Why I love the English language..
If you can pronounce correctly every word in this poem, you will be speaking English better than 90% of the native English speakers in the world. After trying the verses, a Frenchman said he’d prefer six months of hard labour to reading six lines aloud.
Dearest creature in creation,
Study English pronunciation.
I will teach you in my verse
Sounds like corpse, corps, horse, and worse.
I will keep you, Suzy, busy,
Make your head with heat grow dizzy.
Tear in eye, your dress will tear.
So shall I! Oh hear my prayer.
Just compare heart, beard, and heard,
Dies and diet, lord and word,
Sword and sward, retain and Britain.
(Mind the latter, how it’s written.)
Now I surely will not plague you
With such words as plaque and ague.
But be careful how you speak:
Say break and steak, but bleak and streak;
Cloven, oven, how and low,
Script, receipt, show, poem, and toe.
Hear me say, devoid of trickery,
Daughter, laughter, and Terpsichore,
Typhoid, measles, topsails, aisles,
Exiles, similes, and reviles;
Scholar, vicar, and cigar,
Solar, mica, war and far;
One, anemone, Balmoral,
Kitchen, lichen, laundry, laurel;
Gertrude, German, wind and mind,
Scene, Melpomene, mankind.
Billet does not rhyme with ballet,
Bouquet, wallet, mallet, chalet.
Blood and flood are not like food,
Nor is mould like should and would.
Viscous, viscount, load and broad,
Toward, to forward, to reward.
And your pronunciation’s OK
When you correctly say croquet,
Rounded, wounded, grieve and sieve,
Friend and fiend, alive and live.
Ivy, privy, famous; clamour
And enamour rhyme with hammer.
River, rival, tomb, bomb, comb,
Doll and roll and some and home.
Stranger does not rhyme with anger,
Neither does devour with clangour.
Souls but foul, haunt but aunt,
Font, front, wont, want, grand, and grant,
Shoes, goes, does. Now first say finger,
And then singer, ginger, linger,
Real, zeal, mauve, gauze, gouge and gauge,
Marriage, foliage, mirage, and age.
Query does not rhyme with very,
Nor does fury sound like bury.
Dost, lost, post and doth, cloth, loth.
Job, nob, bosom, transom, oath.
Though the differences seem little,
We say actual but victual.
Refer does not rhyme with deafer.
Foeffer does, and zephyr, heifer.
Mint, pint, senate and sedate;
Dull, bull, and George ate late.
Scenic, Arabic, Pacific,
Science, conscience, scientific.
Liberty, library, heave and heaven,
Rachel, ache, moustache, eleven.
We say hallowed, but allowed,
People, leopard, towed, but vowed.
Mark the differences, moreover,
Between mover, cover, clover;
Leeches, breeches, wise, precise,
Chalice, but police and lice;
Camel, constable, unstable,
Principle, disciple, label.
Petal, panel, and canal,
Wait, surprise, plait, promise, pal.
Worm and storm, chaise, chaos, chair,
Senator, spectator, mayor.
Tour, but our and succour, four.
Gas, alas, and Arkansas.
Sea, idea, Korea, area,
Psalm, Maria, but malaria.
Youth, south, southern, cleanse and clean.
Doctrine, turpentine, marine.
Compare alien with Italian,
Dandelion and battalion.
Sally with ally, yea, ye,
Eye, I, ay, aye, whey, and key.
Say aver, but ever, fever,
Neither, leisure, skein, deceiver.
Heron, granary, canary.
Crevice and device and aerie.
Face, but preface, not efface.
Phlegm, phlegmatic, ass, glass, bass.
Large, but target, gin, give, verging,
Ought, out, joust and scour, scourging.
Ear, but earn and wear and tear
Do not rhyme with here but ere.
Seven is right, but so is even,
Hyphen, roughen, nephew Stephen,
Monkey, donkey, Turk and jerk,
Ask, grasp, wasp, and cork and work.
Pronunciation (think of Psyche!)
Is a paling stout and spikey?
Won’t it make you lose your wits,
Writing groats and saying grits?
It’s a dark abyss or tunnel:
Strewn with stones, stowed, solace, gunwale,
Islington and Isle of Wight,
Housewife, verdict and indict.
Finally, which rhymes with enough,
Though, through, plough, or dough, or cough?
Hiccough has the sound of cup.
My advice is to give up!!!
English Pronunciation by G. Nolst Trenité
Source: spelling.wordpress.com
…”Men and Woman Lie….but Numbers Dont”
This infographic …. is so simple but on the other hand soo damn Mind Numbing !!
Source: the-clash
A New Year
I’m not one to make new year’s resolutions. I feel like if you want to change, you don’t have to wait until the beginning of the new year to ceremoniously declare that there are things about yourself that you want to change. But then I realized that there are some things that I just take too long to put into effect. I know I want to change yet I plan on making the change for months, never actually changing anything. So this year, I am making a set of resolutions - hard, fast resolutions that I intend to stick to. And measure with cold, hard data!
