Mad about you, orchestrally.feel the vibe, feel the terror, feel the painmore quotes

# words: beautiful

In Silico Flurries: Computing a world of snow. Scientific American. 23 December 2017

# daily quotation server archives

In the late 90’s I started (a good decade for starts) a daily quotation server project at www.quoteserver.ca. The domain is now defunct—some pages are partially viewable at the Way Back Machine.

Below is the list of quotes I had collected by the end of the life of the project. Most are about love—duh—and a few are jolly jests from funny trenches. You know, that place where mustard gas makes your eyes water.

The quotes weren’t scraped from quote archives—each is meaningful and hand-picked.

## the quote archive

And now for full list of 1,600 other things worth reading. Such as everything Dorothy Parker has written and ... yes, even the Pinky and Brain quotes, which are a special kind of special.

Quote collections about love, heart, desire, life, death, god, mind, science.

Feeling lucky? Read 10 random quotes. Well, will you, punk?

13
Take not God’s name in vain: select a time when it will have effect.
48
I love thee with the love I seemed to lose
With my lost saints,—I love thee with the breath
Smiles, tears, of all my life!—and, if God choose,
I shall but love thee better after death.
Sonnets from the Portuguese
104
Poems are made by fools like me,
But only God can make a tree.
Trees
117
All are but parts of one stupendous whole,
Whose body Nature is, and God the soul.
Essay on Man, Epistle i. 267.
243
Conceit is God’s gift to little men.
298
Poems are made by fools like me,
But only God can make a tree.
Trees
370
Men rarely (if ever) manage to dream up a god superior to themselves. Most gods have the manners and morals of a spoiled child.
Time Enough for Love
414
And, while with silent, lifting mind I’ve trod
The high untrespassed sanctity of space,
Put out my hand, and touched the face of God.
452
God must love stupid people, He made so many of them.
457
If it turns out that there is a God, I don’t think that he’s evil. But the worst that you can say about him is that basically he’s an underachiever.
556
% ar m God
ar: God does not exist
561
God made the integers; all else is the work of Man.
591
If you believe in the light, it’s because of obscurity.
If you believe in happiness it’s because of unhappiness.
If you believe in God, then you have to believe in the Devil.
Church of Notre Dame, Paris
609
O God, if there be a God, save my soul, if I have a soul!
611
See the happy moron,
He doesn’t give a damn.
I wish I were a moron.
My God! Perhaps I am!
706
An Apology for the Devil: It must be remembered that
we have only heard one side of the case. God has written
all the books.
Note Books
741
And almost every one when age,
Disease, or sorrows strike him,
Inclines to think there is a God,
Or something very like Him.
Dipsychus, sc. vi
783
So nigh is grandeur to our dust,
So near is God to man,
When Duty whispers low, Thou must,
The youth replies, I can.
Voluntaries, iii
957
Gott ist tot: aber so wie die Art der Menschen ist,
wird es vielleicht noch jahrtausendlang Hohlen geben,
in denen man seinen Schatten zeigt.
[God is dead: but considering the state the species Man
is in, there will perhaps be caves, for ages yet, in which
Ecce Homo
1082
O God! methinks it were a happy life,
To be no better than a homely swain;
To sit upon a hill, as I do now,
To carve out dials, quaintly, point by point,
Thereby to see the minutes how they run,
How many make the hour full complete;
How many hours bring about the day;
How many days will finish up the year;
How many years a mortal man may live.
Henry VII, Part III, II.v.21
1213
I loved you without hope, a mute offender;
What jealous pangs, what shy despairs I knew!
A love as deep as this, as true, as tender,
God grant another may yet offer you.
I Loved You Once
1218
I have heard the song of the blossoms and the old chant of the sea,
And seen strange lands from under the arched white sails of ships;
But the loveliest things of beauty God ever has showed to me,
Are her voice, and her hair, and eyes, and the dear red curve of her lips.
Beauty
1284
And this, O love, my pitiable plight
Whenever from my circling arms you stray;
This little world of mine has lost its light....
I hope to God, my dear, that you can say
The same to me.
Rondeau Redouble
1333
How can I believe in God when just last week I got
my tongue caught in the roller of an electric typewriter?
1334
Man is a god in ruins.
1386
God made everything out of nothing, but the nothingness
shows through.
1402
Which is it, is man one of God’s blunders or is
God one of man’s?
1416
Thank God men cannot as yet fly and lay waste the
sky as well as the earth!
1477
Nothing but blackness above
And nothing that moves but the cars...
God, if you wish for our love,
Fling us a handful of stars!
Caliban in the Coal Mines
1526
God is the immemorial refuge of the incompetent, the helpless, the miserable. They find not only sanctuary in His arms, but also a kind of superiority, soothing to their macerated egos; He will set them above their betters.
1534
If the average man is made in God’s image, then a man such as Beethoven or Aristotle is plainly superior to God, and so God may be jealous of him, and eager to see his superiority perish with his bodily frame. All animal breeders know how difficult it is to maintain a fine strain. The universe seems to be in a conspiracy to encourage the endless reproduction of peasants and Socialists, but a subtle and mysterious opposition stands eternally against the reproduction of philosophers.
In Defense of Women
1602
We turn toward God only to obtain the impossible.
VIEW ALL

