And whatever I do will become forever what I've done.don't rehearsemore quotes

# feynman point: beautiful

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

# visualization + design

The 2018 Pi Day art celebrates the 30th anniversary of $\pi$ day and connects friends stitching road maps from around the world. Pack a sandwich and let's go!

# $\pi$ Day 2014 Art Posters

2018 $\pi$ day shrinks the world and celebrates road trips by stitching streets from around the world together. In this version, we look at the boonies, burbs and boutique of $\pi$ by drawing progressively denser patches of streets. Let's go places.
2017 $\pi$ day
2016 $\pi$ approximation day
2016 $\pi$ day
2015 $\pi$ day
2014 $\pi$ approx day
2014 $\pi$ day
2013 $\pi$ day
Circular $\pi$ art

On March 14th celebrate $\pi$ Day. Hug $\pi$—find a way to do it.

For those who favour $\tau=2\pi$ will have to postpone celebrations until July 26th. That's what you get for thinking that $\pi$ is wrong.

If you're not into details, you may opt to party on July 22nd, which is $\pi$ approximation day ($\pi$ ≈ 22/7). It's 20% more accurate that the official $\pi$ day!

Finally, if you believe that $\pi = 3$, you should read why $\pi$ is not equal to 3.

All art posters are available for purchase.
I take custom requests.

For the 2014 $\pi$ day, two styles of posters are available: folded paths and frequency circles.

The folded paths show $\pi$ on a path that maximizes adjacent prime digits and were created using a protein-folding algorithm.

The frequency circles colourfully depict the ratio of digits in groupings of 3 or 6. Oh, look, there's the Feynman Point!

Explore the Feynman Point and other such artefacts in $\pi$ using René Hansen's interactive version of this year's posters.

## The Feynman Point

The Feynman Point is the first place where 6 9s occur in π . This happens at digit 762, which is much sooner than expected. Here is π to 1,000 decimal places showing the location of the Feynman point.

$3.1415926535 8979323846 2643383279 5028841971 6939937510 : 50 5820974944 5923078164 0628620899 8628034825 3421170679 : 100 8214808651 3282306647 0938446095 5058223172 5359408128 : 150 4811174502 8410270193 8521105559 6446229489 5493038196 : 200 4428810975 6659334461 2847564823 3786783165 2712019091 : 250 4564856692 3460348610 4543266482 1339360726 0249141273 : 300 7245870066 0631558817 4881520920 9628292540 9171536436 : 350 7892590360 0113305305 4882046652 1384146951 9415116094 : 400 3305727036 5759591953 0921861173 8193261179 3105118548 : 450 0744623799 6274956735 1885752724 8912279381 8301194912 : 500 9833673362 4406566430 8602139494 6395224737 1907021798 : 550 6094370277 0539217176 2931767523 8467481846 7669405132 : 600 0005681271 4526356082 7785771342 7577896091 7363717872 : 650 1468440901 2249534301 4654958537 1050792279 6892589235 : 700 4201995611 2129021960 8640344181 5981362977 4771309960 : 750 5187072113 4999999837 2978049951 0597317328 1609631859 : 800 5024459455 3469083026 4252230825 3344685035 2619311881 : 850 7101000313 7838752886 5875332083 8142061717 7669147303 : 900 5982534904 2875546873 1159562863 8823537875 9375195778 : 950 1857780532 1712268066 1300192787 6611195909 2164201989 : 1000$

As the story goes, Feynman said that he'd like to memorize π up to the first 6 9s so that he could finish the recitation with "nine nine nine nine nine nine and so on". To find a scheme that suggests that π is rational is mathematical humor of the highest order.

We can ask a general question — where is the first place where digit d occurs n times? For example, where do we see the first 5555 (d=5,n=4) or 777777 (d=7,n=6) .

I look into these kinds of locations in π below. If you are interested in integer sequences in general, see the on-line encyclopedia of integer sequences (OEIS)

## (d,n) points in π — sequences of repeating digits

I call a location at which digit d is seen n times in a row the (d,n) point. The Feynman point is a specific case — it is a (d=9,n=6) point.

One can talk about all the (d,n) points (i.e. (d,n,0) , (d,n,1) , (d,n,2) ...), which is the set of all locations at which d appears n times. It should be clear from the context whether one point is being referenced, or a set of points.

We can add a location index i to refer to a specific instance of the sequence. The ith appearance of the digit sequence is (d,n,i) . If we're talking about a single point and i is not specified then i=0 is assumed, which refers to the first appearance of the sequence for a given d and n. That is, (d,n) = (d,n,i=0) .

The point (d,1) trivially corresponds to the first occurence of digit d.

