MobilityX Pitch Competition Part 2: Machine Learning

I recently attended a pitch competition at MobilityX in Austin, TX, to hear eight companies share their ideas on how to use Artificial Intelligence (AI) and immersive media for urban mobility. In my last blog, I talked about the companies using Natural Language Processing (NLP) Artificial Intelligence to tackle mobility problems. In this piece, I am going to talk about the companies that tackled Machine Learning Artificial Intelligence.

 

Machine Learning: Decision Tree Learning Models and Neural Networks

If you want to learn more about the present state of Machine Learning (ML) and in particular its mobile implementation, you can do no better than this article about how HBO created the Not Hot Dog app from Silicon Valley.

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As the article describes, Machine Learning is about systematically improving the machine output of a known input or set of inputs. Put another way, if a computer process outputs hot dog/not hot dog at 50% accuracy, a successful machine learning endeavor will increase that accuracy to say 60%. 60% isn’t all that much bang for your buck though, so I’ll plug the above article again for more insight on the journey to 90%+ accuracy.

 

So let’s take a simple use case and see how we might build it out using machine learning. For our use case we’ll think about a machine that can recognize fruit through image processing. For an idea of the level of complexity inherent in this problem, please check out this relevant xkcd.

 

To start with we’ll consider the case of trying to determine the difference between a banana and a strawberry. At our disposal is any characteristic we can think of with our fruit. In this simple example we might write some code that says if our fruit is yellow then it is a banana, and if it is red then it is a strawberry (for now we’ll ignore the non-trivial problems of how our computer knows what is fruit and not fruit in the image, and the fact that both unripe bananas and unripe strawberries can be green).

 

Next let’s bump up the complexity and add in watermelon. Watermelon are multi-colored, but they’re mostly green so we can use our color logic from the first phase and add in a bit about if green then our fruit is a watermelon (we’ll ignore the non-trivial problem of watermelons being red when sliced for now). But what happens if we then want to add in lemons? Now our color logic is insufficient. To that end let’s add in a second layer to our process that checks the roundness of our fruit. Lemons are pretty round, rounder than bananas at least, so now we can say if a fruit is yellow AND round then it’s a lemon, and if it’s not round it’s a banana.

 

Super! We keep adding fruits to our model and after some period of time we have let’s say five layers that check for the color, roundness, size, hardness, and furriness (kiwis and coconuts are weird). We want to generalize these for all fruits and so we give our layers different weights based on what is selected. For example color might matter a lot for an orange because there aren’t many orange fruit that aren’t oranges, but red doesn’t help narrow the field all that much (cherries, apples, raspberries, strawberries, etc).

 

In order to figure out what these weights should be we get into the machine learning piece finally. What we do is take a bunch of pictures of fruit, let’s say n, and run it through our process which will give us some accuracy, let’s call it A. We tweak the weights and run our n pictures through again. In fact we run them through a bunch of times, let’s say m times, tweaking the weights every time to try and improve A. Ideally we can make n and m large enough that we can get to the A we want (97%?). Our images are called a training set, and this repeatable process is called supervised learning. If our weighted process is straightforward we’ve made a decision tree learning model, and if instead it’s some fancy pants non-linear process then we’ve made a neural network.

 

My Top for ML

At the MobilityX pitch competition, one startup stood out for its use of Machine Learning.

 

Cerebri – While identifying fruit may be a fun academic thought experiment, here in the US we’re in the business of making money, and that’s where Cerebri comes in. Cerebri is working to develop the neural networks necessary to process that big data your CTO told you to start collecting five years ago and turn it into useful customer-centric and actionable business intelligence. Despite their demo having unlabeled axes, please see this second relevant xkcd, these guys are bring ML to a compelling space and building a real organization around serious ML development.

 

It was a pleasure to attend the MobilityX pitch competition. If you want to know what is happening on the cutting edge of the mobility space, keep an eye on my picks, and MobilityX as a whole in the months to come.

