Date published: March 11th, 2020

I am personally growing worried by the lack of testing for COVID-19 in the US, as it seems that this is a concerted effort to keep the public from panicking.

Things we Know

Median time from disease onset to death: 18.5 days (Table 2 discussion)

Mean incubation period: 5–6 days pg 12

Crude fatality rate: 3.8% pg 12

Calculated crude fatality ratio: 3.67%. This number was calculated using the Johns Hopkin’s COVID-19 dashboard: total deaths / total confirmed cases. At time of writing: 4610(deaths)/125664(confirmed).

Logistic regression of cases outside of china yields a growth factor of ~1.188

Current Situation in US

The afore-linked dashboard from Johns Hopkins currently lists 1135 confirmed cases and 32 confirmed deaths in the US.

The thing is, these 32 deaths died from a population of cases that existed >18.5 days ago. We can use the crude fatality rate, to back calculate how many cases we should have had ~19 days ago in the US. (There are many problems with this approach as not all of these people died today / were infected in the US).

32(deaths) / 3.67%(deaths/cases) => 872 cases (~20 days ago)

We can then (crudely) apply this to our ideal exponential growth function:

This yields:

Final Notes

There are many problems and errors in this analysis, and I actively invite anyone to call out these errors as you see them.

It has been eye-opening for me to see just how blind we are flying without proper testing in the US. We have no idea how many cases there are and furthermore stand zero chance of containing the spread. It could be way less, it could be way more, we haven’t any idea.

If you do come down with even mild symptoms, please inform those you had contact with over the preceding 5 days so they can self-quarantine and do their part to stop the spread.

Links

• My COVID-19 Projections spreadsheet

Links to smarter people doing this:

• Estimating Scale of COVID-19 Epidemic in US
• Tweet thread from Alex Perkins

Mathematical Thinking

I am currently taking Dr. Keith Devlin’s Introduction to Mathematical Thinking with my sister in high school. We are talking about a great many number of the core ideas about mathematics that make the field truly interesting.

I count myself as a recent convert to the joys of mathematics. Like many students, my first introductions to mathematics were dreadfully boring problem sets applying algorithms and numerical tricks to solve equations for hour after hour. This charade was repeated for 2 more segments of calculus in college at which point I was fully convinced scholars of math are actually masochists. I think many people never progress past this stage, because there has never been a proper presentation of what Mathematics can really be. Recently, I’ve discovered math is less about solving for a particular number or value and more about using the things you know to reason about things that you don’t know. It’s about building and applying abstractions to problems that you would have previously never considered tractable.

Now I want to issue a disclaimer that I did not earn any sort of degree in mathematics, nor do I consider myself a mathematician. However, I have learned to embrace the notation, desire for generalization, and logic that mathematics truly allows. Mathematics is a tool, like programming, that can and should be wielded by all scientifically minded individuals.

When I have found myself reading papers about Partially Observable Markov Decision Processes or Motion Planning in Autonomous Vehicles, the formal presentations of the ideas can proffer profound insights that the text never fully captures. Presently, for really the first time in my life, slowly coming to terms with the equations of a paper can offer the deepest insights. A few lines of mathematical notation can very succinctly capture the essence of a paper many pages long.

However, there still are (and will always be) many ideas that truly confound me. I have spent many hours of investigation digging into a concept from machine learning known as Rademacher Complexity. Despite my greatest efforts, I remain light-years from an intuitive understanding of the basic principles. But, I think this perpetual confusion is perfectly acceptable! We are all struggling for relative improvement!

To put things in perspective, it helps to look back over your journey as a student on a longer time-scale. The problems I struggled with so tragically as a first-year computer science student are trivial for me today. I am the same creature that struggled for hours over the most trivial sorting algorithms just a few short years ago. But I wouldn’t be the person I am today without the understanding that I gained through the hours spent trying to wrap my mind around the concepts. Understanding can only be earned through the struggle; by becoming subsumed by a problem before re-emerging with some new insight to be carried in from the borders of your understanding. These insights and lessons build on each other incrementally. Eventually, while working on a problem, you can take a step back and admire all of the complex insights, learnings, and concepts that are required to even fathom the problem statement.

It’s a marvelous moment for all of us struggling to learn new things to recognize how far you’ve come already!

Living in a New Place

After I graduated in 2017, I moved to Shanghai for 8 months to work at a Chinese 3D mapping company. My coworkers did not speak English and I was forced to put my Chinese minor to work everyday to communicate. Despite my intensive preparations before going to China, I was rather overwhelmed by the total lack of familiarity in this new place. This alienness is even more overwhelming when it includes the language you use to interface with the world around you. In general, language is tool of basic social interactions that is so fundamental that native speakers rarely have need to contemplate it. The problems we spend most of our time thinking about take place at a higher plane of abstraction. We talk about and discuss problems using language. We ask clarifying questions to eliminate ambiguity using specialized jargon. We argue, tell jokes, and discuss ideas that excite us.

