Emerging-Technologies
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Driving down the cost of preserving genetic material, Acorn Biolabs raises $3.3 million
Acorn Biolabs wants consumers to pay them to store genetic material in a bet that the increasing advances in targeted genetic therapies will yield better healthcare results down the line.
The company’s pitch is to “Save young cells today, live a longer, better, tomorrow.” It’s a gamble on the frontiers of healthcare technology that has managed to net the company $3.3 million in seed financing from some of Canada’s busiest investors.
For the Toronto-based company, the pitch isn’t just around banking genetic material — a practice that’s been around for years — it’s about making that process cheaper and easier.
Acorn has come up with a way to collect and preserve the genetic material contained in hair follicles, giving its customers a way to collect full-genome information at home rather than having to come in to a facility and getting bone marrow drawn (the practice at one of its competitors, Forever Labs) .
“We have developed a proprietary media that cells are submerged in that maintains the viability of those cells as they’re being transported to our labs for processing,” says Acorn Biolabs chief executive Dr. Drew Taylor.
“Rapid advancements in the therapeutic use of cells, including the ability to grow human tissue sections, cartilage, artificial skin and stem cells, are already being delivered. Entire heart, liver and kidneys are really just around the corner. The urgency around collecting, preserving and banking youthful cells for future use is real and freezing the clock on your cells will ensure you can leverage them later when you need them,” Taylor said in a statement.
Typically, the cost of banking a full genome test is roughly $2,000 to $3,000, and Acorn says they can drop that cost to less than $1,000. Beyond the cost of taking the sample and storing it, Acorn says it will reduce to roughly $100 a year the fees to store such genetic materials.
It’s important to note that healthcare doesn’t cover any of this. It’s a voluntary service for those neurotic enough or concerned enough about the future of healthcare and their potential health.
There’s also no services that Acorn will provide on the back end of the storage… yet.
“What people do need to realize is that there is power with that data that can improve healthcare. Down the road we will be able to use that data to help people collect that data and power studies,” says Taylor.
The $3.3 million the company raised came from Real Ventures, Globalive Technology, Pool Global Partners and Epic Capital Management and other undisclosed investors.
“Until now, any live cell collection solutions have been highly expensive, invasive and often painful, as well as being geographically limited to specialized clinics,” said Anthony Lacavera, founder and chairman at Globalive. “Acorn is an industry-leading example of how technology can bring real innovation to enable future healthcare solutions that will have meaningful impact on people’s wellbeing and longevity, while at the same time — make it easy, affordable and frictionless for everyone.”
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D-Wave offers the first public access to a quantum computer
Outside the crop of construction cranes that now dot Vancouver’s bright, downtown greenways, in a suburban business park that reminds you more of dentists and tax preparers, is a small office building belonging to D-Wave. This office — squat, angular and sun-dappled one recent cool Autumn morning — is unique in that it contains an infinite collection of parallel universes.
Founded in 1999 by Geordie Rose, D-Wave worked in relative obscurity on esoteric problems associated with quantum computing. When Rose was a PhD student at the University of British Columbia, he turned in an assignment that outlined a quantum computing company. His entrepreneurship teacher at the time, Haig Farris, found the young physicists ideas compelling enough to give him $1,000 to buy a computer and a printer to type up a business plan.
The company consulted with academics until 2005, when Rose and his team decided to focus on building usable quantum computers. The result, the Orion, launched in 2007, and was used to classify drug molecules and play Sodoku. The business now sells computers for up to $10 million to clients like Google, Microsoft and Northrop Grumman.
“We’ve been focused on making quantum computing practical since day one. In 2010 we started offering remote cloud access to customers and today, we have 100 early applications running on our computers (70 percent of which were built in the cloud),” said CEO Vern Brownell. “Through this work, our customers have told us it takes more than just access to real quantum hardware to benefit from quantum computing. In order to build a true quantum ecosystem, millions of developers need the access and tools to get started with quantum.”
