When I was a very young man, just beginning to make my way, I was invited to dine at the home of a distinguished New York philanthropist. After dinner our hostess led us to an enormous drawing room. Other guests were pouring in, and my eyes beheld two unnerving sights: servants were arranging small gilt chairs in long, neat rows; and up front, leaning against the wall, were musical instruments. Apparently I was in for an evening of Chamber music.
I use the phrase “in for” because music meant nothing to me. I am almost tone deaf. Only with great effort can I carry the simplest tune, and serious music was to me no more than an arrangement of noises. So I did what I always did when trapped: I sat down and when the music started I fixed my face in what I hoped was an expression of intelligent appreciation, closed my ears from the inside and submerged myself in my own completely irrelevant thoughts.
After a while, becoming aware that the people around me were applauding, I concluded it was safe to unplug my ears. At once I heard a gentle but surprisingly penetrating voice on my right.
“You are fond of Bach?” the voice said.
I knew as much about Bach as I know about nuclear fission. But I did know one of the most famous faces in the world, with the renowned shock of untidy white hair and the ever-present pipe between the teeth. I was sitting next to Albert Einstein.
“Well,” I said uncomfortably, and hesitated. I had been asked a casual question. All I had to do was be I equally casual in my reply. But I could see from the look in my neighbor’s extraordinary eyes that their owner was not merely going through the perfunctory duties of elementary politeness. Regardless of what value I placed on my part in the verbal exchange, to this man his part in it mattered very much. Above all, I could feel that this was a man to whom you did not tell a lie, however small.
“I don’t know anything about Bach,” I said awkwardly. “I’ve never heard any of his music.”
A look of perplexed astonishment washed across Einstein’s mobile face.
“You have never heard Bach?”
He made it sound as though I had said I’d never taken a bath.
“It isn’t that I don’t want to like Bach,” I replied hastily. “It’s just that I’m tone deaf, or almost tone deaf, and I’ve never really heard anybody’s music.”
A look of concern came into the old man’s face. “Please,” he said abruptly, “You will come with me?”
He stood up and took my arm. I stood up. As he led me across that crowded room I kept my embarrassed glance fixed on the carpet. A rising murmur of puzzled speculation followed us out into the hall. Einstein paid no attention to it.
Resolutely he led me upstairs. He obviously knew the house well. On the floor above he opened the door into a book-lined study, drew me in and shut the door.
“Now,” he said with a small, troubled smile. “You will tell me, please, how long you have felt this way about music?”
“All my life,” I said, feeling awful. “I wish you would go back downstairs and listen, Dr. Einstein. The fact that I don’t enjoy it doesn’t matter.”
He shook his head and scowled, as though I had introduced an irrelevance.
“Tell me, please,” he said. “Is there any kind of music that you do like?”
“Well,” I answered, “I like songs that have words, and the kind of music where I can follow the tune.”
He smiled and nodded, obviously pleased. “You can give me an example, perhaps?”
“Well,” I ventured, “almost anything by Bing Crosby.”
He nodded again, briskly. “Good!”
But where scientific research is concerned, this recently accelerated shift to data-centric science has a dark side, which boils down to this: the skills required to be a successful scientific researcher are increasingly indistinguishable from the skills required to be successful in industry. While academia, with typical inertia, gradually shifts to accommodate this, the rest of the world has already begun to embrace and reward these skills to a much greater degree. The unfortunate result is that some of the most promising upcoming researchers are finding no place for themselves in the academic community, while the for-profit world of industry stands by with deep pockets and open arms.
The Unreasonable Effectiveness of Data
In 1960, the physicist Eugene Wigner published his famous essay, The Unreasonable Effectiveness of Mathematics in the Natural Sciences. It expounds on the surprising extent to which abstract mathematical concepts seem to hold validity in contexts far beyond those in which they were developed. After all, who would have guessed that Riemann's 19th century studies in non-Euclidean geometry would form the basis of Einstein's rethinking of gravitation, or that a codification of the rotation groups of abstract solids might eventually lead physicists to successfully predict the existence of the Higgs Boson?
Echoing this, in 2009 Google researchers Alon Halevy, Peter Norvig, and Fernando Pereira penned an article under the title The Unreasonable Effectiveness of Data. In it, they describe the surprising insight that given enough data, often the choice of mathematical model stops being as important — that particularly for their task of automated language translation, "simple models and a lot of data trump more elaborate models based on less data."
If we make the leap and assume that this insight can be at least partially extended to fields beyond natural language processing, what we can expect is a situation in which domain knowledge is increasingly trumped by "mere" data-mining skills. I would argue that this prediction has already begun to pan-out: in a wide array of academic fields, the ability to effectively process data is superseding other more classical modes of research.
Usually, I'll whiteboard the question and we'll spend a moment talking about possible approaches. But the goal is to get the candidate to start writing code quickly so we can get to a solution.
We've all been faced with the terrible, knowledge-based, "I could look that up in 1 minute but I don't have a computer" question. Worse are the gotcha questions that you wouldn't be able to solve unless you happen upon a moment of brilliant insight. The questions I ask aim to avoid that.
Good questions are fun and engaging for candidates. Good questions also always have a path forward. If the candidate is stuck, I should be able to give a hint that allows them to get unstuck but that doesn't give everything away.
