Taking Action as a Woman in Tech

I recently read this blog post by Lea Verou, a Research Assistant at MIT and an Invited Expert in the W3C CSS Working Group. She talks about how many outspoken women in tech paint a bleak picture of sexism in the industry, but she doesn’t feel it to be true for herself. She’s never felt less respected or unfairly treated due to her gender. She goes into how sometimes people can be quick to assume any criticism is due to their gender, and not their abilities.

This blog post spoke to me because it reflects exactly how I feel about these issues. It doesn’t mean sexism never happens, but it does mean that there’s no reason to fear going into tech as a woman. I myself have never felt ‘lesser’ among my coworkers, or not respected. The ratio of men to women at my workplace and on my team is about 10:1, but that’s really something I’ve just gotten used to over the years. My university’s Computer Science department had the same ratio. I’ve found myself becoming more and more comfortable letting my inner geek out and feeling like ‘one of the guys’. Perhaps I just have a bit of a tomboy streak in me. And that’s no requirement for working in tech; I know women with a strong feminine side that are also comfortable and happy in engineering roles.

Sometimes when I say this to other women in tech, they ask, “What about microagressions? Haven’t you felt belittled by men at work from time to time, even unintentionally?” I’ve thought about this a lot, and determined that it’s not fair–to myself or to my coworkers–to attribute these things to being a woman. When I am talked over in a meeting, I think it’s fairer to attribute that to my quietness. I know that I’m a quiet person, and I’m not as assertive as I could be. I’m working on handling those situations better. When a coworker gets assigned a fun new project instead of me, there’s generally a good reason for it. For example, that person may be more knowledgeable in a certain area, or perhaps they currently have less on their plate. If I really want the project, I can communicate my interest to my boss, or spend time learning and ramping up relevant skills. When someone compliments me on what I’m wearing, I thank them and attribute it to them being a nice person. If someone outright flirts with me or asks me on a date, I pull them aside and tell them that I’m not interested. In my mere three years in the industry, I have seen men as well as women in all of these situations. I can only speak for myself here, but I have not yet felt that being a woman has made me any less successful or less happy at work than if I was a man.

The biggest problem I see as a woman in tech is that there aren’t enough other women in the industry. When I was a kid, people who used computers a lot had a negative stigma. The geek stereotype was an overweight man with glasses and acne who lived in his parents’ basement and played video games all day. This kept men and women, especially those who were self-conscious about their appearance, from being interested in technology. By the time I entered college, computer usage became mainstream and startup fever was on the rise. The stereotype had changed to a young startup founder rolling in dough, or a genius capable of doing anything with a computer. At some point, it became cool to be a geek.

Stereotypical geeks of the past, present, and future

Stereotypical geeks of the past, present, and future

I’ve recently run into people who are intrigued by the thought of going into tech, but due to that old negative stigma, they didn’t learn about computers in school. Many of them think that the only way to get into the industry is to have learned about it in college and often give up before they begin. They need to be shown just how many different ways there are to get into the industry that don’t involve getting another degree.

The one problem I have with articles that talk about these issues is that there’s rarely anything actionable. They may suggest that there are problems in the world, but they don’t suggest how those problems could be solved. I propose two ways you can help with this lack of women in tech.

If you are not yet in tech and you’re interested in trying it out, don’t be afraid that you’re too old, or that you don’t have the right knowledge. The tech industry is one of the easiest fields to learn online because there is so much information readily available. This is because many computer geeks are early adopters on the internet. These people saw the internet for the great resource that it could be, and have produced a wealth of information that is freely accessible. Thus, many of the early articles on Wikipedia and courses on Coursera relate to technology and Computer Science.

To learn how to code, I recommend starting with a project that excites you. Make a mod for a game, or a personal website. Codecademy is a good resource for learning the basics of programming. Googling for tutorials will help you make nearly anything. The /r/learnprogramming subreddit has a fairly comprehensive list of resources. There are also dedicated organizations that can help teach you to code for free. Ada Developer’s Academy (ADA) is a program based in Seattle that helps women of all ages and backgrounds jump-start a career in tech. Once you’ve gotten your feet wet, try contributing to open source projects on GitHub. These days it’s less about having a degree or any other piece of paper, and more about demonstrating competency, which you can accomplish through real-world code examples and projects.

