Friday Links: Online education, crime algorithm, health care

I was looking at my Blogger stats and discovered that someone found my site by googling "help i'm addicted to wasting time on the internet." I googled it and asked an acquaintance to do the same (since Google results are personalized), and it turns out my article on Internet time-wasting is hit #3-#5, depending on who searches for it. Nice. Interestingly, it goes down with "i'm addicted to wasting time to the Internet," and doesn't come up if you just google "addicted to wasting time on the internet."

http://www.nytimes.com/2012/07/20/opinion/the-trouble-with-online-education.html?_r=1&smid=fb-share

Those who started reading my blog from the beginning know that I started off by explaining an interesting example from an online course on Coursera. To me, online courses are one way of exposing my mind to new ideas that break my preconceptions and defy conventional wisdom. Letting go of long-held ideas is critical to getting anywhere in science. But while I currently treat online education as a fun side project, there is a good debate going on as to what the role of online education will be in general for K-12 and college. Since it's the new thing, everyone wants to know to what extent it will replace traditional classroom learning. In this NY Times article Mark Edmundson, a professor at University of Virginia appears to argue against the widespread use of online education, saying there's nothing you can get from an online course that you can't get from a good book. I largely disagree.

I agree only with his thesis, "But can online education ever be education of the very best sort?" Well sure, it can't be the BEST by itself, but it sure beats the average educational experience in the US. In my opinion, education isn't about learning facts, it's about learning how to make arguments and how to understand and solve problems. And yes (agreeing with Edmundson), this can't be accomplished in a one-way didactic lecture. It needs dialogue and requires students to get to the answer themselves, with proper cultivation from the professor. However, I disagree that this says anything about the value of online education in the grand scheme of things. I don't think online education is meant to completely replace classroom learning, and certainly won't replace the best professors at the best universities. Learning how to solve problems requires some starting facts (and in science, LOTS of starting facts), and those facts should be communicated in the most efficient and organized manner. When done properly, one-way didactic lectures synergize with books (rather than being redundant). And no, I don't find it likely that every teacher individually optimizes the communication of those facts. Furthermore, in many school systems this is so inefficient that they spend all their time lecturing facts and no time on critical thinking. Only a few lucky students get real dialogue learning, since you inherently need small classroom sizes for that. Thus online education, taught by THE best educators (like, the best in the world), would go a long way to improving K-12 education. Then teachers can focus on problem solving sessions rather than lecturing facts.

I envision two parts to future education (both K-12 and college): 1) one-way lectures taught by the very best people who have developed the best ways to explain something. These can be online and available for everyone in the world. 2) actual teachers or TAs that focus entirely on face-to-face dialogue. They don't provide any actual information- they present a problem and work with the students to reason through the problem, using information that they learned in the lectures. They are more like older colleagues than anything else. In college, I often learned way more in small discussion sections than in lecture. Lectures are necessary but not sufficient for education.

In fact, when I served as a TA, there was one module that I didn't know anything about. So what did I do? I studied it just enough to get an intuitive feel for it, and then I just pummeled my students with questions while working through problems. I didn't provide a single answer for them (because I didn't know how to solve the problem), and if they asked a question I just asked a question back. The result? In my student evaluations, they specifically mentioned how well I taught that module. Real teachers don't need to know the answer.

http://www.technologyreview.com/news/428354/la-cops-embrace-crime-predicting-algorithm/

A crime-prediction algorithm takes crime data and balances information on the day, week, month, year, and decade scale to figure out where crime is statistically most likely to strike next in the city. Maybe a certain part of the city sees more crime frequently around the holidays, for example. Humans can't physically process and balance all of the data, so let a computer do it. This leaves more time for humans to do what they do best- interact with other humans. They show up, talk to people, and just by having a presence decrease crime.

http://ayoungmomsmusings.blogspot.com/2012/07/how-i-lost-my-fear-of-universal-health.html

Blog of a die-hard conservative Republican who moves to Canada and of course fears Universal Health Care. However, she soon discovers it's great and that more government control = more freedom for individuals to choose. A far more complicated issue than I'd want to address in a Friday Links entry.

Other random links:

http://www.newscientist.com/article/dn22088-heart-muscle-helps-cyborg-jellyfish-come-alive.html?DCMP=OTC-rss&nsref=online-news

http://www.huffingtonpost.com/2012/07/07/pacu-testicle-eating-fish-illinois-lake_n_1656015.html?utm_hp_ref=mostpopular

The future: no more secrets

The Internet lets people share the problems that are befuddling them. At the same time, the Internet allows for the dissemination of information for other people to solve those problems. And there's nothing stopping those people from connecting, other than the potential mistrust. I think one tantalizing idea is that we're moving towards a world where everyone (including companies) will share their information freely on the Internet. In other words, maybe everything will become open access. Nothing proprietary.

