Honeywell and Cambridge Quantum form joint venture to build a new full-stack quantum business

Honeywell, which only recently announced its entry into the quantum computing race, and Cambridge Quantum Computing (CQ), which focuses on building software for quantum computers, today announced that they are combining Honeywell’s Quantum Solutions (HQS) business with Cambridge Quantum in the form of a new joint venture.

Honeywell has long partnered with CQ and invested in the company last year, too. The idea here is to combine Honeywell’s hardware expertise with CQ’s software focus to build what the two companies call “the world’s highest-performing quantum computer and a full suite of quantum software, including the first and most advanced quantum operating system.”

The merged companies (or ‘combination,’ as the companies’ press releases calls it) expect the deal to be completed in the third quarter of 2021. Honeywell Chairman and CEO Darius Adamczyk will become the chairman of the new company. CQ founder and CEO Ilyas Khan will become the CEO and current Honeywell Quantum Solutions President Tony Uttley will remain in this role at the new company.

The idea here is for Honeywell to spin off HQS and combine it with CQC to form a new company, while still playing a role in its leadership and finances. Honeywell will own a majority stake in the new company and invest between $270 and $300 million. It will also have a long-term agreement with the new company to build the ion traps at the core of its quantum hardware. CQ’s shareholders will own 45% of the new company.

Image Credits: Honeywell

“The new company will have the best talent in the industry, the world’s highest-performing quantum computer, the first and most advanced quantum operating system, and comprehensive, hardware-agnostic software that will drive the future of the quantum computing industry,” said Adamczyk. “The new company will be extremely well positioned to create value in the near-term within the quantum computing industry by offering the critical global infrastructure needed to support the sector’s explosive growth.”

The companies argue that a successful quantum business will need to be supported by large-scale investments and offer a one-stop shop for customers that combines hardware and software. By combining the two companies now, they note, they’ll be able to build on their respective leadership positions in their areas of expertise and scale their businesses while also accelerate their R&D and product roadmaps.

“Since we first announced Honeywell’s quantum business in 2018, we have heard from many investors who have been eager to invest directly in our leading technologies at the forefront of this exciting and dynamic industry – now, they will be able to do so,” Adamczyk said. “The new company will provide the best avenue for us to onboard new, diverse sources of capital at scale that will help drive rapid growth.”

CQ launched in 2014 and now has about 150 employees. The company raised a total of $72.8 million, including a $45 million round, which it announced last December. Honeywell, IBM Ventures, JSR Corporation, Serendipity Capital, Alvarium Investments and Talipot Holdings invested in this last round — which also means that IBM, which uses a different technology but, in many ways, directly competes with the new company, now owns a (small) part of it.

#ceo, #chairman, #emerging-technologies, #enterprise, #honeywell, #operating-system, #president, #quantum-computing, #tc, #technology

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Dutch startup QphoX raises €2M to connect Quantum computers with a Quantum modem

When eventually they become a working reality, Quantum computers won’t be of much value if they simply sit there on their own. Just like the internet, the value is in the network. But right now there’s scant technology to link these powerful devices together.

That’s where QphoX comes in. Thus Dutch startup has raised €2 million to connect Quantum computers with a ‘Quantum modem’.

The funding round was led by Quantonation, Speedinvest, and High-Tech Gründerfonds, with participation from TU Delft.

QphoX aims to develop the Quantum Modem it created at Delft University of Technology (TU Delft) into a commercial product. This networks separate processors together, allowing quantum computers to scale beyond 10’s or 100’s of qubits. Look out for the Singularity folks…

Simon Gröblacher, CEO and co-founder of QphoX told me: “It is the exact same thing as a classical modem except for quantum computers, so it kind of converts electrical and microwave signals to optical signals coherently, so you don’t do any of the quantum information in the process. It then converts it back so you can really have two quantum computers talk to one another.

I noted that there’s more than one type of quantum computer. He countered “We are in principle agnostic to what kind of quantum computer it is. All we do at the moment is we focus on the microwave part, so we can work with superconducting qubits, topological qubits etc. We can convert microwaves to optical signals and they can talk to each other. Currently, the only competitors I know are all the in the academic world. So this is we’re the first company to actually starts building a real product.”

Rick Hao, Principal with Speedinvest’s Deep Tech team, added: “ We want to invest in seed-stage deep technology startups that shape the future and QphoX is well-positioned to make a major impact. Over the next couple of years, there will be rapid progress in quantum computers. Quantum Modem, the product developed by QphoX, enables the development of quantum computers that demonstrate quantum advantage by combining separate quantum processors.”

#delft-university-of-technology, #emerging-technologies, #europe, #microwave, #quantum-computing, #quantum-mechanics, #quantum-supremacy, #qubit, #tc

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The energy ecosystem should move to make the ‘energy internet’ a reality

As vice president of Innovation at National Grid Partners, I’m responsible for developing initiatives that not only benefit National Grid’s current business but also have the potential to become stand-alone businesses. So I obviously have strong views about the future of the energy industry.

But I don’t have a crystal ball; no one does. To be a good steward of our innovation portfolio, my job isn’t to guess what the right “basket” is for our “eggs.” It’s to optimally allocate our finite eggs across multiple baskets with the greatest collective upside.

Put another way, global and regional trends make it clear that the Next Big Thing isn’t any single thing at all. Instead, the future is about open innovation and integration of elements across the entire energy supply chain. Only with such an open energy ecosystem can we adapt to the highly volatile — some might even say unpredictable — market conditions we face in the energy industry.

Just as the digital internet rewards innovation wherever it serves the market — whether you build a better app or design a cooler smartphone — so too will the energy internet offer greater opportunities across the energy supply chain.

I like to think of this open, innovation-enabling approach as the “energy internet,” and I believe it represents the most important opportunity in the energy sector today.

The internet analogy

Here’s why I find the concept of the energy internet helpful. Before the digital internet (a term I’m using here to encompass all the hardware, software and standards that comprise it), we had multiple silos of technology such as mainframes, PCs, databases, desktop applications and private networks.

As the digital internet evolved, however, the walls between these silos disappeared. You can now utilize any platform on the back end of your digital services, including mainframes, commodity server hardware and virtual machines in the cloud.

You can transport digital payloads across networks that connect to any customer, supplier or partner on the planet with whatever combination of speed, security, capacity and cost you deem most appropriate. That payload can be data, sound or video, and your endpoint can be a desktop browser, smartphone, IoT sensor, security camera or retail kiosk.

This mix-and-match internet created an open digital supply chain that has driven an epochal boom in online innovation. Entrepreneurs and inventors can focus on specific value propositions anywhere across that supply chain rather than having to continually reinvent the supply chain itself.

The energy sector must move in the same direction. We need to be able to treat our various generation modalities like server platforms. We need our transmission grids to be as accessible as our data networks, and we need to be able to deliver energy to any consumption endpoint just as flexibly. We need to encourage innovation at those endpoints, too — just as the tech sector did.

Just as the digital internet rewards innovation wherever it serves the market — whether you build a better app or design a cooler smartphone — so too will the energy internet offer greater opportunities across the energy supply chain.

The 5D future

So what is the energy internet? As a foundation, let’s start with a model that takes the existing industry talk of digitalization, decentralization and decarbonization a few steps further:

Digitalization: Innovation depends on information about demand, supply, efficiency, trends and events. That data must be accurate, complete, timely and sharable. Digitalization efforts such as IoE, open energy, and what many refer to as the “smart grid” are instrumental because they ensure innovators have the insights they need to continuously improve the physics, logistics and economics of energy delivery.

Decentralization: The internet changed the world in part because it took the power of computing out of a few centralized data centers and distributed it wherever it made sense. The energy internet will do likewise. Digitalization supports decentralization by letting assets be integrated into an open energy supply chain. But decentralization is much more than just the integration of existing assets — it’s the proliferation of new assets wherever they’re needed.

Decarbonization: Decarbonization is, of course, the whole point of the exercise. We must move to greener supply chains built on decentralized infrastructure that leverage energy supply everywhere to meet energy demand anywhere. The market is demanding it and regulators are requiring it. The energy internet is therefore more than just an investment opportunity — it’s an existential imperative.

Democratization: Much of the innovation associated with the internet arose from the fact that, in addition to decentralizing technology physically, it also democratized technology demographically. Democratization is about putting power (literally, in this case) into the hands of the people. Vastly increasing the number of minds and hands tackling the energy industry’s challenges will also accelerate innovation and enhance our ability to respond to market dynamics.

Diversity: As I asserted above, no one has a crystal ball. So anyone investing in innovation at scale should diversify — not just to mitigate risk and optimize returns, but as an enablement strategy. After all, if we truly believe the energy internet (or Grid 2.0, if you prefer that term) will require that all the elements of the energy supply chain work together, we must diversify our innovation initiatives across those elements to promote interoperability and integration.

That’s how the digital internet was built. Standards bodies played an important role, but those standards and their implementations were driven by industry players like Microsoft and Cisco — as well as top VCs — who ensured the ecosystem’s success by driving integration across the supply chain.

