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Duolingo can’t teach you how to speak a language, but now it wants to try
Duolingo has been wildly successful. It has pulled in 500 million total registered learners, 40 million active users, 1.5 million premium subscribers and $190 million in booked revenues in 2020. It has a popular and meme-ified mascot in the form of the owl Duo, a creative and engaging product, and ambitious plans for expansion.There’s just one key question in the midst of all those milestones: Does anyone actually learn a language using Duolingo?
“Language is first and foremost a social, relational phenomenon,” said Sébastien Dubreil, a teaching professor at Carnegie Mellon University. “It is something that allows people to make meaning and talk to each other and conduct the business of living — and when you do this, you use a tone of different kinds of resources that are not packaged in the vocabulary and grammar.”
Duolingo CEO and co-founder Luis von Ahn estimates that Duolingo’s upcoming product developments will get users from zero to a knowledge job in a different language within the next two to three years. But for now, he is honest about the limits of the platform today.
“I won’t say that with Duolingo, you can start from zero and make your English as good as mine,” he said. “That’s not true. But that’s also not true with learning a language in a university, that’s not true with buying books, that’s not true with any other app.”
Luis von Ahn, the co-founder of Duolingo, visiting President Obama in 2015. Image Credits: Duolingo
While Dubreil doesn’t think Duolingo can teach someone to speak a language, he does think it has taught consistency — a hard nut to crack in edtech. “What Duolingo does is to potentially entice students to do things you cannot pay them enough time to actually do, which is to spend time in that textbook and reinforce vocabulary and the grammar,” he said.
That’s been the key focus for the company since the beginning. “I said this when we started Duolingo and I still really strongly believe it: The hardest thing about learning a language is staying motivated,” von Ahn said, comparing it to how people approach exercise: it’s hard to stay motivated, but a little motion a day goes a long way.
With an enviable lead in its category, Duolingo wants to bring the quality and effectiveness of its curriculum on par with the quality of its product and branding. With growth and monetization secured, Duolingo is no longer in survival mode. Instead, it’s in study mode.
In this final part, we will explore how Duolingo is using a variety of strategies, from rewriting its courses to what it dubs Operation Birdbrain, to become a more effective learning tool, all while balancing the need to keep the growth and monetization engines stoked while en route to an IPO.
Duolingo’s office decor. Image Credits: Duolingo
“Just a funny game that is maybe not as bad as Candy Crush.”
Duolingo’s competitors see the app’s massive gamification and solitary experience as inherently contradictory with high-quality language education. Busuu and Babbel, two subscription-based competitors in the market, both focus on users talking in real time to native speakers.
Bernhard Niesner, the co-founder and CEO of Busuu, which was founded in 2008, sees Duolingo as an entry-level tool that can help users migrate to its human-interactive service. “If you want to be fluent, Duolingo needs innovation,” Niesner said. “And that’s where we come in: We all believe that you should not be learning a language just by yourself, but [ … ] together, which is our vision.” Busuu has more than 90 million users worldwide.
Duolingo has been the subject of a number of efficacy studies over the years. One of its most positive reports, from September 2020, showed that its Spanish and French courses teach the equivalent of four U.S. university semesters in half the time.
Babbel, which has sold over 10 million subscriptions to its language-learning service, cast doubt on the power of these findings. Christian Hillemeyer, who heads PR for the startup, pointed out that Duolingo only tested for reading and writing efficacy — not for speaking proficiency, even though that is a key part of language learning. He described Duolingo as “just a funny game that is maybe not as bad as Candy Crush.”
Putting the ed back into edtech
One of the ironic legacies of Duolingo’s evolution is that for years it outsourced much of the creation of its education curriculum to volunteers. It’s a legacy the company is still trying to rectify.
The year after its founding, Duolingo launched its Language Incubator in 2013. Similar to its original translation service, the company wanted to leverage crowdsourcing to invent and refine new language courses. Volunteers — at least at first — were seen as a scrappy way to bring new material to the growing Duolingo community and more than 1,000 volunteers have helped bring new language courses to the app.
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WorldGaze uses smartphone cameras to help voice AIs cut to the chase
If you find voice assistants frustratingly dumb, you’re hardly alone. The much-hyped promise of AI-driven vocal convenience very quickly falls through the cracks of robotic pedantry.
A smart AI that has to come back again (and sometimes again) to ask for extra input to execute your request can seem especially dumb — when, for example, it doesn’t get that the most likely repair shop you’re asking about is not any one of them but the one you’re parked outside of right now.
Researchers at the Human-Computer Interaction Institute at Carnegie Mellon University, working with Gierad Laput, a machine learning engineer at Apple, have devised a demo software add-on for voice assistants that lets smartphone users boost the savvy of an on-device AI by giving it a helping hand — or rather a helping head.