But in China, I was distanced from comfort–constantly backpedalling as I struggled to make sense of the rules that govern this new place. Things took place that delighted, shocked, offended, and surprised me, and yet, seemed entirely unremarkable to the many thousands that surround me on those Shanghai streets. It required me to take a hard look at the things that I thought I knew about what a modern society ought to do. I was being shown a vibrant counter-example to my long-held belief that Western Society is the theoretically optimal form of human society. (For a great book on the subject, please read Mahbubani’s brilliant tome)

This departure from the known was a wonderful opportunity to delineate the things that I knew from the ways that I have always expressed or heard them expressed. Ideas were set free from the biases and constraints that English had implicitly placed on them. I was given a new lens to view myself and my thoughts through. At least on a weekly basis I would stumble across a new 成语 (chengyu: idiomatic phrase with a story behind it) that would blow my mind. They provided deep insight into a facet of human character that I maybe always “knew”, but rarely engaged with because I lacked the machinery to do so.

There were countless disagreements that I had with my coworkers about the way we should design or implement one of our projects. Over the course of an hour or so, we would dig down from this key disagreement into the hard details shaping our arguments. Most frequently, we would discover some fundamental misunderstanding in the way that we were communicating. The modes of miscommunication varied widely, but would frequently fall into the following categories:

1. I would misunderstand an important word being used
2. I would mistranslate a concept directly from English to Chinese
3. A difference in the assumptions that we made because of the societies that we were raised in

After unearthing a seemingly trivial difference, we would realize that the majority of the energy of the argument was wasted. We were arguing two entirely different points, and agreed upon each others’ positions fully. I am most thankful for these long struggles, because they provide startling examples of how your background can mould your logical thinking.

Unsurprisingly, I learned my most profound lessons about Chinese business and society through these deep communications failures. In each, I was forced to generalize my thinking in a strange and unexpected direction.

A nice example of one of these differences is comparing Go and Chess. Both games are war-like simulations played on grid-like boards. In Go, play pieces are added incrementally to the board on the intersections of the lines, while in chess the pieces are removed during play and rest in the squares outlined by the lines on the board. These are small differences, but they offer some interesting insight into how this property of “similar, but altogether different” can play out in more nuanced discussions.

Seek New Things

If I have learned anything at all in my life, it is certainly that I know nothing. My thoughts are small, biased, and lesser presentations of the ideas of much smarter humans, but they are mine. I entreat you to always seek new things. Challenge even the most trivial assumptions that you make. Seek to understand something from a new perspective. Talk to an immigrant about their homeland! Talk to a mathematician about their favorite insight that they ever gained!

I think we all fundamentally capable of pushing the needle for humanity as a whole. Not as an isolated individual that can make all of the difference, but by taking part in the frenzy of building new technology.

Generalizing Your Experience was originally published in Q.E.D. on Medium, where people are continuing the conversation by highlighting and responding to this story.

Dan Li is a remarkably bright contributor to humanity. The 25 year-old from Dalian, China has wasted no time in leaving an impact.

Education Unlocks the Future

Education Without Barriers (EWB) is a non-profit started in 2015 by Dan Li while she was an undergrad at Harvard. The organization focuses on providing mentoring and tutoring services to Chinese children forced into orphanages because their parents are incarcerated. Because their parents are still living, these children are almost entirely overlooked by the Chinese government and thus not legally considered orphans. As such, the orphanages that care for the children are run entirely on private donations.

Chronically low funds for the orphanages have forced the institutions to offload most of their cooking and cleaning work onto the orphans themselves. The children are required to complete 20–30 hrs of chores every week. This leaves little time for the children to focus on their studies in their evenings.

Li says that these children were trapped in a loop of poverty, kickstarted by their parent’s incarceration. School is paid for by the orphanage proprietors and because money is tight, all but the highest performing students are only encouraged to attend school until about 7th grade. At which point, if the students don’t have a “promising future” in school, they will be forced to withdraw and begin work in a factory. Because the students withdraw from school at such an early age, the transition to manual labor in a factory will likely be the only form of employment they can ever hope to hold. Leading to a life of many long hours working to barely stay ahead of their living expenses

This unfortunate path is a life that the children did not choose, but were forced into by the actions of their parents and the subsequent neglect of the Chinese government. Li says that for many of the children in this situation, they never imagined a life lived any other way because they have never seen anyone escape from this cycle or deviate from the path laid out for them. Li says that with the right mentorship and tutelage, she hopes to provide the students with opportunities for a better life.

EWB was started with the intention of both introducing to the grand possibilities that education affords as well as providing them with the means to do well in school. The organization encourages long-lasting and personal bonds between students and tutors. Li believes that these long-term relationships are the key to establishing enough trust to inspire the students to strive for goals previously unknown to them.

While the organization is still young, many of Li’s tutees are considering high school and even college. EWB provided these children with the means necessary to break the cycle and reach for a greater future than they ever even knew to dream about.

Act Now: Corrections to Current Injustices Compound Continuously

When I sat down with Dan Li in a restaurant in Shanghai to talk about her dedication to this cause, I couldn’t help but wonder if she felt she has done the right thing. My previous, rather cynical view of volunteering and fundraising was that it was more of a service for the ego of the volunteer than for those helped. The mission trips to Sudan or Haiti with plenty of posed pictures building a bridge or a school. Then back to the developed world, where you could share with your peers how grounded and transformed you are by rubbing shoulders with poverty.