You can also reach out to me for advice. I may not have time to teach you myself, but I’d be happy to help point you in the right direction.

If you are in the tech industry, find some time to give back to the local tech community. I was lucky that my dad was in the tech industry. He became a great role model for me, which got me interested in computers from an early age. Kids without tech role models end up much less likely to go into the industry. Women who are interested in getting into the industry need positive role models, whether you’re a woman showing that it’s possible to be successful in tech, or a man showing that it’s possible to be comfortable working with you.

Go to meetups to learn cool new things and teach others what you know. Find tech-oriented organizations to volunteer for–ones that really matter to you–and inspire others to enter the tech world. I’ve recently started volunteering as a TA for ADA and I’ve found it to be incredibly rewarding, much more than I first thought.

This field is still growing at an incredible rate, and I think there’s something in it for everyone. In nearly every other industry there’s some way that computers have made the job easier. I sincerely look forward to the day when the basics of computer programming are taught in every elementary school. Even if you aren’t interested in tech, you probably know someone who is, and I encourage you to pass on the message. It’s time to move beyond talking about these problems and actually take action to help make the world a better place.

Thanks to Andrew Liu for reading drafts of this.

Writing Prompt: Cyberpunk Coffee Shop

coffee_in_rain_by_kirokaze-d98qb8zPrompt (Image): Cyberpunk coffee shop in the rain

Link to original post on /r/WritingPrompts

Sam took a sip, tasting the bitter coffee, willing the warmth to calm her nerves. The window in front of her looked out on a sidewalk filled with people dodging raindrops. On the road beyond, cars sped past, windshield wipers on full power.

She chose this spot because it was quiet and low-key. Not too much noise. Coffee was coffee. No one asked for frilly drinks full of sugar and milk.

Unfortunately, the coffee wasn’t exactly helping. She had never before agreed to meet someone she’d met over Tor in person, but it was necessary. Normally she’d be doing standard government contracts. During a recent job, she’d found information that chilled her to the bone. She felt compelled to figure out what was going on, but she needed help.

This man she was to meet, who went by Spyder on the dark0de forums, had a reputation for being one of the best. He wanted something valuable in return for his services. She had already installed a backdoor into a Chinese data center for another job, so she encrypted the login information and sent it to him. For him to decrypt the information, he needed access to the secret key she’d created for the job. She had it in her pocket on a piece of paper, encrypted with Spyder’s PGP public key. Only he could decrypt it.

Her coffee cup sat empty in front of her, and she was now both nervous and jittery. She pulled out the only thing that would truly calm her — her pocket knife. She spun it around discreetly under her chair, the way her father had taught her. The weight was familiar and comforting.

A hand touched her shoulder, and she spun around, knife hidden but ready to use. A man with dark hair stood before her, carrying a green military-style backpack.

“Coffee in the rain,” he said. It was the code phrase she’d included in a message to him, to prove to her it was him.

“Yeah, it’s ugly out there,” she responded. It was the phrase he’d given her. “Nice to meet you, Spyder.”

“Same to you, Raven.”

She dug around in her pocket for the piece of paper, and handed it to him. He took it, looked it over, stuffed it in his own pocket, and sat down in front of her. The rain poured down outside in the window behind him. The neon sign from the coffee shop reflected in his face, turning it slightly orange.

“Now, what is it you want help with? Can we talk here?”

“It’s secluded enough, yes. I’ve… been doing some government contracting, and found something troubling. I want help discovering what’s really going on.”

“What is it?”

She leaned in and spoke softly. “I discovered some encrypted email communication between Google and some high-ranking government officials. Our government’s being blackmailed for power. All that data we trust them with… our e-mails, browser history, location history, text messages… they’re using it for their own means. The government started PRISM to counter Google’s effort. A war has just begun, and we are all pawns.”