We must take the same approach with the energy internet. Those with the power and influence to do so must help ensure we aggressively advance integration across the energy supply chain as a whole, even as we improve the individual elements. To this end, National Grid last year kicked off a new industry group called the NextGrid Alliance, which includes senior executives from more than 60 utilities across the world.

Finally, we believe it’s essential to diversify thinking within the energy ecosystem as well. National Grid has sounded alarms about the serious underrepresentation of women in the energy industry and of female undergraduates in STEM programs. On the flip side, research by Deloitte has found diverse teams are 20% more innovative. More than 60% of my own team at NGP are women, and that breadth of perspective has helped National Grid capture powerful insights into companywide innovation efforts.

More winning, less predicting

The concept of the energy internet isn’t some abstract future ideal. We’re already seeing specific examples of how it will transform the market:

Green transnationalism: The energy internet is on its way to becoming as global as the digital internet. The U.K., for instance, is now receiving wind-generated power from Norway and Denmark. This ability to leverage decentralized energy supply across borders will have significant benefits for national economies and create new opportunities for energy arbitrage.

EV charging models: Pumping electricity isn’t like pumping gas, nor should it be. With the right combination of innovation in smart metering and fast-charging end-point design, the energy internet will create new opportunities at office buildings, residential complexes and other places where cars plus convenience can equal cash.

Disaster mitigation: Recent events in Texas have highlighted the negative consequences of not having an energy internet. Responsible utilities and government agencies must embrace digitization and interoperability to more effectively troubleshoot infrastructure and better safeguard communities.

These are just a few of the myriad ways in which an open, any-to-any energy internet will promote innovation, stimulate competition and generate big wins. No one can predict exactly what those big wins will be, but there will surely be many, and they will accrue to the benefit of all.

That’s why even without a crystal ball, we should all commit ourselves to digitalization, decentralization, decarbonization, democratization and diversity. In so doing, we’ll build the energy internet together, and enable a fair, affordable and clean energy future.

#column, #electricity, #emerging-technologies, #energy, #energy-industry, #greentech, #internet-of-things, #national-grid-partners, #opinion, #smart-grid, #tc

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AI is ready to take on a massive healthcare challenge

Which disease results in the highest total economic burden per annum? If you guessed diabetes, cancer, heart disease or even obesity, you guessed wrong. Reaching a mammoth financial burden of $966 billion in 2019, the cost of rare diseases far outpaced diabetes ($327 billion), cancer ($174 billion), heart disease ($214 billion) and other chronic diseases.

Cognitive intelligence, or cognitive computing solutions, blend artificial intelligence technologies like neural networks, machine learning, and natural language processing, and are able to mimic human intelligence.

It’s not surprising that rare diseases didn’t come to mind. By definition, a rare disease affects fewer than 200,000 people. However, collectively, there are thousands of rare diseases and those affect around 400 million people worldwide. About half of rare disease patients are children, and the typical patient, young or old, weather a diagnostic odyssey lasting five years or more during which they undergo countless tests and see numerous specialists before ultimately receiving a diagnosis.

No longer a moonshot challenge

Shortening that diagnostic odyssey and reducing the associated costs was, until recently, a moonshot challenge, but is now within reach. About 80% of rare diseases are genetic, and technology and AI advances are combining to make genetic testing widely accessible.

Whole-genome sequencing, an advanced genetic test that allows us to examine the entire human DNA, now costs under $1,000, and market leader Illumina is targeting a $100 genome in the near future.

The remaining challenge is interpreting that data in the context of human health, which is not a trivial challenge. The typical human contains 5 million unique genetic variants and of those we need to identify a single disease-causing variant. Recent advances in cognitive AI allow us to interrogate a person’s whole genome sequence and identify disease-causing mechanisms automatically, augmenting human capacity.

A shift from narrow to cognitive AI

The path to a broadly usable AI solution required a paradigm shift from narrow to broader machine learning models. Scientists interpreting genomic data review thousands of data points, collected from different sources, in different formats.

An analysis of a human genome can take as long as eight hours, and there are only a few thousand qualified scientists worldwide. When we reach the $100 genome, analysts are expecting 50 million-60 million people will have their DNA sequenced every year. How will we analyze the data generated in the context of their health? That’s where cognitive intelligence comes in.

#artificial-intelligence, #cognitive-computing, #column, #cybernetics, #ec-column, #ec-consumer-health, #emerging-technologies, #genomics, #health, #machine-learning, #natural-language-processing, #neural-networks, #obesity, #precision-medicine

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Just 72 hours left to save $100 on passes to TC Sessions: Mobility 2021

So much can happen in 72 hours, and it’s easy to get distracted — especially when you’re building a startup in the fast lane that is mobility tech. But listen up: you have just 72 hours left to save $100 on your pass to TC Sessions: Mobility 2021 on June 9.

Don’t let “busy” distract you. Buy your pass to Mobility 2021 before the price increase goes into effect on Thursday, May 6 at 11:59 pm (PT).

Why should you attend TC Sessions: Mobility 2021? It’s where you can tap into the latest trends, regulatory concerns, technical and ethical challenges surrounding the technologies that will forever change how we move people and material goods across towns, cities, states, countries — and space.

Or, as Jens Lehmann, technical lead and product manager at SAP, told us:

“TC Sessions Mobility is definitely worth your time, especially if you’re an early-stage founder. You get to connect to people in your field and learn from founders who are literally a year into your same journey. Plus, you can meet and talk to the movers and shakers — the people who are making it happen.”

Take a gander at just some of the fascinating people and topics waiting for you and see the event agenda here.

  • Supercharging Self-Driving Super Vision: Few startups were as prescient as Scale AI when it came to anticipating the need for massive sets of tagged data for use in AI. Co-founder and CEO Alex Wang also made a great bet on addressing the needs of lidar sensing companies early on, which has made the company instrumental in deploying AV networks. We’ll hear about what it takes to make sense of sensor data in driverless cars and look at where the industry is headed.
  • EV Founders in Focus: We sit down with the founders poised to take advantage of the rise in electric vehicle sales. We’ll chat with Ben Schippers, co-founder and CEO of TezLab, an app that operates like a Fitbit for Tesla vehicles (and soon other EVs) and allows drivers to go deep into their driving data. The app also breaks down the exact types and percentages of fossil fuels and renewable energy coming from charging locations.
  • The Future of Flight: Joby Aviation founder JoeBen Bevirt spent more than a decade quietly developing an all-electric, vertical take-off and landing passenger aircraft. Now he is preparing for a new phase of growth as Joby Aviation merges with the special purpose acquisition company formed by famed investor and Linked co-founder Reid Hoffman. Bevirt and Hoffman will come to our virtual stage to talk about how to build a startup (and keep it secret while raising funds), the future of flight and, of course, SPACs.

Pro tip: Between the live stream and video on demand, you can keep your work schedule on track without missing out.

TC Sessions: Mobility 2021 takes place on June 9, but you have only 72 short hours left to save $100 on all the info and opportunity that TC Sessions: Mobility 2021 offers. Kick distractions to the curb. Buy your pass before the early bird price disappears on Thursday, May 6 at 11:59 pm (PT).

Is your company interested in sponsoring or exhibiting at TC Sessions: Mobility 2021? Contact our sponsorship sales team by filling out this form.

#alex-wang, #articles, #artificial-intelligence, #automation, #automotive, #av, #ben-schippers, #co-founder, #electric-aircraft, #emerging-technologies, #fitbit, #joby-aviation, #reid-hoffman, #renewable-energy, #robotics, #sap, #science-and-technology, #self-driving-car, #tc, #tc-sessions-mobility-2021, #technology, #tezlab, #video-on-demand

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Orca AI, which puts computer vision onto cargo ships, raises $13M Series A funding

Tel Aviv’s Orca AI, a computer vision startup that can be retrofitted to cargo ships and improve navigation and collision avoidance, has raised $13 million in a Series A funding, taking its total raised to over $15.5 million. While most cargo ships carry security cameras, computer vision cameras are rare. Orca AI hopes its solution could introduce autonomous guidance to vessels already at sea.

There are over 4,000 annual marine incidents, largely due to human error. The company says this is getting worse as the Coronavirus pandemic makes it harder for regular crew changes. The recent events in the Suez Canal have highlighted how crucial this industry is.

The funding round was led by OCV Partners, with Principal Zohar Loshitzer joining Orca AI’s board. Mizmaa Ventures and Playfair Capital also featured.

The company was founded by naval technology experts, Yarden Gross and Dor Raviv. The latter is an former Israel navy computer vision expert. Customers include Kirby, Ray Car Carriers and NYK.

Orca AI’s AI-based navigation and vessel tracking system supports ships in difficult to tricky to navigate situations and congested waterways, using vision sensors, thermal and low light cameras, plus algorithms that look at the environment and alert crews to dangerous situations.

On the raise, Yarden Gross, CEO, and co-founder said: “The maritime industry… is still far behind aviation with technological innovations. Ships deal with increasingly congested waterways, severe weather and low-visibility conditions creating difficult navigation experiences with often expensive cargo… Our solution provides unique insight and data to any ship in the world, helping to reduce these challenging situations and collisions in the future.” 

