I wound up using Python because of machine learning, in particular TensorFlow and Keras. And Python is ok. I miss strong typing, but all in all it seems pretty solid with some outstanding science libraries. Mostly, I write tools:

I’ve been using IntelliJ Ultimate for my Python work. I came at it from Java and the steaming wreckage that is Eclipse. I like Ultimate a lot. It updates frequently but no too much. Most of the time it’s seamless. And I can switch between C++, Java, Python, and TypeScript/JavaScript.

For a new tool project, I need to have Python analytics served to a thin client running D3. Which meant that it was time to set up a client-server architecture.

These things are always a pain to set up. A “Hello world” app that sends JSON from the server to the client where it is rendered at interactive rates, with user actions being fed back to the server is still a surprisingly clunky thing to set up. So I was expecting some grumbling. I was not expecting everything to be broken. Well, nearly everything.

It is possible to create a Flask project in IntelliJ Ultimate. But you can’t actually launch the server. The goal is to have code like this:

Set up and run a webserver with console output like this:

It turns out that there is an unresolved bug in IntelliJ that prevents the webserver from launching. In IntelliJ, the way you know that everything is working is that the python file – app.py in this case — has parentheses around it (app.py). You can also see this in the launch menu:

If you see those parens, then IntelliJ knows that the file contains a Flask webserver, and will run things accordingly.

So how did I get this to work? I bought the Professional Edition of PyCharm (which has Flask), and used the defaults to create the project. Note that it appears that you have to use the Jinja2 template language. The other option, Mako, fails. I have not tried the None option. It’s been that sort of day.

By the way, if you already have a subscription to Ultimate, you can extend it to include the whole suite for the low low price of about $40/year.

Listening to World Affairs Council

In today’s reality, democracy no longer ends with a revolution or military coup, but with a gradual erosion of political norms. As a growing number of countries are chipping away at liberally democratic values, are these institutions safe from elected, authoritarian leaders? Daniel Ziblatt, professor at Harvard University and co-author of How Democracies Die, discusses the future of liberal democracies with World Affairs CEO Jane Wales.

This is connecting with Clockwork Muse. Martindale talks about the oscillation between primordial and stylistic change. Primordial is big jumps on a rugged fitness landscape and stylistic change is hill climbing through refinement. In politics, this may compare to reactionary/populist – big jumps to simpler answers and progressivism which is hill climbing to locally optimal solutions. In both cases, the role of habituation and arousal potential are important. Elites making incremental progress is not exciting. MAGA is exciting, and for both sides.

This is a story about information at rest and information in motion. Actually, it’s really just a story about information in  motion, mediated by computers. Information at rest is pretty predictable. Go pick up an actual, physical, book. Alone, it’s not going to do much. But it is full of information. It’s your job to put it in motion. The right kind of motion can change the world. The thing is, that change, be it the creation of a political movement, or the discovery of a new field of study, is oddly physical. Out terms that describe it (field, movement) are physical. They have weight, and inertia. But that is a property us us — human beings — evolved meat machines that interact with information using mechanisms evolved over millennia to deal with physical interactions. Information in motion isn’t physical.  But we aren’t equipped to deal with that intuitively. The machines that we have built to manipulate information are. And though they are stunningly effective in this, they do not share our physics-based biases about how to interpret the world.

And that may lead to some ugly surprises.

The laws of physics don’t apply in information space.

Actually, we rarely deal with information. We deal in belief, which is the subset of information that we have opinions about. We don’t care how flat a table is as long as it’s flat enough. But we care a lot about the design of the dining room table that we’re putting in our dining room.

In this belief space, we interpret information using a brain that is evolved based on the behavior of the physical world. That’s a possible reason that we have so many movement terms for describing belief behavior. It is unlikely that we could develop any other intuition, given the brief time that there has even been a concept of information.

There are also some benefits to treating belief as if it has physical properties. It affords group coordination. Beliefs that change gradually can be aligned easier (dimension reduction), allowing groups to reach consensus and compromise. This combined with our need for novelty creates somewhat manageable trajectories. Much of the way that we communicate depends on this linearity. Spoken and written language are linear constructs. Sequential structures like stories contain both information and the order of delivery. Only the sequence differs in music. The notes are the same.