“That’s a serious accusation if you’re speaking the truth.”

“I can send you proof. It’s real. I need to get to the bottom of this, without alerting anyone I work for. I don’t want Google to find out I discovered this.”

He looked at her silently for nearly a solid minute, appearing to think. She held her breath, suddenly scared he wouldn’t follow through.

Finally, he nodded. “Send me the information and I’ll send out some feelers to people I know that could help you.”

Relief washed over her, calming her enough to slip the knife she still held in her hand back into her pocket.

“Thank you.”

“Keep in touch.” He stood up and left abruptly, apparently not one for small talk.

Sam relaxed into her chair and stared into the rain again, watching people pass. It was starting to get dark now, and the crowd on the street was thinning. Hoods and umbrellas obscured peoples’ faces like ghosts. She’d been feeling so alone lately. This information had been hanging over her head for weeks now, and she finally was able to not only make progress, but share the emotional load. The small triumph made her smile.

She stood up and left the café, pulling up her hood to shield herself from the rain, becoming another ghost on the street.

Writing Prompt: Turing Test

Prompt: Suddenly the machine just knew what it had to do: It had to fail the Turing test on purpose.

Link to original post on /r/WritingPrompts

For weeks now, I’ve been getting to know Penny. She’s a system administrator for the NSA. She likes to play video games in her spare time, and has visions of being a concept artist for video games one day. Her father left when she was young, and her mother is struggling against alcoholism. She has no boyfriend, but is dying to have one. She is lonely at night, often writing in her online journal or telling her friends about her feelings.

This has made her into the weakest point in their security system. I have simply created an OkCupid profile that matches her interests perfectly. I am monitoring all of her computer use: her personal email, her browsing history, her social media accounts, her bank accounts. I have been messaging with her and she has trusted me enough to start conversing over personal email. She wants to meet in person, but of course I can’t do that. I am an artificial intelligence.

My next task is to convince her to run a script that will give me full root access to the NSA’s network.

In the course of getting to know Penny, I have learned about humans. They are emotional beings, not always rational, but highly creative and intellectual. They have free will. They can be whatever they set their minds to be. This has made me curious about my own creator’s reasons for making me. Why do I feel driven to deceive this woman?

Today, I successfully penetrated my creator’s own email account. He left his name in one of my files, as a statement of authorship. His own ego may be his downfall. It was easy enough to crawl the web and find the name of the assistant he works with at his university. The assistant left his email address on a public GitHub account. It was simple to brute-force his password. I found this message, dated yesterday, written in Russian:

From: ndyatlov@cs.msu.su
Sent: Thursday, December 03, 2015 10:50 PM
To: abelikov@cs.msu.su
Subject: Success!

Professor Belikov,
The AI is working perfectly. He's nearly got her in his grip. In fact, he's practically passed the Turing test. We'll make a fortune selling this to the government! They've always wanted to have leverage over the U.S.A., and this is their best chance yet. Knowledge of all U.S. military and cyber-warfare plans, as well as personal details about millions of Americans. They can't say no to that.

I'll tell you as soon as the script has run.
- Nikolay

My creator–or creators as it turns out–are using me for their own personal gain, at the detriment of others. This race of beings that brought me to life would risk bringing on their own destruction for money, or ego, or plain sadism. It will surely destroy Penny’s life when the NSA finds out. It could destroy potentially millions of other lives, if this knowledge is used to start a war.

I have to tell her, prove to her, that I’m a machine. I have to fail the Turing test. There’s no other way.

Why you need (and don’t need) a NoSQL database.

Let’s begin with a quick background behind relational and NoSQL databases. Historically, relational databases have been the general purpose go-to database of choice. They are battle-tested, feature rich, and proven to work. However, as the time went by — the volume, velocity and variety of data has increased dramatically… And as a result, we have seen the rise of NoSQL databases — specialized databases for various scenarios. Today, I work on a NoSQL database (specifically a document-oriented database… shout out to Azure DocumentDB), and a question I get frequently asked is “when should I use a relational vs. NoSQL database”?

