Getting Over Uber — Backchannel — Medium

Susan Crawford (Professor of Law) promotes improving taxis in urban areas, as a way to provide reliable and cost-effective mobility to people in general. Uber aims also to provide reliability, but the definitions are fundamentally different.

This following trends makes sense, given the incentives (and purpose) of corporations to produce profit:

Uber drivers have a tough time making a living; they’re responsible for their own cars, fuel, benefits, maintenance, tolls, and certain insurance as well as the kickback to Uber that takes a substantial slice out of every fare they pick up. They may or may not know where they’re going, and they may or may not be driving cars that are safe. Uber consistently squeezes its drivers as tightly as it possibly can; new drivers are paying an even higher cut to Uber than the first generation did.

These trends will be particularly true for the lower-income drivers, who rely on driving as their primary income.

It would be interesting and insightful to do a historical analysis on the AT&T monopoly in telecommunications and draw parallels to this pending monopoly in transportation networks.

So even when private companies provide basic transport and communications services — in America, that’s often how we do it — they do this subject to extensive public obligations. That’s where the whole idea of “common carriage” came from — transport and communications networks operated by private companies that provided a high level of uniform service at uniform rates under uniform promises of safety and reliability.

I’m not sure that taxis are the answer, however, though the model of Mobility-on-Demand (MoD) systems is extremely compelling in urban areas, not necessarily operated by a single player (or even a single private player). As some commenters point out, taxi drivers also face many of the same issues as Uber drivers. Taxis are an old and slow-moving system, not very suitable to “compete” in today’s fast-paced world. Governments and public agencies must be willing to experiment, break their own rules, and move fast, if they wish to protect its people.

One last curiosity: bus drivers in some American cities are paid well and have nice benefits. They welcome riders with a smile and some centralized system optimizes their shifts so they have variety in their weekly routes. Perhaps there is something to be learned from public transit systems when considering MoD systems.

via Getting Over Uber — Backchannel — Medium.

A very nice response by Tim O’Reilly addresses many additional problems and differences between taxi and Uber, most notably access to a private vehicle. (Taxis are rented; Uber drivers are required to have their own vehicle.) He lays out several actionable items for the government:

1. Removing outdated taxi regulations that make it difficult for taxis to compete with Uber and Lyft, even given comparable technology. For one example, consider Washington D.C., served by taxis with geographic restrictions. A taxi driver can pick up a passenger in Montgomery MD and bring her into DC, but must then drive back to Maryland to pick up another passenger, since he is prohibited from picking up in the city. A driver from Virginia can only pick up Virginia-bound passengers at Washington National Airport. And so on.

2. Working closely with Uber and Lyft to understand how well the city is being served. There is some evidence that Uber and Lyft are improving availability in previously under-served neighborhoods. Cities should be working to build on and verify these studies.

3. Understanding whether the reputation systems (and other self-regulatory regimes) of Uber and Lyft are producing results at least as good as the older regulatory regimes under which taxis operate. The passenger experience suggests that they are doing considerably better than the older regulatory regimes, but cities should actively be pursuing data to confirm or disprove this anecdotal evidence, and introducing regulation only when systematic problems have been uncovered.

4. Working with Uber and Lyft to understand the tradeoffs between lower fares for passengers and driver income. There is a risk that in pursuit of low prices for consumers, these companies could end up exploiting workers. Government does have a role in making sure that companies produce great experiences not just for their customers but also for the workers delivering their services. But guess what: government has abdicated that responsibility in low wage industries like retail and fast food, where workers are paid so little that they must supplement their wages with public assistance. (Recent estimates put the taxpayer subsidy to these industries at $153 billion/year.) I’d much rather see government focus on areas like this where there is a clear and present problem rather than in new industries like on-demand transportation where the market has not yet settled on the right balance between value to customers and value to workers. The fact that Uber and Lyft are competing so hard to attract drivers suggests to me that the market still has a lot to say about that balance.

5. Improving crime reporting so that there is a consistent basis for evaluating the relative safety of taxicabs versus Uber and Lyft. While there are many anecdotal accounts of bad Uber experiences, there are also anecdotal experiences of bad taxi experiences, but crime statistics are not reported in a way that allows cities to understand if new safeguards are needed.

via Getting Over Uber — Backchannel — Medium.

