Hyundai has signed a memorandum of understanding (MOU) with the city of Seoul to begin testing six autonomous vehicles on roads in the Gangnam district beginning next month, BusinessKorea reports. The arrangement specifies that six vehicles will begin testing on 23 roads in December. Looking ahead to 2021, there will be as many as 15 of the cars, which are hydrogen fuel cell electric vehicles, testing on the roads.
Seoul will provide smart infrastructure to communicate with the vehicles, including connected traffic signals, and will also relay traffic and other info as frequently as every 0.1 seconds to the Hyundai vehicles. That kind of real-time information flow should help considerably with providing the visibility necessary to optimize safe operation of the autonomous test cars. On the Hyundai said, they’ll be sharing information too — providing data around the self-driving test that will be freely available to schools and other organizations looking to test their own self-driving technology within the city.
Together, Seoul and Hyundai hope to use the partnership to build out a world-leading downtown self-driving technology deployment, and to have that evolve into a commercial service, complete with dedicated autonomous vehicle manufacture by 2024.
MIT researchers have developed a new way to optimize how soft robots perform specific tasks — a huge challenge when it comes to soft robotics in particular, because robots with flexible bodies can basically move in an infinite number of ways at any given moment, so programming them to do something in the best way possible is a monumental task.
To make the whole process easier and less computationally intensive, the research team has developed a way to take what is effectively a robot that can move in infinite possible dimensions and simplify it to a representative “low-dimensional” model that can accurately be used to optimize movement, based on environmental physics and the natural ways that soft objects shaped like any individual soft robot is actually most likely to bend in a giving setting.
So far, the MIT team behind this has demonstrated it in simulation only, but in this simulated environment it has seen significant improvements in terms of both speed and accuracy of programmed movement of robots versus methods used today that are more complex. In fact, across a number of tests of simulated robots with both 2D and 3D designs, and two and four-legged physical designs, the researchers were able to show that optimizations that would normally task as many as 30,000 simulations to achieve were instead possible in just 400.
Why is any of this even important? Because it basically shrinks drastically the amount of computational overhead required to get good movement results out of soft robots, which is a key ingredient in helping make them partial to actually use in real-life applications. If programming a soft robot to do something genuinely useful like navigate and effect an underwater damage assessment and repair requires huge amounts of processing power, and significant actual time, it’s not really viable for anyone to actually deploy.
In the future, the research team hopes to bring their optimization method out of simulation and into real-world testing, as well as full-scale development of soft robots from start to finish.
Earlier this month, at the WebSummit conference in Lisbon, D-Wave and Volkswagen teamed up to manage a fleet of buses using a new system that, among other things, used D-Wave’s quantum technology to help generate the most efficient routes. While D-Wave’s 2000Q only played a small part in this process, it’s nevertheless a sign that quantum computing is slowly getting ready for production use and that D-Wave’s approach, somewhat controversial in its early days, is paying off.
Unlike other players in the quantum computing market, D-Wave always bet on quantum annealing as its core technology. This technology lends itself perfectly to optimization problems like the kind of routing problem the company tackled with VW, as well as sampling problems, which, in the context of quantum computing, are useful for improving machine learning models, for example. Depending on their complexity, some of these problems are nearly impossible to solve with classical computers (at least in a reasonable time).
Grossly simplified, with quantum annealing, you are building a system that almost naturally optimizes itself for the lowest energy state, which then represents the solution to your problem.
Microsoft, IBM, Rigetti and others are mostly focused on building gate-model quantum computers and they are starting to see results (with the exception of Microsoft, which doesn’t have a working computer just yet and is hence betting on partnerships for the time being). But this is also a far more complex problem. And while you can’t really compare these technologies qubit to qubit, it’s telling that D-Wave’s latest machines, the Advantage, will feature 5,000 qubits — while the state of the art among the gate-model proponents is just over 50. Scaling these machines up is hard, though, especially given that the industry is still trying to figure out how to manage the noise issues.
D-Wave remains the only major player that’s betting on annealing, but the company’s CEO Vern Brownell remains optimistic that this is the right approach. “We feel more strongly about our decision to do quantum annealing now that there are a few companies that actually have quantum computers that people can access,” he said in an interview earlier this month.
“We have customers, Volkswagen included, that have run problems against those other computers and seeing what they can actually do and it’s vastly different. Our capability is many orders of magnitude faster for most problems than what you can do with other quantum computers. And that is because of the choice of quantum annealing. And that is because quantum healing is more robust to errors.” Error correction, he argues, remains the fundamental problem, and will hamper the performance of these systems for the foreseeable future. “And in order to move into the enterprise or any kind of practical application, that error correction needs to be wrestled with,” he noted.
CRISPR, the revolutionary ability to snip out and alter genes with scissor-like precision, has exploded in popularity over the last few years and is generally seen as the standalone wizard of modern gene-editing. However, it’s not a perfect system, sometimes cutting at the wrong place, not working as intended and leaving scientists scratching their heads. Well, now there’s a new, more exacting upgrade to CRISPR called Prime, with the ability to, in theory, snip out more than 90 percent of all genetic diseases.
Just what is this new method and how does it work? We turned to IEEE fellow, biomedical researcher and dean of graduate education at Tuft University’s school of engineering Karen Panetta for an explanation.
CRISPR is a powerful genome editor. It utilizes an enzyme called Cas9 that uses an RNA molecule as a guide to navigate to its target DNA. It then edits or modifies the DNA, which can deactivate genes or insert a desired sequence to achieve a behavior. Currently, we are most familiar with the application of genetically modified crops that are resistant to disease.
However, its most promising application is to genetically modify cells to overcome genetic defects or its potential to conquer diseases like cancer.
