Year: 2018

Dockless electric scooter company GOAT has launched in Austin after receiving official permits from the city’s transportation department for its pilot program. Unlike what’s happened in San Francisco with startups Bird, Lime and Spin, GOAT says it wants to work in tandem with city officials in Austin. GOAT is currently bootstrapped, but says it plans to continue partnering with local cities to launch its electric scooter service across the nation.

GOAT has permission to launch up to 500 scooters as part of the pilot program, but is currently incrementally deploying scooters 20 at a time. The company tells TechCrunch it’s also working with other cities in pursuing permits in multiple areas.

“In April we watched two California-based companies enter our market, ignore the balance, and exploit the policies and patience of our local city government but today we’re thankful for the due diligence the City of Austin put into place to ensure dockless mobility is a viable option to support their long-term objectives that we’ve worked to support,” GOAT CEO Michael Schramm said in a statement. “Since the City of Austin’s rules were established our team has worked tirelessly to prepare for a launch in our city that meets all of the criteria set forth by the ordinance for dockless mobility.”

Similar to other scooter services, GOAT costs $1 to ride and 15 cents for every minute. GOAT says it also works to educate users around rider safety, red zones and local parking rules. GOAT also offers free helmets to “active” riders, according to its website. And, for those of you wondering, GOAT is indeed going for “greatest of all time.”

“Every time you ride GOAT, we want it to lead you to the best the city has to offer, so each experience with GOAT has the opportunity to be the greatest of all time,” GOAT CMO and co-founder Jennie Whitaker said in a press release. “Coming into the market during the ‘wild west’ of electric scooters is an adventure on its own, so focusing on what makes us unique will guide our brand. By combining our tech competencies with a sincere desire to do good for the people and communities we serve, we look forward to the places GOAT will go as we help solve short distance transportation issues with integrity.”

For reference, here’s how GOAT stacks up to other scooter companies in terms of financing.

Netflix’s market cap is now larger than Comcast, which is pretty much just a symbolic thing given that the companies are valued very differently but is like one of those moments where Apple was larger than Exxon and may be some kind of watershed moment for technology. Or not.

A couple notes on this largely symbolic and not really important thing:

• Netflix users are going up. That’s a number that people look at. It’s why Netflix’s magic market number is going up.
• People are cutting cable TV cords. Netflix has no cable TV cords. It does, however, require a cord connected to the internet. So it still needs a cord of some sort, unless everything goes wireless.
• Netflix is spending a lot of money on content. People consume content. Cable is also content, but it is expensive content. Also, Comcast will start bundling in Netflix into its cable subscriptions.
• They have a very different price-to-earnings ratio. Comcast is valued as a real company. Netflix is valued as a… well, something that is growing that will maybe be a business more massive than Comcast. Maybe.
• Comcast makes much more money than Netflix. Netflix had $3.7 billion in revenue in Q1. Comcast had $22.8 billion and free cash flow of $3.1 billion. Netflix says it will have -$3 billion to -$4 billion in free cash flow in 2018.

Anyway, Netflix will report its next earnings in a couple months, and this number is definitely going to change, because it’s pretty arbitrary given that Netflix is not valued like other companies. The stock price doesn’t swing as much as Bitcoin, but things can be pretty random.

In the mean time, Riverdale Season 2 is on Netflix, so maybe that’s why it’s more valuable than Comcast . See you guys in a few hours.

Following the unrelenting wave of controversy around Russian interference in the 2016 presidential election, Twitter announced new guidelines today for political advertisements on the social networking site.

The policy, which will go into effect this summer ahead of midterm elections, will look towards preventing foreign election interference by requiring organizations to self-identify and certify that they are based in the U.S., this will entail organization registered by the Federal Elections Committee to present their FEC ID, while other orgs will have to present a notarized form, the company says.

Orgs buying political ads will also have to comply with a stricter set of rules for how they present their profiles. Twitter will mandate that the account header, profile photo and organization name are consistent with how the organization presents itself online elsewhere, a policy likely designed to ensure that orgs don’t try to obfuscate their identity or present their accounts in a way that would confuse users that the account belonged to a political organization.

In a blog post, the company noted that there would also be a special type of identifying badge for promoted content from these certified advertisers in the future.