In the TED talk below, Don Tapscott tells the story of a gold prospector has collected a bunch of data on a geological site that he is evaluating. However, the geologists that he works with aren't able to locate the gold and aren't able to make recommendations as to where to start digging. So he thinks: maybe someone else would be able to figure it out. So he does something that is unheard of in business: he decides to publish his data online for everyone to see and held a competition for someone to locate the gold. The result? Someone found the gold, told him where to dig, and he made a bazillion dollars. Could he potentially have gotten scooped? Maybe someone would have sat on his result until the gold prospector gave up and sold his rights to the land, and then swooped in to grab the gold. But I think that's highly unlikely, since each person is competing against EVERYONE else on the Internet. If the malicious person decided to wait, then some other person would probably figure it out and then win his share of the gold in a FAIR manner. In this hypothetical Internet world where people freely share their "trade secrets," people who try to take advantage of other people will not thrive.

I think this is applicable to any field, especially science. If a PI needs something that doesn't exist yet but will likely require expertise outside their field, they currently have two options: hire a guy to work in the lab and develop it, or try to convince another specific PI to collaborate with them. The viability of both options is influenced by factors such as who is in your social network (i.e. having the right connections), investing time to find a person you can trust, and investing time to convince that person that it's a worthwhile pursuit. It's a lot of work and it is highly unlikely that you've found the ideal person for it, especially since you are not the expert and you aren't yet sure what is required to solve the problem. The biggest problem is that the PI is unlikely to disclose any detailed information about the project until he has found the person he wants to work on it. But this is totally backwards. You don't know if a person is going to be able to solve a problem until they've actually looked at the problem. Thus, if someone needs outside help, they can simply release all the relevant information onto the Internet and look for the guy who can solve it. Getting scooped is not an issue because everyone on the Internet already knows exactly what you did and what you contributed. (By the way, I think peer-review is going to get crowdsourced in the future, so it won't matter anymore who is first to publish a result in some paywalled journal). In this system, every professional scientist (or non-scientist, for that matter) could do a little science freelancing on the side, and personally I think it would be fun as hell. 

This also applies to companies. Right now, there's little doubt that the current pharmaceutical industry model sucks. What follows is a simplification, but one issue is that each company sits on a wealth of proprietary information but usually has insufficient power to utilize it. Note that these data sets are ASTRONOMICAL- drug libraries, clinical trial data, synthesis methods, preclinical data that shows that X drugs affect Y biological processes, etc. etc. There's no way that the employees of one company are going to use those data sets to their full potential. The companies are waiting for the chance to make money off of it, but because they have insufficient brainpower to tackle massive data sets, many drugs are not directed to their "ideal" patient populations, so many of the drugs fail. Plus, companies use the patent system to actively prevent others using that information, even if they independently discover it. And because the patent system is not perfect, everyone wastes time suing everyone else. Why bother with all the secrets? I think a lot more drugs would be successfully developed if every person in the world could look at pharmaceutical data and make their suggestions as to which drugs are promising for what diseases.

Remember, the goal shouldn't be to beat other people to the right answer. The goal is to find the right answer. Secrets were viable in the past because problems were simpler. Science didn't involve massive amounts of data. A small group of people could solve the problems without letting anyone else know what they're doing. But no more.

Please comment on my naivety.

Beer with the Worm Guys in the Badger State

Zero blog posts last week- I apologize! But it was a crazy week trying to get experiments done, because I had to take off on Thursday for the annual C. elegans meeting in Madison, WI on Aging, Metabolism, Stress, Pathogenesis, and Small RNAs! I thought maybe I would have time to write on my trip- but no way. A 4-day constant deluge of awesome talks (mostly on topics directly or indirectly related to my own research), meeting tons of people, no sleep, and yes, partying and enjoying the city. I spent a majority of today debriefing myself on the meeting and following up on e-mails, and I'm only about 1/3 done. I consider this my official induction into the Worm community.

Madison is a beautiful cosmopolitan city. Campus is integrated into the bustling cityscape similar to Harvard's Longwood campus, except in a much more efficient and relaxing way compared to the mess in Boston. There are numerous student-friendly areas (completely absent from Harvard/MIT imho) reminiscent of Ann Arbor's bars and cafes, except with far more choices. Like Ann Arbor, it has a disproportionate amount of culture relative to size. The State Capitol is sandwiched between two lakes, and about half our conference took place at the Memorial Union which is right on the larger lake.

