Hilarious, inspirational and loaded with cosmic coincidences, this was one of the best evenings ever! Many of our currently favourite themes were mentioned in the show (such as the controversy of Francis Galton, the BED/ Banana Equivalent Dose, sound wave visualizations, laser, drawing and playing with ellipses, Euler’s formula). Plus Simon got to meet his teachers from several favourite educational YouTube channels, Numberphile, StandUpMaths and Steve Mould.

# Simon introducing himself for the World Science Scholars program

This is Simon’s introductory video for the World Science Scholars program (initiative of The World Science Festival). In May this year, Simon has been chosen as one of the 30 young students worldwide, joining the 2019 cohort for exceptional talents in mathematics. Most of the other students are 14 to 17 years old, age was not a factor in the selection process. To help the students and their future mentors to get to know one another, every World Science Scholar was asked to record an introductory video, no longer than 3 minutes, answering a few questions such as what is the biggest misconception about math, what your favourite branches of math and science are and who among the living mathematicians you’d like to meet.

Throughout the program, the students are given access to over a dozen unique interdisciplinary online courses and have the option to complete an applied math project, alone or as a team, consulting real experts in the field of their project. Simon has already started the first course module, on Special Relativity by Professor Brian Greene. The course has been specifically recorded for the World Science Scholars and reflects the program’s ethos: it’s self-paced, no grades, it relies on beautiful animations and visualizations, it’s full of subtle humour, is dynamic, thought-provoking and quite advanced (exactly in The Goldilocks Zone for Simon, as far as I could judge), yet broken up into easy-to-digest pieces. It’s difficult to predict how Simon’s path as a World Science Scholar will unfold (I’m afraid of making any predictions as he is extremely autodidact), but so far we have been very pleased with the nature of this program and it seems to match our non-coercive, self-directed learning style. I have especially liked one of the course’s main postulates: “Simultaneity is in the eye of the beholder”.

# Prime Generation Algorithm in Python

Simon has written a code in Python that generates primes using the finite list from Euclid’s proof that there are infinitely many primes. “Starting with one prime (2) the code uses the finite list to generate a couple more numbers that aren’t in the list but are primes. It may not even get to all the primes in the long run!” There is only one problem with Simon’s algorithm…

Simon has written down Euclid’s proof in his own words first https://imgur.com/ML2tI6n

and then decided to program it in Python.

Resources:

https://www.programiz.com/python-programming/methods/list/remove

https://www.geeksforgeeks.org/iterate-over-a-set-in-python/

https://www.youtube.com/watch?v=OWJCfOvochA

https://numbermatics.com/n/10650056950807/

https://defuse.ca/big-number-calculator.htm

# Simon building an 8-bit Computer from scratch. Parts 1 & 2.

Parts 1 and 2 in Simon’s new series showing him attempting to build an 8-bit computer from scratch, using the materials from Ben Eater’s Complete 8-bit breadboard computer kit bundle.

Simon is learning this from Ben Eater’s playlist about how to build an 8-bit computer.

# Simon has been bitten by the hardware bug again!

It’s all Ben Eater‘s fault! Simon is more of a software and pure math champion, but Ben Eater’s videos have sparked Simon’s interest in logic and electronics, anew. Back in mid July (yes, I know, I’m a little behind with the blog), while waiting for his Complete 8-bit breadboard computer kit bundle to arrive from the US, Simon was playing with virtual circuits that he built on two wonderful platforms: Circuitverse.org and Logic.ly. You can view Simon’s page on Circuitverse at https://circuitverse.org/users/7241

Simon’s favourite was building the Master-Slave JK Flip-Flop https://circuitverse.org/simulator/edit/20037

Simon gave me a whole lecture on the differences between Sequential and Combinational Logic: in the former, there’s a presence of a feedback loop (the output actually goes back to somewhere else in the circuit), and the latter has everything going in one direction (the inputs come in and the outputs go out).

It’s a little bit like the difference between a Feed Forward neural network where the output only depends on the input and a recurrent neural network where the output also depends on what the output was previously,

Simon explained.

Here’s a problem with sequential logic circuits: they go crazy like this very often (confused NOR gate). That’s why most sequential logic circuits have a clock in them. A clock acts like a delay so that it won’t go crazy.

That’s the power of sequential logic: you can have the same input but a different output. This is useful for storing data: I release the input, but the data is stored. It can only be archived in sequential logic.

The delay comes in error detection (on the rising edge of the square wave).

The following circuits are buit in Logicly https://logic.ly/demo

# It takes the sun to the ground, and violet on the observer’s eye.

Simon writes:

This amazing sentence is generated by a Markov Text-Generation Algorithm. What is a Markov Algorithm? Simply put, it generates the rules from a

source text, and it generates a new text that also follows those rules. The rules are often called theMarkov Blanket, and the new text is also called theMarkov Chain. OK, how does this all work?

Let’s take an example: let’s consider the source text to be “Hello, world!”. Then we pick a number called the

order. The higher the number, the more sense the text makes. We’ll pick 1 for the first examples, we’ll examine what happens with higher numbers later.

Then we generate the

Markov Blanket. This is a deterministic process. We start from the beginning: “H”. So we put H in our Markov Blanket. Then we come across “e”. So we put e in our Markov Blanket, but to specify that it’s next from H, we connect H to e by an arrow. Then we stumble on “l”. So we put l in our Markov Blanket, but again, to specify that it’s next from e, we connect e to l by an arrow.