The prototype system makes simultaneous use of a smartphone’s front and rear cameras to be able to locate the user’s head in physical space, and more specifically within the immediate surroundings — which are parsed to identify objects in the vicinity using computer vision technology.
The user is then able to use their head as a pointer to direct their gaze at whatever they’re talking about — i.e. “that garage” — wordlessly filling in contextual gaps in the AI’s understanding in a way the researchers contend is more natural.
So, instead of needing to talk like a robot in order to tap the utility of a voice AI, you can sound a bit more, well, human. Asking stuff like “‘Siri, when does that Starbucks close?” Or — in a retail setting — “are there other color options for that sofa?” Or asking for an instant price comparison between “this chair and that one.” Or for a lamp to be added to your wish-list.
In a home/office scenario, the system could also let the user remotely control a variety of devices within their field of vision — without needing to be hyper-specific about it. Instead they could just look toward the smart TV or thermostat and speak the required volume/temperature adjustment.
The team has put together a demo video (below) showing the prototype — which they’ve called WorldGaze — in action. “We use the iPhone’s front-facing camera to track the head in 3D, including its direction vector. Because the geometry of the front and back cameras are known, we can raycast the head vector into the world as seen by the rear-facing camera,” they explain in the video.
“This allows the user to intuitively define an object or region of interest using the head gaze. Voice assistants can then use this contextual information to make enquiries that are more precise and natural.”
In a research paper presenting the prototype they also suggest it could be used to “help to socialize mobile AR experiences, currently typified by people walking down the street looking down at their devices.”
Asked to expand on this, CMU researcher Chris Harrison told TechCrunch: “People are always walking and looking down at their phones, which isn’t very social. They aren’t engaging with other people, or even looking at the beautiful world around them. With something like WorldGaze, people can look out into the world, but still ask questions to their smartphone. If I’m walking down the street, I can inquire and listen about restaurant reviews or add things to my shopping list without having to look down at my phone. But the phone still has all the smarts. I don’t have to buy something extra or special.”
In the paper they note there is a long body of research related to tracking users’ gaze for interactive purposes — but a key aim of their work here was to develop “a functional, real-time prototype, constraining ourselves to hardware found on commodity smartphones.” (Although the rear camera’s field of view is one potential limitation they discuss, including suggesting a partial workaround for any hardware that falls short.)
“Although WorldGaze could be launched as a standalone application, we believe it is more likely for WorldGaze to be integrated as a background service that wakes upon a voice assistant trigger (e.g., ‘Hey Siri’),” they also write. “Although opening both cameras and performing computer vision processing is energy consumptive, the duty cycle would be so low as to not significantly impact battery life of today’s smartphones. It may even be that only a single frame is needed from both cameras, after which they can turn back off (WorldGaze startup time is 7 sec). Using bench equipment, we estimated power consumption at ~0.1 mWh per inquiry.”
Of course there’s still something a bit awkward about a human holding a screen up in front of their face and talking to it — but Harrison confirms the software could work just as easily hands-free on a pair of smart spectacles.
“Both are possible,” he told us. “We choose to focus on smartphones simply because everyone has one (and WorldGaze could literally be a software update), while almost no one has AR glasses (yet). But the premise of using where you are looking to supercharge voice assistants applies to both.”
“Increasingly, AR glasses include sensors to track gaze location (e.g., Magic Leap, which uses it for focusing reasons), so in that case, one only needs outwards facing cameras,” he added.
Taking a further leap it’s possible to imagine such a system being combined with facial recognition technology — to allow a smart spec-wearer to quietly tip their head and ask “who’s that?” — assuming the necessary facial data was legally available in the AI’s memory banks.
Features such as “add to contacts” or “when did we last meet” could then be unlocked to augment a networking or socializing experience. Although, at this point, the privacy implications of unleashing such a system into the real world look rather more challenging than stitching together the engineering. (See, for example, Apple banning Clearview AI’s app for violating its rules.)
“There would have to be a level of security and permissions to go along with this, and it’s not something we are contemplating right now, but it’s an interesting (and potentially scary idea),” agrees Harrison when we ask about such a possibility.
The team was due to present the research at ACM CHI — but the conference was canceled due to the coronavirus.
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Activity-monitoring startup Zensors repurposes its tech to help coronavirus response
Computer vision techniques used for commercial purposes are turning out to be valuable tools for monitoring people’s behavior during the present pandemic. Zensors, a startup that uses machine learning to track things like restaurant occupancy, lines and so on, is making its platform available for free to airports and other places desperate to take systematic measures against infection.