Back in April — in the midst of Facebook’s Cambridge Analytica scandal — Twitter publicly shared its support for the Honest Ads Act. This Political Campaigning Policy will be followed up by the company’s work on a unified Ads Transparency Center which the company has promised “will dramatically increase transparency for political and issue ads, providing people with significant detail on the origin of each ad.”

Sorry. Your next car probably won’t be autonomous. But, it will still have artificial intelligence (AI).

While most of the attention has been on advanced driver assistance systems (ADAS) and autonomous driving, AI will penetrate far deeper into the car. These overlooked areas offer fertile ground for incumbents and startups alike. Where is the fertile ground for these features? And where is the opportunity for startups?

Inside the cabin

Inward-facing AI cameras can be used to prevent accidents before they occur. These are currently widely deployed in commercial vehicles and trucks to monitor drivers to detect inebriation, distraction, drowsiness and fatigue to alert the driver. ADAS, inward-facing cameras and coaching have shown to drastically decrease insurance costs for commercial vehicle fleets.

The same technology is beginning to penetrate personal vehicles to monitor driver-related behavior for safety purposes. AI-powered cameras also can identify when children and pets are left in the vehicle to prevent heat-related deaths (on average, 37 children die from heat-related vehicle deaths in the U.S. each year).

Autonomous ridesharing will need to detect passenger occupancy and seat belt engagement, so that an autonomous vehicle can ensure passengers are safely on board a vehicle before driving off. They’ll also need to identify that items such as purses or cellphones are not left in the vehicle upon departure.

AI also can help reduce crash severity in the event of an accident. Computer vision and sensor fusion will detect whether seat belts are fastened and estimate body size to calibrate airbag deployment. Real-time passenger tracking and calibration of airbags and other safety features will become a critical design consideration for the cabin of the future.

Beyond safety, AI also will improve the user experience. Vehicles as a consumer product have lagged far behind laptops, tablets, TVs and mobile phones. Gesture recognition and natural language processing make perfect sense in the vehicle, and will make it easier for drivers and passengers to adjust driving settings, control the stereo and navigate.

Under the hood

AI also can be used to help diagnose and even predict maintenance events. Currently, vehicle sensors produce a huge amount of data, but only spit out simple codes that a mechanic can use for diagnosis. Machine learning may be able to make sense of widely disparate signals from all the various sensors for predictive maintenance and to prevent mechanical issues. This type of technology will be increasingly valuable for autonomous vehicles, which will not have access to hands-on interaction and interpretation.

AI also can be used to detect software anomalies and cybersecurity attacks. Whether the anomaly is malicious or just buggy code, it may have the same effect. Vehicles will need to identify problems quickly before they can propagate on the network.

Cars as mobile probes

In addition to providing ADAS and self-driving features, AI can be deployed on vision systems (e.g. cameras, radar, lidar) to turn the vehicle into a mobile probe. AI can be used to create high-definition maps that can be used for vehicle localization, identifying road locations and facades of addresses to supplement in-dash navigation systems, monitoring traffic and pedestrian movements and monitoring crime, as well as a variety of new emerging use cases.

Efficient AI will win

Automakers and suppliers are experimenting to see which features are technologically possible and commercially feasible. Many startups are tackling niche problems, and some of these solutions will prove their value. In the longer-term, there will be so many features that are possible (some cataloged here and some yet unknown) that they will compete for space on cost-constrained hardware.

Making a car is not cheap, and consumers are price-sensitive. Hardware tends to be the cost driver, so these piecewise AI solutions will need to be deployed simultaneously on the same hardware. The power requirements will add up quickly, and even contribute significantly to the total energy consumption of the vehicle.

It has been shown that for some computations, algorithmic advances have outpaced Moore’s Law for hardware. Several companies have started building processors designed for AI, but these won’t be cheap. Algorithmic development in AI will go a long way to enabling the intelligent car of the future. Fast, accurate, low-memory, low-power algorithms, like XNOR.ai* will be required to “stack” these features on low-cost, automotive-grade hardware.

Your next car will likely have several embedded AI features, even if it doesn’t drive itself.

* Full disclosure: XNOR.ai is an Autotech Ventures portfolio company.

Hitlist, a several-years old app for finding cheap flights has begun rolling out a subscription tier that will turn it into something more akin to your own mobile travel agent. While the core app experience which monitor airlines for flight deals will continue to be free, the new premium upgrade will unlock a handful of other useful features, including advanced filtering, exclusive members-only fares, and even custom travel advice from the Hitlist team.