My conversation with Dan Li clearly elucidated the fragility of my frustrations with volunteering. Li’s brand of aid was refined, focused, and had aspirations to transform the opportunities of a child’s entire life.

She admitted that she wasn’t in the ideal situation to have enacted the greatest change. She wasn’t rich or necessarily well-connected. But once she understood the hardships facing these children, she couldn’t sleep soundly unless she was doing something to help. I can put it no more poignantly than Li did herself:

“What little I do today will have long-term, compounding impacts”

She admitted that if she had waited until she was established in her career, with a stable income she would be far better equipped to provide a “larger” impact, that would come at the expense of years of inactivity. Li said that no matter how struggles finding funding or long hours on international calls and flights, her commitment to the cause is always reaffirmed when she sees the kids. She sees that regardless of the scale, she has already made an unquestionable impact on the course of these children’s lives by acting today.

Dan Li is a visionary and a committed humanitarian. She has worked selflessly for years to better the lives of children who face an unfortunately dire collusion of obstacles. I admire the work that she has done and furthermore I admire her commitment to bettering the future of our species. Li is a guardian of the forgotten and an example of what can be achieved when you commit to helping others succeed first and foremost.

If you would like to learn more about, donate to, or even volunteer your time to Education Without Barriers, please visit their website!

This article presents no new research or novel innovations, but rather paints a picture of what we could achieve with the proper technology.

Historically, humans have found computers useful because they have offloaded the more tedious aspects of problem solving onto these machines. Humans have then been able to redistribute their efforts to where they are truly needed; to where they can make an impact. White collar labor has been reduced to thee set of tasks that are not easy for machines to do automatically (yet).

We as a species now heavily rely upon computers in disparate fields. Mathematics, manufacturing, pricing, delivery / pick-up logistics, searching, and stock trading are key examples that stick out to me. Each of these applications of computing power have (at least) one thing in common: they are all receiving problems as input and providing answers as output.

There are two principal methods by which computers can solve problems: finding a pre-existing answer or calculating a new answer from the inputs.

Google is the poster child for bringing answers to problem-laden humans. It combs through trillions of web pages and does buckets of interesting processing to bring you pages that seem to contain answers to the present question. This is currently really helpful for humans who need to quickly find answers to only the present problem. It is also a helpful abstraction for the programmers. Instead of trying to answer all the problems themselves (much as WolframAlpha does), Google only attempts to bring the answers that already exist on the internet to any given problem that a user supplies to said user’s attention.

This method of solving problems is in direct contrast with some of the other areas of computing that aim to calculate an optimal state for some large set of variables. As an obvious example, stock trading bots don’t need a link to article about how the stock market works, it needs to calculate a prediction for a number of stock’s performance of the next interval of time.

The Internet is Big

As previously mentioned in our foray into Google’s usefulness, the internet is currently estimated to have well over 60 trillion pages. That is more information than any human could ever hope to read or understand. It’s a testament to our achievement as humans in our mastery over information. We currently have and create more data now than ever before in the history of mankind. At last count (March, 2017), there were 300 hours of video being uploaded to YouTube every minute. Each minute, 350,000 tweets are sent (Aug 2013). Each year in the US, there are 600k-1M books published. These numbers are both meant to scare you and demonstrate that information is being created faster than humans can consume it.

With this massive influx of new data, how could we possibly hope to ever stay on top of it all? How can we possibly hope to continue to parse these ever increasing quantities of data? The answer, I would think, is that without new forms of technology meant to drive our ability to consume information to higher levels, we cannot. I feel that I am currently riding the line between informed and information overload. I don’t see myself ever pushing past the current level of 45–60 emails per day that require my attention.

We need new tools that sift out the fluff and provide us with minimized versions of the information we are trying to learn and understand.

Recently Interesting Tools

There are some tools that are beginning to crop up that are meant extend the limits of human data consumption. It is proving to be an especially difficult problem that is being tackled from several different directions.

One of the more interesting vectors of attack in this problem space is actually changing the method by which data is consumed. Rather than creating summaries of datasets, or bubbling relevant data to the top, tools in this category are trying to augment natural human consumption abilities by offering a paradigm shift. Spritz is an interesting example of this type of tool. For the uninitiated, Spritz is a speed-reading utility can help you read at speeds up to 1000 wpm (although people generally don’t). It aligns words around a center locus and then quickly flashes the words at any speed you specify.

The most apparent method to increase the amount of data that humans can take in, is to reduce the amount of data that humans are required to take in. The now defunct Summly is an interesting example of this as it scans the news stories that are being posted on the largest news sources, and boils them down to the essentials and automatically produces sensible summarizations of their content. Summly could provide you with clearly and concise summarizations of a news story, albeit in a terser form.

What we Really Need

But these approaches are not enough to meet the growing strain of parsing, synthesizing, and managing growing data streams. The algorithmic sorting of information that led to personalized “truth” silos of the 2016 election are not enough. Recent attention seems to be focused on bringing “interesting” articles, websites, or images to the feeds of users. We need tools that can synthesize and extract meaningful bits from disparate sources.