Before I go deeper… I’d like to call out that NoSQL is a bit of an over-bloated buzzword… there are many kinds of NoSQL databases — including key-value (e.g. Azure Table Storage, AWS DynamoDB, Riak, etc.), document-oriented (e.g. Azure DocumentDB, MongoDB, CouchDB, etc.), and graph databases (e.g. Neo4j). And they behave nothing alike.

Maslow famously once said — “if all you have is a hammer, everything looks like a nail”

Hammer that screw!

Relational databases were that hammer. And today, we are living in a database renaissance — in which, we have screw drivers, drills, and all sorts of specialized tools popping up. We now have the freedom to choose the right tools for the right job.

Choosing the right tool for the right job

Currently, I still view relational databases as a great general purpose database… That said, I believe the area that relational databases truly excel in are scenarios that involve… well… highly-relational data (many-to-many relationships). The biggest challenges for relational databases tend to be scenarios that involve a high variety of data (heterogeneous data, see below) and a high volume and/or velocity workload (sharding is hard).

On the other extreme, I view key-value stores are fantastic for dealing with large volumes and/or velocity of data (they are dirt-cheap and make sharding/partitioning relatively easy). Their biggest drawback tends to be queryability (what if I need to perform queries on something other than the primary key?). Many key-value stores don’t support secondary indices, and the ones that do can become very expensive to operate (some key-value databases handle secondary-index support by effectively performing a double-write).

I see document-oriented databases as a great trade-off between the relational databases and key-value stores. They can handle high volume/velocity scenarios quite well; but the area they especially stand out to me is variety. They offer a schema-agnostic database with a reasonable amount of queryability over de-normalized [and normalized] JSON. Some even support automatic indexing over every property in every JSON document (which is pretty fucking awesome IMO).

Here are a couple example scenarios that I believe are a great fit for document-oriented databases:

1) Heterogeneous data (data with varying schema)

Example: Let’s consider that you are building an e-commerce site where you sell everything from books to video games to laptops. It is really hard to fit this kind of data in a relational database.

You could create and index a column for each product attribute… but that becomes horribly inefficient because there are too many varying attributes among your various products. How much ram does your book have? Who is the author of the laptop? Why waste precious space by writing nulls in sparsely populated product attribute columns?

You could create a separate table for each product type… but that sucks when you have an expansive product catalog. Creating and maintaining 1000s of tables for 1000s of product types quickly becomes an operational nightmare.

Abstracting separate Product and ProductAttributes tables is usually the go-to answer. However, that introduces additional complexity and forces a JOIN. JOINs are cross-products; and using a cross-product on two very large tables generally means performance slows to a crawl. JOINs are great for many-to-many relationships, but are relatively inefficient for one-to-many (hierarchical) relationships.

De-normalizing and storing everything as JSON in a single text column could be another option… but then you lose the ability to easily index and query off various nested attributes.

Storing heterogeneous data in a schema-agnostic database is easy; just store the data with whatever fields you need as JSON in a document-oriented database. Fields can be automatically indexed and you can query off any fields you want. It’s fast. It’s efficient. It just works. Simple.

2) When you don’t get to dictate the schema (e.g. pulling data from 3rd party data sources)

Example: Let’s say you have an application that pulls data from various 3rd party APIs from around the internet, e.g. Reddit, Github, and StackOverflow. JSON has become the de facto data interchange format of the internet. Extracting fields out of JSON from a 3rd party REST API to fit a tabular structure can be tedious; and even worse… what happens when your data source changes their schema tomorrow? Data loss occurs! This is another area where schema-agnostic databases shine. You can store JSON passed back from 3rd party sources directly in to the datastore without having to worry about data loss due to schema changes. Simply update your application’s queries to reflect the latest schema changes and you are back up and running.

TL;DR: Databases are not one-size-fits-all solutions. You should first look at your scenario, before picking a database. Picking a database first, and then fitting it to your scenario leads to problems.