Some applications of genome editing technology include:
Of course, as with every technology, CRISPR isn’t perfect. It works by cutting the double-stranded DNA at precise locations in the genome. When the cell’s natural repair process takes over, it can cause damage or, in the case where the modified DNA is inserted at the cut site, it can create unwanted off-target mutations.
Some genetic disorders are known to mutate specific DNA bases, so having the ability to edit these bases would be enormously beneficial in terms of overcoming many genetic disorders. However, CRISPR is not well suited for intentionally introducing specific DNA bases, the As, Cs, Ts, and Gs that make up the double helix.
Prime editing was intended to overcome this disadvantage, as well as other limitations of CRISPR.
Prime editing can do multi-letter base-editing, which could tackle fatal genetic disorders such as Tay-Sachs, which is caused by a mutation of four DNA letters.
It’s also more precise. I view this as analogous to the precision lasers brought to surgery versus using a hand-held scalpel. It minimized damage, so the healing process was more efficient.
Prime editing can insert, modify or delete individual DNA letters; it can also insert a sequence of multiple letters into a genome with minimal damage to DNA strands.
Imagine being able to prevent cancer and/or hereditary diseases, like breast cancer, from ever occurring by editing out the genes that are makers for cancer. Cancer treatments are usually long, debilitating processes that physically and emotionally drain patients. It also devastates patients’ loved ones who must endure watching helpless on the sidelines as the patient battles to survive.
“Editing out” genetic disorders and/or hereditary diseases to prevent them from ever coming to fruition could also have an enormous impact on reducing the costs of healthcare, effectively helping redefine methods of medical treatment.
It could change lives so that long-term disability care for diseases like Alzheimer’s and special needs education costs could be significantly reduced or never needed.
How did the scientific community get to this point – where did CRISPR/prime editing “come from?”
Scientists recognized CRISPR’s ability to prevent bacteria from infecting more cells and the natural repair mechanism that it initiates after damage occurs, thus having the capacity to halt bacterial infections via genome editing. Essentially, it showed adaptive immunity capabilities.
It’s already out there! It has been used for treating sickle-cell anemia and in human embryos to prevent HIV infections from being transmitted to offspring of HIV parents.
IEEE Engineers, like myself, are always seeking to take the fundamental science and expand it beyond the petri dish to benefit humanity.
In the short term, I think that Prime editing will help generate the type of fetal like cells that are needed to help patients recover and heal as well as developing new vaccines against deadly diseases. It will also allow researchers new lower cost alternatives and access to Alzheimer’s like cells without obtaining them post-mortem.
Also, AI and deep learning is modeled after human neural networks, so the process of genome editing could potentially help inform and influence new computer algorithms for self-diagnosis and repair, which will become an important aspect of future autonomous systems.
Flying cars are fine – but why use a car when you can have a motorcycle instead? YC-backed startup JetPack Aviation wants to answer that question with the world’s first flying motorcycle, a personal aircraft dubbed ‘The Speeder,’ a name that Star Wars fans will surely appreciate. Now, JetPack has raised a seed round of $2 million from investors indulging Draper Associates, Skype co-founder Jaan Tallinn, YC, Catheis Ventures and a group of angels that it says will fund the development of the Speeder’s first functional prototype.
Back in March, JetPack revealed its plans for the Speeder, which it says will provide a fully stabilized ride that’s either pilot-controlled or fully autonomous. It can take off and land vertically, and reach top speeds of potentially over 400 MPH. There are not exposed rotors systems, which make it a lot safer and easier to operate than a lot of other VTOL designs and helicopters, and the company says it can also be refuelled in under 5 minutes, which is a dramatically shorter turn around time for powering up vs. an electric vehicle.
This isn’t JetPack’s first aerial rodeo: The company, led by CEO and founder David Mayman, has already created an actual jet pack. Mayman himself has demonstrated the personal aerial jet pack numerous times, and it’s been certified by the FAA, plus it landed a CARADA agreement with the U.S. Navy Special Forces for use in short-distance troop transportation. The jet pack also boasts a lot of features that sound, on paper, like diene fiction: Over 100 mph top seed, and suitcase-sized portability, for instance.
That track record is why when Mayman tells me this $2 million round “should fully fund the first full scale flying prototype, including all modelling designs and build,” I tend to believe him more than I would just about anyone else in the world making a similar claim.
Part of the reason the Speeder is more viable near-term than other VTOL designs is that it will rely on turbine propulsion, rather than battery-based flight systems. This is because, in Mayman’s opinion, “current battery energy density is just too low for most electrically powered VTOLs to be truly practical,” and that timelines optimistically for that to change are in the 5 to 10 year range. The Speeder, by comparison, should feasibly be able to provide quick cargo transportation for emergency services and military (its first planned uses before moving on to the consumer market) in a much shorter period.
UPS is rolling along with its drone delivery program, working with partner CVS Pharmacy to deliver prescription drugs to customer doorsteps via its newly deployed commercial drones. UPS delivered medications to two paying customers on November 1 using the M2 drone system that the logistics company developed in partnership with Matternet.
UPS received approval last month from the FAA to fly its fleet of commercial drones in service of customers, and now it plans to iterate its drone delivery program “in the coming months,” with the aim of ensuring that it can deploy UAVs in a commercial capacity at increasing scale. It also launched “UPS Flight Forward,” a dedicated division focused on autonomous drone delivery.
For these early deliveries, drones were loaded with prescriptions filled by pharmacists at a CVS location in Cary, NC. Once a UPS employee loaded the cargo onto the drones, they flew autonomously from the store location to nearby customer homes, dropping off the packages from a hover height of around 20 feet above these locations. One of the customers has mobility challenges that would make travel to a CVS store for prescription pickup difficult, UPS points out.