A great effort is being put together by Distill, by approaching the difficulties of understanding new research. It is a journal that aims at disseminating breakthroughs in Machine Learning with the masses. Researchers and writers work together to create interactive visualizations that will make clear presentations of the interesting pieces of recent research. This is a great step in the right direction. We need a re-emphasis on constructing a well-paved path up the mountain of information that we are currently building our newest industries upon.

Potentially the only way to a future where modern research is accessible to the common people is through publications like Distill. However, I argue that a more direct path is through more advanced technology tools. Tools that aim to bring the most important pieces of an article, scientific publication, book to the surface rather than only the bits that you might find interesting.

I know there is a lot of brilliant research happening in the realms of text extraction and automatic text summarization, but we need to commoditize this research. There are currently only 17 companies on Angel’s “Content Summarization” list. How can it be that we have more people interested disrupting wedding planning than innovating on consuming information? We need to build tools that summarize, extract, and distill without distancing readers from the more detailed, underlying source material.

Applications to Education

For anyone who has recently attended college, you know that textbook prices are sincerely out of control, rising over 1000% since the 1970s. Textbooks are long, inaccessible, and boring. Allen Downey’s Textbook Manifesto is a brilliant critique of this trend. Downey admonishes students and textbook writers alike for their willingness to accept this situation. The lack of engaging and concise textbooks is only adding to the modern burden of information overload. The act of parsing the actionable data from a vast sea of presently irrelevant data is wasteful to all parties.

What if we could quiet these stormy seas and replace them with modern tools for exploring information at various levels of detail? What if we could rely upon algorithmically generated chronologies of the latest PR scandal? Peruse bullet-point summaries of the State of the Union and quickly scan a generated, paragraph summary of what makes a research paper novel in the context of its field? Tools with these capabilities would have the potential to revamp the way that we think about education. Education could truly become a lifelong pursuit rather than only the occupation of the first 25% of our lives.

These technologies could easily be adapted to create personalized curricula on any topic imaginable. You could quickly generate explanatory material, tutorials, worksheets, quizzes tests, summaries, study guides, and more on the any subject of your choosing. You could quite literally self study your way into a PhD. While such a degree would be of dubious value, given the extreme social nature of most professional work and the stark lack of research funding you would have on your own.

Software Engineering is somewhat a special case in the scheme of professional education, as you are required to keep up with modern technological advancements. But I think this state of professional re-education should and will become standard if these type of tools are created and properly commoditized. Our educational system itself would need to be redesigned from the ground up to cope with the changes. How could continual, incremental study be monitored, validated, and leveraged to drive impact?

The internet has provided us with nearly free access to information, but there are still obstacles to properly making use of it all. Multi-resolution automatic text-summarization technology could truly dismantle the artificially constructed barriers to information that have formed along economic and political axes.

I speak of a future that we haven’t quite found a path to yet, but I encourage my readers to consider the profound, positive implications that competent, algorithmic text summarization could have on our modern world.

How Text Summarization Could Change the Way we Educate was originally published in TDS Archive on Medium, where people are continuing the conversation by highlighting and responding to this story.

Four years ago, I switched my keyboard layout to Dvorak. I did it because it sounded like it would not only be easier to use, but also make me a faster typist. I’ve read multiple accounts of various software engineers that did not experience significant improvements when they switched to Dvorak, but this post is meant to be a counterexample to those naysayers.

The rest of this post can be found on my personal website. Sorry for the headache, but I’m migrating all of my blog’s content off of Medium and to my private domain to make it easier to read.

Medium isn’t a blogspot replacement anymore, it’s more a place for professional writers to create content.

I will remit that Tesla’s Autopilot is truly amazing feat of engineering. It is capable of reading and respond to traffic lights & speed limit signs, maintaining proper spacing with the car in front, managing in-lane steering, navigational turning and a whole host of other features.

However, because this system has been packaged as a seemingly all-inclusive “solution” to the monotony of driving, many members of the public assume the system will never require human intervention. Which is absolutely not the case. It is such a misnomer in fact, that Germany has mandated Tesla to stop calling its technology “Autopilot”.

Many of the readers may be familiar with the “Levels of Driving Automation” that is defined in SAE International’s J3016. This document explicitly defines 5 levels of automated Driving systems, and Tesla’s Autopilot is very clearly a Level 2 system.

Not all Automation Levels are Safe for Use

At CES 2017, Gill Pratt gave the world a much needed reality check on the current state of car automation technology. Pratt helped launch the Toyota Research Institute (TRI), which is cutting edge hub for automation research where Toyota pledged to invest $1 billion over the the next five years. He elucidated the many reasons why driverless car tech is still many years away from completion. He discussed the many ideological pitfalls that the public has currently been falling into with regards to the vehicular automation.

I have created a graphic for representing the relative dangers associated with the various levels of vehicle automation, because the relative danger can vary wildly in unexpected situations based on the level of vehicular automation. This variance in danger is due to the drifting attention spans of the human operator. If a driver is only expected to pay attention in emergency situations that the car doesn’t know how to handle, then drivers will not be engaged enough in the act of driving the car at the time of the incident in order to take control. Driver attentiveness drops off remarkably sharply as the time between required intervention incidents increases.