I like to arrive at some running code that solves at least a subset of the problem at the end of every interview. If my question just wasn't going well with a candidate, getting something running keeps them from spiraling down a mental failure vortex, and allows them to relax and focus on the next interview.
Preparing to Ask a Question
A lot of work happens before you ever see a particular interview question. First, I myself have probably solved it in one or two possible ways. The first time I solve it, I try to give myself the same constraints a candidate would have -- limited time, no previous knowledge, no specific preparation.
Next, if I'm the one who came up with the question, I will ask it to a few of my coworkers to get a basic calibration. If someone else came up with the question, then I have probably sat in on (shadowed) a few interviews where the question was asked.
By the time I ask you, I am familiar enough to quickly know the various dead ends and blind alleys that you can fall into. I know of a few ways to steer you towards something that would work. Finally, I know how people of various experience and skill levels usually perform. I know enough to be amazed at your quick and clean approach. Alternatively, I've seen how good whiteboarding goes bad and results in spaghetti code that's impossible to debug.
Over time, I’ve come across quite a few references to this concept of taking a lot of care over who you invite into your circle. I thought it could be useful to share them:
Here’s what Seneca said in Letters from a Stoic:
Even Socrates, Cato, and Laelius might have been shaken in their moral strength by a crowd that was unlike them; so true it is that none of us, no matter how much he cultivates his abilities, can withstand the shock of faults that approach, as it were, with so great a retinue.
Jim Rohn had a famous line:
You are the average of the five people you spend the most time with.
Something a little more direct, from Steve Winterburn:
Before you diagnose yourself with depression or low self-esteem, first make sure that you are not, in fact, just surrounded by assholes.
From my favorite Zen Buddhist monk, Thich Nhat Hanh:
A Vietnamese proverb says that if a tiger comes down from its mountain, it will be killed by humans. This also applies to practitioners. If they abandon the sangha they will be lost, for the conditions of life in the world will sooner or later drag them back into forgetfulness and sleep. If they are to resist this current, they must rely on the bastion of protection which is the sangha.
For reference, here is Thich Nhat Hanh’s definition of sangha:
A quarterback has the ball for about ninety seconds each game, during which time the best quarterbacks make seven successful passes per ten attempts. The worst quarterbacks make five successful passes per ten attempts. Those two extra passes are mostly a result of faster thinking, and the faster thinking is mostly a result of better studying.
How fast does an expert quarterback think?
He has less than one second to solve his first problem: How many opponents are trying to stop him from throwing the pass? Three or four is normal. More than that is a “blitz,” a metaphor of war taken from the German tactic of overwhelming force called blitzkrieg. If the quarterback sees a blitz, or if the line of teammates protecting him has been breached, he must make a quick pass, take evasive action, or sacrifice himself to keep the ball.
If he judges he will survive, he moves on to his next problem: where and when to throw. Potential receivers are spread across the 160-foot-wide field running planned, assigned routes. Each route includes an agreed upon moment when the receiver will turn and look for the ball. The quarterback must gauge each receiver’s prospects. Who is in pursuit? Who can interfere? His decision is based not only on position but also on speed and direction. The fastest receivers run nineteen miles per hour — faster than an Olympic sprinter starting the 100 meters. The quarterback can throw the ball at fifty miles per hour. He must predict what each receiver’s situation will be when the ball arrives. He must think about this while moving and avoiding collisions, and with an obstructed field of vision. If he takes a fraction of a second too long, his options collapse. Opponents converge. Receivers exhaust their routes. On most plays he has fewer than three seconds to make a decision and act.
Rule no1 was: don’t just advertise, social media is social so don’t just shout out all your products every day, but add value to your readers. Give them insight in your operation, give them interesting tips on using your products or go back to the core of your business, your mission, and use Facebook to help with your mission.
The race for the attention
As soon as people’s Timelines started to get flooded with messages, a big problem surfaced: Facebook had to figure out how to get the most important messages in front of the readers. They came up with Edgerank.
EdgeRank was put in place to ensure users don’t get overwhelmed by content and to reduce spammy content in favor for interesting content.
Edgerank is a calculation that decides how important a message is to the reader, and thus how high in the Timeline this particular piece of content shows up. The Edgerank formula consists of three things:
If you spend more time talking or interacting with certain friends or pages, your affinity with those people or pages will increase.
Weight is the level of interaction a piece of content gets (likes, comments and shares).
Over time, a posts relevance decays, making it less important and pushing it down the feed.
The doorbell rings.
Wow! It’s your cousin who you haven’t seen in ages!
Your current best friend.
Your best friend from high school followed by your sister and brother-in-law.
No way! “Lindsay?! Is that you? I sent you an invite, but I wasn’t really sure if you would make it or not. It’s been forever. How cool is this?!”
The party is going great. Everybody is talking about what’s been happening for the last several years, sharing pictures, bands they’re listening to, movies they’ve seen, and you already have twenty-eight of your closest friends who’ve made it!
“Ed?” You dig through your memory banks. That’s right. Ed sat behind you in 3rd grade.
“Hey man, I saw that some old school friends were getting together here. Mind if I join you?”
“Sure, sure. It would be great to catch up.” You never even talked to Ed in the 3rd grade, but he seemed like a nice kid and who knows? Maybe he can help you network out a bit more for your new freelancing gig.