From Mobile Data, Drawing Social Circles | MIT Spectrum | Fall 2015

MIT CEE group utilized cell phone traces from 25mil people in 155 cities in France, Portugal, and Spain over 6 months (7+ bil records).

Through this data, González and her team deduced that one-fifth of urban movement is for social purposes.

González is currently working with the metropolitan planning office in the Saudi Arabian capital of Riyadh to help policymakers coordinate a bus system that reflects urban travel patterns. … Building a bus system with stops and frequencies that anticipate how groups of people actually travel will hopefully encourage more passengers to use it.

This summer, González is working in Rio de Janeiro, Brazil, upcoming site of the 2016 Olympic Games. The city is trying to coordinate traffic routes, as street capacity will be reduced during the events. Understanding how to encourage travelers to use fewer cars will be key. “I’m analyzing how similar people might have similar mobility patterns. Knowing how people move helps us propose solutions,” such as carpooling, González says. … By quantifying how much urban movement is social, it could be possible to pair like-minded travelers through social media apps that increase traveling efficiency. González points to ride-sharing service Uber as a company that leverages this kind of dynamic social mapping.

“The information that we generate can be captured in real time, from people using their devices, and we can actually see mobility in a city. This is the age of instant information, and it can directly affect policy. Imagine you have a set number of people traveling along a certain route, and you want to add an extra lane—this data can tell the mayor that you need it, and you can really quantify the need,” she says. “It’s hugely exciting.”

via From Mobile Data, Drawing Social Circles | MIT Spectrum | Fall 2015.

HT Alex

Greek town glimpses mass transit future: driverless buses – US News

The buses go no faster than 20 kph (12 1/2 mph), but the trials in Trikala (pronounced TREE-kah-lah) potentially represent a major advance for automated transport.

Trikala already has already tested EU-funded pilot medical programs, including schemes to relay heart test data from home to the doctor’s office and use tracker devices for Alzheimer patients. In the center of the city, a “digital tree” with solar panels allows benches to carry phone-charging outlets.

The 28-nation European Union is targeting gasoline use for city transport as one area where it wants to reduce carbon emissions. With oil prices and city populations expected to rise in the coming decades, a major shift to battery power and more shared transport could blur the line between private and public vehicles.

Senior transport analyst Philippe Crist at the International Transport Forum, an OECD think-tank based in Paris, says transport trends are hard to predict as the world moves more toward automation.

Crist said researchers looked at “shared and route-optimized on-call taxi-like services replacing all car and bus trips in a mid-sized European city. We found that these systems could deliver almost the same mobility as today but with 95 percent fewer vehicles.”

So far, the CityMobile2 has had mixed reviews on the streets of Trikala. Not everyone is happy to lose parking spots or replace human jobs with machines. Still, retiree Michalis Pantelis said he was proud that his city was selected for the testing.

via Greek town glimpses mass transit future: driverless buses – US News.

The Moral Bucket List – NYTimes.com

This is a philosophy for stumblers. The stumbler scuffs through life, a little off balance. But the stumbler faces her imperfect nature with unvarnished honesty, with the opposite of squeamishness. Recognizing her limitations, the stumbler at least has a serious foe to overcome and transcend. The stumbler has an outstretched arm, ready to receive and offer assistance. Her friends are there for deep conversation, comfort and advice.

The stumbler doesn’t build her life by being better than others, but by being better than she used to be. Unexpectedly, there are transcendent moments of deep tranquillity. For most of their lives their inner and outer ambitions are strong and in balance. But eventually, at moments of rare joy, career ambitions pause, the ego rests, the stumbler looks out at a picnic or dinner or a valley and is overwhelmed by a feeling of limitless gratitude, and an acceptance of the fact that life has treated her much better than she deserves.

via The Moral Bucket List – NYTimes.com.

Cyber-physical systems and smart cities

Today, I am in Seattle attending a National Science Foundation (NSF) workshop for early career investigators on Cyber-Physical Systems (CPS) in Smart Cities (link). There are 2 main purposes of this workshop:

  1. to share, develop, propose new research directions for smart cities, and
  2. to get to know the other early career researchers working in related areas.