This isn’t the first time UPS has deployed drones in a healthcare industry setting: The company has been working with Mattternet and WakeMed Hospital in Raleigh, doing commercial deliveries of medical samples in a B2B setting.
A UK drone registration scheme has opened ahead of the deadline for owners to register their devices coming into force at the end of this month.
The UK government announced its intention to introduce a drone registration scheme two years ago.
The rules apply to drones or model aircraft weighting between 250g and 20kg.
Owners of drones wanting to fly the device themselves must also take and pass a theory test to gain a flyer ID by November 30. Anyone who wishes to fly a drone owned by someone else must also first obtain a flyer ID by passing the theory test.
UK ministers have come in for serious criticism for lagging on drone regulations in recent years after a spate of drone sightings at the country’s busiest airport grounded flights last December, disrupting thousands of travellers. In January flights were also briefly halted at Heathrow airport after another unidentified drone sighting.
This fall the police investigation into the Gatwick drone shutdown found that at least two drones had been involved. In September police also said they had been unable to identify any suspects — ruling out 96 people of interest.
Following the Gatwick disruption the government tightened existing laws around drone flights near airports — extending a no-fly zone from 1km to 5km. But a full drone bill, originally slated for introduction this year, has yet to take off.
As well as introducing a legal requirement for drone owners to register their craft via the Civil Aviation Authority’s website by November 30, the new stop-gap rules require organizations that use drones to register for an operator ID too, also at a cost of £9 per year.
All drones must also be labeled with the operator ID. This must be clearly visible on the main body of the craft, and easy to read when it’s on the ground, written in block capital letters taller than 3mm high.
The registered person who obtains the operator ID must be aged 18 or older and is accountable for managing drones to ensure only individuals with a flyer ID fly them.
Individuals must be aged 13 or older to obtain a flyer ID.
The online test for obtaining the flyer ID involves answering 20 multiple choice questions. The pass mark for the test is set at 16. There’s no limit on how many times the test can be taken.
The Civil Aviation Authority says everything needed to pass the test can be found in The Drone and Model Aircraft Code. There’s no charge for taking the test or obtaining the flyer ID.
Alphabet -owned drone delivery spin-out Wing is starting to service U.S. customers, after becoming the first drone delivery company to get the federal go-ahead to do so earlier this year. Wing is working with FedEx Express and Walgreens on this pilot, and their first customers are Michael and Kelly Collver, who will get a “cough and cold pack,” which includes Tylenol, cough drops, facial tissues, Emergen-C and bottled water (do people who have colds need bottled water?).
The Collvers are receiving their package in Christianburg, Va., which is where Wing and Walgreens will run this inaugural pilot of the drone delivery service. Walgreens gets a noteworthy credit in the bargain, becoming the first U.S. retailer to do a store-to-customer doorstep delivery via drone, while FedEx will be the first logistics provider to deliver an e-commerce drone delivery with a separate shipment.
Wing is also working with Virginia’s Sugar Magnolia, a retailer local to the state, and that part of the equation is focused on proving out how Wing and drone delivery can service last-mile e-commerce customers at their homes. Sugar Magnolia customers can get small items, including chocolates and paper goods, delivered directly to them via drone through the new pilot.
Wing was able to do this with a new Air Carrier Certificate from the FAA that clears it for expanded service, specifically allowing Wing’s pilots to manage multiple aircraft flying without any human pilot on board at the same time, while providing service to the public.
It’s a big milestone when it comes to U.S.-based drone delivery, and another sign that people should get ready for these services to start to be a more regular fixture. Earlier this month, UPS also secured FAA approval to operate a commercial drone delivery service, so the trials will probably come fast and furious at this point — though widespread service is probably still quite a ways off as both regulators and operators look to learn from their first limited deployments.
Autonomous vehicles are often painted as a utopian-like technology that will transform parking lots into parks and eliminate traffic fatalities — a number that reached 1.35 million globally in 2018.
Even if, as many predict, autonomous vehicles are deployed en masse, the road to that future promises to be long, chaotic and complex. The emergence of ride-hailing, car-sharing and micromobility hints at some of the speed bumps between today’s modes of transportation and more futuristic means, like AVs and flying cars. Entire industries face disruption in this new mobility world, perhaps none so thoroughly as automotive.
Autonomous-vehicle ubiquity may be decades away, but automakers, startups and tech companies are already clambering to be king of the ‘future of transportation’ hill.
How does a company, city or country “own” this future of transportation? While there’s no clear winner today, companies as well as local and federal governments can take actions and make investments today to make sure they’re not left behind, according to Zoox CEO Aicha Evans and former Michigan Gov. Jennifer Granholm, who spoke about the future of cities on stage this month at Disrupt SF.
Evolution in mobility is occurring at a global scale, but transportation is also very local, Evans said. Because every local transit system is tailored to the geography and the needs of its residents, these unique requirements create opportunities at a local level and encourages partnerships between different companies.
This is no longer just a Silicon Valley versus Detroit story; Europe, China, Singapore have all piled in as well. Instead of one mobility company that will rule them all, Evans and Granholm predict more partnerships between companies, governments and even economic and tech strongholds like Silicon Valley.
We’re already seeing examples of this in the world of autonomous vehicles. For instance, Ford invested $1 billion into AV startup Argo AI in 2017. Two years later, VW Group announced a partnership with Ford that covers a number of areas, including autonomy (via a new investment by VW in Argo AI) and collaboration on development of electric vehicles.
BMW and Daimler, which agreed in 2018 to merge their urban mobility services into a single holding company, announced in February plans to unify these services and sink $1.1 billion into the effort. The two companies are also part of a consortium that includes Audi, Intel, Continental and Bosch, that owns mapping and location data service company HERE.