 

Immersive Media
Immersive Media is an umbrella term for three technologies, which despite being in the minds of Sci-Fi authors for a while are just now starting to get the enabling hardware necessary to catch up. The first is 360º Video, itself a misnomer since 360º would be 2-dimensional whereas 360º Video can be spherical and thus includes the 180º of the azimuthal angle in spherical coordinates, but 64,800º Video just doesn’t have the same ring to it. 360º Video is pre-recorded content displayed to a user who can choose any spherical angle as their viewing direction.

 

Virtual Reality is the next tech, and it offers the same degree of freedom for a viewer, but with a persistent virtual world. Persistent virtual worlds have been the purview of the gaming industry for years now (Tron was made in 1982!), but only recently have displays, motion tracking, and processing power gotten to the point where people will start to tolerate a couple of screens inches from their eyes. As the technology matures, we will start to see VR applications outside of gaming: teleconferencing, educational simulations, and if we’re being honest Demolition Man-style sex scenes.

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The third is Augmented Reality, which is the rendering of virtual content within the real world. Of the three technologies I think AR is the most interesting, broadest, and least mature. The applications range from Terminator-style information overlain on real-world objects, to immersive AR-gaming experiences, to volumetric motion capture-enabled holography, and probably a whole bunch of things no one’s thought of yet. Just like with AI, AR has to deal with the fact that the real world is very big, and has a lot of stuff in it. Unlike AI, however, in AR the machine is enhancing human engagement rather than trying to replace it. That’s a much richer space to play in.

 

Even the people I’ve met who are bullish on Immersive Media are underestimating just how large the space will become. We’re definitely early, but the ability to virtualize and augment reality opens up the possibility of multiple universes limited only by the collective ingenuity of human beings. After all why build a movie theater in Duluth, when you can build a virtual theater in virtual space that anyone can get to? If for some reason you doubt that we’ll be spending the next 30-50 years recreating the physical world virtually, please keep in mind that after 15,000 years of trying to figure out how to not all be farmers, we now have multiple cross-platform farming simulation franchises. We’re fascinating creatures.

 

So what’s our use case in this gigantic immersive world? The one we’ve been thinking about here at moovel is making an AR display that we can hover above bus stops that will show arrival times, alerts, and other useful information to AR users. While that might not be all that compelling right now when all we have are our phones, I guarantee you it’ll be cool once we get some Black Mirror-inspired augmentolens grafted onto our eyes. This eye to the future of AR content delivery is what gives this use case such an exciting twist. Let’s dive on in.

MobilityX Pitch Competition Part 1: Wrestling with Natural Language Processing

There are few things in the world I enjoy more than getting a bunch of smart people in a room to talk about the future. Nothing does that better than a startup pitch competition. This past week, I had the unique pleasure of attending a pitch competition at MobilityX in Austin, TX, to hear eight companies pitch ideas on using Artificial Intelligence (AI) and Immersive Media in the mobility space.

 

Last March, Capital Factory and Daimler-owned moovel North America (N.A.) partnered to create MobilityX, a transportation technology accelerator aimed at fostering the growth of new mobility-as-a service companies. My role on moovel’s R&D team is largely to research emerging technologies. Combining that with my general love of the sometimes-ridiculous and sometimes-inspiring world of startups, the MobilityX pitch competition was a treat for me.

 

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As I mentioned, the pitch competition was to see how AI and Immersive Media can be used in the mobility space. But these are umbrella terms for a suite of technologies, so let’s first take a moment to get on the same page about what they mean.

 

Immersive Media refers to digital technology or images that actively engage one’s senses and may create an altered mental state.

 

AI (Artificial Intelligence) refers to the nebulous realm of computers
mimicking human intelligence, such as cognition, speech, decision-making,
etc. In mobility technology, the four most prevalent applications of AI are
Natural Language Processing (NLP), Machine Learning (ML), Autonomous Driving,
and Image Recognition.