The idea, says founder and CEO Gillian Morris, goes back to the original idea that inspired her to create Hitlist in the first place.

“Going back to the very beginning, Hitlist was essentially me giving travel advice to friends,” she says. “People had the time, inclination, and money to travel, but didn’t book because they got lost in the search process. When I sent custom advice, like ‘you said you wanted to go to Istanbul, there are $500 direct round trips in May available right now, that’s a good price and the weather will be good and the tulip festival, this unique cultural experience, will be happening’ – 4 out of 5 people would book,” Morris explains.

“I wouldn’t be able to scale that level of advice at the beginning, so we focused on just the flight deals. But now we have four years’ worth of data that we can learn from – browsing and searching within Hitlist – and we can start to build more sophisticated models that will inside and enable people to travel at scale,” she says.

The new subscription feature will offer users the ability to better filter airline deals by things like the carrier, number of stops, and the time of day of both the departure and return.

It’s also working with airlines to market “closed group” fares that aren’t accessible through flight search engines, but are available to select travel agents and other resellers that market to a closed user group. These will be flagged in the app as “members-only” fares.

Hitlist says it’s currently working with one airline and, through a third party, with several more. But because this is still in a pilot phase and is only live with select users, it can’t say which.

Meanwhile, the app will continue to focus on helping users find the best, low-cost fares – not only by tracking deals – but also by bundling low-cost carriers and traditional airlines together. However, it won’t promote dates that are likely to be cancelled by airlines, nor will it venture into legally gray areas like skipping legs of a flight (like Skiplagged) to find cheaper fares.

Beyond just finding cheap flights – which remains a competitive space – Hitlist aims to offer users a more personalized experience, more like what you would have gotten with a travel agent in the past.

For starters, it developed a proprietary machine learning algorithm that sorts through over 50 million fares’ worth of data per day to find deals that appeal to each individual user. It also learns from how you use it – browsing flights, or how you react to alerts, for example.

“The app gets to know you better over time, just like a human travel agent would,” says Morris. “With the premium upgrade, we’re gaining more insight to the traveler’s preferences that helps us to develop even more sophisticated A.I. to provide advice and make sure you’re getting the best deal.”

When you find a flight you like, Hitlist will direct you over to a partner’s site – like the airline or online travel agency such as CheapOair.

Where the app differs from others who are also trying to replace the travel agent – like Lola, Pana or Hyper – is that Hitlist doesn’t offer a chat interface. Morris feels that ultimately, travelers don’t want to talk to a chatbot – they just want to browse and discover, then have an experience that’s tailored for them as the app gets smarter about what they like.

That’s where Hitlist’s editorially curated suggestions come in, which can be as broad as “escape to Mexico” or as weird and quirky as “best cities to find wild kittens.” (Yes really.)

Hitlist will also help travelers by offering a variety of travel advice to help them make a decision – similar to how Morris used to advise her friends. For example, it might suggest the best days to fly (similar to Google Flights or Hopper), or tell you about the baggage fees, or even what sort of events might be happening at a destination.

Since its launch, Hitlist has grown to over a million mostly millennial travelers, who have collectively saved over $25 million on their flights by booking at the right time.

The new subscription plan is live now on iOS as an in-app purchase for $4.99 per month, but offers a better rate for quarterly or annual subscriptions, at $4.00/mo and $3/mo, respectively. It will roll out on Android later in the year.

On a recent work trip, I found myself in a swanky-but-still-hip office of a private tech firm. I was drinking a freshly frothed cappuccino, eyeing a mini-fridge stocked with local beer, and standing amidst a group of hoodie-clad software developers typing away diligently at their laptops against a backdrop of Star Wars and xkcd comic wallpaper.

I wasn’t in Silicon Valley: I was in Johannesburg, South Africa, meeting with a firm that is designing machine learning (ML) tools for a local project backed by the U.S. Agency for International Development.

Around the world, tech startups are partnering with NGOs to bring machine learning and artificial intelligence (AI) to bear on problems that the international aid sector has wrestled with for decades. ML is uncovering new ways to increase crop yields for rural farmers. Computer vision lets us leverage aerial imagery to improve crisis relief efforts. Natural language processing helps usgauge community sentiment in poorly connected areas. I’m excited about what might come from all of this. I’m also worried.