Present at this workshop include people with funding power (NSF) and industry (Amazon), and we had the opportunity to see invited talks from non-profits (US Ignite, 100 Resilient Cities), city government (City of Seattle), and research groups (Netlab at Caltech, Urban@UW). It has been great to learn about efforts towards smart cities from all sorts of perspectives, from foundational research, funding calls, and knowledge transfer, to hackathons, city challenges, and creating environments for more collaboration.

Some insights I liked from today’s invited talks:

  • One key aspect to remember about cyber-physical systems is that there are underlying physical laws that cannot be designed away in these systems; and this leads to our key challenges of non-convexity, large-scale, uncertainty, and multi-timescales. — Steven Low, Netlab, Caltech, on designing controllers for power grids
  • “Inclusive innovation” is key to developing our urban systems, and it’s not just about supporting diversity; the greater the diversity in human specialization, the greater the potential value of exchanges in a system. — Vikram Janhdyala, University of Washington, Urban@UW
  • Think about the communication requirements of your work in CPS; how much bandwidth do you need for your work to affect real people in real cities? Our use of resources is way unsustainable. Now we want to see sensors, government data, open data, etc. used intelligently for providing transparency, changing behavior, and optimizing our resource use. We’re interested in applications for cities operating at 10 Mbps to 100 Gbps. — Glenn Ricart, US Ignite, on the science of smart cities
  • Shocks and stresses like natural disasters, industry collapse, disease outbreak, etc. can bring opportunities for cities to evolve and in some circumstances transform, so how to best use the opportunities is something to plan for. — Jose Baptista, Rockefeller Foundation, 100 Resilient Cities Project, on designing for… resilient cities
  • City government can move fast and break things. By trying a lot of different programs and efforts, by providing government data openly, Seattle was able to demonstrate the potential of collective brainpower for improving city services, e.g. crime prediction, green commuting, stolen vehicle tracking, computer literacy programs, service requests, etc. We often want to move fast, but we also need to make sure we’ve got good brakes, so we can slow down if needed. — Michael Mattmiller, CTO of Seattle

A theme among the invited speakers was an impatience for our research to reach people. There was a clear emphasis from NSF for research to have shorter-term impacts, e.g. 3-5 years, which they called “technological off-ramps.”

Among the lightning talks, I also noticed a few motifs in the ideas:

Addressing the lack of guarantees for CPS

  • Danielle Tarraf (Johns Hopkins) — certification for systems with limited alphabet and memory
  • Dong Wang (Note Dame) — how to provide data correctness guarantees from humans as sensors
  • Vasumathi Raman (Caltech) — providing control as a service via synthesizing correct CPS
  • Sam Coogan (UC Berkeley) — scalable formal methods for transportation systems

Addressing security of CPS

  • Lillian Ratliff (UC Berkeley) — mathematical foundations for the efficiency-vulnerability tradeoff in societal-scale CPS
  • Tamara Bonaci (UW) — cyber-security for teleoperated robots

Addressing neat new CPS applications

  • Tam Chantem (Utah State University) — CPS techniques for a semi-automated emergency response system
  • Min Kyung Lee (CMU) — studying how people react and respond to automated and algorithmic systems
  • Charlies Mydlarz (NYU) — full-scale CPS for acoustic map and noise mission control for New York City

Addressing challenges in the smart grid

  • Baosen Zhang (UW) — powering smart cities through highly decentralized controllers
  • Mahnoosh Alizadeh (Stanford) — coupling power and transportation networks via electric vehicles

With these themes and fresh ideas in mind, I look forward to all the groundbreaking research this week at CPSWeek 2015.

Special thanks to Jaime for feedback on the article!

Data science interviews

The aim of this guide is to provide the “delta” between a software engineering interview and a data science interview, for a mathematically-oriented researcher. This guide is suitable for researchers well-versed in some combination of statistics, probability, machine learning, optimization, and related areas.

If you don’t already have software engineering interview skills, I recommend looking additionally at guides for building those skills. Almost all of those skills (writing code live, designing algorithms, etc.) and good interviewing practices (asking good questions, showing interest, communicating well) still apply here, except there will be fewer computer systems questions and less emphasis on best practices in software engineering.