There are numerous other examples of companies collaborating after concluding that going it alone wasn’t as feasible as they once thought.
Volvo Group has established a new dedicated business group focused on autonomous transportation, with a mandate that covers industry segments like mining, ports and moving goods between logistics hubs of all kinds. The vehicle maker has already been active in putting autonomous technology to work in these industries, with self-driving projects — including at a few quarries and mines, and in the busy port located at Gothenburg, Sweden.
The company sees demand for this kind of autonomous technology use growing, and decided to establish an entire business unit to address it. The newly formed group will be called Volvo Autonomous Solutions, and its official mission is to “accelerate the development, commercialization and sales of autonomous transport solutions,” focused on the kind of transportation “where there is a need to move large volumes of goods and material on pre-defined routes, in receptive flows.”
Their anticipation of the growth of this sector comes in part from direct customer feedback, the automaker notes. It’s seen “significant increase in inquires from customers,” according to a statement from Martin Lundstedt, Volvo Group’s president and CEO.
Officially, Volvo Autonomous Solutions won’t be a formal new business area under its parent company until January 2020, but the company is looking for a new head of the unit already, and it’s clear they see a lot of potential in this burgeoning market.
Unlike autonomous driving for consumer automobiles, this kind of self-driving for fixed-route goods transportation is a nice match to the capabilities of technology as they exist today. These industrial applications eliminate a lot of the chaos and complexity of driving in, say, urban environments and with a lot of other human-driven vehicles on the road, and their routes are predictable and repeatable.
Waymo, the autonomous vehicle company under Alphabet, has started creating 3D maps in some heavily trafficked sections of Los Angeles to better understand congestion there and determine if its self-driving vehicles would be a good fit in the city.
For now, Waymo is bringing just three of its self-driving Chrysler Pacifica minivans to Los Angeles to map downtown and a section of Wilshire Boulevard known as Miracle Mile.
Waymo employees will initially drive the vehicles to create 3D maps of the city. These maps are unlike Google Maps or Waze. Instead, they include topographical features such as lane merges, shared turn lanes and curb heights, as well as road types and the distance and dimensions of the road itself, according to Waymo. That data is combined with traffic control information like signs, the lengths of crosswalks and the locations of traffic lights.
Starting this week, Angelenos might catch a glimpse of Waymo’s cars on the streets of LA! Our cars will be in town exploring how Waymo's tech might fit into LA’s dynamic transportation environment and complement the City’s innovative approach to transportation. pic.twitter.com/REHfxrxqdL
— Waymo (@Waymo) October 7, 2019
Waymo does have a permit to test autonomous vehicles in California and could theoretically deploy its fleet in Los Angeles. But for now, the company is in mapping and assessment mode. Waymo’s foray into Los Angeles is designed to give the company insight into driving conditions there and how its AV technology might someday be used.
The company said it doesn’t plan to launch a rider program like its Waymo One currently operating in the suburbs of Phoenix. Waymo One allows individuals to hail a ride in one of the self-driving cars, which have a human safety driver behind the wheel.
The self-driving car company began testing its autonomous vehicles in and around Mountain View, Calif., before branching out to other cities — and climates — including Novi, Mich., Kirkland, Wash., San Francisco and, more recently, in Florida. But the bulk of the company’s activities have been in the suburbs of Phoenix and around Mountain View — two places with lots of sun, and even blowing dust, in the case of Phoenix.
UPS announced today that it is the first to receive the official nod from the Federal Aviation Administration (FAA) to operate a full “drone airline,” which will allow it to expand its current small drone delivery service pilots into a country-wide network.
In its announcement of the news, UPS said that it will start by building out its drone delivery solutions specific to hospital campuses nationwide in the U.S., and then to other industries outside of healthcare.
UPS racks up a number of firsts as a result of this milestone, thanks to how closely it has been working with the FAA throughout its development and testing process for drone deliveries. As soon as it was awarded the certification, it did a delivery for WakeMed hospital in Raleigh, N.C. using a Matternet drone, and it also became the first commercial operator to perform a drone delivery for an actual paying customer outside of line of sight thanks to an exemption it received from the government.
This certification, officially titled FAA’s “Part 135 Standard certification,” offers far-reaching and broad license to companies who attain it — much more freedom than any commercial drone operation has had previously in the U.S. Here’s a good summary of just how broad UPS can operate under its new designation:
The FAA’s Part 135 Standard certification has no limits on the size or scope of operations. It is the highest level of certification, one that no other company has attained. UPS Flight Forward’s certificate permits the company to fly an unlimited number of drones with an unlimited number of remote operators in command. This enables UPS to scale its operations to meet customer demand. Part 135 Standard also permits the drone and cargo to exceed 55 pounds and fly at night, previous restrictions governing earlier UPS flights.
Obviously, it’s a huge win for UPS Flight Forward, which is the dedicated UPS subsidiary the company announced it had formed back in July to focus entirely on building out the company’s drone delivery business. But there’s still a lot left to do before you can expect UPS drones to be a regular fixture, or even at all visible in the lives of the average American.
The courier outlined its next steps from here, which include expanding service to new hospitals and medial facilities, building out ground-based detection and avoidance systems for its drone fleets, building a central operation control facility and partnering with new drone makers to create different kinds of delivery drones for different payloads.
A major drone incident at the UK’s second business airport last year continues to baffle police.
Last December a series of drone sightings near Gatwick Airport caused chaos as scores of flights were grounded and thousands of travellers had their holiday plans disrupted.
The incident, which took place during a peak travel period ahead of Christmas, led to the airport being closed for 30 hours, disrupting 1,000 flights and more than 140,000 passengers.