AI and ML have huge promise, but they also have limitations. By nature, they learn from and mimic the status quo–whether or not that status quo is fair or just. We’ve seen AI or ML’s potential to hard-wire or amplify discrimination, exclude minorities, or just be rolled out without appropriate safeguards–so we know we should approach these tools with caution. Otherwise, we risk these technologies harming local communities, instead of being engines of progress.

Seemingly benign technical design choices can have far-reaching consequences. In model development, tradeoffs are everywhere. Some are obvious and easily quantifiable — like choosing to optimize a model for speed vs. precision. Sometimes it’s less clear. How you segment data or choose an output variable, for example, may affect predictive fairness across different sub-populations. You could end up tuning a model to excel for the majority while failing for a minority group.

Image courtesy of Getty Images

These issues matter whether you’re working in Silicon Valley or South Africa, but they’re exacerbated in low-income countries. There is often limited local AI expertise to tap into, and the tools’ more troubling aspects can be compounded by histories of ethnic conflict or systemic exclusion. Based on ongoing research and interviews with aid workers and technology firms, we’ve learned five basic things to keep in mind when applying AI and ML in low-income countries:

1. Ask who’s not at the table. Often, the people who build the technology are culturally or geographically removed from their customers. This can lead to user-experience failures like Alexa misunderstanding a person’s accent. Or worse. Distant designers may be ill-equipped to spot problems with fairness or representation. A good rule of thumb: if everyone involved in your project has a lot in common with you, then you should probably work hard to bring in new, local voices.
2. Let other people check your work. Not everyone defines fairness the same way, and even really smart people have blind spots. If you share your training data, design to enable external auditing, or plan for online testing, you’ll help advance the field by providing an example of how to do things right. You’ll also share risk more broadly and better manage your own ignorance. In the end, you’ll probably end up building something that works better.
3. Doubt your data. A lot of AI conversations assume that we’re swimming in data. In places like the U.S., this might be true. In other countries, it isn’t even close. As of 2017, less than a third of Africa’s 1.25 billion people were online. If you want to use online behavior to learn about Africans’ political views or tastes in cinema, your sample will be disproportionately urban, male, and wealthy. Generalize from there and you’re likely to run into trouble.
4. Respect context. A model developed for a particular application may fail catastrophically when taken out of its original context. So pay attention to how things change in different use cases or regions. That may just mean retraining a classifier to recognize new types of buildings, or it could mean challenging ingrained assumptions about human behavior.
5. Automate with care. Keeping humans ‘in the loop’ can slow things down, but their mental models are more nuanced and flexible than your algorithm. Especially when deploying in an unfamiliar environment, it’s safer to take baby steps and make sure things are working the way you thought they would. A poorly-vetted tool can do real harm to real people.

AI and ML are still finding their footing in emerging markets. We have the chance to thoughtfully construct how we build these tools into our work so that fairness, transparency, and a recognition of our own ignorance are part of our process from day one. Otherwise, we may ultimately alienate or harm people who are already at the margins.

The developers I met in South Africa have embraced these concepts. Their work with the non-profit Harambee Youth Employment Accelerator has been structured to balance the perspectives of both the coders and those with deep local expertise in youth unemployment; the software developers are even foregoing time at their hip offices to code alongside Harambee’s team. They’ve prioritized inclusivity and context, and they’re approaching the tools with healthy, methodical skepticism. Harambee clearly recognizes the potential of machine learning to help address youth unemployment in South Africa–and they also recognize how critical it is to ‘get it right’. Here’s hoping that trend catches on with other global startups too.

A Portland family tells KIRO news that their Echo recorded and then sent a private conversation to someone on its list of contacts without telling them. Amazon called it an “extremely rare occurrence.”

Portlander Danielle said that she got a call from one of her husband’s employees one day telling her to “unplug your Alexa devices right now,” and suggesting she’d been hacked. He said that he had received recordings of the couple talking about hardwood floors, which Danielle confirmed.

Amazon, when she eventually got hold of the company, had an engineer check the logs, and he apparently discovered what they said was true. In a statement, Amazon said “We investigated what happened and determined this was an extremely rare occurrence. We are taking steps to avoid this from happening in the future.”