Zohar Loshitzer, Principal from OCV added: “Commercial shipping has historically been a highly regulated and traditional industry. However, we are now “witnessing a positive change in the adoption of tech solutions to increase safety and efficiency.

#articles, #artificial-intelligence, #computer-vision, #emerging-technologies, #europe, #israel, #mizmaa-ventures, #playfair-capital, #science-and-technology, #ship, #tc

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UK drone startup sees.ai gets go ahead to trial beyond-visual-line-of-sight (BVLOS) flights

The UK’s Civil Aviation Authority (CAA) has given the go ahead to local startup sees.ai, which is developing a beyond-visual-line-of-sight (BVLOS) command & control solution to aid data capture for industrial use-cases, to trial a concept for routine BVLOS operations — the first such authorization for a U.K. company, the regulator said today.

The test is taking place under a sandbox program announced back in May 2019 — directing government funding and regulatory support to R&D in the drone space — initially through virtual testing, such as of avoid and detect systems.

Sees.ai, an early participant in the sandbox, has now secured authorization to trial a concept for routine BVLOS operations at three (physical) sites without needing to pre-authorise each flight.

The Techstars-backed startup is focused on drone operations in industrial settings — building tech to scale the use of drones for inspection and maintenance purposes in industries, such as the oil & gas sector, by enabling pilots to remote-control craft from a central location, rather than needing to be on site for each flight.

But it’s clear BVLOS capabilities will be essential for other uses of drone tech — such as delivery — hence the CAA calling the trial “a significant step forward for the drone industry”.

“By testing the concept in industrial environments for inspection, monitoring and maintenance purposes, sees.ai aims to prove the safety of its system within this context initially, before extending it to address increasingly challenging missions over time,” it added.

Under current U.K. rules, drone operators must keep their aircraft within line of sight and follow the country’s drone code — unless they have specific permissions to do otherwise.

One company that previously gained such permission was U.S. tech giant Amazon — which started testing BVLOS delivery drones in the UK back in 2016 — and continues to work on bringing a commercial drone delivery service to market, under its Prime Air brand.

Amazon’s effort has already been years in the making (it’s been running experiments since 2013) — and last year the FT, citing a Prime Air source, reported that it still remains “years” out from realizing the goal of drone deliveries at scale. So while (another) U.K. trial of BVLOS drone tech is being lauded as a significant development for the industry by the regulator, any Brits expecting drone deliveries in the wild anytime soon are likely to be disappointed.

The CAA authorization for the sees.ai trial will enable the BVLOS test flights to operate under 150ft — initially requiring an observer to remain in visual line of sight with the aircraft and be able to communicate with the remote pilot if necessary, per the regulator.

So, technically then, the trials will begin as extended-line-of-sight (EVLOS), which still entail limits vs true BVLOS — enabling drone flights to operate further than 500m from the remote pilot (by deploying flight observers) but not removing on-site observers entirely, as is the ultimate industry goal.

In a regulatory roadmap published last fall the CAA wrote that many steps are required to arrive at the sought-for situation of BVLOS being ‘business as usual’ in non-segregated airspace — so there still looks to be a long road ahead before commercial drones will be able to legally whiz around gathering data (or delivering stuff) far from any humans in the loop.

“The long-term aspiration of operators is for BVLOS operations to be a routine part of business across the UK. This vision requires a significant volume of evidence, experience and learning by everyone involved. There will inevitably be a need for innovators and the CAA to build, test, learn and repeat in small steps to work towards the vision,” the CAA roadmap notes.

Commenting on sees.ai’s trial authorization in a statement, CEO John McKenna dubbed it a “significant milestone”, adding: “We are accelerating towards a future where drones fly autonomously at scale — high up alongside manned aviation and low down inside our industrial sites, suburbs and cities. Securing this UK-first permission is a major step on this journey which will deliver big benefits to society across public health & safety, efficiency and environmental impact.”

 

#aerospace, #amazon, #artificial-intelligence, #bvlos, #civil-aviation-authority, #drones, #emerging-technologies, #europe, #prime-air, #regulatory-sandbox, #robotics, #techstars, #united-kingdom

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China’s Xpeng in the race to automate EVs with lidar

Elon Musk famously said any company relying on lidar is “doomed.” Tesla instead believes automated driving functions are built on visual recognition and is even working to remove the radar. China’s Xpeng begs to differ.

Founded in 2014, Xpeng is one of China’s most celebrated electric vehicle startups and went public when it was just six years old. Like Tesla, Xpeng sees automation as an integral part of its strategy; unlike the American giant, Xpeng uses a combination of radar, cameras, high-precision maps powered by Alibaba, localization systems developed in-house, and most recently, lidar to detect and predict road conditions.

“Lidar will provide the 3D drivable space and precise depth estimation to small moving obstacles even like kids and pets, and obviously, other pedestrians and the motorbikes which are a nightmare for anybody who’s working on driving,” Xinzhou Wu, who oversees Xpeng’s autonomous driving R&D center, said in an interview with TechCrunch.

“On top of that, we have the usual radar which gives you location and speed. Then you have the camera which has very rich, basic semantic information.”

Xpeng is adding lidar to its mass-produced EV model P5, which will begin delivering in the second half of this year. The car, a family sedan, will later be able to drive from point A to B based on a navigation route set by the driver on highways and certain urban roads in China that are covered by Alibaba’s maps. An older model without lidar already enables assisted driving on highways.

The system, called Navigation Guided Pilot, is benchmarked against Tesla’s Navigate On Autopilot, said Wu. It can, for example, automatically change lanes, enter or exit ramps, overtake other vehicles, and maneuver another car’s sudden cut-in, a common sight in China’s complex road conditions.

“The city is super hard compared to the highway but with lidar and precise perception capability, we will have essentially three layers of redundancy for sensing,” said Wu.

By definition, NGP is an advanced driver-assistance system (ADAS) as drivers still need to keep their hands on the wheel and take control at any time (Chinese laws don’t allow drivers to be hands-off on the road). The carmaker’s ambition is to remove the driver, that is, reach Level 4 autonomy two to four years from now, but real-life implementation will hinge on regulations, said Wu.

“But I’m not worried about that too much. I understand the Chinese government is actually the most flexible in terms of technology regulation.”

The lidar camp

Musk’s disdain for lidar stems from the high costs of the remote sensing method that uses lasers. In the early days, a lidar unit spinning on top of a robotaxi could cost as much as $100,000, said Wu.

“Right now, [the cost] is at least two orders low,” said Wu. After 13 years with Qualcomm in the U.S., Wu joined Xpeng in late 2018 to work on automating the company’s electric cars. He currently leads a core autonomous driving R&D team of 500 staff and said the force will double in headcount by the end of this year.

“Our next vehicle is targeting the economy class. I would say it’s mid-range in terms of price,” he said, referring to the firm’s new lidar-powered sedan.

The lidar sensors powering Xpeng come from Livox, a firm touting more affordable lidar and an affiliate of DJI, the Shenzhen-based drone giant. Xpeng’s headquarters is in the adjacent city of Guangzhou about 1.5 hours’ drive away.

Xpeng isn’t the only one embracing lidar. Nio, a Chinese rival to Xpeng targeting a more premium market, unveiled a lidar-powered car in January but the model won’t start production until 2022. Arcfox, a new EV brand of Chinese state-owned carmaker BAIC, recently said it would be launching an electric car equipped with Huawei’s lidar.

Musk recently hinted that Tesla may remove radar from production outright as it inches closer to pure vision based on camera and machine learning. The billionaire founder isn’t particularly a fan of Xpeng, which he alleged owned a copy of Tesla’s old source code.

In 2019, Tesla filed a lawsuit against Cao Guangzhi alleging that the former Tesla engineer stole trade secrets and brought them to Xpeng. XPeng has repeatedly denied any wrongdoing. Cao no longer works at Xpeng.

Supply challenges

While Livox claims to be an independent entity “incubated” by DJI, a source told TechCrunch previously that it is just a “team within DJI” positioned as a separate company. The intention to distance from DJI comes as no one’s surprise as the drone maker is on the U.S. government’s Entity List, which has cut key suppliers off from a multitude of Chinese tech firms including Huawei.

Other critical parts that Xpeng uses include NVIDIA’s Xavier system-on-the-chip computing platform and Bosch’s iBooster brake system. Globally, the ongoing semiconductor shortage is pushing auto executives to ponder over future scenarios where self-driving cars become even more dependent on chips.

Xpeng is well aware of supply chain risks. “Basically, safety is very important,” said Wu. “It’s more than the tension between countries around the world right now. Covid-19 is also creating a lot of issues for some of the suppliers, so having redundancy in the suppliers is some strategy we are looking very closely at.”

Taking on robotaxis

Xpeng could have easily tapped the flurry of autonomous driving solution providers in China, including Pony.ai and WeRide in its backyard Guangzhou. Instead, Xpeng becomes their competitor, working on automation in-house and pledges to outrival the artificial intelligence startups.