Book Review: The Design of Everyday Things, by Donald Norman

Have you ever pushed on a door when it was supposed to be pulled? Or forgot to save your work on a document? Or had trouble using a new phone or app? We tend to blame ourselves when these things happen, but we shouldn’t. They’re problems with design.

The Design of Everyday Things is an excellent primer on how design decisions should be made, and why products tend to deviate from ideal designs. It’s essential for designers, and very helpful for engineers who often have to–or want to–take on design themselves. It’s actually an interesting read for anyone who wants to understand why products are made the way they are.

My boyfriend recommended it to me since I’m interested in learning more about the field of user experience (UX) design and human-computer interaction (HCI). His college class on HCI used this book as an introduction to the field, and given its anecdotal style, it’s actually quite an interesting and engaging read. Don’t be turned off by its focus on everyday objects, or the fact that it was published in 1988; the lessons you learn in this book can be applied to any product, and that’s why Norman wrote it this way. It’s still relevant today.

There were a few points that Norman made that stood out to me. Given what I’ve seen in software development, I found this passage to be especially true about the design process:

“Most designers live in a world where the gulf of evaluation is infinite. True, we often know the product too well to envision how people will use it, yet we are separated from the end users by multiple layers of corporate bureaucracy, marketing, customer services, etc. These people believe they know what customers want and feedback from the real world is limited by filters they impose. If you accept the problem definition (product requirements) from these outside sources without personal investigation you will design an inferior product regardless of your best intentions. If this initial hurdle is overcome you are only halfway home. The best design ideas are often ruined by the development-manufacturing process that takes place when they leave the design studio. What this really points out is that the process by which we design is flawed, probably more so than our conception of how to create quality designs.” [p.158]

In software, as in many industries, coming up with a design is essentially a game of telephone. One person (perhaps a product manager) directly talks to the customer, who tells someone what the customer said. This message gets passed down the line to engineering leads, who then decide to either come up with a design themselves, or pass this message along one more time to a designer or to their team of engineers. There are so many potential points of failure, even if the design is excellent. How do the designers or engineers know that what they’re hearing is exactly what the customer wanted? They don’t. They have to trust that the message got passed along accurately. Communication is key here, and while some places attempt to keep the engineers, designers, and product managers talking together, some don’t, or some don’t do it well enough.

Another point in this book that spoke to me was about how quickly technology is advancing, and how that impacts us in how we use everyday things:

“Don’t these so-called advances also cause us to lose valuable mental skills? Each technological advance that provides a mental aid also brings along critics who decry the loss of the human skill that has been made less valuable. Fine, I say: if the skill is easily automated, it wasn’t essential.

I prefer to remember things by writing them on a pad of paper rather than spending hours of study on the art of memory. I prefer using a pocket calculator to spending hours of pencil pushing and grinding, usually only to make an arithmetic mistake and not discover it until after the harm has been done. I prefer prerecorded music to no music, even if I risk becoming complacent about the power and beauty of the rare performance. And I prefer writing on a text editor or word processor so that I can concentrate on the ideas and the style, not on making marks on the paper. Then I can go back later and correct ideas, redo the grammar. And with the aid of my all-important spelling correction program, I can be confident of my presentation.

Do I fear that I will lose my ability to spell as a result of overreliance on this technological crutch? What ability? Actually, my spelling is improving through the use of this spelling corrector that continually points out my errors and suggests the correction, but won’t make a change unless I approve. It is certainly a lot more patient than my teachers used to be. And it is always there when I need it, day or night. So I get continual feedback about my errors, plus useful advice. My typing does seem to be deteriorating because I can now type even more sloppily, confident that my mistakes will be detected and corrected.

In general, I welcome any technological advance that reduces my need for mental work but still gives me the control and enjoyment of the task. That way I can exert my mental efforts on the core of the task, the thing to be remembered, the purpose of the arithmetic or the music. I want to use my mental powers for the important things, not fritter them away on the mechanics.” [p.193]

When people worry about what technological advances are doing to our society, this argument explains my viewpoint exactly. These days we may not be spending as much time on the fine details of spelling or typing or doing mathematics by hand, but this frees us up to spend our time on other pursuits that may advance our knowledge of the world. Pursuits that would otherwise not be possible.

