Year: 2019

AutoX, the Hong Kong and San Jose, Calif.-based autonomous vehicle technology company, is pushing past its grocery delivery roots and into the AV supplier and robotaxi business.

And now, it’s taking its business to Europe.

AutoX has partnered with NEVS — the Swedish holding company and electric vehicle manufacturer that bought Saab’s assets out of bankruptcy — to deploy a robotaxi pilot service in Europe by the end of 2020. Under the exclusive partnership, AutoX will to integrate its autonomous drive technology into a next-generation electric vehicle inspired by NEVS’s “InMotion” concept that was shown at the CES Asia in 2017.

This next-generation vehicle is being developed by NEVS in Trollhättan, Sweden. Testing of the autonomous NEVs vehicles will begin in the third quarter of 2019. The vehicles will hit public roads in Europe next year, the companies said. 

AutoX founder and CEO Jianxiong Xiao, commonly referred to as Professor X, noted that this particular vehicle is ideal for an autonomous taxi service because it is purpose-built for this specific application, doesn’t produce tailpipe emissions, can be used 24 hours a day and can help reduce the number of vehicles in the street.

The companies ultimately want to deploy a large fleet of robotaxis globally.

The partnership with NEVs is the latest sign that AutoX has broader ambitions for its autonomous vehicle technology than delivery services. AutoX launched in 2016 and was initially focused on using self-driving vehicles for delivering packages, namely groceries. Last August, the startup kicked off a grocery delivery and mobile store pilot in a limited area in San Jose in partnership with GrubMarket.com and local high-end grocery store DeMartini Orchard.

But more recently, the company, which has raised about $58 million from venture and strategic investors, has expanded its plans. The company now wants to supply manufacturers with autonomous vehicle technology and launch its own robotaxi service.

In June, AutoX became the second company to receive permission from California regulators to transport passengers in its robotaxis. AutoX is calling its California robotaxi service xTaxi.

The California Public Utilities Commission has also granted Pony.ai, Waymo and Zoox permits to participate in the state’s Autonomous Vehicle Passenger Service pilot, which prohibits the company from charging for these robotaxi rides.

Professor X has previously said his mission is to open up autonomous vehicles to everyone, and so this expansion shouldn’t come as a surprise. It’s a goal the company contends can be reached using economical (and better) hardware. The company does use light detection and ranging radar, known as LiDAR. But instead of loading up its self-driving vehicles with numerous expensive LiDAR units, AutoX relies more on cameras, which it argues have better resolution. The company’s proprietary AI algorithms tie everything together.

For now, the xTaxi pilot in California will be rather limited. It will operate in the same operational design domain as the delivery service in San Jose, an area about 5 square miles. But the company clearly has ambitions to expand both in size and geographic reach. AutoX has more than 115 employees today and plans to hire more than 50 people this year.

The company is also working with San Jose city government to launch another pilot downtown. It has yet to reveal details, although the pilot could launch as early as next month.

AutoX also has permit to operate a robotaxi service in Shenzhen, China. It’s not clear whether the company will operate this service on its own or follow the model it set in Europe with NEVS. It’s possible AutoX will partner with BYD in China. AutoX has already working with the Chinese company to integrate its AV tech into BYD vehicles.

AutoX, the Hong Kong and San Jose, Calif.-based autonomous vehicle technology company, is pushing past its grocery delivery roots and into the AV supplier and robotaxi business.

And now, it’s taking its business to Europe.

AutoX has partnered with NEVS — the Swedish holding company and electric vehicle manufacturer that bought Saab’s assets out of bankruptcy — to deploy a robotaxi pilot service in Europe by the end of 2020. Under the exclusive partnership, AutoX will to integrate its autonomous drive technology into a next-generation electric vehicle inspired by NEVS’s “InMotion” concept that was shown at the CES Asia in 2017.

This next-generation vehicle is being developed by NEVS in Trollhättan, Sweden. Testing of the autonomous NEVs vehicles will begin in the third quarter of 2019. The vehicles will hit public roads in Europe next year, the companies said. 

