Summer news, Ackermann related observations and things to come

Light summer post, want more then follow the links.

1. after a chemlambda demo appeared on hackernews  (link) I saw a lot of interest from hacker brains (hopefully). Even if slightly misplaced (title reads: D3.js visualization of a lambda calculus) it is an entry gate into this fascinating subject.

2. Sreejith S (aka 4lhc) works on a python port for chemlambda, called chemlambda-py. It works already, I look forward to try it when back home. Thank you Sreejith! Follow his effort and, why not, contribute?

3. Herman Bergwerf set out to write a chemlambda IDE, another great initiative which I am very eager to see it working, just by looking at Herman beautiful MolView.

4. During discussions with Sreejith, I noticed some funny facts about the way chemlambda computes the Ackermann function. Some preliminaries: without cheats (i.e. closed forms) or without memoization, caching, etc, it is hopeless to try to compute Ack(4,2). The problem is not as much the fact that the function takes huge values, but the fact that in order to compute even modest values, there are lots and lots of calls. See the rosettacode entry for the Ackermann function about that. Compared with those examples, the implementation of chemlambda in awk does not behave bad at all. There are now several mol files for various ackermann function values which you may try. The only one which takes lots of time (but not huge memory, if you except the html output, which you can eliminate by commenting with a # all printf lines in the awk script) is ackermann_4_1. `This one works, but I still have to see how much time it takes. The interesting observation is that the number of steps (in the deterministic version) of chemlambda (mind: steps not rewrites!) is at 1 or 2 difference than the number of steps of this beautiful stacks based script: ackermann script. It means that somehow the chemlambda version records in space the intermediary values, instead of stacking them for further use. Very strange, to explore!

5. There exist, on paper, the chemlambda v3 “enzo”. It’s very very nice, you’ll see!

Permutation-replication-composition all-in-one

This is the permutation cycle 1 – > 2 – > 3 – > 4 – > 5 – > 6 – > 7 – > 8 – > 1 which is replicated and composed with itself in the same time.


Done with pwheel_8_compo.mol from the chemlambda repo, and with, in the deterministic variant (all weights set to 0. The result is a pair of cycles 1 – > 3 – > 5 – > 7 – > 1  and 2 – > 4 – > 6 – > 8 – > 2.

See other plays with permutations in the


The mesh is the computer

The article

The mesh is a network of microtubule connectors that stabilizes individual kinetochore fibers of the mitotic spindle

announces the discovery of a new structure in the cell: the mesh.

From the abstract:

Kinetochore fibers (K-fibers) of the mitotic spindle are force-generating units that power chromosome movement during mitosis. K-fibers are composed of many microtubules that are held together throughout their length.
Here, we show, using 3D electron microscopy, that K-fiber microtubules (MTs) are connected by a network of MT connectors. We term this network ‘the mesh’.
The K-fiber mesh is made of linked multipolar connectors. Each connector has up to four struts, so that a single connector can link up to four MTs.  […]
Optimal stabilization of K-fibers by the mesh is required for normal progression through mitosis.
We propose that the mesh stabilizes K-fibers by pulling MTs together and thereby maintaining the integrity of the fiber. “

My speculation is that the mesh has not only the role of a scaffold for the microtubule structure.

Together with the microtubules and with some other (yet undiscovered or, on the contrary, very well known) parts, this is the computer.



DNA, which fascinates us, is more like a memory device.

But the computation may be as in  chemlambda. The dynamical reorganization of the mesh-microtubule-other proteins structure is very much resembling to a chemlambda molecule (or even to a chemlambda quine.



Mentioned this here, because there is an evocative image


Inceptionism: an AI built on pragmatic principles turns out to be an artificial Derrida

Not willing to accept this, now they say that the artificial neural network dreams. Name: Google Deep Dream.

The problem is that the Google Deep Dream images can be compared with dreams by us humans.  Or in the general case of an automatic classifier of big data there is no term of comparison.  How can we, the pragmatic scientists, know that the output obtained from data (by training a neural network on other data) is full of dreamy dog eyes or not?

If we can’t trust the meaning obtained from big data, by pragmatic means, then we might as well renounce at analytic philosophy and turn towards continental (so called) philosophy.

That is seen as not serious, of course, which means that the ANN dreams, whatever that means. With this stance we transform a  kick in the ass of our most fundamental beliefs into a perceived progress.


The inner artificial life of a cell, a game proposal

The  inner life of a cell is an excellent, but passive window

It is also scripted according to usual human expectations: synchronous moves, orchestrated reactions at a large scale. This is of course either something emergent in real life, or totally unrealistic.

As you know, I propose to use the artificial chemistry chemlambda for making real, individual molecular computing devices, as explained in Molecular computers.

But much more can be aimed at, even before the confirmation that molecular computers, as described there,  can be built by us humans.

Of course that Nature builds them everywhere, we are made of them. It works without any external control, not as a sequence of lab operations, asynchronously, in a random environment, and it is very hard to understand if there is a meaning behind the inner life of a living cell, but it works nevertheless without the need of a semantics to streamline its workings.

So obvious, however so far from IT way of seeing computation.

Despite the huge and exponential advances in synthetic biology, despite the fact that many of these advances are related to IT, despite that more and more ways to control biological workings, I believe that there has to be a way to attack the problem of computations in biology from the basis. Empirical understanding is great and will fuel for some time this amazing synthetic biology evolution, but why not thinking about understanding how life works, instead of using biological parts to make functions, gates and other components of the actual IT paradigm?

As a step, I propose to try to build a game-like artificial life cell, based on chemlambda. It should look and feel like the Inner life of a cell video, only that it would be interactive.

There are many bricks already available, some molecules (each with its own story and motivation) are in the chemlambda repository, other are briefly described, with animations, in the chemlambda collection.

For example:
– a centrosome and the generated microtubules like in

kinesins  as adapted walkers like in

– molecules built from other ones like RNA from DNA

– programmed computations (mixing logic and biologic)

– all in an environment looking somehow like this

Like in a game, you would not be able to see the whole universe at once, but you could choose to concentrate to this part or that part.
You could build superstructures from chemlambda quines and other bricks, then you could see what happens either in a random environment or in one where, for example, reductions happen triggered by the neighbourhood of your
mouse pointer (as if the mouse pointer is a fountain of invisible enzymes).

Videos like this, about the internal working of a neuron

would become tasks for the gamer.


Artificial life which can be programmed

Artificial life


which can be programmed