Now their computers are simulating weather patterns and tsunamis, optimizing hotel ad displays, solving complex network problems and, thanks to a new, open-source platform, could help you ride the quantum wave of computer programming.
Inside the box
When I went to visit D-Wave they gave us unprecedented access to the inside of one of their quantum machines. The computers, which are about the size of a garden shed, have a control unit on the front that manages the temperature as well as queuing system to translate and communicate the problems sent in by users.
Inside the machine is a tube that, when fully operational, contains a small chip super-cooled to 0.015 Kelvin, or -459.643 degrees Fahrenheit or -273.135 degrees Celsius. The entire system looks like something out of the Death Star — a cylinder of pure data that the heroes must access by walking through a little door in the side of a jet-black cube.
It’s quite thrilling to see this odd little chip inside its super-cooled home. As the computer revolution maintained its predilection toward room-temperature chips, these odd and unique machines are a connection to an alternate timeline where physics is wrestled into submission in order to do some truly remarkable things.
And now anyone — from kids to PhDs to everyone in-between — can try it.
Into the ocean
Learning to program a quantum computer takes time. Because the processor doesn’t work like a classic universal computer, you have to train the chip to perform simple functions that your own cellphone can do in seconds. However, in some cases, researchers have found the chips can outperform classic computers by 3,600 times. This trade-off — the movement from the known to the unknown — is why D-Wave exposed their product to the world.
“We built Leap to give millions of developers access to quantum computing. We built the first quantum application environment so any software developer interested in quantum computing can start writing and running applications — you don’t need deep quantum knowledge to get started. If you know Python, you can build applications on Leap,” said Brownell.
To get started on the road to quantum computing, D-Wave built the Leap platform. The Leap is an open-source toolkit for developers. When you sign up you receive one minute’s worth of quantum processing unit time which, given that most problems run in milliseconds, is more than enough to begin experimenting. A queue manager lines up your code and runs it in the order received and the answers are spit out almost instantly.
You can code on the QPU with Python or via Jupiter notebooks, and it allows you to connect to the QPU with an API token. After writing your code, you can send commands directly to the QPU and then output the results. The programs are currently pretty esoteric and require a basic knowledge of quantum programming but, it should be remembered, classic computer programming was once daunting to the average user.
I downloaded and ran most of the demonstrations without a hitch. These demonstrations — factoring programs, network generators and the like — essentially turned the concepts of classical programming into quantum questions. Instead of iterating through a list of factors, for example, the quantum computer creates a “parallel universe” of answers and then collapses each one until it finds the right answer. If this sounds odd it’s because it is. The researchers at D-Wave argue all the time about how to imagine a quantum computer’s various processes. One camp sees the physical implementation of a quantum computer to be simply a faster methodology for rendering answers. The other camp, itself aligned with Professor David Deutsch’s ideas presented in The Beginning of Infinity, sees the sheer number of possible permutations a quantum computer can traverse as evidence of parallel universes.
What does the code look like? It’s hard to read without understanding the basics, a fact that D-Wave engineers factored for in offering online documentation. For example, below is most of the factoring code for one of their demo programs, a bit of code that can be reduced to about five lines on a classical computer. However, when this function uses a quantum processor, the entire process takes milliseconds versus minutes or hours.
Classical
# Python Program to find the factors of a number
define a function
def print_factors(x):
This function takes a number and prints the factors
print(“The factors of”,x,”are:”)
for i in range(1, x + 1):
if x % i == 0:
print(i)
change this value for a different result.