“The availability of massive computing for cars at affordable costs and the fast dropping price of lidar is making the two camps really the same,” Wu said of the dynamics between EV makers and robotaxi startups.

“[The robotaxi companies] have to work very hard to find a path to a mass-production vehicle. If they don’t do that, two years from now, they will find the technology is already available in mass production and their value become will become much less than today’s,” he added.

“We know how to mass-produce a technology up to the safety requirement and the quarantine required of the auto industry. This is a super high bar for anybody wanting to survive.”

Xpeng has no plans of going visual-only. Options of automotive technologies like lidar are becoming cheaper and more abundant, so “why do we have to bind our hands right now and say camera only?” Wu asked.

“We have a lot of respect for Elon and his company. We wish them all the best. But we will, as Xiaopeng [founder of Xpeng] said in one of his famous speeches, compete in China and hopefully in the rest of the world as well with different technologies.”

5G, coupled with cloud computing and cabin intelligence, will accelerate Xpeng’s path to achieve full automation, though Wu couldn’t share much detail on how 5G is used. When unmanned driving is viable, Xpeng will explore “a lot of exciting features” that go into a car when the driver’s hands are freed. Xpeng’s electric SUV is already available in Norway, and the company is looking to further expand globally.

#alibaba, #artificial-intelligence, #asia, #automation, #automotive, #baic, #bosch, #cars, #china, #cloud-computing, #driver, #electric-car, #elon-musk, #emerging-technologies, #engineer, #founder, #huawei, #lasers, #li-auto, #lidar, #livox, #machine-learning, #nio, #norway, #nvidia, #qualcomm, #robotaxi, #robotics, #self-driving-cars, #semiconductor, #shenzhen, #tc, #tesla, #transport, #transportation, #u-s-government, #united-states, #wu, #xavier, #xiaopeng, #xpeng

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IonQ now supports IBM’s Qiskit quantum development kit

IonQ, the trapped ion quantum computing company that recently went public via a SPAC, today announced that it is integrating its quantum computing platform with the open-source Qiskit software development kit. This means Qiskit users can now bring their programs to IonQ’s platform without any major modifications to their code.

At first glance, that seems relatively unremarkable, but it’s worth noting that Qiskit was founded by IBM Research and is IBM’s default tool for working with its quantum computers. There is a healthy bit of competition between IBM and IonQ (and, to be fair, many others in this space), in part because both are betting on very different technologies at the core of their platforms. While IonQ is betting on trapped ions, which allows its machines able to run at room temperature, IBM’s technique requires its machine to be supercooled.

IonQ has now released a new provider library for Qiskit that is available as part of the Qiskit Partner repository on GitHub and via the Python Package Index.

“IonQ is excited to make our quantum computers and APIs easily accessible to the Qiskit community,” said IonQ CEO & President Peter Chapman. “Open source has already revolutionized traditional software development. With this integration, we’re bringing the world one step closer to the first generation of widely-applicable quantum applications.”

On the one hand, it’s hard not to look at this as IonQ needling IBM a bit, but it’s also an acknowledgment that Qiskit has become somewhat of a standard for developers who want to work with quantum computers. But putting these rivalries aside, we’re also in the early days of quantum computing and with no clear leader yet, anything that makes these various platforms more interoperable is a win for developers who want to dip their feet into writing for them.

#azure-quantum, #braket, #emerging-technologies, #github, #ibm, #ionq, #peter-chapman, #python, #quantum, #quantum-computing, #tc

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Hyundai IONIQ 5 will be Motional and Lyft’s first robotaxi

Motional will integrate its driverless technology into Hyundai’s new all-electric SUV to create the company’s first robotaxi. At the start of 2023, customers in certain markets will be able to book the fully electric, fully autonomous taxi through the Lyft app.

The Hyundai IONIQ 5, which was revealed in February with a consumer release date expected later this year, will be fully integrated with Motional’s driverless system. The vehicles will be equipped with the hardware and software needed for Level 4 autonomous driving capabilities, including LiDAR, radar and cameras to provide the vehicle’s sensing system with 360 degrees of vision, and the ability to see up to 300 meters away. This level of driverless technology means a human will not be required to take over driving.

The interior living space will be similar to the consumer model, but additionally equipped with features needed for robotaxi operation, according to a Motional spokesperson. Motional did not reveal whether or not the vehicle would still have a steering wheel, and images of the robotaxi aren’t yet available.

Motional’s IONIQ 5 robotaxis have already begun testing on public roads and closed courses, and they’ll be put through more months of testing and real-world experience before being deployed on Lyft’s platform. The company says it’ll complete testing only once it’s confident that the taxis are safer than a human driver.

Motional, the Aptiv-Hyundai $4 billion joint venture aimed at commercializing driverless cars, announced its partnership with Lyft in December, signaling the ride-hailing company’s primary involvement in Motional’s plans. The company recently announced that it began testing its driverless tech on public roads in Las Vegas. Hyundai’s IONIQ 5 is Motional’s second platform to go driverless on public roads.

#aptiv, #automation, #driver, #emerging-technologies, #hyundai, #hyundai-motor-company, #las-vegas, #lyft, #mobility, #motional, #robotaxi, #robotics, #self-driving-cars, #tc, #technology, #transport

0

IBM brings its Quantum System One to the Cleveland Clinic

IBM has installed a couple of its own Quantum System One machines across the world in recent years, but today it announced its first private-sector U.S. deployment thanks to a new ten-year partnership with the Cleveland Clinic. This not only marks IBM’s first U.S. install of one of its quantum computers outside of its own facilities, but also the first time a healthcare institute purchases and houses a quantum computer. And thanks to this deal, Cleveland will also get access to IBM’s upcoming next-gen 1,000+ qubit quantum system.

We’re still in the very early days of commercializing quantum computing and for most current users, having access to a system over the cloud is sufficient for the experiments they are running. But increasingly, we are seeing research institutes and even some commercial users who are looking to install on-premises quantum computers to have full access to a dedicated machine.

This new deal is part of a larger partnership between IBM and the Cleveland Clinic, which also involves IBM’s hybrid cloud portfolio for high-performance computing and its AI tools. The partnership also forms the foundation of Cleveland Clinic’s new Center for Pathogen Research & Human Health, which is supported by $500 million in investments from the State of Ohio, Jobs Ohio and Cleveland Clinic.

“What we’re announcing here is the first — I’m going to call them private sector or nonprofit — but still, it’s the first sort of non-government organization that is going to have not only fully dedicated systems, but what is really, really remarkable is our commitment for the decades,” Dario Gil, IBM’s SVP and Director of IBM Research, told me. “In a way, they are partnering with us for the entire roadmap. So it’s not only taking receipt and getting access to a fleet of quantum computers and the next-generation quantum computer for next year. They’re also the first ones who are signing up and says, ‘I want the first 1,000+ qubit system.”

He noted that it takes a very forward-looking organization to invest heavily in quantum computing today. It’s one thing for a nation-state to start working with this nascent technology, given the potential it has in a wide variety of fields, but it’s another for a non-profit to make a similar bet. “The level of ambition is really, really high on their end because they’re thinking about the future,” Gil said of the Cleveland Clinic’s leadership.

Gil noted that as part of the overall deal, Cleveland Clinic’s researchers will also get access to IBM’s entire quantum portfolio in the cloud. IBM will maintain and support the on-premises quantum computer and they will remain IBM-owned machines, similar to its deals with government research labs in Japan and Germany, he explained.

“Maintaining it and supporting it is really critical,” Gil said about why that’s the case. “And they need us and our expertise to be able to do that. And also, you know, we do it because it’s like one of the most sensitive technologies that we have in IBM. So we are exquisitely focused on maintaining the security and safety for the machines.”

As part of the overall deal, IBM and Cleveland Clinic will also work on building skills among Cleveland Clinic’s researchers in quantum computing, but also AI and high-performance computing.

“Through this innovative collaboration, we have a unique opportunity to bring the future to life,” said Tom Mihaljevic, M.D., President and CEO of Cleveland Clinic. “These new computing technologies will revolutionize discovery in the life sciences and ultimately improve people’s lives. The Discovery Accelerator will enable our renowned teams to build a forward-looking digital infrastructure and transform medicine, while training the workforce of the future and growing our economy.”

#artificial-intelligence, #cleveland, #computing, #dario-gil, #emerging-technologies, #germany, #ibm, #japan, #quantum-computing, #qubit, #science, #tc, #united-states

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IBM launches its first quantum developer certification

IBM today announced the launch of its first developer certification for programming quantum computers.

While quantum computing may still be in its infancy, most pundits in the industry will tell you that now is the time to learn the basic concepts. And while there is little that’s immediately intuitive on the hardware side of quantum computing, the actual software tools that most players in the industry are developing today should feel somewhat familiar to virtually any developer.

Unsurprisingly, the ‘IBM Quantum Developer Certification,’ as it’s officially called, focuses on IBM’s own software tools and especially Qiskit, its SDK for working with quantum computers. Qiskit has already proven quite popular, with more than 600,000 installs and when IBM Quantum and the Qiskit team hosted a quantum summer school last year, almost 5,000 developers participated.

