDC software extravaganzas, plan to experiment with digital events.
Universities too are sending students home. This might have meant an extended break from school not too long ago. But no more. As lecture halls go empty, an experiment into digital learning at scale is ramping up. In the US alone, over 100 universities, from Harvard to Duke, are offering online classes to students to keep the semester going.
While digital learning has been improving for some time, Covid-19 may not only tip us further into a more digitally connected reality, but also help us better appreciate its benefits. This is important because historically, digital learning has been viewed as inferior to traditional learning. But that may be changing.
The Inversion
We often think about digital technologies as ways to reach people without access to traditional services—online learning for children who don’t have schools nearby or telemedicine for patients with no access to doctors. And while these solutions have helped millions of people, they’re often viewed as “second best” and “better than nothing.” Even in more resource-rich environments, there’s an assumption one should pay more to attend an event in person—a concert, a football game, an exercise class—while digital equivalents are extremely cheap or free. Why is this? And is the situation about to change?
Take the case of Dr. Sanjeev Arora, a professor of medicine at the University of New Mexico. Arora started Project Echo because he was frustrated by how many late-stage cases of hepatitis C he encountered in rural New Mexico. He realized that if he had reached patients sooner, he could have prevented needless deaths. The solution? Digital learning for local health workers.
Project Echo connects rural healthcare practitioners to specialists at top health centers by video. The approach is collaborative: Specialists share best practices and work through cases with participants to apply them in the real world and learn from edge cases. Added to expert presentations, there are lots of opportunities to ask questions and interact with specialists.
The method forms a digital loop of learning, practice, assessment, and adjustment.
Since 2003, Project Echo has scaled to 800 locations in 39 countries and trained over 90,000 healthcare providers. Most notably, a study in The New England Journal of Medicine found that the outcomes of hepatitis C treatment given by Project Echo trained healthcare workers in rural and underserved areas were similar to outcomes at university medical centers. That is, digital learning in this context was equivalent to high quality in-person learning.
If that is possible today, with simple tools, will they surpass traditional medical centers and schools in the future? Can digital learning more generally follow suit and have the same success? Perhaps. Going digital brings its own special toolset to the table too.
The Benefits of Digital
If you’re training people online, you can record the session to better understand their engagement levels—or even add artificial intelligence to analyze it in real time. Ahura AI, for example, founded by Bryan Talebi, aims to upskill workers through online training. Early study of their method suggests they can significantly speed up learning by analyzing users’ real-time emotions—like frustration or distraction—and adjusting the lesson plan or difficulty on the fly.
Other benefits of digital learning include the near-instantaneous download of course materials—rather than printing and shipping books—and being able to more easily report grades and other results, a requirement for many schools and social services organizations. And of course, as other digitized industries show, digital learning can grow and scale further at much lower costs.
To that last point, 360ed, a digital learning startup founded in 2016 by Hla Hla Win, now serves millions of children in Myanmar with augmented reality lesson plans. And Global Startup Ecosystem, founded by Christine Souffrant Ntim and Einstein Kofi Ntim in 2015, is the world’s first and largest digital accelerator program. Their entirely online programs support over 1,000 companies in 90 countries. It’s astonishing how fast both of these organizations have grown.
Notably, both examples include offline experiences too. Many of the 360ed lesson plans come with paper flashcards children use with their smartphones because the online-offline interaction improves learning. The Global Startup Ecosystem also hosts about 10 additional in-person tech summits around the world on various topics through a related initiative.
Looking further ahead, probably the most important benefit of online learning will be its potential to integrate with other digital systems in the workplace.
Imagine a medical center that has perfect information about every patient and treatment in real time and that this information is (anonymously and privately) centralized, analyzed, and shared with medical centers, research labs, pharmaceutical companies, clinical trials, policy makers, and medical students around the world. Just as self-driving cars can learn to drive better by having access to the experiences of other self-driving cars, so too can any group working to solve complex, time-sensitive challenges learn from and build on each other’s experiences.
Why This Matters
While in the long term the world will likely end up combining the best aspects of traditional and digital learning, it’s important in the near term to be more aware of the assumptions we make about digital technologies. Some of the most pioneering work in education, healthcare, and other industries may not be highly visible right now because it is in a virtual setting. Most people are unaware, for example, that the busiest emergency room in rural America is already virtual.
Once they start converging with other digital technologies, these innovations will likely become the mainstream system for all of us. Which raises more questions: What is the best business model for these virtual services? If they start delivering better healthcare and educational outcomes than traditional institutions, should they charge more? Hopefully, we will see an even bigger shift occurring, in which technology allows us to provide high quality education, healthcare, and other services to everyone at more affordable prices than today.
These are some of the topics we can consider as Covid-19 forces us into uncharted territory.
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