Ciro Santilli @cirosantilli  40

As of 2018, Ciro Santilli believes that this could be the next big thing in biology technology.

"De novo" means "starting from scratch", that is: you type the desired sequence into a computer, and the synthesize it.

The "de novo" part is important, because it distinguishes this from the already well solved problem of duplicating DNA from an existing DNA template, which is what all our cells do daily, and which can already be done very efficiently in vitro with polymerase chain reaction.

Many companies are attempting to create more efficient de novo synthesis methods:

Notably, the dream of most of those companies is to have a machine that sits on a lab bench, which synthesises whatever you want.

TODO current de novo synthesis costs/time to delivery after ordering a custom sequence.

The initial main applications are likely going to be:but the real pipe dream is building and bootstraping entire artificial chromosomes

News coverage:

High level DNA studies? :-)

Ways in which it can happen:

Given a bunch of points in $n$ dimensions, PCA maps those points to a new $p$ dimensional space with $p\le n$.

$p$ is a hyperparameter, $p=1$ and $p=2$ are common choices when doing dataset exploration, as they can be easily visualized on a planar plot.

The mapping is done by projecting all points to a $p$ dimensional hyperplane. PCA is an algorithm for choosing this hyperplane and the coordinate system within this hyperplane.

The hyperplane choice is done as follows:

www.sartorius.com/en/knowledge/science-snippets/what-is-principal-component-analysis-pca-and-how-it-is-used-507186 provides an OK-ish example with a concrete context. In there, each point is a country, and the input data is the consumption of different kinds of foods per year, e.g.:so in this example, we would have input points in 4D.

The question is then: we want to be able to identify the country by what they eat.

Suppose that every country consumes the same amount of flour every year. Then, that number doesn't tell us much about which country each point represents (has the least variance), and the first PCA axes would basically never point anywhere near that direction.

Another cool thing is that PCA seems to automatically account for linear dependencies in the data, so it skips selecting highly correlated axes multiple times. For example, suppose that dry codfish and olive oil consumption are very high in Portugal and Spain, but very low in Germany and Poland. Therefore, the variation is very high in those two parameters, and contains a lot of information.

However, suppose that dry codfish consumption is also directly proportional to olive oil consumption. Because of this, it would be kind of wasteful if we selected:since the information about codfish already tells us the olive oil. PCA apparently recognizes this, and instead picks the first axis at a 45 degree angle to both dry codfish and olive oil, and then moves on to something else for the second axis.

We can see that much like the rest of machine learning, PCA can be seen as a form of compression.

Our minimal definition of "electronic money" is the following.

Instead of creating legal tender such as Dollars as banknotes or transactions in some complex obscure banking system, the government offers an official simple centralized API that represents it instead.

Each citizen or legal entity has an account there, and transfers between registered users are just simple API calls.

So for example you would e able to put all your money in the government account instead of using useless banks. And then you would invest it as you want with the investment company of your choice, without tying the "my money is here" with "this is the best investment" aspects of banks.

Forms a normal subgroup of the general linear group.