Journal | September 2018

It was a busy month of extra-curriculars, making the most of our last weeks in Canada before returning to greet the Aussie summer.

Eureka!

The big aha! moment was finally understanding the pleas to remove “for babies” from the titles of the Baby University books. On 17 September I visited two elementary schools in the suburbs of Chicago: Rollins Elementary School and May Watts Elementary.

I had a great time at both, but I knew I had to carefully navigate “for babies”. So, I did read the title and immediately asked, “are there any babies here?” “No!” was the expected and resounding answer. I think I won them over with that. But when the cover image popped up on screen I still heard a few “hey! It says for babies” from the audience. The school avoided the “for babies” problem by selling my two picture books, Goodnight Lab and Scientist, Scientist, Who do you See?.

I can sympathize with teachers and librarians when they tell me about the difficulty in reading the “for babies” books. I am also honored that my baby books want a wider audience! In the meantime, while we figure out a solution to the “for babies” problem in the classroom, I think I’ll stick to reading the picture books at schools.

Reading!

Children’s Literature Recommendations

Twinkle Twinkle Little Star, I Know Exactly What You Are by Julia Kregenow and Carmen Saldaña

Filled with rhyming facts about stars that can be sung to the cadence of the classic nursery rhyme. Easy to read and look at for all ages.

How Did I Get Here? by Philip Bunting

Adorable illustrations accompany the history of the universe from the Big Bang, through conception (yep), until now. Easy for the the kids to listen to and point at.

Adult Literature September Reads

Humility Is the New Smart by Edward D. Hess and Katherine Ludwig

I found this difficult to read because it is heavy on repeating buzzwords and technobabble. There are some great nuggets of wisdom in here which are drawn from well-laid-out examples of people and companies that have put humility ahead of arbitrary measures of merit.

How not to be Wrong by Jordan Ellenberg

This book is about math applied to real life. Some of the explanations are abstract and others follow closely with recent, and mostly quirky, stories. I thoroughly enjoyed it. However, I suspect that the author demands a little too much from the casual reader.

Currently reading: Scale by Geoffrey West

Writing!

We are in the final editorial stages of ABC’s of Engineering, Robotics for Babies, and Neural Networks for Babies, all co-authored by my friend Sarah Kaiser. Look for these in January of 2019. They are going to be awesome. Conversations about them included the sentence, “I hate to have to tell you this… but we can’t rickroll babies.”

5b2c752fNae7923f1

One of the questions I get most is are you working on any books for older kids? Yes, yes I am. But at the moment it is too early to give anything away. Stay tuned!

I completed a few more early manuscripts in the Red Kangaroo Physics series. Next year, they will begin to be translated (or untranslated 😄) and available in English. If this is news to you, this is a series of picture books each of which discusses a topic in physics. The story follows a dialogue between me and a curious Red Kangaroo. The first 15 are available now in Chinese.

Arithmetic! (academic news)

Both my students recently submitted their first papers and presented them at an international conference this month. Congrats to Maria and Akram!

I finally got the advertisements up for two postdoctoral positions which are funded by a $3 million grant from the Australian government. This is a collaboration with Gerardo Paz Silva, Howard Wiseman, and Andrea Morello that I am keen to get going.

Mostly an exercise in catharsis, I am reminding myself to say no to every invitation to chair, organise, or join a committee. My future self won’t heed this warning—so here’s hoping it is another thing that gets easier and less time consuming with practice.

Events!

Lots of great opportunities this past month. I met many great people and learned a lot!

  • I gave a public lecture at the Institute for Quantum Computing on 13 September call Big Ideas for Little Minds. I won’t say too much about it as it will be posted online soon. I also gave the same talk at Google on 19 September, which will also appear on Talks at Google.

  • I finally met (in person) Cara Florance my co-author of ABC’s of Biology, Organic Chemistry for Babies, and Evolution for Babies! We did a joint event at the MIT Coop bookstore. How did it go? Well, Cara built a DIY cloud chamber and had a Geiger counter—’nuff said. I also met an MIT professor that bought a copy of Statistical Physics for Babies for every student in his class 😳!
  • I joined Nikola Tesla for some reading, banter, and science demonstrations at the Rochester Museum and Science Center. This was the first time I read 8 Little Planets and was really pleased with the response from both the children and parents! The science centre itself was awesome and I even got a private showing of musical Tesla coils!

