Emerging-Technologies
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As pandemic drags on, interest in automation surges
Today, the U.S. exceeded three million COVID-19 cases and 132,000 deaths. In several states, new hotspots have rolled back plans to reopen businesses. The novel coronavirus has — and will continue — to profoundly impact the way we live and work.
For the moment, that includes a shift in the employment status of many Americans. More than 50 million people have filed for unemployment since mid-March. And while many states have made efforts to reopen businesses and return some sense of normality, these moves have led to a spike in cases and may prolong the pandemic and its ongoing economic impact.
Technology has been a lifeline for many, from food delivery to the 3D printing I highlighted last week, which has worked to address a nation suffering from personal protective equipment shortages. Automation and robotics have also been a constant in conversations around tech’s battle against COVID-19.
Robots don’t get sick, tired or emotionally burnt out, and unlike us, they aren’t walking, talking disease vectors. Automation advocates like to point to the “three Ds” of dull, dirty and dangerous jobs that will eventually be replaced by a robotic workforce, but in the age of COVID-19, nearly any essential job qualifies.
The robotic invasion has already begun in earnest. The service, delivery, health care and sanitation industries in particular have all opened a massive gap over the past several months that automation has been more than happy to roll right through. A recent report from The Brookings Institute notes that automation arrives in the workforce in fits and starts — most notably, during times of economic downturn.
“Robots’ infiltration of the workforce doesn’t occur at a steady, gradual pace. Instead, automation happens in bursts, concentrated especially in bad times such as in the wake of economic shocks, when humans become relatively more expensive as firms’ revenues rapidly decline,” the study found. “At these moments, employers shed less-skilled workers and replace them with technology and higher-skilled workers, which increases labor productivity as a recession tapers off.”
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AR 1.0 is dead: Here’s what it got wrong
The first wave of AR startups offering smart glasses is now over, with a few exceptions.
Google acquired North this week for an undisclosed sum. The Canadian company had raised nearly $200 million, but the release of its Focals 2.0 smart glasses has been cancelled, a bittersweet end for its soft landing.
Many AR startups before North made huge promises and raised huge amounts of capital before flaring out in a similarly dramatic fashion.
The technology was almost there in a lot of cases, but the real issue was that the stakes to beat the major players to market were so high that many entrants pushed out boring, general consumer products. In a race to be everything for everybody, the industry relied on nascent developer platforms to do the dirty work of building their early use cases, which contributed heavily to nonexistent user adoption.
A key error of this batch was thinking that an AR glasses company was hardware-first, when the reality is that the missing value is almost entirely centered on missing first-party software experiences. To succeed, the next generation of consumer AR glasses will have to nail this.
Image Credits: ODG
App ecosystems alone don’t create product-market fit
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Manufacturing startup Divergent 3D reduces staff by one-third
Divergent, the Los Angeles-based startup aiming to revolutionize vehicle manufacturing, has cut about one-third of its staff amid the COVID-19 pandemic that has upended startups and major corporations alike.
The company, which employed about 160 people, laid off 57 workers, according to documents filed with the California Employment Development Department. Founder and CEO Kevin Czinger didn’t provide specific numbers. However, he did confirm to TechCrunch that he had to reduce staff due to the COVID-19 pandemic. A core team remains, he said.
“Whenever you’re doing something that’s affecting people’s jobs — and especially in a company where I basically recruited everyone and knew everyone by face and name — it’s obviously super painful to do that under any circumstance,” Czinger said in an interview this week.
The company’s No. 1 priority was to ensure long-term financial stability and secure the core team, technology development and customer programs no matter what the scenario, Czinger said, adding that there is still enormous uncertainty surrounding the real impact and duration of the COVID-19 pandemic.
“This was about making the company as totally weatherproof as possible,” Czinger said.
Divergent 3D is essentially a Tier 1 supplier for the automotive and aerospace industry. But it can hardly be considered a traditional supplier. After resigning as CEO of the now-defunct EV startup Coda Automotive in 2010, Czinger began to focus on how the vehicle manufacturing process could become more efficient and less wasteful.
Divergent 3D was born out of that initial exploration. The company developed an additive manufacturing platform designed to make it easier and faster to design and build new cars at a fraction of the cost — all while reducing the environmental impact that traditional factories have.