# Statistics vs Machine Learning

Tue 03-04-2018
We conclude our series on Machine Learning with a comparison of two approaches: classical statistical inference and machine learning. The boundary between them is subject to debate, but important generalizations can be made.

Inference creates a mathematical model of the datageneration process to formalize understanding or test a hypothesis about how the system behaves. Prediction aims at forecasting unobserved outcomes or future behavior. Typically we want to do both and know how biological processes work and what will happen next. Inference and ML are complementary in pointing us to biologically meaningful conclusions.

Nature Methods Points of Significance column: Statistics vs machine learning. (read)

Statistics asks us to choose a model that incorporates our knowledge of the system, and ML requires us to choose a predictive algorithm by relying on its empirical capabilities. Justification for an inference model typically rests on whether we feel it adequately captures the essence of the system. The choice of pattern-learning algorithms often depends on measures of past performance in similar scenarios.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Statistics vs machine learning. Nature Methods 15:233–234.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: supervised methods. Nature Methods 15:5–6.

# Happy 2018 $\pi$ Day—Boonies, burbs and boutiques of $\pi$

Wed 14-03-2018

Celebrate $\pi$ Day (March 14th) and go to brand new places. Together with Jake Lever, this year we shrink the world and play with road maps.

Streets are seamlessly streets from across the world. Finally, a halva shop on the same block!

A great 10 km run loop between Istanbul, Copenhagen, San Francisco and Dublin. Stop off for halva, smÃ¸rrebrÃ¸d, espresso and a Guinness on the way. (details)

Intriguing and personal patterns of urban development for each city appear in the Boonies, Burbs and Boutiques series.

In the Boonies, Burbs and Boutiques of $\pi$ we draw progressively denser patches using the digit sequence 159 to inform density. (details)

No color—just lines. Lines from Marrakesh, Prague, Istanbul, Nice and other destinations for the mind and the heart.

Roads from cities rearranged according to the digits of $\pi$. (details)

The art is featured in the Pi City on the Scientific American SA Visual blog.

Check out art from previous years: 2013 $\pi$ Day and 2014 $\pi$ Day, 2015 $\pi$ Day, 2016 $\pi$ Day and 2017 $\pi$ Day.

# Machine learning: supervised methods (SVM & kNN)

Thu 18-01-2018
Supervised learning algorithms extract general principles from observed examples guided by a specific prediction objective.

We examine two very common supervised machine learning methods: linear support vector machines (SVM) and k-nearest neighbors (kNN).

SVM is often less computationally demanding than kNN and is easier to interpret, but it can identify only a limited set of patterns. On the other hand, kNN can find very complex patterns, but its output is more challenging to interpret.

Nature Methods Points of Significance column: Machine learning: supervised methods (SVM & kNN). (read)

We illustrate SVM using a data set in which points fall into two categories, which are separated in SVM by a straight line "margin". SVM can be tuned using a parameter that influences the width and location of the margin, permitting points to fall within the margin or on the wrong side of the margin. We then show how kNN relaxes explicit boundary definitions, such as the straight line in SVM, and how kNN too can be tuned to create more robust classification.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Machine learning: a primer. Nature Methods 15:5–6.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

# Human Versus Machine

Tue 16-01-2018
Balancing subjective design with objective optimization.

In a Nature graphics blog article, I present my process behind designing the stark black-and-white Nature 10 cover.

Nature 10, 18 December 2017

# Machine learning: a primer

Thu 18-01-2018
Machine learning extracts patterns from data without explicit instructions.

In this primer, we focus on essential ML principles— a modeling strategy to let the data speak for themselves, to the extent possible.

The benefits of ML arise from its use of a large number of tuning parameters or weights, which control the algorithmâs complexity and are estimated from the data using numerical optimization. Often ML algorithms are motivated by heuristics such as models of interacting neurons or natural evolutionâeven if the underlying mechanism of the biological system being studied is substantially different. The utility of ML algorithms is typically assessed empirically by how well extracted patterns generalize to new observations.

Nature Methods Points of Significance column: Machine learning: a primer. (read)

We present a data scenario in which we fit to a model with 5 predictors using polynomials and show what to expect from ML when noise and sample size vary. We also demonstrate the consequences of excluding an important predictor or including a spurious one.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.