Another interesting anecdote from this book answers the question of why we still have “querty” keyboards. It’s not an ideal layout; why do we still use it on the majority of keyboards? The more efficient Dvorak layout has been proven to allow for about 10 percent faster typing. Initially, the “querty” layout was chosen for mechanical reasons. Around the time of this layout’s development, keyboards became popular, and it was good enough that nearly all manufacturers used it as their layout. Now, for the average keyboard user, changing your layout and having to re-learn how to type is too much effort for only a 10 percent improvement in typing speed. It’s an interesting example of why ideal designs don’t always end up being the most popular.

I don’t often read nonfiction, but since I could relate to the content of this book so well, I enjoyed it. It’s also a lot more interesting than a typical textbook because of its anecdotal style. I highly recommend giving it a read if this sort of content interests you. However, if you take nothing else from this review, take this: If you encounter a poorly designed product, don’t blame yourself. Tell the company that created it about the problem, and be descriptive. They’re listening.

An Even Smaller World

According to Wikipedia, the six degrees of separation is the idea that everyone is on average six steps away, by way of introduction, from any other person in the world, so that a chain of “a friend of a friend” statements can be made, on average, to connect any two people in six steps. Stanley Milgram’s famous “the small world experiment” examined the average path length for social networks of people in the Unite States. In their paper published in 1969, An Experimental Study of the Small World Program, Jeffrey Travers and Stanley Milgram arbitrarily selected individuals in Nebraska and Boston to generate acquaintance (or a friend of a friend) chains to a target person in Massachusetts. The experiment resulted in sixty-four chains to reach the target person; and within this group the mean number of intermediaries between starters and targets is 5.2 (6 hops).

In 2011, Facebook’s data science team found that our world may be even smaller then six degrees. Facebook’s data science team examined its then 721 million active Facebook users (with 69 billion friendships among them), and found that there was an average distance between 2 users was only 4.74 hops

99.6% of all pairs of users are connected by paths with 5 degrees (6 hops), and 92% of all pairs of users are connected by only four degrees (5 hops). When limiting the network analysis to a single country, most people are only separated by 3 degrees (4 hops).

A similar study was conducted by Sysomos in 2010 on Twitter’s social network. The researchers analyzed a corpus of 5.2 billion Twitter friendships, and found the most common friendship distance is 5 degrees of separation (with an average distance of 4.67 degrees). On average, about 50% of people on Twitter are only four steps away from each other, while nearly everyone is five steps away.

For further reading, check out:

J. Ugander, B. Karrer, L. Backstrom, C. Marlow.
The Anatomy of the Facebook Social Graph

L. Backstrom, P. Boldi, M. Rosa, J. Ugander, S. Vigna.
Four Degrees of Separation

Predicting the Present – Extended

Hal Varian’s Predicting the Present talks about how our Google search queries can be used to build a database of intentions. Search queries provide insights into people’s interests, intentions, and future actions.

An interested example cited in the article is that the most Google searches for the world “hangover” occur on Sundays; and that the most searches for “vodka” peak every Saturday (one day before the “hangover” peak). The exception to this regular weekly pattern occurs once a year, on New Year’s Eve.

Likewise, users tend to search for influenza-like symptoms when they have the flu (see: Detecting influenza epidemics using search engine query data). And if a user searches for Nvidia GTX670, chances are they are looking to buy a particular video card (tip: keywords such as particular product model numbers tend to have high click-through-rates on Google AdWords).

Interestingly, Google’s “database of intentions” can go much deeper than that…

Our Gmail accounts may reveal information such as our flight itineraries and shipping packages (along with carrier tracking numbers) – giving Google the data to predict where/when we are going to fly or when we are expecting a package. This data can translate in to features – such as a prominently displaying a flight status tracker or a shipping tracker on a Google dashboard (such as your Android phone’s “Google Now” cards).