The platform is an end-to-end digital production system that uses high-speed 3D printers to make complex parts out of metal alloys. This system produces the structures of vehicles, such as the full frame, subframes and suspension structures that are part of the crash-performance structure of the vehicle.
In its early years as a company, Divergent 3D was perhaps best known for Blade, the first automobile to use 3D printing to form the body and chassis. Divergent 3D made Blade — which was on the auto show circuit in 2016 — to demonstrate the technology platform.
It was enough to get the attention of investors and at least two global OEMs as customers. Divergent can’t name the customers because of non-disclosure agreements.
The company has raised about $150 million from investors that include venture capital fund Horizons Ventures, automotive and aerospace engineering services company Altran Technologies and Chinese backers O Luxe Holdings, an investment conglomerate backed by the Hong Kong-based real estate investment magnate Li Ka-shing and Shanghai Alliance Investment Limited, an investment arm of the Shanghai Municipal Government.
The latest example of Divergent’s technology is the 21C, a hypercar unveiled in March that was built using the additive manufacturing platform. The high-performance 3D-printed vehicle was produced by Czinger Vehicles. Divergent 3D and Czinger Vehicles are wholly owned subsidiaries under Divergent Technologies.
Image Credits: Czinger Vehicles
Czinger said the company is poised to navigate the pandemic and ultimately survive. Divergent 3D has two global OEMs as customers. Structures such as chassis components and subframes, for which Divergent has supply contracts, are going through various testing and validation stages, depending on the program. Those programs, which are for serial production vehicles, are moving forward, Czinger said.
There will be delays as automakers have slowed or stopped operations. Czinger is hopeful that by 2021 the company will be able to announce that its 3D-printed structures will be production vehicles.
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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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R&D Roundup: Ultrasound/AI medical imaging, assistive exoskeletons and neural weather modeling
In the time of COVID-19, much of what transpires from the science world to the general public relates to the virus, and understandably so. But other domains, even within medical research, are still active — and as usual, there are tons of interesting (and heartening) stories out there that shouldn’t be lost in the furious activity of coronavirus coverage. This last week brought good news for several medical conditions as well as some innovations that could improve weather reporting and maybe save a few lives in Cambodia.
Ultrasound and AI promise better diagnosis of arrhythmia
Arrhythmia is a relatively common condition in which the heart beats at an abnormal rate, causing a variety of effects, including, potentially, death. Detecting it is done using an electrocardiogram, and while the technique is sound and widely used, it has its limitations: first, it relies heavily on an expert interpreting the signal, and second, even an expert’s diagnosis doesn’t give a good idea of what the issue looks like in that particular heart. Knowing exactly where the flaw is makes treatment much easier.
Ultrasound is used for internal imaging in lots of ways, but two recent studies establish it as perhaps the next major step in arrhythmia treatment. Researchers at Columbia University used a form of ultrasound monitoring called Electromechanical Wave Imaging to create 3D animations of the patient’s heart as it beat, which helped specialists predict 96% of arrhythmia locations compared with 71% when using the ECG. The two could be used together to provide a more accurate picture of the heart’s condition before undergoing treatment.
Another approach from Stanford applies deep learning techniques to ultrasound imagery and shows that an AI agent can recognize the parts of the heart and record the efficiency with which it is moving blood with accuracy comparable to experts. As with other medical imagery AIs, this isn’t about replacing a doctor but augmenting them; an automated system can help triage and prioritize effectively, suggest things the doctor might have missed or provide an impartial concurrence with their opinion. The code and data set of EchoNet are available for download and inspection.
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Anorak’s Greg Castle on early-stage investing during a crisis
As the venture landscape adjusts to the COVID-19 pandemic and seismic shifts in public markets, early-stage VCs are reassessing which bets they’re making, along with questions they’re asking of founders who are exploring bleeding-edge technology.
Anorak’s Greg Castle
Anorak Ventures is a small seed-investment firm that bets on emerging tech like AR/VR, machine learning and robotics. I recently hopped on a Zoom call with founder Greg Castle to talk about what he’s seen recently in seed investing and how the sector is responding to the crisis. Castle was an early investor in Oculus; his other bets at Anorak include Against Gravity, 6D.ai and Anduril.