But on top of knowing their way around the basics of Qiskit (think defining and executing quantum circuits) developers also need to learn some of the basics of quantum computing itself. Once you know your way around Bloch spheres, Pauli matrices and Bell states, you’ll probably be in good shape for taking the certification exam, which will be administered on the Pearson VUE platform.

Abe Asfaw, the global lead for Quantum Education and Open Science at IBM, told me that this is just the first of a series of planned quantum certifications.

“What we’ve built is a multi-tiered developer certification,” he told me. “The first tier is what we’re releasing in this announcement and that tier gets developers introduced to how to work with quantum circuits. How do you use Qiskit […] and how do you run it on a quantum computer? And once you run it on a quantum computer, how do you look at the results and how do you interpret the results? This sets the stage for the next series of certifications that we’re developing, which are then going to be attached to use cases that are being explored in optimization, chemistry and finance. All of these can now be sort of integrated into the developer workflow once we have enabled someone to show that they can work with quantum circuits.”

Image Credits: IBM

Asfaw stressed that IBM has focused on education developers about quantum computing for quite a while now, in part because it takes some time to develop the skills and intuition to build quantum circuits. He also noted that the open-source Qiskit project has integrated a lot of the tools that developers need to work at both the circuit level — which is a bit closer to writing in C or maybe even assembly in the classical computing world — and at the application level, where a lot of that is abstracted away.

“The idea is to make it easy for someone who is currently developing, whether it’s in the cloud, whether it’s using Python, to be able to run these tools and integrate quantum computing into their workflow,” Asfaw said. “I think the hardest part, to be very honest, is just giving someone the comfort to know that quantum computing is real today and that you can work with quantum computers. It’s as easy as opening up a Jupyter notebook and writing some code in Python.”

He noted that IBM already often helps upskill developers in its partner companies who are interested in quantum computing. So far, though, this has been a very ad hoc process. With the new certification program, developers can now formally demonstrate their skills and show that they are in a position to utilize quantum computing in their workflow.

#c, #computing, #developer, #developers, #emerging-technologies, #finance, #ibm, #python, #quantum-certification, #quantum-computing, #quantum-development, #quantum-education, #tc, #technology

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DeepSee.ai raises $22.6M Series A for its AI-centric process automation platform

DeepSee.ai, a startup that helps enterprises use AI to automate line-of-business problems, today announced that it has raised a $22.6 million Series A funding round led by led by ForgePoint Capital. Previous investors AllegisCyber Capital and Signal Peak Ventures also participated in this round, which brings the Salt Lake City-based company’s total funding to date to $30.7 million.

The company argues that it offers enterprises a different take on process automation. The industry buzzword these days is ‘robotic process automation,’ but DeepSee.ai argues that what it does is different. I describe its system as ‘knowledge process automation’ (KPA). The company itself defines this as a system that “mines unstructured data, operationalizes AI-powered insights, and automates results into real-time action for the enterprise.” But the company also argues that today’s bots focus on basic task automation that doesn’t offer the kind of deeper insights that sophisticated machine learning models can bring to the table. The company also stresses that it doesn’t aim to replace knowledge workers but help them leverage AI to turn the plethora of data that businesses now collect into actionable insights.

Image Credits: DeepSee.ai

“Executives are telling me they need business outcomes and not science projects,” writes DeepSee.ai CEO Steve Shillingford. “And today, the burgeoning frustration with most AI-centric deployments in large-scale enterprises is they look great in theory but largely fail in production. We think that’s because right now the current ‘AI approach’ lacks a holistic business context relevance. It’s unthinking, rigid, and without the contextual input of subject-matter experts on the ground. We founded DeepSee to bridge the gap between powerful technology and line-of-business, with adaptable solutions that empower our customers to operationalize AI-powered automation – delivering faster, better, and cheaper results for our users.”

To help businesses get started with the platform, DeepSee.ai offers three core tools. There’s DeepSee Assembler, which ingests unstructured data and gets it ready for labeling, model review and analysis. Then, DeepSee Atlas can use this data to train AI models that can understand a company’s business processes and help subject-matter experts define templates, rules and logic for automating a company’s internal processes. The third tool, DeepSee Advisor, meanwhile focuses on using text analysis to help companies better understand and evaluate their business processes.

Currently, the company’s focus is on providing these tools for insurance companies, the public sector and capital markets. In the insurance space, use cases include fraud detection, claims prediction and processing, and using large amounts of unstructured data to identify patterns in agent audits, for example.

That’s a relatively limited number of industries for a startup to operate in, but the company says it will use its new funding to accelerate product development and expand to new verticals.

“Using KPA, line-of-business executives can bridge data science and enterprise outcomes, operationalize AI/ML-powered automation at scale, and use predictive insights in real time to grow revenue, reduce cost, and mitigate risk,” said Sean Cunningham, Managing Director of ForgePoint Capital. “As a leading cybersecurity investor, ForgePoint sees the daily security challenges around insider threat, data visibility, and compliance. This investment in DeepSee accelerates the ability to reduce risk with business automation and delivers much-needed AI transparency required by customers for implementation.”

#allegiscyber-capital, #articles, #artificial-intelligence, #automation, #automation-anywhere, #business-process-automation, #business-process-management, #business-software, #cloud, #emerging-technologies, #enterprise, #forgepoint-capital, #machine-learning, #recent-funding, #robotic-process-automation, #salt-lake-city, #signal-peak-ventures, #startups

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Mighty Buildings nabs $40M Series B to 3D print your next house

Once upon a time, the idea of 3D-printed homes felt like a thing of the future.

But as housing gets less and less affordable — especially in ultra-expensive markets such as the Bay Area — companies are getting creative in their quest to build more affordable homes using technology.

One of those companies, Oakland-based Mighty Buildings, just raised $40 million in Series B funding for its quest to create homes that it says are “beautiful, sustainable and affordable” using 3D printing, robotics and automation. It claims to be able to 3D print structures “two times as quickly with 95% less labor hours and 10-times less waste” than conventional construction. For example, it says it can 3D print a 350-square-foot studio apartment in just 24 hours.

The four-year-old startup’s efforts caught the eye of Khosla Ventures, which co-led the financing along with Zeno Ventures. 

Ryno Blignaut, an operating partner at Khosla, believes that Mighty Buildings — which launched out of stealth last August — has the potential to cut both the cost and carbon footprint of home construction “by 50% or more.”

The company takes a hybrid approach to home construction, combining 3D printing and prefab (meaning built offsite) building, according to co-founder and COO Alexey Dubov. It has invented a proprietary thermoset composite material called Light Stone Material (LSM) as part of its effort to reduce the home construction industry’s reliance on concrete and steel. 

The material can be 3D printed and hardens almost immediately, according to the company, while also maintaining cohesion between layers to create a monolithic structure. Mighty Buildings can then 3D print elements like overhangs or ceilings without the need for additional supporting formwork. That way, it’s able to fully print a structure and not just the walls. 

Robotic arms can post-process the composite, which combined with the company’s ability to automate the pouring of insulation and the 3D printing gives Mighty Buildings the ability to automate up to 80% of the construction process, the company claims.

Khosla was drawn to the Mighty Buildings’ innovative approach.

“We believe in dematerializing buildings and non-linearly reducing the amount of cement and steel used, thereby reducing the cost of construction in order to increase affordable access to housing together with improved sustainability,” Blignaut wrote via email.

Mighty Building’s use of 3D printing, advanced manufacturing techniques, modern robotics and “new lighter and stronger materials” gives it an edge, he added.

Since its launch, the company has produced and installed a number of accessory dwelling units (ADUs) and is now taking orders for Mighty Houses — its newest product line that will range from 864 to 1,440 square feet at an estimated cost of $304,000 to $420,500. (Similarly sized houses in some parts of the Bay Area can sell for upwards of $1 million).

The units are created with a 3D-printed exterior panelized shell while certain elements — such as bathrooms for example — are prefabricated in the company’s 79,000-square-foot production facility in Oakland. 

For now, the company is only building in California, but Dubov says it’s open to exploring other markets as its factory can be replicated.

Also, Mighty Buildings plans this year to market its Mighty Kit System and a new fiber-reinforced material for multi-story projects as part of a planned B2B platform for developers. In fact, the company already has secured contracts with developers for its single family housing product line. It also plans to use the new capital in part to scale its production capacity with increased automation.

Ultimately, Mighty Building’s vision is to provide production-as-a-service, with builders and architects designing their own structures and then developers using Mighty Factories to produce them at scale.

Mighty Buildings is not the only startup doing 3D-printed homes. Last August, Austin-based ICON raised $35 million in Series A funding. The company also aims to reinvent building affordable homes with the use of 3D printers, robotics and advanced materials. The biggest difference between the two companies, according to Dubov, is that ICON does primarily onsite construction while Mighty Buildings prefabricates in a factory.