Anyway, here is how I prepared for my data science interviews.

Step 0: what is data science?

Learn your audience. It’s good for context to know what exactly people mean when they are talking about data science. it’s sufficient to read the top articles from google, and I’ve provided some here.

Step 1: imagine the problems that the company will ask you

What are the questions that the company would like to know from their data? What kind of data do they have? How can the data be related to the company’s mission / bottom line / target audience? How can their data affect their day-to-day operations? How can it affect their longer-term business decisions? What kinds of methods would you use to solve these problems? What kinds of visualizations would be appropriate? How do some of these questions then translate into products or features?

Step 2: pseudo-structured wandering through wikipedia++

The goal here is primarily to familiarize oneself with the terminology of data mining, with an emphasis on the high-level concepts and what people tend to use in practice. I’ve listed a bunch of topics I reviewed below, and the goal is to develop the following for each area:

  • Have an intuitive understanding (and way to explain) the concept / technique / method
  • Be able to write the (main) relevant equations; know the key properties
  • Have an example at hand to demonstrate usage/understanding
  • Understand when it is used and why; know related tools and when it is better to use one vs the other; what are considered “good” values and how to determine/assess/validate them

Topics

Thesaurus

  • Learning rate (machine learning) == step size (convex optimization)
  • Spectrum analysis == frequency domain analysis == spectral density estimation
  • Predictive analytics == predictive modeling and forecasting
  • Multinomial logistic regression == softmax regression == multinomial logit == maximum entropy (MaxEnt) classifier == conditional maximum entropy model

Step 3: okay, what do other people do for interview prep?

Additionally, there is an explosion of data science books (e.g. this or this) and blogs that I’m sure are also very useful for data science interviews.


If you are a grad student in a technical field, leave a comment with your interview preparation techniques!

Insightful advice for academia from WICSE

Today I attended the WISCE Berkeley-Stanford annual meetup and there was quite a bit I learned that I think applies to anyone in the field, so I wanted to share it here (along with more women-specific things). In particular, I found the academia panel to be very insightful and wise. Here were the key points.

First off, there are lots of resources specific to women + careers in EECS from an annual conference called Rising Stars in EECS. It’s neat because the talks are all online and the advice is quite specific to EECS and covers both academia and industry.

The panelists

The first invited panelist is Professor Ruzena Bajcsy (Berkeley), who has been in the field for 40+ years and thus has been through many of the shifts through the generation. The second panelist is Professor Tsu-Jae King Liu (Berkeley, current EECS department chair). She was immediately asked a bunch of questions about time management, chair responsibilities and whatnot, to which she says that of course being chair is a lot of work and easily takes up half of her time. The third and last panelist is Dr. Sadia Afroz (Berkeley, current postdoc).

Shifts in academia

When asked about what the biggest changes in academia have been, Ruzena commented that she’s noticed almost an obsession with money among young researchers… people are always talking about their salary, grant funding, etc. She cautioned (and reminded) us that she chose academia because she wanted to do her own projects, to think independently… not to be led where the money lies.

Advice for new faculty

Ruzena, now 80+ years old, still advises a full lab, comes to work every day, stays fully engaged in the community and the field. I find it remarkable and admirable and awesome. When asked the question “What advice would you give a new young faculty?” she gave the advice: you really want to have a job where every day you are happy to go to work. She commented that she is literally happy to come into the lab every day, including weekends (our meetup was on a Saturday). Specific to academia, she advised that if you are not curious, don’t like teaching, don’t like exploring wild ideas (and keep in mind that many of those ideas won’t work out), don’t do it.

A lot of these qualities actually ring true with me. I love teaching, mentoring; I like exploring new ideas, I like thinking about the future, and certainly I’m curious. But at the same time, it’s really important to me that the things that I work on see the light of day and actually make a real difference in people’s lives. So I asked the question: is curiosity in conflict with broad impact? It seems clear to me that, at some point, if you want to carry an idea out further and broader, then you need to spend the time and effort to invest in that single idea… which of course detracts from other curiosities. So then is academia still suitable if your life drive is the impact and results rather than the curiosity?