Our conversation has been edited for length and clarity.
TechCrunch: Has this pandemic affected the types of companies that you’re looking at?
Greg Castle: From my experience as an investor thus far, being reactive as an investor and looking at “hot” areas has a lot of pitfalls to be mindful of. I think a lot of the areas that excite me as an investor could benefit from what’s going on here, those areas including robotics, automation, immersive entertainment and immersive computing.
Just generally, do you feel like a recession is likely to negatively impact emerging tech more so than other areas?
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Helm.ai raises $13M on its unsupervised learning approach to driverless car AI
Four years ago, mathematician Vlad Voroninski saw an opportunity to remove some of the bottlenecks in the development of autonomous vehicle technology thanks to breakthroughs in deep learning.
Now, Helm.ai, the startup he co-founded in 2016 with Tudor Achim, is coming out of stealth with an announcement that it has raised $13 million in a seed round that includes investment from A.Capital Ventures, Amplo, Binnacle Partners, Sound Ventures, Fontinalis Partners and SV Angel. More than a dozen angel investors also participated, including Berggruen Holdings founder Nicolas Berggruen, Quora co-founders Charlie Cheever and Adam D’Angelo, professional NBA player Kevin Durant, Gen. David Petraeus, Matician co-founder and CEO Navneet Dalal, Quiet Capital managing partner Lee Linden and Robinhood co-founder Vladimir Tenev, among others.
Helm.ai will put the $13 million in seed funding toward advanced engineering and R&D and hiring more employees, as well as locking in and fulfilling deals with customers.
Helm.ai is focused solely on the software. It isn’t building the compute platform or sensors that are also required in a self-driving vehicle. Instead, it is agnostic to those variables. In the most basic terms, Helm.ai is creating software that tries to understand sensor data as well as a human would, in order to be able to drive, Voroninski said.
That aim doesn’t sound different from other companies. It’s Helm.ai’s approach to software that is noteworthy. Autonomous vehicle developers often rely on a combination of simulation and on-road testing, along with reams of data sets that have been annotated by humans, to train and improve the so-called “brain” of the self-driving vehicle.
Helm.ai says it has developed software that can skip those steps, which expedites the timeline and reduces costs. The startup uses an unsupervised learning approach to develop software that can train neural networks without the need for large-scale fleet data, simulation or annotation.
“There’s this very long tail end and an endless sea of corner cases to go through when developing AI software for autonomous vehicles, Voroninski explained. “What really matters is the unit of efficiency of how much does it cost to solve any given corner case, and how quickly can you do it? And so that’s the part that we really innovated on.”
Voroninski first became interested in autonomous driving at UCLA, where he learned about the technology from his undergrad adviser who had participated in the DARPA Grand Challenge, a driverless car competition in the U.S. funded by the Defense Advanced Research Projects Agency. And while Voroninski turned his attention to applied mathematics for the next decade — earning a PhD in math at UC Berkeley and then joining the faculty in the MIT mathematics department — he knew he’d eventually come back to autonomous vehicles.
By 2016, Voroninski said breakthroughs in deep learning created opportunities to jump in. Voroninski left MIT and Sift Security, a cybersecurity startup later acquired by Netskope, to start Helm.ai with Achim in November 2016.
“We identified some key challenges that we felt like weren’t being addressed with the traditional approaches,” Voroninski said. “We built some prototypes early on that made us believe that we can actually take this all the way.”
Helm.ai is still a small team of about 15 people. Its business aim is to license its software for two use cases — Level 2 (and a newer term called Level 2+) advanced driver assistance systems found in passenger vehicles and Level 4 autonomous vehicle fleets.
Helm.ai does have customers, some of which have gone beyond the pilot phase, Voroninski said, adding that he couldn’t name them.
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Quantum Machines raises $17.5M for its Quantum Orchestration Platform
Quantum Machines, a Tel Aviv-based startup that is building both hardware and software to operate quantum computers, today announced that it has raised a $17.5 million Series A funding round. The round was led by Israeli tech entrepreneur Avigdor Willenz (who, among other companies, co-founded Habana Labs and Anapurna Labs and sold them to Intel and Amazon, respectively) and Harel Insurance Investments.