 

The companies that stood out the most for me were in the Artificial Intelligence space – specifically using Natural Language Processing (NLP) and Machine Learning (ML). Three companies using NLP caught my eye at the pitch competition. But before I reveal who these companies are, it’s worth taking a moment to expand on what NLP means in the context of mobility – and why any startup that successfully embraces it should be commended. Also, in my next blog, I will report on startups with a novel approach on ML in mobility.

Why Natural Language Processing (NLP) is So Tricky

NLP has been around since the theoretical underpinnings of computing were laid down by Alan Turing. The famous Turing test is probably the ultimate use case for NLP implementations. By the 70s, computer scientists were saying over-the-top things like, “translation and communication were solved problems,” and, “a fully functioning NLP AI solution was years away.” As is often the case with technology, these scientists underestimated humanity’s ability to be nuanced and difficult.

 

To get a feel for the trouble in NLP, let’s take a look at that first use case: something that seems simple, but is fairly complex under the hood. For example, let’s say I’m chatting with a chatbot and I say the phrase, “I’m headed to Milwaukee next week.” There are three relevant pieces of information in that one phrase: “headed to”, “Milwaukee”, and “next week”. In NLP parlance, these are called entities (the process a computer uses here is called Named-Entity Recognition), and they all pertain to different sub-routines for understanding.

 

First, let’s try to figure out what I mean by Milwaukee. If we were chatting at the bar, you might know that I live in Portland, OR. and that I’m from Chicago, IL. (even if you didn’t know that, my propensity for wearing Bears gear would help you out). Now, in your attempt to figure out which Milwaukee I’m headed to, you might narrow it down to Milwaukie, OR. or Milwaukee, WI. Our intrepid chatbot, armed with the same information, might do the same. Of course, that would require the persistence of per-user historical information, and that’s a big, big software system. Without that, maybe our bot checks spelling, but that assumes I know how to spell the right Milwaukee, which, if I wasn’t writing a blog about it, is very unlikely.

 

So you think to yourself, ‘ok, maybe we can’t use per-user historical data or spelling. Let’s instead try to disambiguate location based on a probabilistic model based on something like population and/or proximity’. That might work for ‘Milwaukees’, but what about ‘Springfields’ or something less-specific like ‘City Hall’ or the ‘Courthouse’? There aren’t enough interns in the world to hire to make and maintain that type of location-based disambiguator, and it’s unclear how you’d shove that into a UI in the first place. To get an idea of how it might work, I just searched for “city hall” in Google Maps, which took me to the Springfield City Hall at Springfield, OR. Google’s utilizing historical information from my search history along with my location to attempt to present me with the most relevant city hall. This is, of course, a nifty UX, but it’s difficult to raise money by telling people you’re going to recreate Google. And there’s also the fact that Google is wrong. I was actually thinking about the City Hall in Matawan, NJ (bwahahahah!).

 

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A recurring theme in the world of AI is that the real world is very big and complex, and making meaningful intelligence for it at scale is rather difficult. After all, we didn’t even consider the fact that I might have been talking about ‘Milwaukee’, the power tools company, which itself has multiple locations across multiple states and brings its own disambiguation nightmare to the experience. Our range of languages and our penchant for arbitrarily naming and renaming things (thank goodness for %s/Bombay/Mumbai/g – know what I mean?) makes a general purpose NLP Interface a difficult prospect at this time.

My Top Three for NLP

Thankfully, the three companies pitching NLP and Conversational UIs, AppVuze, Upswing’s, and Blink.AI, understood the need for more context-aware domain-specific solutions. Let’s take a look:

 

AppVuze, the MobilityX challenge winner, set out to recreate customer support software (think the support portion of a CRM) for mobile. Mobile is an interesting space at the moment as it is on the cusp of proliferation with new devices for the Immersive Media space. That AppVuze has focused on making their offering available not only on iOS and Android, but on the majority of the cross-platform solutions in the space show that they get how to make your offering compelling in the mobile space. While Natural Language Processing (NLP) in customer service isn’t entirely new (you can say, “new card questions”, or “main menu”), being able to assign an app-specific context to that chat is novel. AppVuze is focusing on support for enterprise mobile solutions, which, when combined with things like Google’s refocusing of Google Glass to enterprise solutions, has some definite promise in the coming years.