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Just hanging out in a Faraday cage ⚡⚡⚡

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  • I got to visit Sourcebooks headquarters in Chicago. It was great to meet all the people behind the scenes that make the children’s books possible. Everyone I met was so passionate about making books, especially the amazing Dominique Raccah!
  • Check out a quick discussion about Baby University on Global TV’s The Morning Show. It’s a great opportunity to reach a large television audience. Too bad the time is so short and the questions so quick!
  • I did some reading and activities at the Oxford County Library in Ingersoll on 6 September. It was amazing to see how close members of a small community are with their library. The librarians even knew the interests of the children! Very eye opening as this has not been my experience in Sydney.

Up next!

October is going to be another busy month. We need to get settled back into Sydney and I don’t even want to think about the backlog of administration I have been ignoring at the uni. But I am also really excited for Quantum Gates, Jumps, and Machines and of course the release of 8 Little Planets!

The power of simulation: birthday paradox

The birthday paradox goes… in a room of 23 people there is a 50-50 chance that two of them share a birthday.

OK, so the first step in introducing a paradox is to explain why it is a paradox in the first place. One might think that for each person, there is 1/365 chance of another person having the same birthday as them. Indeed, I can think of only one other person I’ve met that has the same birthday as meand he is my twin brother! Since I’ve met far more than 23 people, how can this be true?

This reasoning is flawed for several reasons, the first of which is that the question wasn’t asking about if there was another person in the room with a specific birthdayany pair of people (or more!) can share a birthday to increase the chances of the statement being true.

The complete answer gets heavy into the math, but I want to show you how to convince yourself it is true by simulating the experiment. Simulation is programming a computer or model to act as if the real thing was happening. Usually, you set this up so that the cost of simulation is much less than doing the actual thing. For example, putting a model airplane wing in a wind tunnel is a simulation. I’ve simulate the birthday paradox in a computer programming language called Python and this post is available in notebook-style here. Indeed, this is much easier than being in a room with 23 people.

Below I will not present the code (again, that’s over here), but I  will describe how the simulation works and present the results.

The simulation

Call the number of people we need to ask before we get a repeated birthday n. This is what is called a random variable because its value is not known and may change due to conditions we have no control over (like who happens to be in the room).

Now we simulate an experiment realising a value for n as follows.

  1. Pick a random person and ask their birthday.
  2. Check to see if someone else has given you that answer.
  3. Repeat step 1 and 2 until a birthday is said twice.
  4. Count the number of people that were asked and call that n.

Getting to step 4 constitutes a single experiment. The number that comes out may be n = 2 or n = 100. It all depends on who is in the room. So we repeat all the steps many many times and look at how the numbers fall. The more times we repeat, the more data we obtain and the better our understanding of what’s happening.

Here is what it looks like when we run the experiment one million times.

exponemil
Simulating the birthday paradox. On the horizontal axis is n, the number of people we needed to ask before a repeated birthday was found. We did the experiment one million times and tallied the results.

So what do all those numbers mean? Well, let’s look at how many times n = 2 occurred, for example. In these one million trials, the result 2 occurred 2679 times, which is relatively 0.2679%. Note that this is close to 1/365 ≈ 0.274%, which is expected since the probability that the second person has the same as the first is exactly 1/365. So each number of occurrences divided by one million is approximately the probability that we would see that number in a single experiment.

We can then plot the same data considering the vertical axis the probability of needing to see n people before a repeated birthday.

prob
Same as the previous plot but now each bar is interpreted as a probability.

Adding up the value of each of these bars sums to 100%. This is because one of the values must occur when we do an experiment. OK, so now we can just add up these probabilities starting at n = 2 and increasing until we get to 50%. Visually, it is the number which splits the coloured area above into two equal parts. That number will be the number of people we need to meet to have a 50-50 shot at getting a repeated birthday. Can you guess what it will be?

Drum roll… 23! Tada! The birthday paradox simulated and solved by simulation!

But, wait! There’s more.