During dinner and other breaks we would sit out on the Terrace listening to live bands, watch the boaters and swimmers enjoying the wonderful weather, and drinking Wisconsin's signature beer along with a thousand other people (you can buy beer everywhere all the time in Wisconsin). The other half of our conference was at the brand new Union South, and like many of the newer campus buildings, it was way nicer than anything I've seen at Harvard, MIT or Michigan. I wandered into the Wisconsin Institutes of Discovery and found myself wondering: why don't we too have fancy bars, numerous fountains and a froyo place right in the lobby of our lab building? Madison is set up as an intellectual's dream city.

It was incredible - this meeting motivated me like nothing else since I started working in a C. elegans aging lab a year ago. The whole experience was a blast, but one thing really pumped me up:

Getting to know my peers. I always thought hydrogen sulfide research was awesome, and now I get to party and schmooze with the very same people who did that work! Almost surreal - especially when we started jumping in the lake at 4am. Watching the distinguished professor from Germany do the Twist in hot pink pants at the dance party didn't hurt either. Science is hard work. Benchwork can become lonely, and reading a zillion papers can become really abstract. Without a face and without knowing if you'll ever meet them, it's hard to think of the authors of papers you read as real people. What better way to motivate yourself than to meet the research community? It's only rational- we are social animals. Feeling connected to people who authored the papers you read really helps you feel connected to the research itself, and it helps you appreciate opposing viewpoints. But I've found I love the worm community, with its social dynamism running contrary to all societally-imprinted misconceptions of scientists as awkward and anti-social.

Everyone at this meeting works on related problems (aging/longevity/epigenetics) in the same system (C. elegans). Which means that all of the long-standing mysteries in the aging literature that have fascinated me for years were addressed in some way at this meeting. These are the very people working and solving these problems. Being one of the first people to hear even the partial solutions to these mysteries has made me fall in love with the field all over again. Even if I had a massive hangover while listening to all the talks.

Lastly, I was nicknamed "creepy and delicious"- and yes that was affectionate.

Friday Links: DNA + dark matter, E.O. Wilson, skeleton racing

More Friday Links! 

http://www.technologyreview.com/view/428391/revolutionary-dna-tracking-chamber-could-detect/

A really cool research venture: using DNA to detect dark matter. Deep sequencing technologies require you to compactly array DNA molecules (all of different sequences) on a solid surface, and biologists have standard techniques (PCR + sequencing) to uniquely identify a DNA molecule from any given spot in the array. Guess what? That's a great setup for detecting dark matter. The hypothesis is that the Earth should be plowing through dark matter as it revolves and/or rotates, assuming that dark matter is diffusely distributed.

Essentially, here's how the DNA dark matter detector works:

1) Earth rotates, brushing through dark matter in a predictable rhythm that varies directionally throughout the day

2) DNA molecules are arrayed on a gold sheet. Dark matter can knock gold nuclei out of the sheet and into the array of DNA molecules.

3) The gold nucleus cuts a swath through the forest of DNA molecules, severing them

4) severed DNA molecules fall from the array and are collected, then amplified by PCR and sequenced so the biologists can figure out exactly which DNA molecules were severed

5) since they know where each DNA molecule was anchored, they can put together the path that the gold nucleus took

6) Match the path with the direction from which you'd expect the dark matter to be coming from at the given time of day.

One word: awesome.

http://www.ted.com/talks/e_o_wilson_advice_to_young_scientists.html

E.O. Wilson, the famed evolutionary biologist who studies eusocial organisms, advocates the kind of cross-pollination exemplified by the above DNA-dark matter example. His message to scientists-in-training: learn broadly and collaborate broadly. Too many PhDs spend all their time doing experiments in a narrow field and never venture into other areas. But they are missing out: new discoveries are found in non-intuitive connections between different fields.

One idea I had relevant to MD/PhDs was inspired by E.O. Wilson's two strategies for doing good science:

1) Medicine shows the problems. Seek to learn all the problems (literally, ALL) then look for scientific phenomena that can explain the problems and provide a means to intervene

2) Science observes phenomena without necessarily knowing if they have consequences relevant to human well-being. Seek to learn all (literally, ALL) the phenomena then look for problems that might be linked to the phenomena and apply your knowledge to the problem

http://www.freakonomics.com/2012/06/29/how-to-make-a-better-athlete/

Every sport is a unique combination of agents (players + equipment) and rules that those agents follow (official rules + rules of physics). Fortunately, that's all you need to create a model of something, with the purpose of identifying the most important factors, and to predict and explain emergent properties.

Here, Freakonomics spotlights the Australian team in the skeleton, an Olympic sledding sport. They don't have career skeleton athletes (except one) and they don't have a chance to practice because, well, they're in Australia. You might think that the skill and practice time of the athlete are the two most important factors. In fact, that might be true in most sports. But if you carefully examine the rules of the game, you'll realize that there are two components to a race: the actual sledding, and the 30-meter sprint beforehand to get the sled going. Guess what? Australia has plenty of good sprinters. And as it turns out, using a little bit of modeling, you can show that the actual sledding is of minimal importance in terms of time. Skill might prevent you from wiping out, but as long as you stay on course you're not going to shave much time off with good sledding. So instead, they focused their efforts on finding really good sprinters and training them.