num = 320
uncomment the following line to take input from the user
#num = int(input(“Enter a number: “))
print_factors(num)
Quantum
@qpu_ha
def factor(P, use_saved_embedding=True):
####################################################################################################
get circuit
####################################################################################################
construction_start_time = time.time()
validate_input(P, range(2 ** 6))
get constraint satisfaction problem
csp = dbc.factories.multiplication_circuit(3)
get binary quadratic model
bqm = dbc.stitch(csp, min_classical_gap=.1)
we know that multiplication_circuit() has created these variables
p_vars = [‘p0’, ‘p1’, ‘p2’, ‘p3’, ‘p4’, ‘p5’]
convert P from decimal to binary
fixed_variables = dict(zip(reversed(p_vars), “{:06b}”.format(P)))
fixed_variables = {var: int(x) for(var, x) in fixed_variables.items()}
fix product qubits
for var, value in fixed_variables.items():
bqm.fix_variable(var, value)
log.debug(‘bqm construction time: %s’, time.time() – construction_start_time)
####################################################################################################
run problem
####################################################################################################
sample_time = time.time()
get QPU sampler
sampler = DWaveSampler(solver_features=dict(online=True, name=’DW_2000Q.*’))
_, target_edgelist, target_adjacency = sampler.structure
if use_saved_embedding:
load a pre-calculated embedding
from factoring.embedding import embeddings
embedding = embeddings[sampler.solver.id]
else:
get the embedding
embedding = minorminer.find_embedding(bqm.quadratic, target_edgelist)
if bqm and not embedding:
raise ValueError(“no embedding found”)
apply the embedding to the given problem to map it to the sampler
bqm_embedded = dimod.embed_bqm(bqm, embedding, target_adjacency, 3.0)
draw samples from the QPU
kwargs = {}
if ‘num_reads’ in sampler.parameters:
kwargs[‘num_reads’] = 50
if ‘answer_mode’ in sampler.parameters:
kwargs[‘answer_mode’] = ‘histogram’
response = sampler.sample(bqm_embedded, **kwargs)
convert back to the original problem space
response = dimod.unembed_response(response, embedding, source_bqm=bqm)
sampler.client.close()
log.debug(’embedding and sampling time: %s’, time.time() – sample_time)
“The industry is at an inflection point and we’ve moved beyond the theoretical, and into the practical era of quantum applications. It’s time to open this up to more smart, curious developers so they can build the first quantum killer app. Leap’s combination of immediate access to live quantum computers, along with tools, resources, and a community, will fuel that,” said Brownell. “For Leap’s future, we see millions of developers using this to share ideas, learn from each other and contribute open-source code. It’s that kind of collaborative developer community that we think will lead us to the first quantum killer app.”
The folks at D-Wave created a number of tutorials as well as a forum where users can learn and ask questions. The entire project is truly the first of its kind and promises unprecedented access to what amounts to the foreseeable future of computing. I’ve seen lots of technology over the years, and nothing quite replicated the strange frisson associated with plugging into a quantum computer. Like the teletype and green-screen terminals used by the early hackers like Bill Gates and Steve Wozniak, D-Wave has opened up a strange new world. How we explore it us up to us.
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Meet the startups in the latest Alchemist class
Alchemist is the Valley’s premiere enterprise accelerator and every season they feature a group of promising startups. They are also trying something new this year: they’re putting a reserve button next to each company, allowing angels to express their interest in investing immediately. It’s a clever addition to the demo day model.
You can watch the live stream at 3pm PST here.
Videoflow – Videoflow allows broadcasters to personalize live TV. The founding team is a duo of brothers — one from the creative side of TV as a designer, the other a computer scientist. Their SaaS product delivers personalized and targeted content on top of live video streams to viewers. Completely bootstrapped to date, they’ve landed NBC, ABC, and CBS Sports as paying customers and appear to be growing fast, having booked over $300k in revenue this year.
Redbird Health Tech – Redbird is a lab-in-a-box for convenient health monitoring in emerging market pharmacies, starting with Africa. Africa has the fastest growing middle class in the world — but also the fastest growing rate of diabetes (double North America’s). Redbird supplies local pharmacies with software and rapid tests to transform them into health monitoring points – for anything from blood sugar to malaria to cholesterol. The founding team includes a Princeton Chemical Engineer, 2 Peace Corps alums, and a Pharmacist from Ghana’s top engineering school. They have 20 customers, and are growing 36% week over week.