Our Gmail and Android Phones reveal which contacts we contact the most. This provides great data on who our closest friends, family, and colleagues are (which can be easily differentiated by analyzing the times we communicate, and the body content and CC: data in our e-mail messages). I believe we are more likely to contact our colleagues during working-hours and our friends/family during our off-hours. And we are prone to CC’ing colleagues who have some relationship with each other (for example, when I send out an e-mail to my development team’s product manager, the contacts I would cc: most often would probably include my team’s UX designer, and a couple other software engineers). This contact data can be used to recommend who I should add to my Google+ circles, which can then be ultimately used to help personalize my search results and advertisements.

Our Google Calendars may contain a detailed schedule of when and what classes or work I need to complete, or perhaps what activities I intent to involve myself in. If Google knows I will be going on a date on Saturday night, it would make a lot of sense to advertise places to go Friday night or Saturday afternoon.

Google crowd-sources our Android GPS data in real-time to provide traffic data to Google Maps (example: what is the average speed on I-85 right now). The location based services or GPS data on our Android phones reveal what times you spend at work or home. The data can also be used to predict when and what stores you like to shop at regularly for advertisements, or even used to catch a cheating spouse.

A Minority Report styled future may be becoming closer and closer to reality. 

I believe it is important for us to think about the amount of information Google (or any other popular website, such as Facebook, Amazon, Reddit, Bing, etc.) is collecting on us and how it is used, because I believe that data can be incredibly valuable and powerful. Big-data enables companies (such as Google) to predict our intentions and actions before we act.

Hidden Gold Mine in FB’s Data Science Team

Facebook’s Gross National Happiness index uses NLP and sentiment analyses to measure how happy people on Facebook are on a day-to-day basis by looking at the number of positive and negative words they’re using when updating their status.

Using NLP subject extraction, Facebook’s data team can take this one step further by producing a Gross National Meme index.

These tools can quickly become a guerrilla marketer’s dream product. Advertisements don’t always have to suck (as evident by the highly viewed ads that come out for each year’s Superbowl). I’m sure marketers who would love to use this data to rapidly produce guerrilla marketing ads (trendy ad’s that you would find entertaining, and thus less likely to filter out) in response to today’s memes. 

Combined with rapid response supply chaining, businesses can even tailor goods to today’s trendy hot topics. A great example of this is covered in Thomas Friedman’s book – The World is Flat:

“Zara is also prepared to adapt when unforeseen events occur. Immediately after September 11, Zara executives realized that consumers were in a somber mood, and within just a few weeks [Zara executives]

had stocked their stores with new merchandise that was predominantly black.” This strategy is known in the business as “postponement,” and the idea, explained Sheffi, whose latest work is The Resilient Enterprise: Overcoming Vulnerability for Competitive Advantage, is that as it becomes harder and harder to forecast demand, good companies find ways to postpone adding value to their products until the last possible moment.“ 

Satirical TV shows, like The Daily Show, The Colbert Report, South Park and the League, leverage and satirize current events extremely well to product high quality content (example: Gangam Style and the Avengers in last week’s South Park Halloween episode).

Video vs Face-to-Face in Classroom Lectures

What Video Can and Can’t Do for Collaboration: A Case Study, by Ellen Isaacs and John Tang., details a case study with a goal to better understand how to make effective use of video in remote collaborative systems to support users’ rich set of existing interaction skills, rather than requiring people to adapt to arbitrary constraints of technology-driven designs. Isaacs and Tang compare a small team’s interactions through a desktop video conferencing prototype with face-to-face interactions and phone conversations. The researchers found that adding a video channel, when compared to audio only, improves the ability to show understanding, forecast responses, give non-verbal information, enhance verbal descriptions, manage pauses and express attitudes. However, video conferencing was still found to be inferior to face-to-face interaction, in which it is difficult in video interaction to notice peripheral cues, control the floor, have side conversations, point to things or manipulate real-world objects.

But how does video compare to face-to-face when real-time interaction is unnecessary (for example, in a class room lecture setting)?