AutoX founder and CEO Jianxiong Xiao, commonly referred to as Professor X, noted that this particular vehicle is ideal for an autonomous taxi service because it is purpose-built for this specific application, doesn’t produce tailpipe emissions, can be used 24 hours a day and can help reduce the number of vehicles in the street.

The companies ultimately want to deploy a large fleet of robotaxis globally.

The partnership with NEVs is the latest sign that AutoX has broader ambitions for its autonomous vehicle technology than delivery services. AutoX launched in 2016 and was initially focused on using self-driving vehicles for delivering packages, namely groceries. Last August, the startup kicked off a grocery delivery and mobile store pilot in a limited area in San Jose in partnership with GrubMarket.com and local high-end grocery store DeMartini Orchard.

But more recently, the company, which has raised about $58 million from venture and strategic investors, has expanded its plans. The company now wants to supply manufacturers with autonomous vehicle technology and launch its own robotaxi service.

In June, AutoX became the second company to receive permission from California regulators to transport passengers in its robotaxis. AutoX is calling its California robotaxi service xTaxi.

The California Public Utilities Commission has also granted Pony.ai, Waymo and Zoox permits to participate in the state’s Autonomous Vehicle Passenger Service pilot, which prohibits the company from charging for these robotaxi rides.

Professor X has previously said his mission is to open up autonomous vehicles to everyone, and so this expansion shouldn’t come as a surprise. It’s a goal the company contends can be reached using economical (and better) hardware. The company does use light detection and ranging radar, known as LiDAR. But instead of loading up its self-driving vehicles with numerous expensive LiDAR units, AutoX relies more on cameras, which it argues have better resolution. The company’s proprietary AI algorithms tie everything together.

For now, the xTaxi pilot in California will be rather limited. It will operate in the same operational design domain as the delivery service in San Jose, an area about 5 square miles. But the company clearly has ambitions to expand both in size and geographic reach. AutoX has more than 115 employees today and plans to hire more than 50 people this year.

The company is also working with San Jose city government to launch another pilot downtown. It has yet to reveal details, although the pilot could launch as early as next month.

AutoX also has permit to operate a robotaxi service in Shenzhen, China. It’s not clear whether the company will operate this service on its own or follow the model it set in Europe with NEVS. It’s possible AutoX will partner with BYD in China. AutoX has already working with the Chinese company to integrate its AV tech into BYD vehicles.

There comes a time for many startup companies where they either realize they need to do a nationwide roll-out, or they need to actively target buyers in the middle of the country. If you are a startup on either the east or the west coasts, it’s worth thinking about how this market might present its own set of unique challenges, and how you plan to overcome them.

There are a lot of misconceptions about what some people call “flyover country”, and as a San Francisco native who spent two decades in NY, DC, and Boston before moving to Pittsburgh, I can assure you they are almost all wrong. Without getting into specifics, the reality of “middle America” is that it’s the same as anywhere else.

Income, education, world view, and waistlines are all varied. It’s pretty accurate that San Francisco possesses a culture obsessed with fitness and entrepreneurship. But, California isn’t necessarily all like that, and if you think it is, I encourage you to go to Bakersfield, the Central Valley, or Eureka sometime.

In addition, just because the stereotypes are wrong doesn’t mean there’s nothing different about doing business here. As you think about how to conduct your rollout, here are some things you should consider:

Table of Contents

• Research
• Audience 
• Launch Calendar
• Advertising 
• Youth
• Public Relations
• Pitfalls

Research

As with any market, research is key since it informs every other aspect of the rollout. Start by looking into who your competition is.

Since there are fewer VC backed startups in middle America, and smaller companies tend to get less press, the research may be harder. However, there are some major universities that are actively putting money into their own Entrepreneurship programs and those spinoffs often do very well.