Ruzena’s response was actually unexpected but heart-warming and wise. So, Ruzena had just seen the talk I presented at this meetup, which was focused on developing large urban systems. In my particular context, she agreed that academia is probably best suited for this type of work because it can connect me with a wide group of people in many different related fields, and this is necessary since urban systems touch on so many different studies, e.g. EECS, civil engineering, urban planning, economics, public policy, etc. But more generally, she advised that everyone take advantage of the breadth of courses and resources that are available to us (at a top school); to explore a bit because anything narrow that we learn now will become out of date. And so it is prudent to pick up a breadth of skills.

Nice gals lose?

Often times, women (and actually many people in EECS) feel like they need to be more aggressive in order to succeed, but feel conflicted because it is counter to their personality. When asked the question of “Should I be more aggressive if I want to attain professorship?”, Liu responded that confidence is everything; that the key is to develop confidence in your own abilities, over time and to recall that often times people are just curious… there’s no need to get defensive. In order to have people recognize you as an individual rather than as a woman is to have confidence from within, display that confidence, and engage in conversations.

Advice for yourself?

The next question was: now, putting yourself back in your own shoes as a grad student, what’s one piece of advice that you’d give yourself, in order to succeed in academia? Ruzena responded succinctly: learn as much mathematics as you can. Liu’s advice was to really get to know your classmates; in fact, that also helps with building confidence. Sadia’s response was to not be afraid to ask questions, make the critical comments about other people’s work — to not assume that other people know better — because often times, you will be right. And I full-heartedly agree with all of the responses. The bottom line is really to gradually build up your own confidence.

Relatedly, what’s one thing you did right?

To this, Liu responded with seeking out a good mentor, one who is separate from your academic advisor. This person should believe in you, be able to provide emotional support. Ruzena, being the only female in robotics at the time (eeeesh) and immersed in a somewhat WASP-y culture (white anglo-saxon protestant), simply tried to make friends, looking for those (male) colleagues who were open to equality. She slowly built up a network and community for herself, slowly but surely. It wasn’t revolutionary by any means, she commented, but she was really supported by this network despite the opposing culture. What we have here now with WISCE is not far off. It’s a great community of like-minded people, and it’s really important that we stick together and support one another.

Thanks to Alex Lee for feedback on this article!

The Data You’ll Get Your Hands On | HubHacks 2 | ChallengePost

Every 911 call made to Police with time and address (except domestic abuse)
Every 911 call made to Fire with time. Address included only for non-medical calls.
Every 911 call made to EMS.
Every parking ticket written.
GPS location of every bus every minute
Every user reported alert (jam, double parked car, pothole, accident) on Waze
Other City data including Permitting Detail, Entertainment Licenses, Licensing Board, Zoning
Data sets from the Boston Public Library, Big Belly, RunKeeper and ZipCar

via The Data You'll Get Your Hands On | HubHacks 2 | ChallengePost.

City of Boston data hub: link

AT&T to hook up its automated home and connected car services | Reuters

AT&T said it had about 20 million connected devices from cars to cargo ship container sensors in 2014, up 21 percent from the year earlier. It has not yet revealed its revenue from its "Internet of Things" business.

via AT&T to hook up its automated home and connected car services | Reuters.

Ready for all sorts of demand inference, route inference, etc.

The Government’s Bad Diet Advice – NYTimes.com

Instead of accepting that this evidence was inadequate to give sound advice, strong-willed scientists overstated the significance of their studies.

Uncertain science should no longer guide our nutrition policy. Indeed, cutting fat and cholesterol, as Americans have conscientiously done, may have even worsened our health. In clearing our plates of meat, eggs and cheese (fat and protein), we ate more grains, pasta and starchy vegetables (carbohydrates). Over the past 50 years, we cut fat intake by 25 percent and increased carbohydrates by more than 30 percent, according to a new analysis of government data. Yet recent science has increasingly shown that a high-carb diet rich in sugar and refined grains increases the risk of obesity, diabetes and heart disease — much more so than a diet high in fat and cholesterol.

via The Government’s Bad Diet Advice – NYTimes.com.

Sigh, science is hard.

HT Yang