 

Upswing’s ballywick is in the educational space, and I have to give them credit for diverting some resources to apply their tech to the mobility space. Upswing combines student support – tutoring, mentoring, and advising – with data science. For the competition, Upswing presented us a Facebook Messenger bot based on a simple decision-tree-based UI. While everyone’s puzzled looks regarding Facebook Messenger bots made me a little doubtful regarding the prevalence of its platform, decision-tree-based UIs have been a hallmark of modern gaming and extending them into other spaces seems like a no-brainer. Education seems like a natural fit for multiple-choice domain-specific conversation UIs, and I’m excited to see how Upswing can grow in that space.

 

Blink.AI is taking more of a platform approach to the NLP space and endeavoring to provide an abstraction for the creation of context-aware domain-specific conversational UIs. While earlier in the product lifecycle than their NLP compatriots, they definitely know what they’re talking about in the space. That depth of knowledge and understanding of how it can apply to the mobility space was sufficient for second place at the challenge. After all, taking something complex and making it simple enough to enable others to build cool stuff is How To Make Money 101, and I wouldn’t be surprised if Blink.AI ended up powering some intrepid chatbot in the not so distant future.

 

In my next blog, I will look at the startups from the MobilityX competition that are wrestling with Machine Learning that are on my radar – and should be on yours, as well.

This Week in the Headlines: July 17th – July 23rd, 2017

Welcome to Move Forward’s weekly news wrap-up, featuring the mobility stories you don’t want to miss. This week’s edition features coverage highlights from TriMet’s Hop FastPass launch and moovel Lab’s What The Street?! Project. We’re also featuring news on public transit woes in New York City, an article on the benefit of micro-transit, thought leadership articles from industry experts, and more.

 

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Fastpass & Nat Parker Featured in KOIN segement:


KOIN Channel 6 featured a segment on the launch of Hop Fastpass, including video footage from Monday’s launch event with TriMet and moovel. moovel is highlighted at 1:45, while moovel CEO Nat Parker describes the importance of keeping riders’ information safe
KOIN: “TriMet’s Hop Fastpass officially launches” by Staff, July 17, 2017.

 

WTS?! makes waves in national media:

An article in Fast Company dives into moovel Lab’s mobility space report project, What The Street?! The piece gives an in-depth look into the data presented in the project, particularly diving into the issue of wasted space.
Fast Company: “Just See How Much Of A City’s Land Is Used For Parking Spaces” by Adele Peters, July 20, 2017.

 

MassTransit highlights moovel smart card account management:


MassTransit Magazine features the Hop Fastpass launch news, in particular describing how this technology is the second mobile smart card account management platform in the country, and the first on the West Coast
MassTransit Magazine: “moovel Powers Landmark Mobile Smart Card Account Management Platform” by Staff, July 21, 2017

 

Autonomous vehicles shake up automotive world:

A new study indicates that automakers face challenging roads ahead as autonomous vehicles begin to control the future of the automotive industry, citing financial backing and competition with technology companies as main issues.
Government Technology: “Study: Autonomous Future Looks Tough For Automakers” by Brent Snavely, July 14, 2017.

 

Penn Station

 

Summer of hell begins in NYC:

Construction in New York’s Penn Station has proved to be nightmarish for commuters, with city and statewide delays. These strains on the city’s transit system – which is already operating at maximum capacity – has further proved that public transportation is in need of a reboot.
The Wall Street Journal: “Commuters’ ‘Summer of Hell’ Starts One Week Late” by Paul Berger, Corinne Ramey and, Zolan Kanno-Youngs, July 17, 2017.