Lyft has announced an expansion of its new program designed to make airports pickups less confusing for riders and drivers alike, by directing riders to a designated pickup spot where they’ll show the driver a PIN code. The program is launching this weekend at New York’s LaGuardia Airport, the company says, and the plan to roll out a similar experience at other airports in the future still remains.

Starting on Saturday, July 20 at LaGuardia, users who request a Lyft ride won’t have to search for their vehicle at the usual and often busy pickup areas. Instead, they’ll be directed through the Lyft app to a designated Lyft pickup spot line, located in the Terminal B Garage, Area G.

A screen will pop-up after the user enters their ride request and destination that explains how to get to the Lyft pickup area, and will display a button that says “Get Code.” Riders tap this button for a unique code they’ll show their driver which matches them to that ride.

This way, Lyft users can hop in the first available car as opposed to waiting for a particular driver to arrive.

The company says it will have Lyft ambassadors on hand to assist, as the new program rolls out.

The end result is effectively a ride-sharing alternative to an airport taxi line. It also comes shortly after Lyft announced a similar program in May in partnership with the Portland International Airport (PDX), as did Uber. That made Portland the first U.S. airport to participate in Uber’s pilot, following its trials of PIN pickups in Bangalore.

LaGuardia, meanwhile, will be the first East Coast airport to offer such an option, according to reports.

Only standard Lyft rides are available for the feature, Lyft notes.

The company didn’t say what other airports will receive the feature in the future. But it may not always make sense, as it requires the airport to offer a designated pickup spot — and capacity for that could be limited in some cases. In addition, the location of the pickup spot plays a key role as to whether such a feature is even useful, as both Uber and Lyft are now finding out.

Both companies have been making the headlines in recent days due to their pickup problems at the San Francisco International Airport (SFO), following their move to a new pickup location. With wait times pushing 24-28 minutes, both services saw increased cancellations, according to local reports.

Meanwhile, LaGuardia has been experiencing all kinds of problems of its own, but related to construction. This included traffic backups that led to Uber and Lyft drivers getting stuck trying to get to the pickup spot. Because of these problems, LaGuardia may not have been the best airport for this latest expansion, as Lyft won’t know the feature’s true impact on efficiency for some time.

Sense and compute are the electronic eyes and ears that will be the ultimate power behind automating menial work and encouraging humans to cultivate their creativity. 

These new capabilities for machines will depend on the best and brightest talent and investors who are building and financing companies aiming to deliver the AI chips destined to be the neurons and synapses of robotic brains.

Like any other herculean task, this one is expected to come with big rewards.  And it will bring with it big promises, outrageous claims, and suspect results. Right now, it’s still the Wild West when it comes to measuring AI chips up against each other.

Remember laptop shopping before Apple made it easy? Cores, buses, gigabytes and GHz have given way to “Pro” and “Air.” Not so for AI chips.

Roboticists are struggling to make heads and tails out of the claims made by AI chip companies.  Every passing day without autonomous cars puts more lives at risk of human drivers. Factories want humans to be more productive while out of harm’s way. Amazon wants to get as close as possible to Star Trek’s replicator by getting products to consumers faster.

A key component of that is the AI chips that will power them.  A talented engineer making a bet on her career to build AI chips, an investor looking to underwrite the best AI chip company, and AV developers seeking the best AI chips, need objective measures to make important decisions that can have huge consequences. 

A metric that gets thrown around frequently is TOPS, or trillions of operations per second, to measure performance.  TOPS/W, or trillions of operations per second per Watt, is used to measure energy efficiency. These metrics are as ambiguous as they sound. 

What are the operations being performed on? What’s an operation? Under what circumstances are these operations being performed? How does the timing by which you schedule these operations impact the function you are trying to perform?  Is your chip equipped with the expensive memory it needs to maintain performance when running “real-world” models? Phrased differently, do these chips actually deliver these performance numbers in the intended application?

Image via Getty Images / antoniokhr

What’s an operation?