The company, founded two years ago but covered by TechCrunch in 2016, was among the early adopters of computer vision as a means to extract value from things like security camera feeds. It may seem obvious now that cameras covering a restaurant can and should count open tables and track that data over time, but a few years ago it wasn’t so easy to come up with or accomplish that.
Since then Zensors has built a suite of tools tailored to specific businesses and spaces, like airports, offices and retail environments. They can count open and occupied seats, spot trash, estimate lines and all that kind of thing. Coincidentally, this is exactly the kind of data that managers of these spaces are now very interested in watching closely given the present social distancing measures.
Zensors co-founder Anuraag Jain told Carnegie Mellon University — which the company was spun out of — that it had received a number of inquiries from the likes of airports regarding applying the technology to public health considerations.
Software that counts how many people are in line can be easily adapted to, for example, estimate how close people are standing and send an alert if too many people are congregating or passing through a small space.
“Rather than profiting off them, we thought we would give our help for free,” said Jain. And so, for the next two months at least, Zensors is providing its platform for free to “selected entities who are on the forefront of responding to this crisis, including our airport clients.”
The system has already been augmented to answer COVID-19-specific questions, like whether there are too many people in a given area, when a surface was last cleaned and whether cleaning should be expedited, and how many of a given group are wearing face masks.
Airports surely track some of this information already, but perhaps in a much less structured way. Using a system like this could be helpful for maintaining cleanliness and reducing risk, and no doubt Zensors hopes that having had a taste via what amounts to a free trial, some of these users will become paying clients. Interested parties should get in touch with Zensors via its usual contact page.
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Scientists discover a new way to provide plants the nutrients they need to thrive
Researchers at Carnegie Mellon University have discovered a new method for delivering key nutrients to plant roots – without having to ensure they’re present in the soil where the plants are growing.
The landmark study greatly increases the efficiency of surface delivery of nutrients and pesticides to plants. Currently, when crops are sprayed with stuff that’s supposed to help them grow faster or better, the vast majority of that (up to 95 percent, according to CMU’s engineering blog) will just end up either as concentrated deposits in the surrounding soil, or dissolving into ground water. In both cases, accumulation over time can have negative knock-on effects, in addition to being terribly inefficient at their primary task.
This method, described by researchers in detail in a new academic paper, would manage to improve efficiency to nearly 100% absorption of nutrients and pesticides delivered as nanoparticles (particles smaller than 50 nanometers across – a human hair is about 75,000 nanometers wide, for context) sprayed onto the leaves of plants, which then make their way through the plant’s internal vascular system all the way down into the root system.
Using this method, agricultural professionals could also greatly improve delivery of plant antibiotics, making it easier and more cost-effective to treat plant diseases affecting crop yields. It would be cheaper to delivery all nutrients and pesticides, too, because the big bump in efficiency of uptake by the plants means you can use much less of anything you want to deliver to achieve your desired effect.
This research could have huge impact in terms of addressing growing global food supply needs while making the most existing agricultural land footprint and decreasing the need for potentially damaging expansion of the same.
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Diving deep into Africa’s blossoming tech scene
Jumia may be the first startup you’ve heard of from Africa. But the e-commerce venture that recently listed on the NYSE is definitely not the first or last word in African tech.
The continent has an expansive digital innovation scene, the components of which are intersecting rapidly across Africa’s 54 countries and 1.2 billion people.
When measured by monetary values, Africa’s tech ecosystem is tiny by Shenzen or Silicon Valley standards.
But when you look at volumes and year over year expansion in VC, startup formation, and tech hubs, it’s one of the fastest growing tech markets in the world. In 2017, the continent also saw the largest global increase in internet users—20 percent.
If you’re a VC or founder in London, Bangalore, or San Francisco, you’ll likely interact with some part of Africa’s tech landscape for the first time—or more—in the near future.
That’s why TechCrunch put together this Extra-Crunch deep-dive on Africa’s technology sector.
Tech Hubs
A foundation for African tech is the continent’s 442 active hubs, accelerators, and incubators (as tallied by GSMA). These spaces have become focal points for startup formation, digital skills building, events, and IT activity on the continent.
Prominent tech hubs in Africa include CcHub in Nigeria, Pan-African incubator MEST, and Kenya’s iHub, with over 200 resident members. More of these organizations are receiving funds from DFIs, such as the World Bank, and aid agencies, including France’s $76 million African tech fund.
Blue-chip companies such as Google and Microsoft are also providing money and support. In 2018 Facebook opened its own Hub_NG in Lagos with partner CcHub, to foster startups using AI and machine learning.