Shuttle – Shuttle is getting a head start on the future of space travel by building a commercial spaceflight booking platform. Space tourism may be coming sooner than you think. Shuttle wants to democratize access to the heavens above. Founded by a Stanford Computer Science alum active in Stanford’s Student Space Society, Shuttle has partnerships with the leading spaceflight operators, including Virgin Galactic, Space Adventures, and Zero-G. Tickets to space today will set you back a cool $250K, but Shuttle believes that prices will drop exponentially as reusable rockets and landing pads become pervasive. They have $1.6m in reservations and growing.
Birdnest – Threading the needle between communal and private, Birdnest is the Goldilocks of office space for startups. Communal coworking spaces are accessible but have too many distractions. Traditional office spaces are private but inflexible on their terms. Birdnest brings the best of each without the drawbacks: finding, leasing, and operating a network of underutilized spaces inside of private offices. The cofounders, a duo of Duke and Kellogg MBA grads, are at $300K ARR with a fast-growing 50+ client waitlist.
Tag.bio – Tag.bio wants to make data science actionable in healthtech. The founding team is comprised of a former Ayasdi bioinformatician and a former Honda Racing engineer with a Stanford MBA. They’ve developed a next-generation data science platform that makes it easy and fast to build data apps for end users, or as they say, “WordPress for data science.” The result they claim is lightning-fast analysis apps that can be run by end users, dramatically accelerating insight discovery. They count the UCSF Medical Center and a “large Swiss pharma company” as early customers.
nCorium – They’ve built a new server architecture to handle the onslaught of AI to come with what they claim is the world’s first AI accelerator on memory to deliver 30x greater performance than the status quo. The quad founding team is intimidatingly technical — including a UCSD Professor, and former engineers from Qualcomm and Intel with 40 patents among them. They have $300K in pilots.
Spiio – Software eats landscaping with Spiio, which combines cloud-driven AI with physical sensors to monitor watering and landscaping for big companies. Their smart system knows when to water and when not to. This reduces water consumption by 50%, which means their system pays for itself in less than 30 days for big companies. They want to connect every plant to the internet, and look like they are off to a good start — $100K in orders from brand name Valley tech firms, and they are doubling monthly.
Element42 – Fraud is a major problem — For example, if you buy a Rolex on eBay, you run the risk of winding up with a counterfeit. Started by ex-VPs from Citibank, the founders are using risk models and technologies that banks use to help brands combat fraud and counterfeiting. Designed with token economics, they also incentivize customers to buy genuine products by serving exclusive content and promotions only to genuine product holders. Built on blockchain at the core, they claim to be the world’s first peer-to-peer authentication platform for physical assets. They have 45 customers across two industry verticals, 800K in ARR and are a member of World Economic Forum’s global initiatives against corruption.
My90 – Distrust between the public and the police has rarely been more strained than it is today. My90 wants to solve that by collecting data about interactions between the police and the public—think traffic stops, service calls, etc.—and turn these into actionable intelligence via an online analytics dashboard. Users text My90 anonymously about their interactions, and My90’s dashboard analyzes the results using natural language processing. Customers include major city police departments like the San Jose Police Department and the world’s largest community policing program. They have booked $150K in pilots and are expanding aggressively across the US.
Nunetz – A Stanford Computer Science grad and UCSF Neurosurgeon have come together to try to build a single unifying interface to replace the deluge of monitors and data sources in today’s clinical health environment. The goal is to prepare a daily “battle map” for physicians, nurses, and other providers, with an initial focus on the Intensive Care Unit (ICU). They have closed 3 paid pilots with hospitals through grants.