So here we go:
1) Get a job. Enough said. I graduated with great grades, a ton of student involvement, loads of projects and a lot of networking. Seven months of unemployment is not cool. Yes, immigration policies don’t work in my favor, but that’s not an excuse.
2) Have a laser focus. I think I work pretty hard. But I also spend a good 10 minutes of every hour getting distracted, whether it’s searching for the optimal Spotify playlist, reading my never-ending RSS feeds, or the most evil of all, trolling Facebook. In Steve Job’s biography, Walter Isaacson frequently talks about Jobs’ ability to get things done.
Jobs’s intensity was also evident in his ability to focus. He would set priorities, aim his laser attention on them, and filter out distractions. If something engaged him.. he was relentless.
So my first resolution is to cultivate such a GTD attitude.
3) Get fit. Every year I make a silent resolution to eat healthier, work out more, and take long walks. Every year (since 2007) that resolution dies as silently as it was birthed. So this year, it is a proclamation. I also have the added incentive that I’m getting married in 5 months and I need to look good for the role.
4)Build something. I have a million ideas, thousands of objectives, hundreds of sketches and loads of attempts to start something. This year I will start and finish a lot of great projects.
5) Increase technical skills and create a web presence. Harder said than done. I’ve attempted to contribute to Hacker News, poked around on Stack Overflow, created a website, learned a few languages and such. But I won’t stop there. Here’s to a lot more knowledge attainment!
So there. Maybe this year, with a public-ish list, I will get things done!!!!
Source: theoatmeal.com
Data visualization
My girlfriend has finally got around to calling me a geek. The tipping point, which I’m surprised it took so long to arrive at, was the inclusion of books on data, design and data visualization in my Christmas list. I love getting gifts, and for me to include books instead of the usual fare underlines my love for data.
I’ve always been fascinated with data and all its possibilites but in the past I’ve only looked at it from a collection point of view, which is definitely not fun. Not for me at least. I enjoyed my econometrics classes, playing with SAS, and making sense out of data sets but I was limited with what I could do with the output. As much as I love data, SAS output sheets are so old school. With all the tools we have available, data can be manipulated in so many ways and the best part is companies are finally playing around with it in fun ways that interest more than just data lovers like me.
Take Facebook for instance. I’m no expert but its a fair assumption that they probably have the world’s largest data set. Their Engineering team recently published a post titled Anatomy of Facebook, in which they visit a well known hypothesis - Six Degrees of Separation. The idea was put forth by a Hungarian author Frigyes Karinthy in a short story titled Chains (the link is in Hungarian, from the original text, but Google will kindly translate it, all you have to do is ask). Without going into too much detail, he suggested that you could reach almost any one in the world, through at most five acquaintances. Unfortunately for him, it wasn’t really feasible to test such a hypothesis in his day; but we can. Or rather, Facebook can. Their results on this study, considered the largest study of social networking, are fascinating. What they have found is that as Facebook has grown, the degree of separation has become smaller and is currently at an average of four degrees of separation - the variations come from different friend sizes between users. What amazed me further was the size of the data set - Facebook has access to 721 million active accounts that encompass 10% of the world population and a total of 69 billion friendships. Drool haha.
Another one of their posts, Interning at Facebook: Who Goes Where When (and Why It Matters) written by, duh, an intern, shows some of the useful stuff they’re doing with their data. They’re using Facebook check-ins at different locations to develop a Open-Close schedule for these businesses and an awesome recommendation engine. When you type in a location to check-in at on your Facebook app, Facebook checks the time and cross-references your input against the list of businesses they have marked as “Open” during the time that you are checking in. So typing “Ni..” in the afternoon will not auto complete to “Nightclub”. Pretty neat.
Popular iPhone photo sharing service, Instagram, recently published a post on their engineering blog, Instagram Engineering, showing users the things they do behind the scenes with their data. Like most other tech firms, they posted this as a challenge to developers, to see whether they could optimize the solution. The challenge involves taking a shredded image, un-shredding it and reconstituting it to make the original picture by reading the picture data. I’m far from being able to solve a puzzle like this but it’s definitely on my to-do list.
Other examples include Foursquare, the company that (IMO) does location based check-ins best. They’ve taken their huge data set of over 10 million people checking-into 28 million different venues all over the world and visualized that on a global activity map. To me that activity map revealed more than just check-in activity. Firstly, it is an indicator of smartphone prevalence across the globe and more importantly it is an indicator of privacy concerns. I know a lot of people who won’t check-in anywhere because they believe it’s a violation of privacy. So in areas where theres a large cluster of activity, does it mean people aren’t that fraught about revealing their location? To me it does anyway.
These are just few and simple examples of how one can use data to get meaningful information that even lay people can understand. You don’t have to be a SAS expert to understand Facebook’s findings. I have a lot more work to do before I can play around with the data I’m collecting, but hopefully, I’ll get there sooner than I think.