Level 1

The canonical example of Level 1 tech is braking assistance or lane guidance systems. Level 1 tech is defined by reducing cognitive load for drivers, while still requiring their active operation of the vehicle.

Level 2: The Most Dangerous Level of Automation

Partial Automation automation systems (Level 2) are slightly more dangerous because most pieces of the driving task in most driving environments are handled by the system itself. The definition of “driving environments” encompasses weather, traffic, city/highway etc. and most trips will involve several such environments.

“Partial Automation” implies that the driver is constantly monitoring the operation of the vehicle. Such a system wrongly assumes that at any time, the operators are engaged enough in the act of driving the car to seamlessly assume full control of the vehicle.

Level 3: Drivers Don’t Need to be Engaged

Level 3 automation systems do not require constant monitoring from their human operators and will transition control to the driver only after properly alerting them for the need to take control. Not surprisingly, the longer the driver is disengaged from the task of driving, the longer the response time is for them to get back up to speed (no pun intended) on the events surrounding the vehicle itself.

This level of automation represents a system while technically is more difficult to build, is similarly dangerous as Level 2 automation. In order to properly transition control of the car to the driver in unexpected conditions (such as a car wreck), the driver needs to adequately warned of this scenario at least 10 seconds before the human needs to regain control.

While for many scenarios, even if you were reading a book or texting your friends, it would be reasonable to assume that you could safely assume control of the vehicle within that time. Especially if the potential issue can be mapped and the car can be forewarned of the issue several minutes before the car will encounter it. Deteriorating weather conditions and areas with many jay-walking pedestrians are two simple scenarios that come to mind.

However, there are many more nuanced and sudden scenarios that come up that cannot be safely predicted that automated systems could be unable to properly cope with. Several example scenarios: debris on the interstate, car going the wrong way, small animals in the roadway, sudden and sporadic illumination from lightning, etc. While many of these feel contrived and rare, the average driver is sure to encounter them with enough frequency to understand the vast number of diverse situations that can arise on the roadway.

Furthermore, the automated system must not only be able to detect these numerous situations, but detect them with enough spare time to give the driver time to regain control. This task is difficult to reason about and theorized to nearly impossible to do with acceptable safety margins.

Level 4: Our aim for 5–10 years from now

In a stipulated set of driving scenarios (certain roads, time of day, traffic conditions, weather conditions etc.), the car can operate itself fully autonomously without human intervention ever. It is expected that we will see a good number of these types of vehicles on the road by 2025.

Level 5: The Ideal Automation System

I am not doubtful that I will see a Level 5 automated vehicle on the road within my lifetime, but they will appear only after many millions of Level 4 cars have been on the roads for a couple decades. The software systems that will be required to back Level 5 vehicles are more complex than anything human beings have ever built before. They will require incredible innovations in computer vision, machine learning, automated behavior planning, and sensing technology. Such systems will require unprecedented reliability from the complex computer systems that will power their situational awareness and decision-making tiers.

Acceptable Level of Safety

Engineers of automatic systems will be required to determine at what point has a particular automatic driving system reached a Level 3 or Level 4 or even Level 5 level of autonomousness. These determinations will require the systems’ performance in unforeseen circumstances to be stratified into a score that will hopefully properly represent car performance. These systems will be required to respond to and handle many diverse problems in a social environment, while toeing the line between convenience and safety.

It is most likely the case that we will not accept the “human-level” performance from our automatic vehicles. I fully expect the political tension and public outcry to demand superhuman levels of safety.

What We Should Expect in the Early Days

Because of the many issues that I have presented with regards to the issues arising in each of the several levels of automation tech, we should expect extreme timidity in the first several generations of self-driving vehicles. Cars will be hard-wired to drive more cautiously (read: frustratingly) than your grandmother.

A plastic bag fluttering across the highway will trigger hard braking attempts. I predict that pedestrians will aggressively jaywalk in front of driverless vehicles, especially if one can easily discern a self-driving vehicle from a human-operated vehicle.

The age-old question of “Standard or Manual?” will stop referring the transmission, but rather if one’s car is capable of automatic operation or not.

Political Pushback

While over 30,000 fatalities occur in the US each year due to car wrecks, fatalities caused by cars operating in automatic mode will spawn many media firestorms and protests. Technophobia spawned by the capricious release of underdeveloped safety systems has the potential to seriously delay the integration of automatic vehicles into our roadways.

Buggy firmware updates will cause overnight spikes in driver and pedestrian fatalities. Lawsuits will be filed against the software engineers and engineering managers that ok’d the roll-out of those updates.

While I am optimistic that these things will not stop the widespread adoption of such ground-breaking technology, I do believe that these events will happen. If our entrepreneurs and our technologists are not prepared for these realities or if public opinion can be swayed against this technology, then we will have a long and slow period of recovery.

Egotistical Driver Action

Where parking is hard to find, automatic cars will be sent to circle the block while the owner runs into the store to grab something, thus increasing road-way congestion with entirely empty vehicles. There will be incredible legal tension whenever such a car is involved in a fatal collision with a pedestrian.