When Labs – If you hate managing people, When Labs wants to unburden you. Using an AI-powered assistant that texts with employees to negotiate assignments for hourly work, WhenLabs is trying to free customers like Hilton from spending money on managers who would normally do this manually. As the system gets smarter, they claim employees will prefer interfacing with their AI bot more than a human. AI and HR is a crowded space, but this might be the team to separate from the pack: the founding team’s previous company had a 9 figure exit to IBM.
FirstCut – FirstCut helps businesses put video content out at scale. Video dominates social media — it creates 10x more comments than text — and is emerging as a necessity for B2B media. But putting video out if you are a B2B marketer normally requires using agencies that charge hefty fees. FirstCut wants to disrupt the agencies with software and marketplaces. They use software automation and an on-demand talent marketplace to offer a fixed price product for video content. They are at $180k revenue, and most of it is moving to recurring subscriptions.
LynxCare – LynxCare claims that 90% of healthcare data goes untapped when doctors make critical decisions about your life. Further, they claim the average person’s life could be extended by 4 years if that data can be converted into insights. Their team of clinicians and data scientists aims to do just that — building a data platform that aggregates disparate data sets and drive insight for better clinical outcomes. And it looks like their platform has fans: they are active in 9 hospitals, count Pharma companies like Pfizer as Partners, and grew 4x over the past year and now are at $800K ARR.
ADIAN – Adian is a B2B SaaS product that digitizes the complex agrochemical supply chain in order to improve the sales process between manufacturers and distributors. The company claims manufacturers reduce costs by 20% and increase sales by 4% by using their online framework. $1.5 Billion and 70,000 orders have gone through the platform to date.
Hardin Scientific – Hardin is building IoT-enabled, Smart Lab Equipment. The hardware becomes a gateway to become the hub for monitoring, controlling, and sharing scientific data across teams. They’ve closed over $1.5m in revenue, and raised $15m in equity and debt financing. One of their smart devices is being used to 3D print bio-tissues and human organs in space.
ZaiNar – This team of 5 Stanford grads — 3 PhD’s and 2 MBAs — joined up with the Co-Founder of BlueKai to build the world’s best time synchronization technology. ZaiNar claims their ability to wirelessly synchronize and distribute time between networked devices is a thousand times better than existing technologies. This enables them to locate RF-emitting devices (i.e. phones, cars, drones, & RFID) at long distances with sub-meter accuracy. Beyond location, this technology has applications across data transmission, 5G communications, and energy grids. ZaiNar has raised a $1.7 million seed from AME Cloud and Softbank, and has built an extensive patent portfolio.
SMART Brain Aging – This startup claims to reduce the onset of dementia by 2.25 years with software. They are the only company approved by Medicare to get reimbursed on a preventative basis for the treatment of dementia. In conjunction with Harvard University, they have developed 20,000 exercises that are clinically proven to reduce the onset of dementia and, they claim, help build neurotransmitters. The company works with 300 patients per week ($2.2 million annual revenue) and is building to a goal of helping 22,000 people in 24 months.
Phoneic – Phoneic believes the data trapped in voice calls from cellphones is a gold mine waiting to be unleashed. Their app records and transcribes cell phones conversations, and the company has built an integration layer to enterprise AI and CRM systems that traditionally didn’t have access to voice data. The team is led by the co-founder of 3jam, one of the first group SMS and virtual number companies, which was acquired by Skype in 2011. He is keenly aware of the power of virality — and like Skype, the use of Phoneic spreads its adoption. The company has already raised $800,000 in seed funding.
Arkose Labs – Whether or not you think Russia interfered with the 2016 election, it’s no secret that bots are having significant impact on society. Arkose Labs wants to fight fraud, without adding friction to legit users. Most fraud prevention platforms today focus on gathering info from the user and providing a probability score that the traffic is good or bad. This leaves companies with a difficult decision where they may be blocking revenue generating users. Arkose has a different approach, and uses a bilateral approach that doesn’t force this tradeoff. They claim to be the only solution to offer a 100% SLA on fraud prevention. Big companies like Singapore Airlines and Electronic Arts are customers. USVP led a $6 million investment into the company.