What about those leap year babies? In fact, isn’t the assumption that birthdays are equally distributed wrong? If we actually tried this experiment out in real life, would we get 23 or some other number?

Happily, we can test this hypothesis with real data! At least for US births, you can find the data over at fivethirteight’s github page. Here is what the actual distribution looks like.

dist_birth.png
Distribution of births in US from 1994-2014, by day of year.

Perhaps by eye it doesn’t look too uniform. You can clearly see 25 Dec and 31 Dec have massive dips. Much has been written about this and many beautiful visualizations are out there. But, our question is whether this has an effect on the birthday paradox. Perhaps the fact that not many people are born on 25 Dec means it is easy to find a shared birthday on the remaining days, for example. Let’s test this hypothesis by simulating the experiment with the real distribution of birthdays.

To do this, we perform the same 4 steps as above, but randomly sampling answers from the actual distribution of birthdays. The result of another one million experiments is plotted below.

truehist.png
Simulating the birthday paradox on the true distribution of births. On the horizontal axis is n, the number of people we needed to ask before a repeated birthday was found. We perform the experiment one million times and tallied the results.

And the answer is the same! The birthday paradox persists with the actual distribution of birthdays.

Nerd sniping

The above discussion is very good evidence that the birthday paradox is robust to the actual distribution of births. However, it does not constitute a mathematical proof. An experiment can only provide evidence. So I will end this with a technical question for those mathematical curiosos out there. (What I am about to do is also called Nerd Sniping.)

Here is the broad problem: quantify the above observation. I think there is more than one question here. For example, it should be possible to bound the 50-50 threshold as a function of the deviation from a uniform distribution.

(Cover image credit: Ed g2s, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=303792)

My visit to a Chinese kindergarten class

I think this means it went well.

Recently, I had a video chat with the kindergarten class of Dragon Bay Kindergarten in Beijing. It was a lot of fun to see how excited the children and teachers were to read my books. They even had an entire Science Fair based around my Physics for Babies books!

Beijing_babyphys.png

During our video call, the students and teachers asked many questions. I’ve transcribed them here.

Student questions

How can I see atoms in the real world?

We can not see atoms with our eyes. They are too small. We can use other ways to take pictures of atoms. In the labs where I work, physicists shine laser light on the atoms. The electrons take the energy to move up in their energy levels. When they fall back down, they release light that we can see with a camera.

Can you introduce particles and entanglement to us?

Atoms themselves are made of even smaller things called particles. Electrons are one kind of particle. Not all particles make atoms though. Photons, what light is made of, are another kind of particle which is not part of an atom.
Entanglement is tricky to explain in everyday language. It is something we see in the math of quantum physics. Even scientists today argue about how to understand it. But, we can use the math to show us how to build quantum technology where entanglement is used.

What does an atom look like? How are they different?

Electron microscopes take pictures of atoms which look like blurry little balls. Most atoms look the same but some are bigger than others. When electrons move between energy levels, they send out light at very specific colors. Each atom makes a different color, which is how we can tell them apart.

How do you know everything was made by atoms?

We can see them with today’s technology!

How can I touch the atom?

Since everything is made of atoms, you are touching them right now!

Why don’t you wear the clothes of a physicist?

In pictures of scientists, they are often wearing lab coats. In real life, physicists do not wear lab coats. Some work in a lab and others, like me, work in an office with computers and whiteboards.

Teacher questions

What made you think that babies need to learn about quantum entanglement?

A lot of science is a language which we learn by listening and talking to other scientists, just like learning your first language. So, the sooner you start to hear the language, the sooner you will speak it.

Will entangled particles always be measured the same or can they just be influenced?

Entanglement has a quality to it which might not make it perfect. Experimental technology is always a bit unreliable. But perfect entanglement, like that described in the book, means that particles will be measured the same every time.

Do your children like and understand your books?

My children like the books and can often repeat some of the sentences. I talk with them about it, but they will not be doing any quantum physics research yet.

How does your work place look like?

There are labs. Some use lasers which means they must be dark. Some have big refrigerators which keep things really really cold. Above the labs is office space. Here it looks like a regular office, but with whiteboards that have lots of math on them.