The core mathematical function performed in training and running neural networks is a convolution, which is simply a sum of multiplications. A multiplication itself is a bunch of summations (or accumulation), so are all the summations being lumped together as one “operation,” or does each summation count as an operation? This little detail can result in difference of 2x or more in a TOPS calculation. For the purpose of this discussion, we’ll use a complete multiply and accumulate (or MAC), as “two operations.” 

What are the conditions?

Is this chip operating full-bore at close to a volt or is it sipping electrons at half a volt? Will there be sophisticated cooling or is it expected to bake in the sun? Running chips hot, and tricking electrons into them, slows them down.  Conversely, operating at modest temperature while being generous with power, allows you to extract better performance out of a given design. Furthermore, does the energy measurement include loading up and preparing for an operation? As you will see below, overhead from “prep” can be as costly as performing the operation itself.

What’s the utilization?

Here is where it gets confusing.  Just because a chip is rated at a certain number of TOPS, it doesn’t necessarily mean that when you give it a real-world problem, it can actually deliver the equivalent of the TOPS advertised.  Why? It’s not just about TOPS. It has to do with fetching the weights, or values against which operations are performed, out of memory and setting up the system to perform the calculation. This is a function of what the chip is being used for. Usually, this “setup” takes more time than the process itself.  The workaround is simple: fetch the weights and set up the system for a bunch of calculations, then do a bunch of calculations. Problem with that is that you’re sitting around while everything is being fetched, and then you’re going through the calculations.  

Flex Logix (my firm Lux Capital is an investor) compares the Nvidia Tesla T4’s actual delivered TOPS performance vs. the 130 TOPS it advertises on its website. They use ResNet-50, a common framework used in computer vision: it requires 3.5 billion MACs (equivalent to two operations, per above explanation of a MAC) for a modest 224×224 pixel image. That’s 7 billion operations per image.  The Tesla T4 is rated at 3,920 images/second, so multiply that by the required 7 billion operations per image, and you’re at 27,440 billion operations per second, or 27 TOPS, well shy of the advertised 130 TOPS.  

Batching is a technique where data and weights are loaded into the processor for several computation cycles.  This allows you to make the most of compute capacity, BUT at the expense of added cycles to load up the weights and perform the computations.  Therefore if your hardware can do 100 TOPS, memory and throughput constraints can lead you to only getting a fraction of the nameplate TOPS performance.

Where did the TOPS go? Scheduling, also known as batching, of the setup and loading up the weights followed by the actual number crunching takes us down to a fraction of the speed the core can perform. Some chipmakers overcome this problem by putting a bunch of fast, expensive SRAM on chip, rather than slow, but cheap off-chip DRAM.  But chips with a ton of SRAM, like those from Graphcore and Cerebras, are big and expensive, and more conducive to datacenters.  

There are, however, interesting solutions that some chip companies are pursuing:

Compilers:

Traditional compilers translate instructions into machine code to run on a processor.  With modern multi-core processors, multi-threading has become commonplace, but “scheduling” on a many-core processor is far simpler than the batching we describe above.  Many AI chip companies are relying on generic compilers from Google and Facebook, which will result in many chips companies offering products that perform about the same in real-world conditions. 

Chip companies that build proprietary, advanced compilers specific to their hardware, and offer powerful tools to developers for a variety of applications to make the most of their silicon and Watts will certainly have a distinct edge. Applications will range from driverless cars to factory inspection to manufacturing robotics to logistics automation to household robots to security cameras.  

New compute paradigms:

Simply jamming a bunch of memory close to a bunch of compute results in big chips that sap up a bunch of power.  Digital design is one of tradeoffs, so how can you have your lunch and eat it too? Get creative. Mythic (my firm Lux is an investor) is performing the multiply and accumulates inside of embedded flash memory using analog computation. This empowers them to get superior speed and energy performance on older technology nodes.  Other companies are doing fancy analog and photonics to escape from the grips of Moore’s Law.