Today Sussex Police have released an update on their multi-month investigation into who was operating the drones — with thin findings, saying they have “identified, researched and ruled out 96 people ‘of interest’”.
Although they are now sure that drones played a part in the disruption. The report confirms that at least two drones were involved. The police are also convinced the perpetrator or perpetrators had detailed knowledge of the airport.
“The police investigation has centred on 129 separate sightings of drone activity, 109 of these from credible witnesses used to working in a complex airport environment including a pilot, airport workers and airport police,” the force writes.
“Witness statements show activity happened in ‘groupings’ across the three days on 12 separate occasions, varying in length from between seven and 45 minutes. On six of these occasions, witnesses clearly saw two drones operating simultaneously.”
“The incident was not deemed terror-related and there is no evidence to suggest it was either state-sponsored, campaign or interest-group led. No further arrests have been made,” it adds.
The policing operation during the disruption and subsequent investigation has cost £790,000 so far.
Sussex Police is drawing a line under its investigation at this point, saying without new information coming to light “there are no further realistic lines of enquiry at this time”.
Shortly after the Gatwick debacle drone maker DJI also updated its geofencing system across Europe.
A comprehensive UK drone bill — intended to beef up police powers to curb drone misuse, and which could contain policy on flight information notification systems — has remained stalled.
In a ‘future of drones’ report published at the start of this year ministers said they intended to bring the bill forward this year. But the government is fast running out of parliamentary time to do so.
It had already made provision to introduce mandatory drone registration.
From November 30 it will be a legal requirement for all UK drone operators to register, as well as for drone pilots to complete an online pilot competency test.
While Sussex Police have ruled out the Gatwick drone incident being related to a campaign or interest-group, earlier this month an environmental group attempted to shut down Heathrow using toy drones flown at head height in the legal restriction zone.
Automaker Hyundai is forming a new joint venture with autonomous driving technology company Aptiv, with both parties taking a 50% ownership stake in the new company. The goal of the new venture will be to develop Level 4 and Level 5 production-ready self-driving systems intended for commercialization, with the goal of making those available to robotaxi and fleet operators, as well as other auto makers, by 2022.
The combined investment in the joint venture from both companies will total $4 billion in aggregate value (including the value of combined engineering services, R&D and IP) initially, according to Aptiv and Hyundai, and testing for their fully autonomous systems will begin in 2020 in pursuit of that 2022 commercialization target.
In terms of what each is bringing to the table, Aptiv will be delivering its autonomous driving tech, which it has been developing for many years — originally as part of global automotive industry supplier Delphi — as well as 700 employees working on AV tech. Hyundai Motor Group will provide a combined $1.6 billion in cash from across its subrands, vehicle engineering, R&D and access to its IP.
Heading up the new joint venture will be Karl Iagnemma, the president of Aptiv’s Autonomous Mobility group, and it’ll be headquartered in Boston and supported by additional technology centers in multiple locations in the U.S. and Asia.
Both companies have been demonstrating autonomous vehicle technologies for multiple years now, and Aptiv has been working with Lyft in Las Vegas on a public trial of autonomous robotaxi services since debuting the capabilities at CES in 2018. Aptiv’s Vegas pilot uses BMW 5 Series cars for its autonomous pickup fleet.
This joint venture should help them with bringing the technology to market with the scale of a global automaker, while Hyundai gains by being able to shore up its own work in self-driving with a partner that has invested in developing these solutions as a primary concern over many years.
Khosla Ventures, Jaguar Land Rover’s InMotion Ventures and Chevron Technology Ventures also participated in the round. The company, which operates a ride-hailing service in retirement communities using self-driving cars supported by human safety drivers, has raised a total of $52 million since launching in 2017. The new funding includes a $3 million convertible note.
Voyage CEO Oliver Cameron has big plans for the fresh injection of capital, including hiring and expanding its fleet of self-driving Chrysler Pacifica minivans, which always have a human safety driver behind the wheel.
Ultimately, the expanded G2 fleet and staff are just the means toward Cameron’s grander mission to turn Voyage into a truly driverless and profitable ride-hailing company.
“It’s not just about solving self-driving technology,” Cameron told TechCrunch in a recent interview, explaining that a cost-effective vehicle designed to be driverless is the essential piece required to make this a profitable business.
The company is in the midst of a hiring campaign that Cameron hopes will take its 55-person staff to more than 150 over the next year. Voyage has had some success attracting high-profile people to fill executive-level positions, including CTO Drew Gray, who previously worked at Uber ATG, Otto, Cruise and Tesla, as well as former NIO and Tesla employee Davide Bacchet as director of autonomy.
Funds will also be used to increase its fleet of second-generation self-driving cars (called G2) that are currently being used in a 4,000-resident retirement community in San Jose, Calif., as well as The Villages, a 40-square-mile, 125,000-resident retirement city in Florida. Voyage’s G2 fleet has 12 vehicles. Cameron didn’t provide details on how many vehicles it will add to its G2 fleet, only describing it as a “nice jump that will allow us to serve consumers.”
Voyage used the G2 vehicles to create a template of sorts for its eventual driverless vehicle. This driverless product — a term Cameron has used in a previous post on Medium — will initially be limited to 25 miles per hour, which is the driving speed within the two retirement communities in which Voyage currently tests and operates. The vehicle might operate at a low speed, but they are capable of handling complex traffic interactions, he wrote.
“It won’t be the most cost-effective vehicle ever made because the industry still is in its infancy, but it will be a huge, huge, huge improvement over our G2 vehicle in terms of being be able to scale out a commercial service and make money on each ride,” Cameron said.