Below is a list of all (d,n=6,i=0) points.

$d n i sequence position .....(n x d) ..... 1 6 0 111111 255945 62417(111111)75895 2 6 0 222222 963024 87437(222222)85444 4 6 0 444444 828499 02846(444444)66922 5 6 0 555555 244453 33233(555555)69581 6 6 0 666666 252499 58934(666666)88391 7 6 0 777777 399579 18074(777777)98344 8 6 0 888888 222299 10985(888888)35254 9 6 0 999999 762 21134(999999)83729 # Feynman Point$

## examples of (d,n) points in first 1,000,000 digits of π

There are 49 (d,n,i=0) points in the first 1,000,000 digits of π for n>1. For example, the first (d=0,n=4) , which is the first 0000, occurs at digit 13,390 (...93095000090715). The longest sequence of d=0 happens for n=7(d=0,n=7,i=0) is at position 710,100 (...73537333333386381).

$d n i n x d position ...(n x d)... 0 2 0 00 307 24587(00)66063 1 2 0 11 94 25342(11)70679 2 2 0 22 135 55058(22)31725 3 2 0 33 24 46264(33)83279 4 2 0 44 59 09749(44)59230 5 2 0 55 130 44609(55)05822 6 2 0 66 117 28230(66)47093 7 2 0 77 559 43702(77)05392 8 2 0 88 34 79502(88)41971 9 2 0 99 44 71693(99)37510 0 3 0 000 601 05132(000)56812 1 3 0 111 153 12848(111)74502 2 3 0 222 1735 99219(222)18427 3 3 0 333 1698 39414(333)45477 4 3 0 444 2707 42858(444)79526 5 3 0 555 177 52110(555)96446 6 3 0 666 2440 49954(666)72782 7 3 0 777 4575 94764(777)26224 8 3 0 888 4985 08099(888)68741 9 3 0 999 2949 19217(999)83910 0 4 0 0000 13390 93095(0000)90715 1 4 0 1111 12700 42144(1111)26358 2 4 0 2222 4902 35136(2222)47715 3 4 0 3333 66846 31487(3333)67147 4 4 0 4444 54525 11793(4444)82014 5 4 0 5555 33172 96839(5555)68686 6 4 0 6666 21880 23047(6666)72174 7 4 0 7777 1589 28909(7777)27938 8 4 0 8888 4751 16274(8888)00786 9 4 0 9999 17988 98955(9999)11209 0 5 0 00000 17534 66768(00000)10652 1 5 0 11111 32788 46584(11111)57758 2 5 0 22222 65260 99725(22222)80801 3 5 0 33333 28467 52392(33333)64743 4 5 0 44444 808650 64849(44444)73161 5 5 0 55555 24466 89742(55555)16076 6 5 0 66666 48439 71972(66666)42267 7 5 0 77777 162248 47779(77777)18415 8 5 0 88888 213245 09581(88888)03131 9 5 0 99999 19446 21285(99999)39961 1 6 0 111111 255945 62417(111111)75895 2 6 0 222222 963024 87437(222222)85444 4 6 0 444444 828499 02846(444444)66922 5 6 0 555555 244453 33233(555555)69581 6 6 0 666666 252499 58934(666666)88391 7 6 0 777777 399579 18074(777777)98344 8 6 0 888888 222299 10985(888888)35254 9 6 0 999999 762 21134(999999)83729 3 7 0 3333333 710100 73537(3333333)86381$

When we sort this list by position, we can see just how unusual (i.e. early) the Feynman Point is. We also see (d,n=2) , (d,n=3) , (d,n=4) and (d,n=5) points for all digits d) in the first 1,000,000 digits.