Ultimately, if you’re doing conventional digital design, you’re limited by a single physical constraint: the speed at which charge travels through a transistor at a given process node. Everything else is optimization for a given application.  Want to be good at multiple applications? Think outside the VLSI box!

Parrot announced the AR.Drone back at CES 2010, three years before DJI’s Phantom 1. It was a seemingly odd move by a company best known for making bluetooth speakers and headsets, but over the years it’s continued to release fairly novel takes on the growing category.

Two years back, the French company announced its intentions to shift product away from consumer focused device. Since then, it’s been slowly scaling things back, this week confirming a Wirecutter report that it’s leaving the toys behind.

No doubt seeing an insurmountable challenge from China’s DJI, the company is shuttering all drone lines but Anafi. While the line closely resembles DJI’s Mavic products, Parrot has begun to position the foldable quadcopter at enterprise uses. As we noted in April, the addition of a Flir thermal camera finds the company targeting construction workers and firefighters.

The move comes as the consumer and hobbyist market continues to grow, but those numbers have been utterly dominated by DJI’s offerings in recent years. Of course, DJI has also been tackling the B2B space, both with souped up versions of the Mavic line and higher payload devices like the Matrice and Inspire.

Those products can perform a wide range of different tasks, from pesticide spraying to search and rescue.

Gaurav Maken, the chief executive officer of the online vegan grocery store, Mylk Guys, doesn’t think of his company as a place to just buy food. For him, it’s a testing ground and platform for all of the new food products he expects to be developed as startup entrepreneurs and established food companies start tackling the plant-based and alternative meat market in earnest.

The company has raised $2.5 million in support of that vision from investors including Khosla Ventures, Pear Ventures, and Fifty Years.

“Today we’re an online grocery store,” says Maken. “We are also a place for cultured meats and any genetically engineered food that allows us to scale our food production and allows us to keep feeding people.”

Maken isn’t wedded to plant-based products and envisions a virtual store stocked with products that create more sustainable consumption options for its customers. In fact, 40% of the company’s customers are not vegan, according to Maken. . 

“We don’t only think about vegans. We think about sustainable food systems,” says Maken. “Our audience is an educated consumer who wants to have less of an impact from their diet… They’re just folks trying to do better with their eating habits.”

Right now, the company sells around 1300 products through its site. And the pitch that Maken makes to suppliers is that they can access the data around their customers (unlike other online retailers whose name rhymes with shmamazon).

“We provide analytics and a way for brands to unlock the data coming from their customers,” Maken says. “Our focus is how can we get you a personalized staple that works for you.” 

The company’s top sellers are vegan cheeses like Sparrow Camembert, lines of vegan jerkies, and the Beyond Burger, Maken said.

“You can build brands that are successful that are $1 million brands or $5 million brands and the reason why you haven’t is because they haven’t had the platform to provide national distribution to be successful,” says Maken.  

Mylk Guys launched in 2018 and went through the Y Combinator accelerator program. Now, with its new capital, the company is focusing on expanding its sales and marketing on the East Coast. Opening a new warehouse for distribution and reaching out to the vegan community on the Eastern Seaboard.

The model for selling more sustainable foods directly to the consumer has at least one precedent. Los Angeles-based Thrive Market raised $111 million in a 2016 round of funding for its online sustainable product-focused grocery store.

As recent reports indicate, the sustainable food business is only growing. Citing reports from Ecovia Intelligence, the publication Environmental Leader reported that organic food sales topped $100 billion for the first time in 2018.

Twitter is testing a new way to make conversation threads easier to follow, with the launch of a new test that labels notable replies with special icons. If the original poster replies somewhere in the thread, their tweet will have a small microphone icon next to their profile picture. Other tweets may be labeled, as well — including those from users who were mentioned in the original tweet and replies from people you’re already following on Twitter.

These will be labeled with the at symbol (@) and a small person icon with a checkmark by it, respectively.