More than a dozen other investors also participated in Mighty Building’s latest round, including returning backers Bold Capital Partners, Core Innovation Capital and Foundamental and new investors including ArcTern Ventures, Abies Ventures, Modern Venture Partners, MicroVentures, One Way Ventures, Polyvalent Capital and others. Mighty Buildings was also included in Y Combinator’s Top companies list, all of which have valuations over $150 million (although the company declined to reveal its current valuation). 

For its part, Khosla’s Blignaut believes that buildings are “a big part of our urban landscape and a large consumer of resources.”

“Construction and building account for more carbon emissions in the U.S. than transportation or industry,” he said. Other portfolio companies addressing such challenges include Ori Living, Vicarious, Katerra and Arevo.

#3d-printing, #affordable-housing, #bold-capital-partners, #california, #construction-tech, #core-innovation-capital, #emerging-technologies, #khosla-ventures, #microventures, #mighty-buildings, #oakland, #one-way-ventures, #recent-funding, #startups, #tc, #y-combinator

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Internet of Cars: A driver-side primer on IoT implementation

Billions of devices are currently connected to the Internet of Things (IoT), and researchers are predicting tremendous growth in the coming decade.

One of the most exciting, challenging and potentially lucrative areas of the IoT is the automotive sector. The car is a major component of most people’s daily lives, and a “smart” car could do a lot to save people time and money.

At the same time, the “Internet of Cars” carries with it dystopian visions of increased ad noise and security threats. It’s worth considering for a moment what these scenarios look like — good and bad — and how consumers can educate themselves to ensure that the cars of the future are driving in the right direction.

The car is a major component of most people’s daily lives, and a “smart” car could do a lot to save people time and money.

The promises and problems of connected cars

Imagine if your car was able to call your mechanic when the engine was showing signs of trouble. Imagine if the mechanic could read a data report from your engine and order the required parts ahead of time. Imagine if the data on those parts could be aggregated to warn of the need for any mass recalls? What if your car could communicate with other cars around it in a traffic jam, and the cars could all work together to space out and ease congestion?

What if your car could pay automatically at parking garages and drive-throughs? Anyone that owns a car is familiar with all these pain points, and the prospect of a new system that erases these spots of friction would be a welcome development.

But how can we ensure that all of this new data from our smart cars will be handled in a secure and private way? It seems likely, as car manufacturers work quickly to bring their products online, that tech giants will be the first partners to help implement the Internet of Cars. This might be cause for concern amongst consumers who are growing tired of their data being sold or hacked. The big tech companies aren’t inherently evil, but their basic business models are structured in such a way that consumer privacy and security are not the main priorities.

It’s not hard to imagine how the Internet of Cars could move in a much darker direction: Advertisements with real-time location data updating constantly on your windshield, personal data such as your driving habits stored on central servers, and a myriad of new vulnerabilities for hackers to exploit. How do we bring cars online so that friction in our lives is smoothed down without introducing a unique set of new problems?

Data security must be the foundation of the IoC

Of course Big Tech companies will be eager to offer connectivity for drivers, but it’s most likely going to come at the price of giving personal data over to Big Tech servers. This brings with it, as always, two major problems. The first is that centralized data represents a honeypot for hackers. No matter the strength of the security system, hackers realize that once they break through, they have access to the whole pot. The second problem is that the value of all that data is simply too lucrative for the owner to ignore. The data will always be sold, regardless of all the lip service promising to make it anonymous.

The IoT represents a new layer of IT integration in our lives; it will be at least as much of a game-changer as the internet was originally. Even with the advancement of the mobile internet brought about by smartphones, internet implementations have, until now, basically been carried out through clunky interfaces like screens, keyboards and mouses. The IoT is going to bring a new level of sophistication to how and where we interface online, but this also means a new level of intrusion into our physical reality. In the case of cars, we can be rightly wary that this new development might be problematic, but it doesn’t have to be.

Distributed ledger technology (DLT) represents a path forward for the Internet of Cars, because it builds data security and privacy into the foundations of any connected devices. Any model of DLT includes some basic concepts such that data is carried on a decentralized network of computers and servers. It also means that data is stored permanently, and that new entries of any data are subject to a mathematical verification. DLT is a fundamentally different way to handle massive amounts of data. DLTs have proven to be extremely resilient to attacks, and the data on these networks is nearly impossible to collect and sell.

Picking the right tool for the job

There are millions of internet-connected cars already on the road, albeit mostly with crude subscription services for music and weather apps. With further advances, connection will be much more encompassing, with the average connected car having up to 200 sensors installed, each recording a point of data, minute by minute. The numbers quickly become staggering, and in emergency situations, the need for data agility is apparent. Picture driving on a highway in medium traffic.

If someone’s tire blows out half a mile ahead, this information could be quickly conveyed to surrounding cars, warning of the potential for emergency braking. Any DLT solution would have to include a very nimble verification process for all these new packets of information to be brought into and carried by the network.

Additionally, because of the computational complexity involved, almost all DLTs today charge a fee for each new transaction brought into the network. In fact, the fee is an integral part of the structure of many of these computational models. This is obviously not going to be workable in a system like urban traffic that would be generating billions of “transactions” every day. The truth is that decentralized data networks were never designed to handle these kinds of massive use-case scenarios. Blockchain, for example, is very elegant at censorship-resistance in a network, and this has proven valuable in certain financial use cases.

But a DLT that expects a little money every time a car’s air conditioning reports its output is simply unusable for that application. Any DLT that’s going to give us a high level of security and real-time connectivity will also have to be feeless.

Security, speed and ease of adaptability through a no-fee structure are the three critical points for any network backing up the Internet of Cars. DLTs are clearly the most secure option, but they must also provide scalability and a feeless structure.

The example of being able to pay automatically for a parking garage visit might seem like a trite convenience. In actuality, if we can implement these types of small transactions properly from the beginning, then the hurdles we will jump in solving the complexity and volume of the car traffic data environment will go a long way to creating a safe, consumer-friendly Internet of Things in general.

When thinking about a completely connected physical environment, the alternatives to scalable, fee-less DLT are frankly scary.

#ambient-intelligence, #automotive, #column, #computer-security, #connected-car, #dlt, #emerging-technologies, #internet-of-things, #mobile-internet, #opinion, #privacy, #security, #transportation

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Firehawk Aerospace extends seed funding to $2.5 million with $1.2 million from Harlow Capital

Rocket fuel technology startup Firehawk Aerospace has added $1.2 million to its existing seed financing, bringing the full amount invested in the round to $2.5 million. The new tranche comes from Harlow Capital Management, a Dallas-based firm run by Colby Harlow, who will join Firehawk’s board of directors as part of the deal.

Firewhawk, which was a finalist in our first-ever all-virtual Startup Battlefield at TC Disrupt last September, has developed a new kind of hybrid rocket fuel that greatly enhances rocket launch safety, cost and transportation using additive manufacturing (basically, the grown-up version of 3D printing). Hybrid rocket fuel (which combines aspects of both liquid and solid propellants used previously) isn’t new, but past technology has been unable to compete on cost and efficacy relative to existing nonhybrid alternatives.

The startup’s Chief Scientist Ron Jones was able to get around these limitations with two new approaches: Using a fuel with a hard polymer structure and producing it using additive manufacturing instead of casting via molds with a liquid that hardens.

Firehawk now intends to use its seed funding to test its technology in operational conditions and at the kind of scale required for commercialization, and to build out its partnerships and client list. The startup also intends to grow its R&D and manufacturing operations in both Texas and Oklahoma.

#3d-printing, #additive-manufacturing, #aerospace, #articles, #dallas, #economy, #emerging-technologies, #firehawk-aerospace, #funding, #oklahoma, #rocket, #rocket-propulsion, #rocketry, #startup-company, #startups, #tc, #texas

0

MIT researchers develop a new ‘liquid’ neural network that’s better at adapting to new info

A new type of neural network that’s capable of adapting its underlying behavior after the initial training phase could be the key to big improvements in situations where conditions can change quickly – like autonomous driving, controlling robots, or diagnosing medical conditions. These so-called ‘liquid’ neural networks were devised by MIT Computer Science and Artificial Intelligence Lab’s Ramin Hasani and his team at CSAIL, and they have the potential to greatly expand the flexibility of AI technology after the training phase, when they’re engaged in the actual practical inference work done in the field.

Typically, after the training phase, during which neural network algorithms are provided with a large volume of relevant target data to hone their inference capabilities, and rewarded for correct responses in order to optimize performance, they’re essentially fixed. But Hasani’s team developed a means by which his ‘liquid’ neural net can adapt the parameters for ‘success’ over time in response to new information, which means that if a neural net tasked with perception on a self-driving car goes from clear skies into heavy snow, for instance, it’s better able to deal with the shift in circumstances and maintain a high level of performance.

The main difference in the method introduced by Hasani and his collaborators is that it focuses on time-series adaptability, meaning that rather than being built on training data that is essentially made up of a number of snapshots, or static moments fixed in time, the liquid networks inherently considers time series data – or sequences of images rather than isolated slices.