Decrease in Car Wrecks and Vehicle-Related Fatalities

This one should be obvious simply from the positive results that Tesla has already been receiving. The heightened reaction time of even imperfect automation systems will save many lives. Inevitably scenarios will arise in which a human operator would have almost certainly have been able to avoid a collision / fatality, but I predict that on the whole, collision rates will drop considerably as driverless cars hit the road in droves.

Even if automatic driving systems are unable to avoid a collision completely, the reaction time of such a system does not deteriorate as a human’s does the as acclimatization to driving does in humans. Thus, I hypothesize that many fatal accidents will be converted into accidents with minor injuries to both or one party through system intervention.

Even though the system prevented fatalities, I foresee these accidents being viewed as accidents caused by the self-driving cars instead of fatalities prevented by the self-driving cars. This is a dangerous result of improper framing.

Eventual Long-term Effects

After several generations of self-driving cars, the rough corners will be rounded off and the lives of many will be improved (and prolonged) by self-driving vehicles. They will become more assertive; able to correctly toe the line of aggressive driving that leads to increased roadway throughput and safety.

Improved Accident Avoidance Systems

Accidents that occur due to drivers falling asleep at the wheel can be pre-emptively detected and avoided by monitoring the internal conditions of the vehicle.

When developed to maturity, radar systems should allow for many accidents induced through heavy fog to become a thing of the past.

Additionally, I imagine that emergency actions taken in adverse weather conditions such as snow or rain could quickly surpass the efficacy of that of the average driver. Correctly identifying the weather conditions and selecting the correct course of action will take many years of research, but when developed to maturity, such systems will easily out-perform humans.

The Simplifications are Worth it

The obvious obstacles to roadways dominated by automatic vehicles are mostly technical ones, but I’m honestly much more concerned with the consequences of a poorly informed public emotionally reacting to the development of an earth-changing new technology.

The problem of introducing driverless cars to society will involve a wide variety of growing pains, as we all seek to understand the implications of a new transportation paradigm. We need to spend an adequate amount of energy properly engineering the release of the technology to prevent the plethora of negative reactions from affecting society.

The motivation for this article is to attempt to inform members of the public about the challenges that need to be solved and the potential consequences for buying products that are promising more than they can deliver. Because, if we can keep the unwashed masses skeptical enough of the benefits of a level 2.5 car, then I think there is less chance at a poorly timed lawsuit stifling further technological innovation.

I want to encourage us all to have more conversations about what the future will look like when these vehicles begin to hit the road. Humans will inevitably adapt, but we want to make sure that the natural adaptations will not lead to an increase in recklessness or non-altruistic behavior in the short-term before we can reap the long-term simplifications to our economic system.

Automatic Cars are Definitely the Future

We have many glorious moments of technological development awaiting us in the future. We must ensure that we properly validate these systems before we allow the investors to push immature products to market, as they could stifle our future innovation.

We have great things to look forward too. Let’s ensure that we plot a thoughtful way around the many obstacles that still lie in our path.

NOTE: There is a lot math (which I find interesting) at the top of this post, but there are pretty pictures towards the bottom. Feel free to scroll ahead if cosines scare you.

I recently watched a great video about visualizing the Mandelbrot Set in Processing, and I decided to investigate the topic more. The largest shape visualizations of the Mandelbrot set is a cardioid. It looks a bit like a jelly bean, and can be drawn in an extremely surprising way that I aim to emulate in this post. The gif was the main image on the Cardioid page whenever I created my own, and inspired me to create my own version.

I started by graphing the parametric equations from [0, 2*PI] in Matlab, which produced the below visualization:

This is mostly what we expected to see.

I then graphed both components before realizing that the y component causes the internal flurry:

So if we solve this y component for zero, we can plot our cardioid without those bounds:

Great, so let’s plot again from pi/3 → (5*pi)/3:

And now we finally have something that looks right. Now we can start breaking apart these equations to see how they work.

Creating the Visualization

I used Processing to create these visualizations. My code is available here.

Let’s think about the basic case where x = cos(t) and y = sin(t). At every location in time (t), we are at a specific (x, y) coordinate which defines our position. If we visualize this movement over time, we see that our point is moving around the unit circle:

In our original equations, the first term is x = 2*cos(t) and y = 2*sin(t). So what happens when we visualize that?

Our point is simply moving around in a circle that is twice as big as before. So I’ll draw the moving circle twice as big as before.

Pretty much the same, but now we can see that both of the circles are in fact the same size and we are rolling the outer one along the inner one. So I’ll add a reference line to the rotating circle so that we can see it’s rotation.

If you look carefully, the end of that line is drawing a cardioid! So if we draw a red line wherever the end of our rotating line goes, we can see the cardioid:

I have since published this image on Wikipedia. Feel free to use / modify it. By the time that you read this post, there may very well be a much different image on the Cardioid page, but for now, there’s this one! Hopefully you learned something today!

Thanks for reading!

So I say:

I’m a senior in college studying Computer Science and Chinese.

Then everyone say:

Oh that’s…interesting. Why the f*** are you studying Chinese?

