I recently audited some lectures by friend and China expert Prof. Susan Shirk. She bans computers in her lectures. But one student sitting near me had his machine out and was “busy” with the usual distractions. (Didn’t he know the Associate Dean was a few seats away?) I asked Susan about him after class. “He told me he can’t take notes without a computer.” Obviously the computer is not the big issue on his note taking. Actually, it probably IS the issue – but in a negative way.
Not one computer mirrors the overheads.
James Kwak has beaten the distraction of cell phones – by removing most apps, including browsers.
I know that its enormous powers of distraction also make me lose focus on work, tune out in meetings, stay up too late at night, and, worst of all, ignore people in the same room with me. We all know this. We’re addicted to the dopamine hit we get when we look at our email and there’s actually something good in there, so we keep checking our email hoping to feel it again.
Clay Shirky, an Internet sociologist, has a good discussion of why he recently banned computers in his classrooms. Excerpt:
I came late and reluctantly to this decision — I have been teaching classes about the internet since 1998, and I’ve generally had a laissez-faire attitude towards technology use in the classroom. This was partly because the subject of my classes made technology use feel organic, …. And finally, there’s not wanting to infantilize my students, who are adults, even if young ones — time management is their job, not mine.
Despite these rationales, the practical effects of my decision to allow technology use in class grew worse over time. The level of distraction in my classes seemed to grow, even though it was the same professor and largely the same set of topics, …
Over the years, I’ve noticed that when I do have a specific reason to ask everyone to set aside their devices (‘Lids down’, in the parlance of my department), it’s as if someone has let fresh air into the room. The conversation brightens, and more recently, there is a sense of relief from many of the students. Multi-tasking is cognitively exhausting — when we do it by choice, being asked to stop can come as a welcome change.
So this year, I moved from recommending setting aside laptops and phones to requiring it, adding this to the class rules: “Stay focused. (No devices in class, unless the assignment requires it.)” …
LeCun: Actually, I think the basics of machine learning are quite simple to understand….
A pattern recognition system is like a black box with a camera at one end, a green light and a red light on top, and a whole bunch of knobs on the front. The learning algorithm tries to adjust the knobs so that when, say, a dog is in front of the camera, the red light turns on, and when a car is put in front of the camera, the green light turns on. You show a dog to the machine. If the red light is bright, don’t do anything. If it’s dim, tweak the knobs so that the light gets brighter. If the green light turns on, tweak the knobs so that it gets dimmer. Then show a car, and tweak the knobs so that the red light get dimmer and the green light gets brighter. If you show many examples of the cars and dogs, and you keep adjusting the knobs just a little bit each time, eventually the machine will get the right answer every time.
Why unsupervised learning is critical in the long run, but does not yet work:
The type of learning that we use in actual Deep Learning systems is very restricted. What works in practice in Deep Learning is “supervised” learning. You show a picture to the system, and you tell it it’s a car, and it adjusts its parameters to say “car” next time around. Then you show it a chair. Then a person. And after a few million examples, and after several days or weeks of computing time, depending on the size of the system, it figures it out.
Now, humans and animals don’t learn this way. You’re not told the name of every object you look at when you’re a baby. And yet the notion of objects, the notion that the world is three-dimensional, the notion that when I put an object behind another one, the object is still there—you actually learn those. You’re not born with these concepts; you learn them. We call that type of learning “unsupervised” learning.
And a candidate for worst graph of the year, appearing to show that deaths from a certain class of diseases grew in parallel with some farming trends. ! (Figure 16 in the article, which is at http://www.organic-systems.org/journal/92/JOS_Volume-9_Number-2_Nov_2014-Swanson-et-al.pdf ). Any steadily increasing time series can be plotted so that they lie approximately on top of each other, if you distort the scales enough. Other “causes” they could have plotted, with approximately the same results: cell-phone per capita, percentage of cars on the road with ABS brakes, and (for all I know) average campaign spending per Congressional race.
The Telephone Wires of Manhattan, 1887. Switchboards were a big step forward. This picture also shows an advantage of living in a city: better communications. Still true today, except measured in milliseconds.
[edits Jan. 31] A poli sci friend recently blogged about the Ukranian government’s “text that changed the world,” a mass text message thousands of anti-government demonstrators in Kiev. She asked 1) How did the government know who was in the main square of Kiev that day? (Cell phone location) and 2) How did it send the same message to everyone at once? (Mass SMS)
Demonstrators in Kiev. From CNN
The second question is easy: phone companies routinely provide mass-SMS services to large customers. For example, I’m on the “emergency alert” texting service of UC San Diego’s campus police. It was designed for earthquakes, but it has been used for other kinds of messages “between earthquakes.” The same message goes out to every phone number on their list.
What to do to avoid tracking? Short version: Leave your phone at home. Second best is to shut it off or switch to airplane mode, but those work only if the government is not making an effort to target you.
Have any political scientists tried to model /improve governance of Wikipedia? LOTS of interesting questions there, and seemingly a way for an ambitious young academic to stake out new territory that will be increasingly important. Here’s the author’s view of the cause of problems:
The loose collective running the site today, estimated to be 90 percent male, operates a crushing bureaucracy with an often abrasive atmosphere that deters newcomers who might increase participation in Wikipedia and broaden its coverage.
a few more quotes:
The page explaining a policy called Neutral Point of View, one of “five pillars” fundamental to Wikipedia, is almost 5,000 words long. “That is the real barrier: policy creep,” he says. But whatever role that plays in Wikipedia’s travails, any effort to prune its bureaucracy is hard to imagine. It would have to be led by Wikipedians, and the most active volunteers have come to rely on bureaucratic incantations.
Yet it may be unable to get much closer to its lofty goal of compiling all human knowledge. Wikipedia’s community built a system and resource unique in the history of civilization. It proved a worthy, perhaps fatal, match for conventional ways of building encyclopedias. But that community also constructed barriers that deter the newcomers needed to finish the job. Perhaps it was too much to expect that a crowd of Internet strangers would truly democratize knowledge. Today’s Wikipedia, even with its middling quality and poor representation of the world’s diversity, could be the best encyclopedia we will get.
Another article refuting Hollywood scare tactics. Apparently even Hollywood studios are coming to recognize that piracy provides valuable free PR, though they won’t admit it publicly.
I fear that ongoing trade treaty negotiations are being used as a backdoor for misguided new restrictions on IP. Because these are being negotiated “in secret” (except from large companies), there won’t be time to hold hearings or have a rational discussion of these provisions when the treaty is presented to the Senate for confirmation.