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3DHubs, once a community 3D printing service, is now sourcing all 3D prints internally
3D Hubs, like MakeXYZ, was a community-based 3D printing service that let anyone with a printer sell their prints online. Founded in the heyday of the 3D printing revolution, the service let thousands of makers gather a little cash for making and mailing prints on their home 3D printers.
Now, however, the company has moved to a model in which its high-end partners will be manufacturing plastic, metal, and injection molded parts for customers willing to pay extra for a professional print.
“Indeed, more focus on high end printers run by professional companies,” said founder Brian Garret. “So a smaller pool of manufacturing locations (still hundreds around the world), but with more control on standardized quality and repeatability. Our software takes care of the sourcing, so companies order with 3D Hubs directly.”
Not everyone is happy with the decision. 3DPrint.come editor Joris Peels saw the value in a solid, dedicated community of hobbyists in the 3D space. The decision to move away from hobbyist printers, wrote Peels, “has confused many.”
“The value of 3DHubs is in its community; the community gives it granular local presence and a barrier to entry. Now it is just like any 3D printing service upstart and will lose its community entirely. I’ve always liked 3DHubs, although I have been very skeptical of their Trends Report I like the company and what they’re doing. I liked the idealism coupled with business,” he wrote.
The community, for its part, is angry.
A big F you to @3DHubs today! Switching over from “Locally sourced 3D prints” to the “Closed manufacturing program” basically… This was a big reason for me to own a 3d printer… now it’s all gone!
— 2lol555 (@2lol555) September 12, 2018
Why? Don’t you plan on screwing over the 3d printing community due to greed?
— MikByte (@viperz28) September 12, 2018
Sad news! @3DHubs is closing normal hubs (non Manufacturing Partners/Fulfilled by 3D Hubs). I’ve been pushing for months to get into the Fulfilled by 3D Hubs program, hope they give me one last change to join
pic.twitter.com/R6W51rLEeH
— Diego Trapero (@diegotrap) September 12, 2018
The move will happen on October 1 when all prints will be completed by Fulfilled by 3D Hubs partners, dedicated merchants who will offer “source parts for larger, high value engineering projects.” The company wrote that during the early hobbyist days the “platform at that time was very much free-form, with the goal of serving as many, mostly one-off, custom maker projects as possible.”
This slow movement from hobbyist 3D printing to professional parts manufacturer is not surprising or unexpected, but it is jarring. The 3D printing community is small, vociferous, and dedicated to the technology. In the early days, when 3D printers were rare, it was tempting to buy a mid-price printer and become a small, one-person shop online. Now, with the availability of commodity printers that cost less than some paper printers, the novelty and utility of a low-resolution print has fallen considerably.
3D printing never fulfilled its promise in the home and small office. A one-off print can save some of us a trip to the machine shop or music store but in practice home 3D printing has been a bust.
Like most open source technologies that went commercial, the dedicated zealots will complain and the established players will pivot into profitability. It ruffles feathers, to be sure, but that’s how these things work. To paraphrase the White Stripes, “Well, you’re in your little room and you’re printing something good/ But if it’s really good, you’re gonna need a bigger room/ And when you’re in the bigger room, you might not know what to do/ You might have to think of how you got started sitting in your little room.”
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Peep the future of distributed ledgers with the leaders of Hyperledger, Parity Technologies and Tradeshift
As cryptocurrencies emerge from the speculative bloodletting of the past months, believers in the promise of distributed ledger technologies for business and consumer applications are casting about for what comes next.
On our stage at Disrupt San Francisco we’ll be welcoming some of the leading thinkers in how distributed ledgers can create an entirely new architecture for computing and new processes for almost every conceivable transaction framework.
For Brian Behlendorf, the executive director of Hyperledger, distributed ledger technologies represent a powerful path for the future of networked computing — no matter the underlying technology. That’s why Behlendorf –through the Linux Foundation — is investing resources in ensuring that viable open source distributed ledger projects are supported and coming to market for any number of applications for businesses and consumers.