Because of the way the system is designed, it’s actually also more open to observation and study by researchers, when compared to traditional neural networks. This kind of AI is typically referred to as a ‘black box,’ because while those developing the algorithms know the inputs and the the criteria for determining and encouraging successful behavior, they can’t typically determine what exactly is going on within the neural networks that leads to success. This ‘liquid’ model offers more transparency there, and it’s less costly when it comes to computing because it relies on fewer, but more sophisticated computing nodes.

Meanwhile, performance results indicate that it’s better than other alternatives for accuracy in predicting the future values of known data sets. Th next step for Hasani and his team are to determine how best to make the system even better, and ready it for use in actual practical applications.

#articles, #artificial-intelligence, #artificial-neural-networks, #computing, #emerging-technologies, #neural-network, #neural-networks, #science, #science-and-technology, #self-driving-car, #tc

0

Classiq raises $10.5M Series A round for its quantum software development platform

Classiq, a Tel Aviv-based startup that aims to make it easier for computer scientists and developers to create quantum algorithms and applications, today announced that it has raised a $10.5 million Series A round led by Team8 Capital and Wing Capital. Entrée Capital, crowdfunding platform OurCrowd and Sumitomo Corporation (through IN Venture) also participated in this round, which follows the company’s recent $4 million seed round led by Entrée Capital.

The idea behind Classiq, which currently has just under a dozen members on its team, is that developing quantum algorithms remains a major challenge.

“Today, quantum software development is almost an impossible task,” said Nir Minerbi, CEO and Co-founder of Classiq. “The programming is at the gate level, with almost no abstraction at all. And on the other hand, for many enterprises, that’s exactly what they want to do: come up with game-changing quantum algorithms. So we built the next layer of the quantum software stack, which is the layer of a computer-aided design, automation, synthesis. […] So you can design the quantum algorithm without being aware of the details and the gate level details are automated.”

Image Credits: Classiq

With Microsoft’s Q#, IBM’s Qiskit and their competitors, developers already have access to quantum-specific languages and frameworks. And as Amir Naveh, Classiq’s VP of R&D told me, just like with those tools, developers will define their algorithms as code — in Classiq’s case a variant of Python. With those other languages, though, you will write sequences of gates on the cubits to define your quantum circuit.

“What you’re writing down isn’t gates on cubits, its concepts, its constructs, its constraints — it’s always constraints on what you want the circuit to achieve,” Naveh explained. “And then the circuit is synthesized from the constraints. So in terms of the visual interface, it would look the same [as using other frameworks], but in terms of what’s going through your head, it’s a whole different level of abstraction, you’re describing the circuit at a much higher level.”

This, he said, gives Classiq’s users the ability to more easily describe what they are trying to do. For now, though, that also means that the platform’s users tend to be quantum teams and scientists and developers who are quantum experts and understand how to develop quantum circuits at a very deep level. The team argues, though, that as the technology gets better, developers will need to have less and less of an understanding of how the actual qubits behave.

As Minerbi stressed, the tool is agnostic to the hardware that will eventually run these algorithms. Classiq’s mission, after all, is to provide an additional abstraction layer on top of the hardware. At the same time, though, developers can optimize their algorithms for specific quantum computing hardware as well.

Classiq CTO Dr. Yehuda Naveh also noted that the company is already working with a number of larger companies. These include banks that have used its platform for portfolio optimization, for example, and a semiconductor firm that was looking into a material science problem related to chip manufacturing, an area that is a bit of a sweet spot for quantum computing — at least in its current state.

The team plans to use the new funding to expand its existing team, mostly on the engineering side. A lot of the work the company is doing, after all, is still in R&D. Finding the right software engineers with a background in physics — or quantum information experts who can program — will be of paramount importance for the company. Minerbi believes that is possible, though, and the plan is to soon expand the team to about 25 people.

“We are thrilled to be working with Classiq, assisting the team in achieving their goals of advancing the quantum computing industry,” said Sarit Firon, Managing Partner at Team8 Capital. “As the quantum era takes off, they have managed to solve the missing piece in the quantum computing puzzle, which will enable game-changing quantum algorithms. We look forward to seeing the industry grow, and witnessing how Classiq continues to mark its place as a leader in the industry.”

#computer-science, #emerging-technologies, #entree-capital, #funding, #ourcrowd, #quantum-computing, #recent-funding, #science, #science-and-technology, #startups, #team8, #tel-aviv

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Span, the smart fusebox replacement founded by an ex-Tesla engineer, gets an Alexa upgrade

Automating and controlling devices and energy usage in homes has potentially become a bit easier thanks to an integration between Span, the startup making a digital fusebox replacement, and  Amazon’s voice recognition interface, Alexa.

The integration also comes with a $20 million new cash infusion from Amazon’s Alexa Fund and the massive insurance company Munich Re Ventures’ HSB Fund.

Through the Alexa integration, homeowners using Span’s electrical panels can turn on or off any circuit or appliance in their home, monitor which appliances are using power, and determine which electrical source is generating the most power for a home.

Questions like “Alexa, ask Span what is consuming the most power right now?” will get a response. The Alexa integration opens up new opportunities for home owners to integrate their devices and appliances, because of the connection to the home’s wiring, according to Span chief executive, Arch Rao.

Rao sees the Alexa integration as a way for Span to become the home automation hub that tech companies have been promising for a long time. “There are far too many devices in the hoe today… with too many apps,” Rao said. “The advantage we have is, once installed, we’re persistent in the home and connected to everything electric in the home for the next 30 to 40 years.”

In addition to monitoring energy usage and output, Alexa commands could turn off the power for any device or switch that a homeowner has programmed into the system.

“The most material way to state it is, our panel is providing a virtual interface to the home in the build environment,” said Rao. “We’re building a very capable edge device… it becomes sort of a true aggregation point and nerve center to give you real-time visibility and control.”

Going forward, Rao envisions Span integrating with other devices like water sensors, fire alarm sensors, and other equipment to provide other types of controls that could be useful for insurers like Munich Re.

With the $20 million that the company raised, Rao intends to significantly increase sales and marketing efforts working through partners like Munich Re and Amazon to get Span’s devices into as many homes as possible.

The company has significant tailwinds thanks to home automation and energy efficiency upgrade efforts that are now wending their way through Washington, but could mean subsidies for the deployment of technology’s like Span’s electric panels.

 

Rao also intends to boost headcount at Span. The company currently has 35 employees and Rao would like to see that number double to roughly 70 by the end of the year.

Span’s growth is part of a broad movement in home technologies toward increasingly sustainable options. In many cases that’s the penetration of electrical appliances in things like water heaters and stove tops, but also the integration of electric vehicle charging stations, home energy storage units, and other devices that push energy generation and management to the edge of electricity grids.

“It’s cutting that pipe that’s bringing natural gas to the home and bringing all electric everything… as consumers are continuing to cut the cord on fossils, your existing home system is not efficient. That’s one ecosystem of products where we are starting to see partnership opportunities,” Rao said. “When it comes to applications like monitoring the health of your appliances… and services to the home. Having the data that we provide will be unprecedented.”

#alexa, #amazon, #arch-rao, #automation, #charging-station, #emerging-technologies, #home-automation, #natural-gas, #tc, #voice-recognition, #washington

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Wingcopter raises $22 million to expand to the U.S. and launch a next-generation drone

German drone technology startup Wingcopter has raised a $22 million Series A – its first significant venture capital raise after mostly bootstrapping. The company, which focuses on drone delivery, has come a long way since its founding in 2017, having developed, built and flown its Wingcopter 178 heavy-lift cargo delivery drone using its proprietary and patented tilt-rotor propellant mechanism, which combines all the benefits of vertical take-off and landing with the advantages of fixed-wing aircraft for longer distance horizontal flight.

This new Series A round was led by Silicon Valley VC Xplorer Capital, as well as German growth fund Futury Regio Growth. Wingcopter CEO and founder Tom Plümmer explained to the in an interview that the addition of an SV-based investor is particularly important to the startup, since it’s in the process of preparing its entry into the U.S., with plans for an American facility, both for flight testing to satisfy FAA requirements for operational certification, as well as eventually for U.S.-based drone production.

Wingcopter has already been operating commercially in a few different markets globally, including in Vanuatu in partnership with Unicef for vaccine delivery to remote areas, in Tanzania for two-way medical supply delivery working with Tanzania, and in Ireland where it completed the world’s first delivery of insulin by drone beyond visual line of sight (BVLOS, the industry’s technical term for when a drone flies beyond the visual range of a human operator who has the ability to take control in case of emergencies).

Wingcopter CEO and co-founder Tom Plümmer

While Wingcopter has so far pursued a business as an OEM manufacturer of drones, and has had paying customers eager to purchase its hardware effectively since day one (Plümmer told me that they had at least one customer wiring them money before they even had a bank account set up for the business), but it’s also now getting into the business of offering drone delivery-as-a-service. After doing the hard work of building its technology from the ground up, and seeking out the necessary regulatory approvals to operate in multiple markets around the world, Plümmer says that he and his co-founders realized that operating a service business not only meant a new source of revenue, but also better-served the needs of many of its potential customers.