The result? An Australian qualified for the Olympics within 18 months, getting in about 1/10 as much practice time as a "career skeleton athlete." Very often, working smarter is 10x than working harder. Science knows best, and conventional wisdom fails miserably.

http://www.npr.org/blogs/thetwo-way/2012/07/05/156280450/kaboom-san-diegos-entire-fireworks-show-ignites-at-once

Haha all of San Deigo's July 4 fireworks goes off all at once:

June review, July goals, and sustainability

It's July 1, and it's time to do my monthly review! Specific goals for each month, and be honest with myself about how I did and how much more I can take on. As a reference, here are my June goals.

June

The name of the game is sustainability. Sometimes sustainability can be achieved in non-intuitive ways. For example, in a previous post I described how I was going to warm-up at the beginning of idea sessions by doing a 5x5 idea list (based on things I should be thinking about everyday anyways) before my "real" idea list of the day. While this sounds like way more work, it actually results in finishing my idea habit in LESS time and thus is MORE sustainable. Since I've implemented it, I've never had trouble finishing my idea habit within 30 minutes (including associated reading). And that's because it just gets my brain working on actual ideas rather than worrying "is this going to be a good idea list?"

I work out almost every day without needing to give it a second thought. It's just part of life. I have no problem dropping everything at any given time of day, changing, and running to the gym. This is exactly what I imagined doing with my June goals. The entire point of doing a 30-day trial where I focus intensely on a handful of goals is to overcome the activation energy barrier, and then I can keep doing it without using up mental willpower. In particular, generating ideas and reading the scientific literature on a daily basis proved to have just as much utility as I had originally imagined. Thus, I have a conscious motivation to keep doing them. I just needed to get started. Furthermore, I've simply become used to doing them, and quite possibly I've become emotionally attached to them (in a good way). Once this happens, not only is it easy to continue doing them without all this deliberate tracking, but I subconsciously feel like something is wrong if I haven't completed them yet on a given day. Thus, it is sustainable.

I will continue idea generation and reading, but I won't be tracking them daily.

I'm much happier with my goal of waking up at 6am than the graph would suggest. I've been tracking my goals in a binary fashion- yes I woke up at 6am, or no I didn't. But simply focusing on waking up earlier has helped me stop wasting time at night and just get to bed, and I have more willpower to get my butt out of bed. Hence, my average waking time has shifted from 9:30am to 7:30am, and that has been great for productivity. I used to think I'm not a morning person, using that as an excuse not to wake up early. Boy was I wrong.

July

Conversely, while my "Lab Plan" goal looks like a success on the graph, there was way too much stuff for me to juggle in lab to really plan anything long-term (to remind you, I'm already pretty happy with my ability to plan within a day). While I worked on my Lab Plan most days, it never made any real impact because it was too unfocused.

Fortunately, my research project has gotten to the point where I just need to hammer out and repeat a series of known experiments. The essential story and elements of a publication are all present, and we've decided to scale back some of the more interesting but technically difficult experiments, and save those for a second publication. Hence, July (and August) is going to primarily about getting all these experiments done, and I'm going to focus my Lab Plan on just that. 3 MIEs (most important experiments) per day planned out for the entire month, and stick to it.

This month there's not going to be much time to develop new habits and work on other goals outside of lab. If I take on too much, it will no longer be sustainable. However, I feel like I should solidify one skill into a daily habit: writing. My goal is to write 30 minutes per day, and much of this will be my paper, though some of it will be my blog. There's no reason to have 100% of my experiments done before I start writing a manuscript. Some days I will write more, for example on days that I'm publishing this blog, or simply whenever I have the time and the ideas.

However, I would go crazy if I did nothing new. On this point, to hell with sustainability. (By the way, this isn't really a habit but an attitude: I generally try something new everyday). This may seem random, but I'm going to learn the Python programming language and find ways to use it in my work. I have some experience with Java but as far as I can tell Python is easier and more intuitive. I'll probably start with some csv files and use it to convert experimental data from one format to another, something that I've been doing manually by copying and pasting. If anyone has any ideas on how I can use Python in my work, please let me know.

To summarize, I have 3 primary goals for July.

1) Lab plan. Plan out experiments long-term rather than just daily. Primarily just experiments related to a publication (hopefully!)

2) Write 30 minutes per day

3) Learn Python. Each day, work on it until feel like I've learned or done something interesting. Time limit 30 minutes per day unless I'm applying it to work.