$3 2 0 33 24 46264(33)83279 # first n=2 8 2 0 88 34 79502(88)41971 9 2 0 99 44 71693(99)37510 4 2 0 44 59 09749(44)59230 1 2 0 11 94 25342(11)70679 6 2 0 66 117 28230(66)47093 5 2 0 55 130 44609(55)05822 2 2 0 22 135 55058(22)31725 1 3 0 111 153 12848(111)74502 # first n=3 5 3 0 555 177 52110(555)96446 0 2 0 00 307 24587(00)66063 7 2 0 77 559 43702(77)05392 # last n=2 0 3 0 000 601 05132(000)56812 9 6 0 999999 762 21134(999999)83729 # first n=6 (Feynman Point) 7 4 0 7777 1589 28909(7777)27938 # first n=4 3 3 0 333 1698 39414(333)45477 2 3 0 222 1735 99219(222)18427 6 3 0 666 2440 49954(666)72782 4 3 0 444 2707 42858(444)79526 9 3 0 999 2949 19217(999)83910 7 3 0 777 4575 94764(777)26224 8 4 0 8888 4751 16274(8888)00786 2 4 0 2222 4902 35136(2222)47715 8 3 0 888 4985 08099(888)68741 # last n=3 1 4 0 1111 12700 42144(1111)26358 0 4 0 0000 13390 93095(0000)90715 0 5 0 00000 17534 66768(00000)10652 # first n=5 9 4 0 9999 17988 98955(9999)11209 9 5 0 99999 19446 21285(99999)39961 6 4 0 6666 21880 23047(6666)72174 5 5 0 55555 24466 89742(55555)16076 3 5 0 33333 28467 52392(33333)64743 1 5 0 11111 32788 46584(11111)57758 5 4 0 5555 33172 96839(5555)68686 6 5 0 66666 48439 71972(66666)42267 4 4 0 4444 54525 11793(4444)82014 2 5 0 22222 65260 99725(22222)80801 3 4 0 3333 66846 31487(3333)67147 # last n=4 7 5 0 77777 162248 47779(77777)18415 8 5 0 88888 213245 09581(88888)03131 8 6 0 888888 222299 10985(888888)35254 5 6 0 555555 244453 33233(555555)69581 6 6 0 666666 252499 58934(666666)88391 1 6 0 111111 255945 62417(111111)75895 7 6 0 777777 399579 18074(777777)98344 3 7 0 3333333 710100 73537(3333333)86381 # first n=7 4 5 0 44444 808650 64849(44444)73161 # last n=5 4 6 0 444444 828499 02846(444444)66922 2 6 0 222222 963024 87437(222222)85444$

## when do we expect (d,n) points?

Any given position in π , 10n different combinations of n digits can appear. In only one of them are all the digits the same and equal to d. Thus, the chance of seeing (d,n) at any position for any given d is p=10n.

The probability of not seeing a (d,n) point at a location for a given d is 1-p.

Thus the probability of not seeing a point at k locations and then seeing one at k+1 location is (1-p)kp. This is the definition of the geometric distribution.

For example, the question "how many times must we throw a die until a 1 appears?" can be addressed by this distribution. Here p=1/6, assuming the die is fair. The probability of seeing a 1 for the first time on the k toss is

$k P(X=k) 1 16.7% = (1-1/6)0 * 1/6 2 13.8% = (1-1/6)1 * 1/6 3 11.6% = (1-1/6)2 * 1/6 4 9.6% = (1-1/6)3 * 1/6 5 6.7% = (1-1/6)4 * 1/6 6 5.6% = (1-1/6)5 * 1/6 ...$

The cumulative probability P(Xk) gives us the chance of seeing a 1 for the first time on the 1, 2, 3 ... or k toss). 1-P(Xk) is the chance of not seeing a 1 in the first 1, 2, 3 ... k tosses.

$k P(X≤k) 1-P(X≤k) 1 16.7% 83.3% 2 30.6% 69.4% 3 42.1% 57.9% 4 51.8% 48.2% 5 59.8% 40.2% 6 66.5% 33.5% ...$

We can apply this calculation to the probability of observing a (d,n) point for the first time. Let's pick (d=9,n=6) . Using p=10–6, let's look at 1-P(Xk). Values in the table below for P are shown to one significant digit, except for the Feynman Point position k=962.

$k P(X≤k) 1-P(X≤k) 1 0.000001 0.999999 10 0.00001 0.99999 100 0.0001 0.9999 962 0.000962 0.999037 1000 0.001 0.999 10000 0.01 0.99 100000 0.1 0.9 693147 0.5 0.5$

Now we know just how unlikely the Feynman Point is. The chance of observing 6 9s in the first 962 digits is about 0.000962.

These probabilities are approximate because probability that a sequence of n digits appears at any given position is correlated across a window of n-1 digits. For example if we see (d=9,6) at position j then the probability of seeing (d=9,6) at position j+1 is 1/10, not 10–6, because we are guaranteed the first 5 9s.

## all (d,n) points in 268 million digits of π

If you're interested in more (d,n) points, I calculated all for lengths n≥3 for the first ~268 million digits of π — you can download the complete list of the 2.42 million (d,n) points.

Most of these points are (d,n=3) (2.17 million). The count as function of n is

$n number of (d,n) points 3 2174839 4 217431 5 21749 6 2153 7 204 8 19 9 3$

The Feynman Point is (d=9,n=6,i=0) . With 268 million digits, we can find up to (d=9,n=8,i=0) — the first places in which 8 9's appear in a row. All the (d=9,n,i=9) points in the first 268 million digits of π are

$9 3 0 999 2949 19217(999)83910 9 4 0 9999 17988 98955(9999)11209 9 5 0 99999 19446 21285(99999)39961 9 6 0 999999 762 21134(999999)83729 9 7 0 9999999 1722776 09713(9999999)31766 9 8 0 99999999 36356642 15746(99999999)54228$

The longest sequences are for n=8 and 9, of which there are 50. We see a (d,n=8,0) point for all d.