Voyage initially used modified Ford Fusion vehicles to test its autonomous vehicle technology, then introduced in July 2018 Chrysler Pacifica minivans, its second generation of autonomous vehicles. But the end goal has always been a driverless product.
TechCrunch previously reported that the company has partnered with an automaker to provide this next-generation vehicle that has been designed specifically for autonomous driving. Cameron wouldn’t name the automaker. The vehicle will be electric and it won’t be a retrofit like the Chrysler Pacifica Hybrid vehicles Voyage currently uses or its first-generation vehicle, a Ford Fusion.
Most importantly, and a detail Cameron did share with TechCrunch, is that the vehicle it uses for its driverless service will have redundancies and safety-critical applications built into it.
Voyage also has deals in place with Enterprise rental cars and Intact insurance company to help it scale.
“You can imagine leasing is much more optimal than purchasing and owning vehicles on your balance sheet,” Cameron said. “We have those deals in place that will allow us to not only get the vehicle costs down, but other aspects of the vehicle into the right place as well.”
Starship Technologies is fresh off a recent $40 million funding round, and the robotics startup finds itself in a much-changed market compared to when it got its start in 2014. Founded by software industry veterans, including Skype and Rdio co-founder Janus Friis, Starship’s focus is entirely on building and commercializing fleets of autonomous sidewalk delivery robots.
Starship invented this category when it debuted, but five years later it’s one of a number of companies looking to deploy what essentially amounts to wheeled, self-driven coolers that can carry small packages and everyday freight, including fresh food, to waiting customers. CEO Lex Bayer, a former sales leader from Airbnb, took over the top spot at Starship last year and is eager to focus the company’s efforts in a drive to take full advantage of its technology and experience lead.
The result is transforming what looked, to all external observers, like a long-tail technology play into a thriving commercial enterprise.
“We want to do 100 universities in the next 24 months, and we’ll do about 25 to 50 robots on each campus,” Bayer said in an interview about his company’s plans for the future.
Mo Gawdat, the former Google and Google X executive, is probably best known for his book Solve for Happy: Engineer Your Path to Joy. He left Google X last year. Quite a bit has been written about the events that led to him leaving Google, including the tragic death of his son. While happiness is still very much at the forefront of what he’s doing, he’s also now thinking about his next startup: T0day.
To talk about T0day, I sat down with the Egypt-born Gawdat at the Digital Frontrunners event in Copenhagen, where he gave one of the keynote presentations. Gawdat is currently based in London. He has adopted a minimalist lifestyle, with no more than a suitcase and a carry-on full of things. Unlike many of the Silicon Valley elite that have recently adopted a kind of performative aestheticism, Gawdat’s commitment to minimalism feels genuine — and it also informs his new startup.
“In my current business, I’m building a startup that is all about reinventing consumerism,” he told me. “The problem with retail and consumerism is it’s never been disrupted. E-commerce, even though we think is a massive revolution, it’s just an evolution and it’s still tiny as a fraction of all we buy. It was built for the Silicon Valley mentality of disruption, if you want, while actually, what you need is cooperation. There are so many successful players out there, so many efficient supply chains. We want the traditional retailers to be successful and continue to make money — even make more money.”
What T0day wants to be is a platform that integrates all of the players in the retail ecosystem. That kind of platform, Gawdat argues, never existed before, “because there was never a platform player.”
That sounds like an efficient marketplace for moving goods, but in Gawdat’s imagination, it is also a way to do good for the planet. Most of the fuel burned today isn’t for moving people, he argues, but goods. A lot of the food we buy goes to waste (together with all of the resources it took to grow and ship it) and single-use plastic remains a scourge.
How does T0day fix that? Gawdat argues that today’s e-commerce is nothing but a digital rendering of the same window shopping people have done for ages. “You have to reimagine what it’s like to consume,” he said.
The reimagined way to consume is essentially just-in-time shipping for food and other consumer goods, based on efficient supply chains that outsmart today’s hub and spoke distribution centers and can deliver anything to you in half an hour. If everything you need to cook a meal arrives 15 minutes before you want to start cooking, you only need to order the items you need at that given time and instead of a plastic container, it could come a paper bag. “If I have the right robotics and the right autonomous movements — not just self-driving cars, because self-driving cars are a bit far away — but the right autonomous movements within the enterprise space of the warehouse, I could literally give it to you with the predictability of five minutes within half an hour,” he explained. “If you get everything you need within half an hour, why would you need to buy seven apples? You would buy three.”
Some companies, including the likes of Uber, are obviously building some of the logistics networks that will enable this kind of immediate drop shipping, but Gawdat doesn’t think Uber is the right company for this. “This is going to sound a little spiritual. There is what you do and there is the intention behind why you do it,” he said. “You can do the exact same thing with a different intention and get a very different result.”
That’s an ambitious project, but Gawdat argues that it can be done without using massive amounts of resources. Indeed, he argues that one of the problems with Google X, and especially big moonshot projects like Loon and self-driving cars, was that they weren’t really resource-constrained. “Some things took longer than they should have,” he said. “But I don’t criticize what they did at all. Take the example of Loon and Facebook. Loon took longer than it should have. In my view, it was basically because of an abundance of resources and sometimes innovation requires a shoestring. That’s my only criticism.”
T0day, which Gawdat hasn’t really talked about publicly in the past, is currently self-funded. A lot of people are advising him to raise money for it. “We’re getting a lot of advice that we shouldn’t self-fund,” he said, but he also believes that the company will need some strategic powerhouses on its side, maybe retailers or companies that have already invested in other components of the overall platform.