The new test is the latest in a series of experiments Twitter has been running focused on making its product easier to use, particularly when conversations around a tweet become lengthy.

At the beginning of this year, the company first began a test where it labeled the original poster in a conversation thread as the “Original Tweeter.” That may have been a bit too confusing for some, because a few months later, Twitter changed it to “Author.” It then also added two other labels, for people who were mentioned in the original tweet, and those replies from people you’re following.

These, however, were text labels — meaning they took up valuable screen space on small mobile devices. They also cluttered up the already text-heavy interface with more distracting text to read.

The new icons don’t have that problem. But they’re also small and light gray and white in color, which makes them hard to see. In addition, their meaning isn’t necessarily clear to anyone who doesn’t hang around online forums like Reddit, for example, where it’s common to use a microphone to showcase the original poster’s follow-up comments.

It’s also unclear why Twitter thinks users are clamoring to see this information. Highlighting the original poster is fine, I guess, but the other labels seem extraneous.

While this is a minor change, it’s one of many things Twitter is tweaking in the hopes of making its service simpler and more approachable. It’s also running an experimental prototype app called twttr where it’s trying out new ideas around threaded conversations, like using color-coded replies or branching lines to connect tweets and their responses.

A lot of these changes feel a little unnecessary. Twitter isn’t as difficult to understand as the company believes it is.

At the end of the day, it’s a way to publish a public status update and reply to those others have posted. That’s its core value proposition — not live streaming video, not its clickable newsreels it calls “Moments,” and not its article bookmarking tools. Those are useful and fun additions, sure, but optional.

Instead, Twitter’s challenges around user growth aren’t because the service is overly complex, but because a public platform like this is rife for issues around online bullying and abuse, disinformation and propaganda, hate speech, spambots, and everything else that an unmoderated forum would face.

Twitter tests are live now, but not be showing for all users.

Twitter’s self-service tools when it comes to blocking content you don’t want to see, as well as a growing tendency for users to delete a lot of the content they post, is making some of the conversations on the platform look like Swiss cheese. The company says it will introduce added “context” on content that’s unavailable in conversations in the next few weeks, however, to help make these gaps at least less mystifying.

There are any number of reasons why tweets in a conversation you stumble upon might not be viewable, including that a poster has a private account, that the tweet was taken down due to a policy violation, that it was deleted after the fact or that specific keywords are muted by a user and present in those posts.

Twitter’s support account notes that the fix will involve providing “more context” alongside the notice that tweets in the conversation are “unavailable,” which, especially when presented in high volume, doesn’t really offer much help to a confused user.

Last year, Twitter introduced a new process for adding additional context and transparency to why an individual tweet was deleted, and it generally seems interested in making sure that conversations on the platform are both easy to follow, and easy to access and understand for users who may not be as familiar with Twitter’s behind-the-scenes machinations.

Seems Hongmeng isn’t the Android replacement it’s been pitched as, after all. The initial story certainly tracked, as Huawei has been preparing for the very real possibility of life after Google, but the Chinese hardware giant says the operating system is primarily focused on industrial use.

The latest report arrives courtesy of Chinese state news agency, Xinhua, which notes that the OS has been in development for far longer than the Trump-led Huawei ban has been in effect. Hongmeng is a relatively simple operating system compared to the likes of Android, according to SVP, Catherine Chen. The news echoes another recent report that Huawei had initially developed the software for use on IoT devices.

None of this means that Huawei isn’t working on a full mobile operating system, of course. Or that the sees of this new OS couldn’t be adapted to do more.

And given the recent news, such a move would be a pretty good use of the company’s vast resources. After all, it’s no doubt seen the writing on the wall for some time. While no one anticipated that such a ban would arrive so suddenly, questions about the company have been floated in security circles for years now.

New restrictions from the Trump administration barred Huawei from working with American companies like Google, but temporary reprieves have allowed the smartphone maker to employ Android services — at least temporarily. Questions about the company’s health are still very much up in the air, however, as the ban ramps back up.