My Journey Learning Chinese

I’ve written before about the rising technological scene in China, but I did not originally choose to study Chinese for pragmatic and economic reasons. In high school, I studied Spanish and German, which as a native English speaker feels like cheating. Something like 40% of words in Spanish are cognates for English speakers.

In college, I wanted to step outside my comfort zone and tackle something that was thoroughly alien to me. I choose Chinese because it was a language that evolved entirely separately from Romance languages, essentially meaning I wouldn’t have any idea what was going on. As a side-benefit I was also learning the official language of the world’s most populous nation, what’s not to love?

From my very first lessons in Chinese, I quickly became more invested in the language than I had expected to be. Before I studied Chinese, I remember thinking that Chinese sounded very harsh. Although, now that I have studied it academically, I know have precise pedagogy to reinforce this belief. The “angry” sound that Chinese often takes on for Westerners is principally due to the 4th tone, which is similar to the drop in pitch that accompanies a American mother’s scolding. It was fairly difficult to get used to the fact that in any given sentence I would have to use the same intonation that has always meant I was angry, to simply express my meaning. To this day, I struggle to really commit to the 4th tone, because I don’t want to sound offensive.

So with this difference in mind, you may find it surprising that I have since discovered that there is a marvelous warmness about speaking Chinese that I’ve truly come to enjoy. The modal particles are wonderful agents for expressing and feeling this warmness. Modal particles are characters used to terminate sentences with a splendidly intricate blend of attitude and declaration of tone (of one’s sentence, not voice). They don’t necessarily express an exact meaning, but rather reframe the sentence in the There are modal particles that are used to express a question, surprise, happy surprise, a positive suggestion, and many more nuanced meanings that haven’t quite made themselves entirely clear to me. At any rate, any regular, boring sentence can easily (and often is) turned into a cute / excited exclamation.

我们会去商店。

We’ll go to the store…(ugh).

Turns into:

我们会去商店啊。

We’re going to the store and we’re excited and happy yay!

Now this is certainly hyperbolic, but I think it does a nice job of illustrating the pleasant feeling that you can communicate with Chinese.

My Progress with Chinese

From my humble beginnings, struggling to even construct the simplest characters, my skills have (thankfully) improved. I am fairly conversational and can skate by in most polite conversations. But, when it comes to truly expressing my feelings on abstract concepts, I falter heavily. I’ve included a letter that I wrote to one of my dear friend’s parents to illustrate both my progress, and how far I have yet to go.

For readers that cannot read Chinese: don’t worry that you can’t read this letter. You truly aren’t missing much more than a 2nd grader’s rambling attempts at expressing thanks. My sentence structure is simplistic, and I make many, many grammatical errors. So…I’m going to improve.

In the summer of 2016, I was interning in San Francisco. I walked into an ice cream shop and happened to notice a man using WeChat. So I struck up a conversation with him and after I few minutes of putting my Chinese through it’s paces, I asked if he knew anyone that could help me study Chinese in the mornings before my internship started. He pointed across the room and said, “My wife used to be a kindergarten teacher in China”. So the woman pictured to the right ended up meeting up with me thrice a week in the mornings to tutor me in Chinese for 90 minutes. My Chinese improved and I made a great friend!

Why I’m going back

I want to build companies and products that effect the way that humanity interact with each other. I want to maximize our utilization of existing infrastructure and provide economically sensible choices for consumers to contribute to a future of widespread human collaboration. Businesses are a powerful tool in society, as they create monetary incentives for a very specific behavior. When properly motivated, new businesses and products are tremendous tools for reorganizing the Earth’s economy, so that seems to be the path I want to take forward.

China is a world brimming with possibility, a market that is undergoing remarkable growth at a scale that the Western world troubles fully grasp. I want to place myself in this environment for three reasons:

1. I want to improve my Chinese
2. I want to learn about what it takes to run a business in China
3. I want to improve my familiarity with modern Chinese culture

And I don’t see a better place to do that, other than China itself.

This article is designed to alleviate a bit of that strain by describing how the China of the last century has risen past its humble beginnings in the 1950s to become the rising superpower that it is today.

The Old China

The 20th century marked a period of incredible change for China, as they transitioned from the dynastic tradition of ancient times to the Communist government of the People’s Republic of China that was founded in 1949. Mao Zedong led the newly reformed nation through a period incredible self-imposed tribulation known as the Great Leap Forward (大跃进) from 1958–62. This attempt to modernize China was headlined by gathering nearly 700 million Chinese peasants into communes, meant to drive a period of fervent economic development. Ruefully, however, it is estimated that close to 45 million Chinese citizens died as a direct result of the Great Leap Forward.

After the mistakes made in 1958–62, China did experience immense modernization and economic development, increasing their GDP by a factor of 20 in the forty years between 1960 and 2000. The shifting of economic policies that followed the period of Chinese Economic Reform (改革开放) is mostly responsible for these vast economic gains.