But belief merely creates the illusion that information has qualities like weight. Although the neurons in our brain are slower than solid-state circuits, the electrochemical network that they build is capable of behaving in far less linear and inertial ways. Mental illness can be regarded as a state where the brain network is misbehaving. It can be underdamped, leading to racing thoughts or overdamped manifesting as depression. It can have runaway resonances, as with seizures. In these cases, the functioning of the brain no longer maps successfully to the external, physical environment. There seems to be an evolutionary sweet spot where enough intelligence to model and predict possible outcomes is useful. Functional intelligence appears to be a saddle point, surrounded by regions of instability and stasis.

Computers, which have not evolved under these rules treat information very differently. They are stable in their function as sets of transistors. But the instructions that those transistors execute, the network of action and information is not so well defined. For example, computers can access all information simultaneously. This is one of the qualities of computers that makes them so effective at search. But this capability leads to deeply connected systems with complicated implications that we tend to mask with interfaces that we, the users, find intuitive.

For example, it is possible to add the illusion of physical properties to information. In simulation we model masses, springs and dampers to create sophisticated representations of real-world behaviors. But simply because these systems mimic their real-world counterpart, doesn’t mean that they have these intrinsic properties. Consider the simulation of a set of masses and springs below:

Depending on the solver (physics algorithm), damping, and stiffness, the system will behave in a believable way. Choose the Euler solver, turn down the damping and wind up the stiffness and the systems becomes unstable, or explodes:

The computer, of course, doesn’t know the difference. It can detect instability only if we program or train is specifically to do so. This is not just true in simulations for physical systems, but also training-based systems like neural networks (gradient descent) and genetic algorithms (mutation rate). In all these cases, systems can converge or explode based on the algorithms used and the hyperparameters that configure them.

This is the core of an implicit user interface problem. The more we make our intelligent machines so that they appear to be navigating in belief spaces, the more we will be able to work productively with them in intuitive ways. Rather than describing carefully what we want them to do (either with programming or massive training sets), we will be able to negotiate with them, and arrive at consensus or compromise. The fundamental problem is that this is a facade that does not reflect the underlying hardware. Because no design is perfect, and accidents are inevitable, I think that it is impossible to design systems that will not “explode” in reaction to unpredictable combinations if inputs.

But I think that we can reduce the risks. If we legislate an environment that requires a minimum level of diversity in these systems, from software design through training data,  to hardware platform, we can increase the likelihood that when a nonlinear accident happens it will only happen in one of several loosely coupled systems. The question of design is a socio-cultural one that consists of several elements:

1. How will these systems communicate that guarantees loose coupling?
2. What is the minimum number of systems that should be permitted, and under what contexts?
3. What is the maximum “size” of a single system?

By addressing these issues early, in technical and legislative venues, we have an opportunity to create a resilient socio-technical ecosystem, where novel interactions of humans and machines can create new capabilities and opportunities, but also a resilient environment that is fundamentally resistant to catastrophe.

Trying to think about how intelligence at an individual level is different from intelligence at a population level. At an individual level, the question is how much computation to spend in the presence of imperfect / incomplete information. Does it make sense to be an unquestioning acolite? Follow fashion? Go your own way? These define a spectrum from lowest to highest amount of computation. A population that is evolving over time works with different demands. There is little or no sense of social coordination at a population’s genetic level (though there is coevolution). It seems to me that it is more a question of how to allocate traits in the population in such a way that optimises the duration that the genetic pattern that defines the population. The whole population needs a level of diversity. Clone populations (Aspens, etc) fail quickly. Gene exchange increases the likelihood of survival, even though it is costly. Similarly, explore/exploit and other social traits may be distributed unevenly so that there are always nomadic individuals that move through the larger ecosystem, producing a diaspora that a population can use to recover from a disaster that decimates the main population centers. Genes probably don’t “care” if these nomads are outcasts or willing explorers, but the explorers will be probably be better equipped to survive and create a new population, perpetuating the “explorer” genes at some level in the larger genome, at least within some time horizon where there is a genetic, adaptive “memory” of catastrophe.