One of the leading lights of the internet revolution, Behlendorf’s career shaping the future of the networked world began in 1993 when he co-founded Organic Inc. — the first business dedicated to building commercial websites. Going on to become one of the foundational architects of the Apache http protocol, Behlendorf has served as the chief technology officer of the World Economic Forum and as an executive director for the technology investment fund, Mithril Capital.
Meanwhile, Parity Technologies is attempting to ensure that businesses don’t need to worry about the underlying technologies at all. Selling a suite of services that are all enabled by distributed ledger technologies and cryptographic computing, Jutta Steiner is giving businesses a way through the maze of competing protocols with a service that can enable the creation and adoption of distributed apps for businesses.
“We see it as a way for people to build blockchains that fulfill their particular needs,” Steiner told our own Samantha Stein at our Blockchain event earlier this year in Zug. “One of the challenges we’re addressing in this is to come up with a scalable framework.”
Before Parity, Steiner was responsible for security and partner integration within the Ethereum Foundation when the public blockchain first launched in 2015. Steiner also co-founded Project Provenance — a London based start-up that employs blockchain technology to make supply chains more transparent.
Supply chains are at the heart of Tradeshift’s offerings — and the company is hoping that distributed ledgers will be too. That’s why the company created Tradeshift Frontiers, an innovation lab and incubator that will focus on transforming supply chains through emerging technologies, such as distributed ledgers, artificial intelligence and the Internet of Things.
“The use cases we’re working through Frontiers cover a very wide variety of themes, including supply chain financing, asset liquidity, and supply chain transparency,” said Gert Sylvest, co-founder and GM of Tradeshift Frontiers, at the time. “There is so much more potential than just cryptocurrencies.”
That potential will be one of the things that Sylvest, Steiner, and Behlendorf discuss. We’ll hope you’ll be in the audience to listen.
Disrupt SF will take place in San Francisco’s Moscone Center West from September 5 to 7. The full agenda is here, and you can still buy tickets right here.
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Airobotics makes autonomous drones in a box
Not far from Tel Aviv a drone flies low over a gritty landscape of warehouses and broken pavement. It slowly approaches its home — a refrigerator-sized box inside a mesh fence, and hovers, preparing to dock. It descends like some giant bug, whining all the way, and disappears into its base where it will be cleaned, recharged and sent back out into the air. This drone is doing the nearly impossible: it’s flying and landing autonomously and can fly again and again without human intervention — and it’s doing it all inside a self-contained unit that is one of the coolest things I’ve seen in a long time.
The company that makes the drone, Airobotics, invited us into their headquarters to see their products in action. In this video we talk with the company about how the drones work, how their clients use the drones for mapping and surveillance in hard-to-reach parts of the world and the future of drone autonomy. It’s a fascinating look into technology that will soon be appearing in jungles, deserts and war zones near you.
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The well-funded startups driven to own the autonomous vehicle stack
At some point in the future, while riding along in a car, a kid may ask their parent about a distant time in the past when people used steering wheels and pedals to control an automobile. Of course, the full realization of the “auto” part of the word — in the form of fully autonomous automobiles — is a long way off, but there are nonetheless companies trying to build that future today.
However, changing the face of transportation is a costly business, one that typically requires corporate backing or a lot of venture funding to realize such an ambitious goal. A recent funding round, some $128 million raised in a Series A round by Shenzhen-based Roadstar.ai, got us at Crunchbase News asking a question: Just how many independent, well-funded autonomous vehicles startups are out there?
In short, not as many as you’d think. To investigate further, we took a look at the set of independent companies in Crunchbase’s “autonomous vehicle” category that have raised $50 million or more in venture funding. After a little bit of hand filtering, we found that the companies mostly shook out into two broad categories: those working on sensor technologies, which are integral to any self-driving system, and more “full-stack” hardware and software companies, which incorporate sensors, machine-learned software models and control mechanics into more integrated autonomous systems.