TLV Partners and Battery Ventures also participated in this round. TLV Partners also led the company’s $5.5 million seed round in 2018, in which Battery Partners also participated.
“The race to commercial quantum computers is one of the most exciting technological challenges of our generation,” said Willenz. “Our goal at [Quantum Machines] is to make this happen faster than anticipated and establish ourselves as a key player in this emerging industry.”
The company says it will use the new funding to accelerate the adoption of its Quantum Orchestration Platform. This platform went live earlier this year. What makes it unique is that it’s a combination of custom hardware, which the company designed itself, and software tools that can be used to control virtually any quantum processor. To control a quantum processor, you also need a powerful classical computer, but traditional computers are ill-suited for this task, Quantum Machines argues, and it’ll take specialized hardware for classical computing to harness the power of quantum computing and run complex algorithms on these machines.
“The classical layers of the quantum computer are the real unmet need. They are the bottleneck,” Quantum Machines co-founder Itamar Sivan told me when the platform launched. “We were really looking into what is holding the industry back. What are the things that we can do today to drive this industry forward, but that will also enable faster progress in the future. Since most of the focus in the last years has been devoted to quantum processors, it was only natural that you know we take on this challenge.”
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Honeywell says it will soon launch the world’s most powerful quantum computer
“The best-kept secret in quantum computing.” That’s what Cambridge Quantum Computing (CQC) CEO Ilyas Khan called Honeywell‘s efforts in building the world’s most powerful quantum computer. In a race where most of the major players are vying for attention, Honeywell has quietly worked on its efforts for the last few years (and under strict NDA’s, it seems). But today, the company announced a major breakthrough that it claims will allow it to launch the world’s most powerful quantum computer within the next three months.
In addition, Honeywell also today announced that it has made strategic investments in CQC and Zapata Computing, both of which focus on the software side of quantum computing. The company has also partnered with JPMorgan Chase to develop quantum algorithms using Honeywell’s quantum computer. The company also recently announced a partnership with Microsoft.
Honeywell has long built the kind of complex control systems that power many of the world’s largest industrial sites. It’s that kind of experience that has now allowed it to build an advanced ion trap that is at the core of its efforts.
This ion trap, the company claims in a paper that accompanies today’s announcement, has allowed the team to achieve decoherence times that are significantly longer than those of its competitors.
“It starts really with the heritage that Honeywell had to work from,” Tony Uttley, the president of Honeywell Quantum Solutions, told me. “And we, because of our businesses within aerospace and defense and our business in oil and gas — with solutions that have to do with the integration of complex control systems because of our chemicals and materials businesses — we had all of the underlying pieces for quantum computing, which are just fabulously different from classical computing. You need to have ultra-high vacuum system capabilities. You need to have cryogenic capabilities. You need to have precision control. You need to have lasers and photonic capabilities. You have to have magnetic and vibrational stability capabilities. And for us, we had our own foundry and so we are able to literally design our architecture from the trap up.”
The result of this is a quantum computer that promises to achieve a quantum Volume of 64. Quantum Volume (QV), it’s worth mentioning, is a metric that takes into account both the number of qubits in a system as well as decoherence times. IBM and others have championed this metric as a way to, at least for now, compare the power of various quantum computers.
So far, IBM’s own machines have achieved QV 32, which would make Honeywell’s machine significantly more powerful.
Khan, whose company provides software tools for quantum computing and was one of the first to work with Honeywell on this project, also noted that the focus on the ion trap is giving Honeywell a bit of an advantage. “I think that the choice of the ion trap approach by Honeywell is a reflection of a very deliberate focus on the quality of qubit rather than the number of qubits, which I think is fairly sophisticated,” he said. “Until recently, the headline was always growth, the number of qubits running.”
The Honeywell team noted that many of its current customers are also likely users of its quantum solutions. These customers, after all, are working on exactly the kind of problems in chemistry or material science that quantum computing, at least in its earliest forms, is uniquely suited for.
Currently, Honeywell has about 100 scientists, engineers and developers dedicated to its quantum project.
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