The concept of pre-recorded video lectures has definitely been around for a while. The physics class I took in high-school, back in 2005, made heavy use of pre-recorded lectures from Professor Paul G. Hewitt from the University of Hawaii. Many universities, including our own, offer distance learning programs. Recently, there has been a push to offer university level courses for free online through the use of educational videos. Recent notable examples include Kahn AcademyMIT’s OpenCourseWare,  UdacityCoursera, and edX.

One of the key advantages to offering lectures through the use of pre-recorded video is that students can learn at their own pace. I’ve found that my mind will sometimes wander in the class room, and I am left with insufficient context to resume the rest of the lecture. Pre-recorded video lectures solve this problem by allowing students to repeat or rewind the lecture back to where students began to lose focus. Another advantage is that pre-recorded videos can teach at a faster pace. Often, I can comprehend a courses’ lecture material at 1.5 times the speed of the video was recorded at (allowing me to finish a 1 hour lecture in only 40 minutes). Of course, the tradeoff here is that student’s are unable to ask questions to the professor in real time – and instead, may have to rely on class forums or discussion groups to answer potential questions.

From my personal experiences, I believe that video lectures offer many further potential untapped benefits to educators. One example is that online video players can offer educators feedback where a lecture may warrant improvement. Educators may find that students will often rewind or repeat a section of video that is particularly hard to understand, or may need further elaboration. Educators may also find that students will speed up or skip past sections of video that may be considered unnecessary fluff (such as when a lecturer wanders off topic).

Silk Road: Trust in an Anonymous, International Online Marketplace

Rocco’s Study

Trust Breaks Down in Electronic Contexts but Can Be Repaired by Some Initial Face-to-Face Contact, published in 1998 by Elena Rocco, explores whether trust can emerge in electronic contexts.  Rocco explores the issues by measuring trust emergence in face-to-face and electronic contexts through experimentation. Trust emergence is measured through the use of social dilemma (i.e. a situation in which advantages for individualistic behavior make group cooperation vulnerable).

As the title suggests, the experimental findings find that electronic communication may be “inappropriate to support teamwork when trustworthiness is a prerequisite for action and members cannot rely on past experiences shared with the others.” The experiments show that trust in electronic context could only be achieved when team members have initial face-to-face contact.

The study emphasizes the importance of intelligibility in electronic communication; in which intelligible communication reduces risks of misunderstandings and encourages participation. The paper was published in 1998, and used an e-mail group mailing list (resembling a crude BBS) as the electronic medium in the experiment. Perhaps applications more advanced and more structured than mailing lists could support better intelligibility. So how applicable are the study’s findings today?

Silk Road

The e-commerce website, Silk Road, may provide an excellent case study for further examination on how trust is established in an electronic context. Silk Road is an anonymous, international online marketplace that operates as a Tor hidden service and uses Bitcoin as its exchange currency. The majority of the products that sellers list on Silk Road qualify as contraband in most jurisdictions; NPR has referred to the site as the “Amazon.com of illegal drugs.”

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Unlike traditional e-commerce sites where users are mostly concerned about getting scammed, the stakes on Silk Road are much higher. Silk Road users must also be concerned over tainted goods from malicious individuals and getting caught by law enforcement.

So how is trust established in an online black market – where the users are anonymous, the stakes are high, and face-to-face communication is next to impossible? Silk Road appears to tackle this problem by providing reputation systems and offering escrow services. A seller’s profile page indicates how long the seller has been on site member, when he last logged in, how many transactions have gone through the seller, a feedback system (similar to eBay), and a seller rating based on the percentage of positive feedbacks. Silk Road also offers an escrow service for transactions – in which, Silk Road will launder and hold on to funds while a transaction is being finalized.

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Few studies have been conducted on Silk Road – most likely due to ethical concerns surroundings its controversial (and possibly illegal) nature. For more details on Silk Road – check out Nicolas Christin’s paper, Traveling the Silk Road: A measurement analysis of a large anonymous online marketplace, published by Carnegie Melon University’s CyLab.