What could have happened? It seems likely that the Echo’s voice recognition service misheard something, interpreting it as instructions to record the conversation like a note or message. And then it apparently also misheard them say to send the recording to this particular person. And it did all this without saying anything back.

The house reportedly had multiple Alexa devices, so it’s also possible that the system decided to ask for confirmation on the wrong device — saying “All right, I’ve sent that to Steve” on the living room Echo because the users’ voices carried from the kitchen. Or something.

Naturally no one expects to have their conversations sent out to an acquaintance, but it must also admitted that the Echo is, fundamentally, a device that listens to every conversation you have and constantly sends that data to places on the internet. It also remembers more stuff now. If something does go wrong, “sending your conversation somewhere it isn’t supposed to go” seems a pretty reasonable way for it to happen.

I’ve asked Amazon for more details on what happened, but as the family hasn’t received one, I don’t expect much.

For being one of the most visited websites on the web, Reddit‘s product has rocked a notoriously basic design for much of its existence. The site is in the process of slowly rolling out a major desktop redesign to users, and today the company announced that part of this upgrade will be native support for night mode.

Night mode will likely be a popular feature for the desktop site that seems to have a core group of users that never sleep. Reddit’s mobile apps have notably had a native night mode for a while already.

While night mode won’t likely be too controversial, some Redditors already seem resistant to the redesign change. Nevertheless, I’ve found it to be a pretty friendly upgrade (classic view is still the best) that gels with the surprisingly great mobile apps the company has continued to update. Reddit’s recent heavy integration of native ads is only more apparent in the new design, something that is understandably frustrating a lot of users, but it was surprising the ad-lite good times lasted so long in the first place.

You can access the night mode feature with a toggle in the username dropdown menu in the top-right corner of the site.

At the very beginning, there were 15 startups. After a morning of incredibly fierce competition, we now have a winner.

Startups participating in the Startup Battlefield have all been hand-picked to participate in our highly competitive startup competition. They all presented in front of multiple groups of VCs and tech leaders serving as judges for a chance to win €25,000 and an all-expense paid trip for two to San Francisco to participate in the Startup Battlefield at TechCrunch’s flagship event, Disrupt SF 2018.

After many deliberations, TechCrunch editors pored over the judges’ notes and narrowed the list down to five finalists: Glowee, IOV, Mapify, Wakeo and Wingly.

These startups made their way to the finale to demo in front of our final panel of judges, which included: Brent Hoberman (Founders Factory), Liron Azrielant (Meron Capital), Keld van Schreven (KR1), Roxanne Varza (Station F), Yann de Vries (Atomico) and Matthew Panzarino (TechCrunch).

And now, meet the Startup Battlefield Europe at VivaTech winner.

Winner: Wingly

Wingly is a flight-sharing platform that connects pilots and passengers. Private pilots can add flights they have planned, then potential passengers can book them.

Runner-Up: IOV

IOV is building a decentralized DNS for blockchains. By implementing the Blockchain Communication Protocol, the IOV Wallet will be the first wallet that can receive and exchange any kind of cryptocurrency from a single address of value.

At the very beginning, there were 15 startups. After a morning of incredibly fierce competition, we now have a winner.

Startups participating in the Startup Battlefield have all been hand-picked to participate in our highly competitive startup competition. They all presented in front of multiple groups of VCs and tech leaders serving as judges for a chance to win €25,000 and an all-expense paid trip for two to San Francisco to participate in the Startup Battlefield at TechCrunch’s flagship event, Disrupt SF 2018.

After many deliberations, TechCrunch editors pored over the judges’ notes and narrowed the list down to five finalists: Glowee, IOV, Mapify, Wakeo and Wingly.

These startups made their way to the finale to demo in front of our final panel of judges, which included: Brent Hoberman (Founders Factory), Liron Azrielant (Meron Capital), Keld van Schreven (KR1), Roxanne Varza (Station F), Yann de Vries (Atomico) and Matthew Panzarino (TechCrunch).

And now, meet the Startup Battlefield Europe at VivaTech winner.

Winner: Wingly

Wingly is a flight-sharing platform that connects pilots and passengers. Private pilots can add flights they have planned, then potential passengers can book them.

Runner-Up: IOV

IOV is building a decentralized DNS for blockchains. By implementing the Blockchain Communication Protocol, the IOV Wallet will be the first wallet that can receive and exchange any kind of cryptocurrency from a single address of value.