T0day’s ambitions are massive, but Gawdat thinks that his team can get the basic elements right, be that the fulfillment center design or the routing algorithms and the optimization engines that power it all. He isn’t ready to talk about those, though. What he does think is that T0day won’t be the interface for these services. It’ll be the back end and allow others to build on top. And because his previous jobs have allowed him to live a comfortable life, he isn’t all that worried about margins either, and would actually be happy if others adopted his idea, thereby reducing waste.
For the longest time, even while scientists were working to make it a reality, quantum computing seemed like science fiction. It’s hard enough to make any sense out of quantum physics to begin with, let alone the practical applications of this less than intuitive theory. But we’ve now arrived at a point where companies like D-Wave, Rigetti, IBM and others actually produce real quantum computers.
They are still in their infancy and nowhere near as powerful as necessary to compute anything but very basic programs, simply because they can’t run long enough before the quantum states decohere, but virtually all experts say that these are solvable problems and that now is the time to prepare for the advent of quantum computing. Indeed, Gartner just launched a Quantum Volume metric, based on IBM’s research, that looks to help CIOs prepare for the impact of quantum computing.
To discuss the state of the industry and why now is the time to get ready, I sat down with IBM’s Jay Gambetta, who will also join us for a panel on Quantum Computing at our TC Sessions: Enterprise event in San Francisco on September 5, together with Microsoft’s Krysta Svore and Intel’s Jim Clark.
Artificial intelligence is now being used to make decisions about lives, livelihoods and interactions in the real world in ways that pose real risks to people.
We were all skeptics once. Not that long ago, conventional wisdom held that machine intelligence showed great promise, but it was always just a few years away. Today there is absolute faith that the future has arrived.
It’s not that surprising with cars that (sometimes and under certain conditions) drive themselves and software that beats humans at games like chess and Go. You can’t blame people for being impressed.
But board games, even complicated ones, are a far cry from the messiness and uncertainty of real-life, and autonomous cars still aren’t actually sharing the road with us (at least not without some catastrophic failures).
AI is being used in a surprising number of applications, making judgments about job performance, hiring, loans, and criminal justice among many others. Most people are not aware of the potential risks in these judgments. They should be. There is a general feeling that technology is inherently neutral — even among many of those developing AI solutions. But AI developers make decisions and choose tradeoﬀs that aﬀect outcomes. Developers are embedding ethical choices within the technology but without thinking about their decisions in those terms.
These tradeoﬀs are usually technical and subtle, and the downstream implications are not always obvious at the point the decisions are made.
The fatal Uber accident in Tempe, Arizona, is a (not-subtle) but good illustrative example that makes it easy to see how it happens.
The autonomous vehicle system actually detected the pedestrian in time to stop but the developers had tweaked the emergency braking system in favor of not braking too much, balancing a tradeoﬀ between jerky driving and safety. The Uber developers opted for the more commercially viable choice. Eventually autonomous driving technology will improve to a point that allows for both safety and smooth driving, but will we put autonomous cars on the road before that happens? Proﬁt interests are pushing hard to get them on the road immediately.
Physical risks pose an obvious danger, but there has been real harm from automated decision-making systems as well. AI does, in fact, have the potential to beneﬁt the world. Ideally, we mitigate for the downsides in order to get the beneﬁts with minimal harm.
A signiﬁcant risk is that we advance the use of AI technology at the cost of reducing individual human rights. We’re already seeing that happen. One important example is that the right to appeal judicial decisions is weakened when AI tools are involved. In many other cases, individuals don’t even know that a choice not to hire, promote, or extend a loan to them was informed by a statistical algorithm.
Buyers of the technology are at a disadvantage when they know so much less about it than the sellers do. For the most part decision makers are not equipped to evaluate intelligent systems. In economic terms, there is an information asymmetry that puts AI developers in a more powerful position over those who might use it. (Side note: the subjects of AI decisions generally have no power at all.) The nature of AI is that you simply trust (or not) the decisions it makes. You can’t ask technology why it decided something or if it considered other alternatives or suggest hypotheticals to explore variations on the question you asked. Given the current trust in technology, vendors’ promises about a cheaper and faster way to get the job done can be very enticing.
So far, we as a society have not had a way to assess the value of algorithms against the costs they impose on society. There has been very little public discussion even when government entities decide to adopt new AI solutions. Worse than that, information about the data used for training the system plus its weighting schemes, model selection, and other choices vendors make while developing the software are deemed trade secrets and therefore not available for discussion.
Image via Getty Images / sorbetto
The Yale Journal of Law and Technology published a paper by Robert Brauneis and Ellen P. Goodman where they describe their eﬀorts to test the transparency around government adoption of data analytics tools for predictive algorithms. They ﬁled forty-two open records requests to various public agencies about their use of decision-making support tools.
Their “speciﬁc goal was to assess whether open records processes would enable citizens to discover what policy judgments these algorithms embody and to evaluate their utility and fairness”. Nearly all of the agencies involved were either unwilling or unable to provide information that could lead to an understanding of how the algorithms worked to decide citizens’ fates. Government record-keeping was one of the biggest problems, but companies’ aggressive trade secret and conﬁdentiality claims were also a signiﬁcant factor.
Using data-driven risk assessment tools can be useful especially in cases identifying low-risk individuals who can beneﬁt from reduced prison sentences. Reduced or waived sentences alleviate stresses on the prison system and beneﬁt the individuals, their families, and communities as well. Despite the possible upsides, if these tools interfere with Constitutional rights to due process, they are not worth the risk.
All of us have the right to question the accuracy and relevance of information used in judicial proceedings and in many other situations as well. Unfortunately for the citizens of Wisconsin, the argument that a company’s proﬁt interest outweighs a defendant’s right to due process was aﬃrmed by that state’s supreme court in 2016.
Of course, human judgment is biased too. Indeed, professional cultures have had to evolve to address it. Judges for example, strive to separate their prejudices from their judgments, and there are processes to challenge the fairness of judicial decisions.