“We learned during this process, through applying for permission, receiving these permissions and working now in five continents in multiple countries, flying BVLOS, that actually operating drones is something we are now very good at,” he said. This was actually becoming a really good source of income, and ended up actually making up more than half of our revenue at some point. Also looking at scalability of the business model of being an OEM, it’s kind of […] linear.”

Linear growth with solid revenue and steady demand was fine for Wingcopter as a bootstrapped startup founded by university students supported by a small initial investment from family and friends. But Plümmer says the company say so much potential in the technology it had developed, and the emerging drone delivery market, that the exponential growth curve of its drone delivery-as-a-service model helped make traditional VC backing make sense. In the early days, Plümmer says Wingcopter had been approached by VCs, but at the time it didn’t make sense for what they were trying to do; that’s changed.

“We were really lucky to bootstrap over the last four years,” Plümmer said. “Basically, just by selling drones and creating revenue, we could employ our first 30 employees. But at some point, you realize you want to really plan with that revenue, so you want to have monthly revenues, which generally repeat like a software business – like software as a service.”

Wingcopter 178 cargo drone performing a delivery for Merck.

Wingcopter has also established a useful hedge regarding its service business, not only by being its own hardware supplier, but also by having worked closely with many global flight regulators on their regulatory process through the early days of commercial drone flights. They’re working with the FAA on its certification process now, for instance, with Plümmer saying that they participate in weekly calls with the regulator on its upcoming certification process for BVLOS drone operators. Understanding the regulatory environment, and even helping architect it, is a major selling point for partners who don’t want to have to build out that kind of expertise and regulatory team in-house.

Meanwhile, the company will continue to act as an OEM as well, selling not only its Wingcopter 178 heavy-lift model, which can fly up to 75 miles, at speeds of up to 100 mph, and that can carry payloads up to around 13 lbs. Because of its unique tilt-rotor mechanism, it’s not only more efficient in flight, but it can also fly in much windier conditions – and take-off and land in harsher conditions than most drones, too.

Plümmer tells me that Wingcopter doesn’t intend to rest on its laurels in the hardware department, either; it’s going to be introducing a new model of drone soon, with different capabilities that expand the company’s addressable market, both as an OEM and in its drones-as-a-service business.

With its U.S. expansion, Wingcopter will still look to focus specifically on the delivery market, but Plümmer points out that there’s no reason its unique technology couldn’t also work well to serve markets including observation and inspection, or to address needs in the communication space as well. The one market that Wingcopter doesn’t intend to pursue, however, is military and defense. While these are popular customers in the aerospace and drone industries, Plümmer says that Wingcopter has a mission “to create sustainable and efficient drone solutions for improving and saving lives,” and says the startup looks at every potential customer and ensures that it aligns with its vision – which defense customers do not.

While the company has just announced the close of its Series A round, Plümmer says they’re already in talks with some potential investors to join a Series B. It’s also going to be looking for U.S. based talent in embedded systems software and flight operations testing, to help with the testing process required its certification by the FAA.

Plümmer sees a long tail of value to be built from Wingcopter’s patented tilt-rotor design, with potential applications in a range of industries, and he says that Wingcopter won’t be looking around for any potential via M&A until it has fully realized that value. Meanwhile, the company is also starting to sow the seeds of its own potential future customers, with training programs in drone flights and operations it’s putting on in partnership with UNICEF’s African Drone and Data Academy. Wingcopter clearly envisions a bright future for drone delivery, and its work in focusing its efforts on building differentiating hardware, plus the role it’s playing in setting the regulatory agenda globally, could help position it at the center of that future.

#aerospace, #ceo, #darmstadt, #delivery-drone, #emerging-technologies, #federal-aviation-administration, #ireland, #recent-funding, #robotics, #science-and-technology, #series-a, #software, #startups, #tanzania, #tc, #technology, #unicef, #united-states, #unmanned-aerial-vehicles, #wing, #wingcopter

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Drone-focused construction startup TraceAir raises $3.5M

Bay Area-based construction startup TraceAir today announced a $3.5 million Series A. Led by London-based XTX Ventures, this round brings the company’s total funding up to $7 million. The raise includes existing investor Metropolis VC, along with new additions Liquid 2 Ventures, GEM Capital, GPS Ventures and Andrew Filev.

We first noted the company back in 2016, when it pitched a method for using drones to spot construction errors before they become too expense. It’s a pretty massive field that various technology companies are attempting to solve through a variety of different means, ranging from quadrupedal robots to site-scanning hard hats.

Last February, TraceAir announced a new drone management tool. “Haul Router provides the best mathematically objective hauls for each given drone scan,” the company noted at the time. “Any employee can use the tool to design a haul road and export the results to feed into grading equipment.”

The pandemic has thrown the construction industry for a loop (along with countless others). But unlike other sectors, demand still remains high in many places. TraceAir is hoping its solution will prove beneficial as many outfits seek a way to continue the process in spite of uncertainty.

“The Covid-19 pandemic created new challenges for the U.S. and worldwide construction industries, resulting in delayed projects and growing unemployment rates,” CEO Dmitry Korolev said in a release tied to the news. “Our platform allows industry leaders to manage projects more efficiently and collaborate with their teams remotely, minimizing the need for a physical presence on-site.”

TraceAir says the additional funding will go toward its sales and marketing, along with future product developments, including an unnamed product set for release this quarter.

#andrew-filev, #apps, #construction, #drones, #emerging-technologies, #funding, #liquid-2-ventures, #london, #recent-funding, #science-and-technology, #startups, #technology, #traceair, #united-states, #wireless, #xtx-ventures

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ETH spin-off LatticeFlow raises $2.8M to help build trustworthy AI systems

LatticeFlow, an AI startup that was spun out of ETH Zurich in 2020, today announced that it has raised a $2.8 million seed funding round led by Swiss deep-tech fund btov and Global Founders Capital, which previously backed the likes of Revolut, Slack and Zalando.

The general idea behind LatticeFlow is to build tools that help AI teams build and deploy AI models that are safe, reliable and trustworthy. The problem today, the team argues, is that models get very good at finding the right statistical patterns to hit a given benchmark. That makes them inflexible, though, since these models were optimized for accuracy in a lab setting, not for robustness in the real world.

“One of the most commonly used paradigms for evaluating machine learning models is just aggregate metrics, like accuracy. And, of course, this is a super coarse representation of how good a model really is,” Pavol Bielik, the company’s CTO explained. “What we want to do is, we provide systematic ways of monitoring models, assessing their reliability across different relevant data slices and then also provide tools for improving these models.”

Image Credits: LatticeFlow

Building these kinds of models that are more flexible yet still provide robust results will take a new arsenal of tools, though, as well as the right team with deep expertise in these areas. Clearly, though, this is a founding team with the right background. In addition to CTO Bielik, the founding team includes Petar Tsankov, the company’s CEO and former senior researcher and lecturer at ETH Zurich, as well as ETH professors Martin Vechev, who leads the Secure, Reliable and Intelligence Systems lab at ETH, and Andreas Krause, who leads ETH’s Learning & Adaptive Systems lab. Tsankov’s last startup, DeepCode, was acquired by cybersecurity firm Snyk in 2020.

It’s also worth noting that Vechev, who previously co-founded ETH spin-off ChainSecurity, and his group at ETH previously developed ERAN, a verifier for large deep learning models with millions of parameters, that last year won the first competition for certifying deep neural networks. While the team was already looking at creating a company before winning this competition, Vechev noted that gave the team the confirmation that it was on the right path.

Image Credits: LatticeFlow

“We want to solve the main AI problem, which is making AI usable. This is the overarching goal,” Vechev told me. “[…] I don’t think you can actually found the company just purely based on the certification work. I think the kinds of skills that people have in the company, my group, Andreas [Krause]’s group, they all complement each other and cover a huge space, which I think is very, very unique. I don’t know of other companies who have covered this range of skills in these pressing points and have done groundbreaking work before.”

LatticeWorks already has a set of pilot customers who are trialing its tools. These include Swiss railways (SBB), which is using it to build a tool for automatic rail inspections, Germany’s Federal Cyber Security Bureau and the U.S. Army. The team is also working with other large enterprises that are using its tools to improve their computer vision models.

“Machine Learning (ML) is one of the core topics at SBB, as we see a huge potential in its application for an improved, intelligent and automated monitoring of our railway infrastructure,” said Dr. Ilir Fetai and Andre Roger, the leads of SBB’s AI team. “The project on robust and reliable AI with LatticeFlow, ETH, and Siemens has a crucial role in enabling us to fully exploit the advantages of using ML.”

For now, LatticeFlow remains in early access. The team plans to use the funding to accelerate its product development and bring on new customers. The team also plans to build out a presence in the U.S. in the near future.

#artificial-intelligence, #btov-partners, #deep-neural-networks, #deepcode, #emerging-technologies, #global-founders-capital, #latticeflow, #machine-learning, #recent-funding, #revolut, #siemens, #snyk, #startups, #tc, #united-states, #zalando

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