 

Individual based approach for transportation:

Stephen Goldsmith, Professor of Practice at the Harvard Kennedy School and Director of the Innovations in American Government Program, addresses the problems transit agencies have with identifying the needs of the individual. Goldsmith believes that a user-based approach to identifying these issues is critical for transit companies.
Governing: “The Need to Manage Mobility From the Ground Up” by Stephen Goldsmith, July 18, 2017.

 

Controversial benefits of AVs explained:

Tim Schwanen, Associate Professor and Director of the Transport Studies Unit at University of Oxford, believes that autonomous vehicles may actually create more problems for our transport system than they are predicted to solve. Heathrow states that as many public transit investments tend to favor the middle class, “AV developments risk further increasing transport’s role in enhancing social inequality.”
The Guardian: “Peak car? Driverless technology may actually accelerate car ownership” by Tim Heathrow, July 18, 2017.

 


Please note that this article expresses the opinions of the author and does not reflect the views of Move Forward.

This Week in the Headlines: May 5th- June 12th, 2017

Welcome to Move Forward’s weekly news wrap-up, featuring the mobility stories you don’t want to miss. This week’s edition features takeaways on the future of smart cities from IOT World 2017, new technologies revolutionizing urban transit in Europe, news on Big Data’s role in facilitating transportation efficiencies, and more.

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moovel partner upgrades Atlanta transit:

MassTransit featured Atlanta Streetcar in a write-up discussing the transit authority’s mission to upgrade transportation throughout Atlanta. moovel N.A. was included in the story as a key partner in this mission. “Atlanta Streetcar partnered with moovel N.A., Tozny and the National Institutes of Standards and Technology (NIST) to create a simple and easy-to-use mobile ticketing app.”

MassTransit: “Best Practices: Real-Time Information” by Staff, June 6, 2017.

 

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Miami encourages other cities to participate in MaaS:

Miami-Dade County has released an “Urban Mobility Playbook” on how to organize multi-modal mobility in cities by integrating public transit and private transportation services.

Government Technology: “Miami-Dade County Demonstrates How to Overhaul Urban Mobility Through Integration” by Ryan McCauley, June 1, 2017.

 

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Automakers invest in mobility:

Many automakers are investing heavily in self-driving car initiatives in an effort to compete with “the future of mobility.” “Daimler, judged by Bernstein’s Warburton to be one of the leading automakers in the self-driving tech race, plans to spend 8.1 billion euros on r&d both this year and next year, up from 7.6 billion euros in 2016.”

Automotive News Europe: “Automakers face big, costly challenges to make self-driving cars a reality” by Nick Gibbs, June 4, 2016.

 

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Daimler Trucks N.A. jumps into AV technology:

An interview with Roger Nielson, CEO of Daimler Trucks North America, illuminates why the company has chosen to stay in Portland, along with the company’s future plans to begin implementing autonomous technology in their trucks.

Government Technology: “New Daimler Trucks CEO Talks Autonomy, Active Braking and Driver Attentiveness Monitoring” by Elliot Njus, June 5, 2017.

 

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NYC subway faces more issues:

As each day brings increasing problems to New York City’s subway system, The New York Times looks into the issues facing the region’s public transit system. Gov. Andrew Cuomo said in a recent speech, “it is no secret the New York City subway system is in dire need of upgrades and repairs, not only for the safety of commuters and visitors throughout the metropolitan area, but in order to meet the demand of travelers as ridership continues to grow.”

The New York Times: “How Did the Subway Get So Bad? Look to the C Train” by Marc Santora, June 6, 2017.

 

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Big Data creates safer commutes:

Cities across the U.S. are turning to new technologies, such as sensors, cameras, and machine-learning algorithms, to assess risks at dangerous intersections. This comes on the heels of a recent push for cities to gather data to better understand how their residents move.

StreetsBlog: “Can Algorithms Design Safer Intersections?” by Stephen Miller, June 7, 2017.

 

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Atlanta Streetcar makes moves:

The Georgia Department of Transportation says that Atlanta Streetcar is on track to remedying the 66 problems outlined in state and federal audits this month. However, while ridership has risen in the first five months of 2017, total usage remains lower than usual after the city began charging for rides last year.