In the United States, the 1968 Fair Housing Act was passed to ensure that real-estate professionals conduct their business without discriminating against clients. Technology companies do not have such a culture. Recent news has shown just the opposite. For individual AI developers, the focus is on getting the algorithms correct with high accuracy for whatever deﬁnition of accuracy they assume in their modeling.
I recently listened to a podcast where the conversation wondered whether talk about bias in AI wasn’t holding machines to a diﬀerent standard than humans—seeming to suggest that machines were being put at a disadvantage in some imagined competition with humans.
As true technology believers, the host and guest eventually concluded that once AI researchers have solved the machine bias problem, we’ll have a new, even better standard for humans to live up to, and at that point the machines can teach humans how to avoid bias. The implication is that there is an objective answer out there, and while we humans have struggled to ﬁnd it, the machines can show us the way. The truth is that in many cases there are contradictory notions about what it means to be fair.
A handful of research papers have come out in the past couple of years that tackle the question of fairness from a statistical and mathematical point-of-view. One of the papers, for example, formalizes some basic criteria to determine if a decision is fair.
In their formalization, in most situations, diﬀering ideas about what it means to be fair are not just diﬀerent but actually incompatible. A single objective solution that can be called fair simply doesn’t exist, making it impossible for statistically trained machines to answer these questions. Considered in this light, a conversation about machines giving human beings lessons in fairness sounds more like theater of the absurd than a purported thoughtful conversation about the issues involved.
Image courtesy of TechCrunch/Bryce Durbin
When there are questions of bias, a discussion is necessary. What it means to be fair in contexts like criminal sentencing, granting loans, job and college opportunities, for example, have not been settled and unfortunately contain political elements. We’re being asked to join in an illusion that artiﬁcial intelligence can somehow de-politicize these issues. The fact is, the technology embodies a particular stance, but we don’t know what it is.
Technologists with their heads down focused on algorithms are determining important structural issues and making policy choices. This removes the collective conversation and cuts oﬀ input from other points-of-view. Sociologists, historians, political scientists, and above all stakeholders within the community would have a lot to contribute to the debate. Applying AI for these tricky problems paints a veneer of science that tries to dole out apolitical solutions to diﬃcult questions.
One major driver of the current trend to adopt AI solutions is that the negative externalities from the use of AI are not borne by the companies developing it. Typically, we address this situation with government regulation. Industrial pollution, for example, is restricted because it creates a future cost to society. We also use regulation to protect individuals in situations where they may come to harm.
Both of these potential negative consequences exist in our current uses of AI. For self-driving cars, there are already regulatory bodies involved, so we can expect a public dialog about when and in what ways AI driven vehicles can be used. What about the other uses of AI? Currently, except for some action by New York City, there is exactly zero regulation around the use of AI. The most basic assurances of algorithmic accountability are not guaranteed for either users of technology or the subjects of automated decision making.
Image via Getty Images / nadia_bormotova
Unfortunately, we can’t leave it to companies to police themselves. Facebook’s slogan, “Move fast and break things” has been retired, but the mindset and the culture persist throughout Silicon Valley. An attitude of doing what you think is best and apologizing later continues to dominate.
This has apparently been eﬀective when building systems to upsell consumers or connect riders with drivers. It becomes completely unacceptable when you make decisions aﬀecting people’s lives. Even if well-intentioned, the researchers and developers writing the code don’t have the training or, at the risk of oﬀending some wonderful colleagues, the inclination to think about these issues.
I’ve seen ﬁrsthand too many researchers who demonstrate a surprising nonchalance about the human impact. I recently attended an innovation conference just outside of Silicon Valley. One of the presentations included a doctored video of a very famous person delivering a speech that never actually took place. The manipulation of the video was completely imperceptible.
When the researcher was asked about the implications of deceptive technology, she was dismissive of the question. Her answer was essentially, “I make the technology and then leave those questions to the social scientists to work out.” This is just one of the worst examples I’ve seen from many researchers who don’t have these issues on their radars. I suppose that requiring computer scientists to double major in moral philosophy isn’t practical, but the lack of concern is striking.
Recently we learned that Amazon abandoned an in-house technology that they had been testing to select the best resumes from among their applicants. Amazon discovered that the system they created developed a preference for male candidates, in eﬀect, penalizing women who applied. In this case, Amazon was suﬃciently motivated to ensure their own technology was working as eﬀectively as possible, but will other companies be as vigilant?
As a matter of fact, Reuters reports that other companies are blithely moving ahead with AI for hiring. A third-party vendor selling such technology actually has no incentive to test that it’s not biased unless customers demand it, and as I mentioned, decision makers are mostly not in a position to have that conversation. Again, human bias plays a part in hiring too. But companies can and should deal with that.
With machine learning, they can’t be sure what discriminatory features the system might learn. Absent the market forces, unless companies are compelled to be transparent about the development and their use of opaque technology in domains where fairness matters, it’s not going to happen.
Accountability and transparency are paramount to safely using AI in real-world applications. Regulations could require access to basic information about the technology. Since no solution is completely accurate, the regulation should allow adopters to understand the eﬀects of errors. Are errors relatively minor or major? Uber’s use of AI killed a pedestrian. How bad is the worst-case scenario in other applications? How are algorithms trained? What data was used for training and how was it assessed to determine its ﬁtness for the intended purpose? Does it truly represent the people under consideration? Does it contain biases? Only by having access to this kind of information can stakeholders make informed decisions about appropriate risks and trade-oﬀs.
At this point, we might have to face the fact that our current uses of AI are getting ahead of its capabilities and that using it safely requires a lot more thought than it’s getting now.