The Rise of “The World’s Factory”

As the 20th century wore on, China become an ever-more important member of the world economy, mostly in the role of the manufacturing. Unbelievably low wages allowed western corporations to relocate manufacturing operations to China. These low manufacturing costs came hand-in-hand with low-quality of the produced goods, such that “Made in China” became synonymous with poorly manufactured products. In the nineties, journalists dubbed China the “World’s Factory”, despite China b eing a distant third in terms of manufacturing output (even as late as 2005). Media presentation of China as a manufacturing powerhouse that was leeching the manufacturing jobs away from the US has caused a lot of anxiety. The crisis of millions of jobs evaporating from the continental United States, only to reappear in China, was an unsettling pill for many Americans to swallow. According to a 2013 survey by the Pew Research Institute, only 37% of Americans thought favorably of China.

This negative sentiment was underscored by the implicit form of superiority that arises from a position of economic superiority. This plot shows the ratio of the US GDP to the Chinese GDP from 1960 to 2015. As you can see, the US was sitting at a very comfortable 10 to 17 times greaterthroughout most of the 20th century. Under this backdrop, China’s sudden economic growth in the early 2000s shocked analysts, and certainly instigated many a hysteric and reactionary article attacking Chinese business tactics.

In short, the China that the world knew in the late 1900s was known for cheaply manufacturing goods and their periodic abuse of human rights. In the closing period of the 20th century, China experienced immense economic growth, but remained a relatively unimportant player in the global economy.

Old China’s Technology Sector

China is known for being the place that manufactures iPhones, which very prominently proclaim that they are “Designed in California” in an ultimately American distinction. The line is palpable between the “creators” and those that mechanically produce the objects laid out for them in schematics drawn in the United States.

China is a hard market for international corporations to enter (as Uber has recently discovered), because the Chinese government has kept a white-knuckled grip on China’s access to the internet. The Great Firewall of China (防火长城) is a government-controlled project that aims to restrict Chinese citizens’ ability to openly communicate with each other through various forms of social media.

Google has long been unusable in mainland China as well as other staples of the American Internet such as Facebook, Twitter, Instagram, and YouTube. This lack of global competition has allowed for homegrown Chinese versions of other products to arise. China’s Twitter copycat, Weibo has over 261 million monthly active users (MAU) and sees over 470 million daily video views (source). China’s dominant search engine, Baidu, reported is fulfilling 5 billion search queries per day (as compared to the 3.5 billion that Google was doing in 2013). China has two chat clients that have over 700 million MAUs: WeChat and QQ (previously arguable competitors with WhatsApp and Messenger, but may not be the case anymore).

The New China

The China that has been developing over the last 15 years is a very different beast than the afterimage of the industrializing China of the 20th century. Since China has entered the World Trade Organization (which didn’t happen by the way, until 2001), wages are way up and the government is encouraging young people to start companies with state-sponsored investment programs.

The New China features technology giants such as Alibaba ($194 market capitalization as of 2015) and Huawei ($61 billion revenue in 2015) that are actively investing capital into startups in the US. You may remember Alibaba from its record-setting IPO in 2014, or from its $250 million investment in Lyft, or even from its $800 million investment in AR firm, MagicLeap. Another arising Chinese tech giant, Huawei, is an emerging leader in consumer electronics with a number of flagship devices that are now for sale in the US.

The New China now has a majority control of the BitCoin network. Let me say that one more time to help it sink in: Chinese companies have majority control of the BitCoin network (70% of transactions as of Summer 2016). Chinese companies have surreptitiously moved to a position of power as they slowly gained market share through massive mining farms. In a recent secret meeting between those in positions of power in the world of BitCoin, American delegates attempted to persuade the Chinese BTC companies to alter the BTC protocol in ways that are outside of the scope of this article. However, in stark contrast to the previously consistent position of power that Americans had come to expect, the tables were turned. In a recent NY Times piece, Bobby Lee (CEO of BTC China) makes a statement that I think is one of the most important quotes of 2016, as it very clearly illustrates the shifting balance of power between the US and China:

The New China is an important member of the global economy and has interests of its own, that are independent of those that have historically had economical leverage over China. The New China is an emerging leader in technology that, as Mr. Lee frankly stated, cannot and will not be dictated by American corporations.

This New China has been manifested through the combined effort of millions of Chinese citizens working to achieve the “Chinese Dream” (中国梦). The Chinese Dream is different from the classic American dream in a variety of ways. Most importantly, while the American Dream features an individual family working hard to provide a comfortable life for themselves, the Chinese Dream centers around every Chinese citizen working hard to build a better future for China at large. President Xi Jinping is largely credited with championing this new vision for future development of China.

No Longer the “World’s Factory”

Mirroring the same outsourcing that has plagued American laborers in the last couple of decades, China is beginning to experience an economic shift from its position of manufacturing supremacy. Stalling growth in the manufacturing sector has led Chinese politicians to forecast a transitional stage from manufacturing to “consumption and services”.

The New China certainly isn’t perfect. It may not even be the dominating world leader in technology, but it is a recently added player with an ever-growing amount of influence that the West has yet to fully come to terms with. The New China promises to keep the world on its toes as it fights to restore itself to greatness.

I have since moved this post to my personal blog. Please follow the link and read it there.

I’m moving all my Medium posts to my personal site because Medium is no longer the place that it was when I first joined this site in 2014. It’s pretty annoying and gimmicky to just explore other people’s content without constantly being asked to go pro.