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This robot uses lasers to ‘listen’ to its environment
A new technology from researchers at Carnegie Mellon University will add sound and vibration awareness to create truly context-aware computing. The system, called Ubicoustics, adds additional bits of context to smart device interaction, allowing a smart speaker to know it’s in a kitchen or a smart sensor to know you’re in a tunnel versus on the open road.
“A smart speaker sitting on a kitchen countertop cannot figure out if it is in a kitchen, let alone know what a person is doing in a kitchen,” said Chris Harrison a researcher at CMU’s Human-Computer Interaction Institute. “But if these devices understood what was happening around them, they could be much more helpful.”
The first implementation of the system uses built-in speakers to create “a sound-based activity recognition.” How they are doing this is quite fascinating.
“The main idea here is to leverage the professional sound-effect libraries typically used in the entertainment industry,” said Gierad Laput, a PhD student. “They are clean, properly labeled, well-segmented and diverse. Plus, we can transform and project them into hundreds of different variations, creating volumes of data perfect for training deep-learning models.”
From the release:
Laput said recognizing sounds and placing them in the correct context is challenging, in part because multiple sounds are often present and can interfere with each other. In their tests, Ubicoustics had an accuracy of about 80 percent — competitive with human accuracy, but not yet good enough to support user applications. Better microphones, higher sampling rates and different model architectures all might increase accuracy with further research.
In a separate paper, HCII Ph.D. student Yang Zhang, along with Laput and Harrison, describe what they call Vibrosight, which can detect vibrations in specific locations in a room using laser vibrometry. It is similar to the light-based devices the KGB once used to detect vibrations on reflective surfaces such as windows, allowing them to listen in on the conversations that generated the vibrations.
This system uses a low-power laser and reflectors to sense whether an object is on or off or whether a chair or table has moved. The sensor can monitor multiple objects at once and the tags attached to the objects use no electricity. This would let a single laser monitor multiple objects around a room or even in different rooms, assuming there is line of sight.
The research is still in its early stages, but expect to see robots that can hear when you’re doing the dishes and, depending on their skills, hide or offer to help.
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This tech (scarily) lets video change reality
Researchers at Carnegie Mellon University have created a method to turn one video into the style of another. While this might be a little unclear at first, take a look at the video below. In it, the researchers have taken an entire clip from John Oliver and made it look like Stephen Colbert said it. Further, they were able to mimic the motion of a flower opening with another flower.
In short, they can make anyone (or anything) look like they are doing something they never did.
“I think there are a lot of stories to be told,” said CMU Ph.D. student Aayush Bansal. He and the team created the tool to make it easier to shoot complex films, perhaps by replacing the motion in simple, well-lit scenes and copying it into an entirely different style or environment.
“It’s a tool for the artist that gives them an initial model that they can then improve,” he said.
The system uses something called generative adversarial networks (GANs) to move one style of image onto another without much matching data. GANs, however, create many artifacts that can mess up the video as it is played.
In a GAN, two models are created: a discriminator that learns to detect what is consistent with the style of one image or video, and a generator that learns how to create images or videos that match a certain style. When the two work competitively — the generator trying to trick the discriminator and the discriminator scoring the effectiveness of the generator — the system eventually learns how content can be transformed into a certain style.
The researchers created something called Recycle-GAN that reduces the imperfections by “not only spatial, but temporal information.”
“This additional information, accounting for changes over time, further constrains the process and produces better results,” wrote the researchers.
Recycle-GAN can obviously be used to create so-called Deepfakes, allowing for nefarious folks to simulate someone saying or doing something they never did. Bansal and his team are aware of the problem.
“It was an eye opener to all of us in the field that such fakes would be created and have such an impact. Finding ways to detect them will be important moving forward,” said Bansal.
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How a tap could tame the smart home
Here’s a novel fix for the headache of interacting with all sorts of connected devices: researchers at Carnegie Mellon University have devised a system that lets smartphone users tap their phone against an IoT device in order to have a contextual menu automatically loaded on screen. Read More
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In his new book, The New Urban Crisis, author Richard Florida shows how cities can survive an uncertain future
Richard Florida is most frequently associated with the concept of the rising creative class and his books have described how these high-paid knowledge workers are slowly changing the face of our cities. In his first book he foresaw the growth of high tech hubs in places like San Francisco and Pittsburgh and in his new book, The New Urban Crisis, he describes the dangers we face when… Read More
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Of course a venture capitalist was no match for CMU’s updated poker-playing AI
Libratus, Carnegie Mellon’s poker-playing AI, is damn good. It easily routed four of the world’s best poker players back in January over 120,000 hands of No-Limit Texas Hold-um. So it comes as no surprise that a team of venture capitalists, entrepreneurs and engineers playing against Lengpudashi, Libratus’ bigger, badder brother, would face a similar fate. Read More
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