Now, here’s where it gets interesting. What’s next? Well, it’s “l” again. So now we connect l to

itself, by an arrow. This is interesting because it’s no longer a straight line!

And we keep going. Once we’re done, our Markov Blanket will look something like this:

Once we’ve created our Markov Blanket, then we start generating the

Markov Chainfrom it. Unlike the Markov Blanket, generating the Markov Chain is a stochastic process.

This is just a process of wandering about the Markov Blanket, and noting down where we go. One way to do this, is just to start from the beginning, and follow the path. And whenever we come across some sort of fork, we just spin a wheel to see where we go next. For example, here are some possible Markov Chains:

```
Held!
Helld!
Hellld!
Helorld!
Hello, world!
Helllo, wo, wo, world!
```

That was an easy one, so let’s do something more complex with code.

First, just an interface to enter in the text, and the order:

text = "" # Variable to hold the text print("Type your text here (type END to end it):") while True: line = input("") # Read a line of text from standard input if line != "END": text += line + "\n" # If we didn't enter END, add that line to the text else: break # If we entered END, signify that the text has ended text = text[:len(text)-1] # Remove the last line break order = int(input('Type the order (how much it makes sense) here: ')) input("Generate me a beautiful text") # Just to make it dramatic, print this message, and ask the user to hit ENTER to proceed

Next, the Markov Blanket. Here, we store it in a

dictionary, and store every possible next letter in alist:

def markov_blanket(text, order): result = {} # The Markov Blanket for i in range(len(text) - order + 1): # For every n-gram ngram = "" for off in range(order): ngram += text[i+off] if not ngram in result: # If we didn't see it yet result[ngram] = [] if i < len(text) - order: # If we didn't reach the end result[ngram].append(text[i+order]) # Add the next letter as a possibility return result # Give the result back

Huh? What is this code?

This is what happens when we pick a number >1. Then, instead of making the Markov Blanket for every character, you make it for every couple of characters.

For example, if we pick 2, then we make the Markov Blanket for the 1st and 2nd letter, the 2nd and 3rd, the 3rd and 4th, the 4th and 5th, and so on. When we generate the Markov Chain, we

squashthe ngrams that we visit together. So next, the Markov Chain:

def markov_chain(blanket): keys = blanket.keys() ngram = random.choice(list(keys)) # Starting Point new_text = ngram while True: try: nxt = random.choice(blanket[ngram]) # Choose a next letter new_text += nxt # Add it to the text ngram += nxt # Add it to the ngram and remove the 1st character ngram = ngram[1:] except IndexError: # If we can't choose a next letter, maybe because there is none break return new_text # Give the result back # Now that we know how to do the whole thing, do the whole thing! new_text = markov_chain(markov_blanket(text, order), num) print(new_text) # Print the new text out

OK, now let’s run this:

```
Type your text here (type END to end it):
A rainbow is a meteorological phenomenon that is caused by reflection, refraction and dispersion of light in water droplets resulting in a spectrum of light appearing in the sky. It takes the form of a multicoloured circular arc. Rainbows caused by sunlight always appear in the section of sky directly opposite the sun.
Rainbows can be full circles. However, the observer normally sees only an arc formed by illuminated droplets above the ground, and centered on a line from the sun to the observer's eye.
In a primary rainbow, the arc shows red on the outer part and violet on the inner side. This rainbow is caused by light being refracted when entering a droplet of water, then reflected inside on the back of the droplet and refracted again when leaving it.
In a double rainbow, a second arc is seen outside the primary arc, and has the order of its colours reversed, with red on the inner side of the arc. This is caused by the light being reflectedtwice on the inside of the droplet before leaving it.
END
Type the order (how much it makes sense) here: 5
Generate me a beautiful text
```

And……..it..stops.

Why did it do that?

You see, this is not such a good method. What if our program generated a Markov Blanket that didn’t have an end? Well, our program wouldn’t even get to the end, and it would just wander around and around and around, and never give us a result! Or even if it did, it would be

infinite!

So how do we avoid this?

Well, we set another much bigger number , let’s say 5000, to be a

callout value. If we don’t get to the end within 5000 steps, we give up and output early. Let’s run this again…

And now, it doesn’t stop anymore! Snippets of example generated text:

*It takes the sun to the ground, and violet on the observer’s eye.*

*This rainbow, a second arc formed by illuminated droplets resulting it.In a primary rainbow is a meteorological phenomenon the back of the ground, and has the sky. It takes the order of its coloured circles. However, the sun.*

*Rainbow, a second arc shows red on a line from the section of light in water droplet and has the sun.*

*In a double rainbow is caused by illuminated droplet on the outer part and refracted when leaving in a spectrum of a multicoloured circles. However, the droplet of water droplets resulting it.In a double rainbow is a meteorological phenomenon the droplets resulting in a spectrum of a multicoloured circular arc. Rainbow is caused by the inner side the observer’s eye*

Play with this project online at: https://repl.it/@simontiger/Markov-Text

# Inscribed angle theorem

# Back at Stedelijk

# More Fibonacci

# Ackermann Function

Simon solving the Ackermann function (a function that cannot be de-recursed). It’s computable but the computer’s soon runs out of its computing power (see the last line of code below):