Full-stack self-driving vehicle companies
Let’s start with full-stack companies first. The table below shows the set of independent full-stack autonomous vehicle companies operating in the market today, as well as their focus areas, headquarter’s location and the total amount of venture funding raised:
Note the breakdown in focus area between the companies listed above. In general, these companies are focused on building more generalized technology platforms — perhaps to sell or license to major automakers in the future — whereas others intend to own not just the autonomous car technology, but deploy it in a fleet of on-demand taxi and other transportation services.
Making the eyes and ears of autonomous vehicles
On the sensor side, there is also a trend, one that’s decidedly more concentrated on one area of focus, as you’ll be able to discern from the table below:
Some of the most well-funded startups in the sensing field are developing light detection and ranging (LiDAR) technologies, which basically serve as the depth-perceiving “eyes” of autonomous vehicle systems. CYNGN integrates a number of different sensors, LiDAR included, into its hardware arrays and software tools, which is one heck of a pivot for the mobile phone OS-maker formerly known as Cyanogen.
But there are other problem spaces for these sensor companies, including Nauto’s smart dashcam, which gathers location data and detects distracted driving, or Autotalks’s DSRC technology for vehicle-to-vehicle communication. (Back in April, Crunchbase News covered the $5 million Series A round closed by Comma, which released an open-source dashcam app.)
And unlike some of the full-stack providers mentioned earlier, many of these sensor companies have established vendor relationships with the automotive industry. Quanergy Systems, for example, counts components giant Delphi, luxury carmakers Jaguar and Mercedes-Benz and automakers like Hyundai and Renault-Nissan as partners and investors. Innoviz supplies its solid-state LiDAR technology to the BMW Group, according to its website.
Although radar and even LiDAR are old hat by now, there continues to be innovation in sensors. According to a profile of Oryx Vision’s technology in IEEE Spectrum, its “coherent optical radar” system is kind of like a hybrid of radar and LiDAR technology in that “it uses a laser to illuminate the road ahead [with infrared light], but like a radar it treats the reflected signal as a wave rather than a particle.” Its technology is able to deliver higher-resolution sensing over a longer distance than traditional radar or newer LiDAR technologies.
Can startups stack up against big corporate competitors?
There are plenty of autonomous vehicle initiatives backed by deep corporate pockets. There’s Waymo, a subsidiary of Alphabet, which is subsidized by the huge amount of search profit flung off by Google . Uber has an autonomous vehicles initiative too, although it has encountered a whole host of legal and safety issues, including holding the unfortunate distinction of being the first to kill a pedestrian earlier this year.
Tesla, too, has invested considerable resources into developing assistive technologies for its vehicles, but it too has encountered some roadblocks as its head of Autopilot (its in-house autonomy solution) left in April. The company also deals with a rash of safety concerns of its own. And although Apple’s self-driving car program has been less publicized than others, it continues to roll on in the background. Chinese companies like Baidu and Didi Chuxing have also launched fill-stack R&D facilities in Silicon Valley.
Traditional automakers have also jumped into the fray. Back in 2016, for the price of a cool $1 billion, General Motors folded Cruise Automation into its R&D efforts in a widely publicized buyout. And, not to be left behind, Ford acquired a majority stake in Argo AI, also for $1 billion.
That leaves us with a question: Do even the well-funded startups mentioned earlier stand a chance of either usurping market dominance from corporate incumbents or at least joining their ranks? Perhaps.
The reason why so much investor cash is going to these companies is because the market opportunity presented by autonomous vehicle technology is almost comically enormous. It’s not just a matter of the car market itself — projected to be over 80 million car sales globally in 2018 alone — but how we’ll spend all the time and mental bandwidth freed up by letting computers take the wheel. It’s no wonder that so many companies, and their backers, want even a tiny piece of that pie.
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