The Atlanta Journal Constitution: “Atlanta Streetcar fixes problems, looks to expansion” by David Wickert, June 7, 2017.

 


Please note that this article expresses the opinions of the author and does not reflect the views of Move Forward.

This Week in the Headlines: May 29th- June 4th, 2017

Welcome to Move Forward’s weekly news wrap-up, featuring the mobility stories you don’t want to miss. This week’s edition features takeaways on the future of smart cities from IOT World 2017, new technologies revolutionizing urban transit in Europe, news on Big Data’s role in facilitating transportation efficiencies, and more.

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Daimler invests in sustainable energy:

Daimler recently unveiled a fleet of new battery-powered electric vehicles in preparation for a new era of clean driving. Daimler has also entered a partnership with Utah-based Vivant Solar– a move that indicates the company is working to provide EV users with the option to source their electricity via solar systems.

Just Means: “Daimler’s Sustainability Drives Races Ahead with EV Technology” by Antonio Passolini, May 25, 2017.

 

BMW thinks outside of the box for urban mobility:

Last week, BMW unveiled the new BMW Motorrad Concept Link- an emission-less, two-wheeled vehicle that they believe is the future of urban mobility.

Alphr: “BMW wants to reinvent urban mobility on two wheels” by Staff, May 26, 2017.

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More adults turn to ride-sharing:

According to a new survey, nearly 25% of adults in the U.S. have sold or traded in a vehicle in the past year. Further, 9% of that group has turned to ride-sharing services as their primary means of transportation.

Fortune: “The Latest Uber Effect: People Are Ditching Their Cars” by Kate Samuelson, May 26, 2017.

 

Takeaways from IOT World 2017:

TechRepublic shares several key takeaways from a panel at the IoT World 2017 conference that focused on how smart cities incorporate sustainability and IoT. Notable points include: 1) Cities must start by improving infrastructure to become more efficient and 2) Solving public transportation problems is critical to smart city advancements.

TechRepublic: “5 lessons from IoT leaders creating sustainable, smart cities” by Teena Maddox, May 26, 2017.

 

New transit initiatives in Europe:

Deutsche Bahn, Germany’s largest train and bus operator, has begun testing driverless vehicles for public transportation use. The goal of this initiative is to eventually offer on-demand driverless services that will connect to existing public transportation networks.

The New York Times: “The Future of European Transit: Driverless and Utilitarian” by Mark Scott, May 28, 2017.

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“Internet of Trains” creates reliability:

Siemens AG, one of the world’s largest providers of railway infrastructure, is implementing big data, sensors and predictive analytics to guarantee their customers close to 100% reliability. Called the “Internet of Trains”, this technology is used to improve asset availability and utilization while increasing energy efficiency.

Forbes: “How Siemens Is Using Big Data And IoT To Build The Internet Of Trains” by Bernard Marr, May 30, 2017.

 

Complex relationships in ride-sharing industry:

An infographic from axios shows the intertwined relationships between companies in the ride-sharing business.

axios: “The complex web of self-driving car relationships” by Kia Kokalitcheva, Dan Primack, & Lazaro Gamio, May 30, 2017.

 

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The World’s Smartest Cities:

IESE Cities in Motion Index published their rankings of “The Top 10 Smartest Cities” in the world, with New York City taking the #1 slot, followed by London and Paris.

Forbes: “The Smartest Cities In The World For 2017” by IESE School of Business, May 31, 2017.

 

Technologies shape future of public transit:

span style=”font-weight: 400;”>D Magazine explores the differences in how European and American cities are using new technologies to shape the future of public transportation, and how this relates, specifically, to transit in Dallas. The author suggests that, regardless of location, “the rules of mobility and public transit are about to be rewritten.”

D Magazine: “European Researchers Are Reinventing the Rules of Public Transit” by Peter Simek, May, 31 2017.


Please note that this article expresses the opinions of the author and does not reflect the views of Move Forward.