$0 8 0 00000000 172330850 65581(00000000)12202 0 8 1 00000000 184688988 50614(00000000)27944 1 8 0 11111111 159090113 46225(11111111)09751 1 8 1 11111111 174624972 36529(11111111)76023 1 8 2 11111111 199394968 58734(11111111)25842 2 8 0 22222222 175820910 95134(22222222)07695 3 8 0 33333333 36488176 81791(33333333)25108 3 8 1 33333333 248922246 51334(33333333)47253 4 8 0 44444444 22931745 74369(44444444)36403 4 8 1 44444444 65122865 35213(44444444)82112 4 8 2 44444444 221749424 95631(44444444)79916 5 8 0 55555555 168743355 75041(55555555)01882 6 8 0 66666666 55616210 64263(66666666)81935 6 8 1 66666666 129423072 80429(66666666)81580 6 8 2 66666666 160301327 23994(66666666)89064 7 8 0 77777777 82144203 84454(77777777)83966 8 8 0 88888888 239798471 04446(88888888)16169 9 8 0 99999999 36356642 15746(99999999)54228 9 8 1 99999999 66780105 26137(99999999)31798 6 9 0 666666666 45681781 79094(666666666)71734 7 9 0 777777777 24658601 85304(777777777)24846 8 9 0 888888888 46663520 87842(888888888)07509$

## subsequences of π in 268 million digits of π

Finally, it's interesting to see where subsequences of π occurs in π . Because π is random and non-terminating, it has infinitely many subsequences of itself in it — this is hard to think about.

In the first 268 million digits of π you see subsequences up to 8 digits, not counting the trivial one that starts at zero.

$3 1 0 9 59265(3)58979 31 2 0 137 05822(31)72535 314 3 0 2120 96514(314)29809 3141 4 0 3496 67110(3141)26711 31415 5 0 88008 96265(31415)14138 314159 6 0 176451 24573(314159)78761 3141592 7 0 25198140 60173(3141592)14513 31415926 8 0 50366472 17005(31415926)09521$

We can also find e (2.718281828) up to 9 digits in the first 268 digits of π .

$2 1 0 6 14159(2)65358 27 2 0 28 43383(27)95028 271 3 0 241 83165(271)20190 2718 4 0 11706 24322(2718)85159 27182 5 0 28024 10077(27182)71874 271828 6 0 33789 50445(271828)92749 2718281 7 0 1526800 07421(2718281)53375 27182818 8 0 73154827 92454(27182818)56113 271828182 9 0 246890641 99305(271828182)91617$

as well as φ (1.618033989) up to 8 digits,

$1 1 0 1 3(1)41592 16 2 0 40 84197(16)93993 161 3 0 1610 70600(161)45249 1618 4 0 6004 60290(1618)76679 16180 5 0 105857 23121(16180)99462 161803 6 0 144979 54397(161803)03956 1618033 7 0 1205122 51502(1618033)21817 16180339 8 0 19445230 33490(16180339)99933$

as well as 1/ φ = 1– φ up to 6 digits.

$0 1 0 32 32795(0)28841 06 2 0 71 78164(06)28620 061 3 0 885 38142(061)71776 0618 4 0 14423 26086(0618)72455 06180 5 0 84671 08181(06180)21002 061803 6 0 933127 07779(061803)00371 0618033 7 0 12074179 53993(0618033)75138 06180339 8 0 200380161 62492(06180339)55938$
VIEW ALL

# Curse(s) of dimensionality

Tue 05-06-2018
There is such a thing as too much of a good thing.

We discuss the many ways in which analysis can be confounded when data has a large number of dimensions (variables). Collectively, these are called the "curses of dimensionality".

Nature Methods Points of Significance column: Curse(s) of dimensionality. (read)

Some of these are unintuitive, such as the fact that the volume of the hypersphere increases and then shrinks beyond about 7 dimensions, while the volume of the hypercube always increases. This means that high-dimensional space is "mostly corners" and the distance between points increases greatly with dimension. This has consequences on correlation and classification.

Altman, N. & Krzywinski, M. (2018) Points of significance: Curse(s) of dimensionality Nature Methods 15:399–400.

# 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, smørrebrød, 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 algorithm’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 evolution—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.