TuringTest

> I would really enjoy a class that was nothing but modeling various kinds of computation entirely with recursion.

I had the luck of having a whole semester doing just that with various functional and logic languages (Haskell and Prolog) with a great teacher.

You start by learning accumulator parameters and tail recursion, then you build and learn to use foldl and lazy functions; and then you may learn monads as a design pattern to reduce boilerplate for those techniques.

Maybe you're thinking of the zipper data structure? [1]

Where you represent the composite structure hanging from the point you want to mutate, so updating that point is merely dropping the header and appending the updated value as a new prefix.

1 - https://en.m.wikipedia.org/wiki/Zipper_(data_structure)

You can just use the classic temporal logic approach, where state is passed explicitly as a parameter and the new state is returned.

This is what state monads are doing anyway under a hidden syntax, and for simple cases it may be easier that building a full dedicated monad.

That is unpredictable, but not a side effect. Side effects are changes that are not declared as part of the function parameters.

If you model the controller input as a function parameter, that's just a state argument, and FP can handle it declaratively just fine without having side effects.

Monads are just one design pattern for handling state, and not the only way. It's no different than having an OOP using dependency injection everywhere - it's a design decision that you use only when it makes architectural sense.

A shell is not more imperative than a processor - both are Turing machines, a completely functional mathematical object. Seeing them as imperative is a semantic interpretation of the people watching it, not an inherent property.

FP is not stateless, is state-aware. You need to declare what functions handle state, just like in Java you need to declare which functions handle exceptions.

Faster and simpler coding with less learning wins every time against snappy but esoteric, at least in business applications. It's better for the client, better for the developer and better for the company, even if it doesn't squeezes every processor cycle

The pure advantage of iterating faster on requirements and deploying new functions beats having a very fast but very restricted and possibly incorrect feature set. And if an application takes off, it's always possible to identify its critical path and optimize it for speed, or even re-engineer it starting from a much clear solution to the business case.

> Isn't it just showing your are in-group and well cultured?

Yes, but that is not necessarily a bad thing. Learning that different groups of people may have their own languages and customs is the first step to being able to blend in with a diverse group; it's great when you're able to adopt a register appropriate to the people you are with at any given moment.

Of course it's also possible that the child learns to be a snob and treating with disdain those who are not in your group. But I would say that's a character trait of the person, and it's still worth having the opportunity to learn how to adapt, rather than being unable to modulate your behavior and only knowing how to have a single posture. Having a culture of acceptance starts with knowing the customs of many different groups.

The argument is not that you'd run your IDE on the AR device (although there's people who will want to do that).

The point is that you can hack the device to run your software, or distribute your software so that it's easy for others to use it as well, but not both.

The point is that you're generating diagrams from code that's already been written, you're not modelling, do you? ;-)

Those are a different kind of diagram than the one you create from scratch to decide what goes in the program.

No, that's purely destructive greed.

Ancient civilizations invented the jubilee (loans should be repaid in 7 years) to prevent speculation on them. But unfortunately, preventing extreme concentration of wealth has fallen out of favour

If you're creating diagrams with graphvis, you're just coding on another language. The benefits of diagrams appear when you draw them by hand (or graphically by mouse).

Apples to oranges comparison (no pun intended). SimulaVR aims to be a general computing platform allowing professional "desktop like" workflow, with the performance of a high-end laptop. For what has been revealed, Vision Pro is designed towards tablet-like content consumption.

No shit. In the Spanish version, the column in Explorer showing the 'link target' of shortcuts is translated to the equivalent of 'connect the goal'.

Scripted enemies are "non-player-controlled entity behavior", yet they are not AI. For NPC behaviours to be considered game AI, there needs to be some calculation depending on the current state of the game and objectives of the character. What makes a game behaviour "AI" is its feedback with the player actions and/or events evolving in the game.

Super Mario mushrooms and turtles are not AI controlled; Pac-Man ghosts are. (Possibly the earliest and simplest form of game AI, but quite effective for its purpose).

It is useful when you use it like those knowledgeable of the field. Marketing departments are not using it that way.

> Fancy abstractions, but what are they abstracting away precisely? What are the roots they are trying to hide? Is the foundation of logic itself logical?

Of course the foundations of logic are not logical themselves. Symbolic human thought grows from the natural capabilities of the brain, there are not universal axioms that can be extracted fully formed from the void. That's what rationalist philosophers for wrong (but then, they didn't have neuroscience and electric scanners).

> > It's not a high bar to achieve.

> I'm not sure I agree. It's not trivial at all to produce a system that can support what you call logic and formal processes

We're talking at different levels.

Building the concept of formal rational systems to order thoughts was a huge achievement of philosophers, first the Greeks and later XIX mathematicians like Bool and Russell and the computer builders.

But what I say that's easy is building a new specific system on top of those foundations. It may be as simple as writing a regular expression, which defines a full (if limited) formal grammar. I agree that their power to represent and order the patterns of thought you find in life is more limited than what engineers believe; but when you find a domain of science where it can be applied, it's a very powerful tool to explore consequences and avoid or fix biases and misconceptions.

Rational system is anything that follows a mathematically formal process. This server where we post our messages is a rational system, as well as any deterministic software. It's not a high bar to achieve.

There may be limits to rationality (both for being incomplete to describe the world and for generating results that can't be demonstrated within themselves), but being rational by itself is nothing more than the capability to apply formal logic, i.e the capability to generate a sequence of sentences derived from the previous steps and a limited number of rules of inference.

> Rationality’s building blocks are themselves not rational. I don’t know where this idea came from that logical thought somehow springs into life fully formed at once.

Certainly not from me :-P I'm fully aware that human rationality is one technique trained on top of our common diffuse thinking. Heck, we invented machines to perform rational steps for us without errors.

Once you build a consistent rational system though, you can trust that it will always produce internally coherent knowledge (as long as no bugs external to the system are introduced). That behaviour requires algorithms, not statistical inference.

It has a model, but it is not a rational model. This difference is something that often throws engineers off track when thinking about generative AI.

LLMs models work more like intuitions. They are able to make statements about a problem in context, but they are generated from ideas that "instinctively" make sense given the prior statements and learned corpus (similar to Daniel Kahneman's fast mode of thinking), not logically constructed. These models do not have the capability to build formal inferences of careful steps that try to validate those ideas avoiding contradictions.

Those capabilities can be added outside the model to try to verify the generated text, but so far are not integrated in the learning process, and I don't think anyone knows how to do it.

Don't cross the streams.

(Or maybe I'm mixing up my 80's nostalgia?)

Classics are worth revisiting

http://www.pera.net/Stonesoup.html

> Is compositing different bitmaps an embarrassingly parallel problem?

If you can make an initial approximate guess of the area for each section, you can split regions with lower and upper size constraints that can be laid out in parallel. A global branch and bound algorithm can coordinate those processes, tightening the upper constraints of one region when another connected region exceededs its minimum bounds.

Indeed there still may be degenerate cases where multiple regions need to be recalculated several times, but overall the default case should be able to finish without much conflict.

Chomsky is one the major experts if the XX century in language structure, as well as a founder of cognitive science. I'd say he is capable of having a fairly good idea of what the language models may and may not be doing, even if he doesn't know how the computer is processing the statistics needed to do it.

> eh, there was probably something similar in the trainset that the LLM just had to tweak slightly

Look for examples of mixed image-text generative AIs, and see how they are able to understand visual jokes and memes and explain the source of their humour.

To do that, the AI not only has to recognize and classify the items in the image. It also has to identify the context of the items in the image, and figure out what elements are unusual in that context.

All that process is something that could be only imitated if the training set contained exactly the same meme; as the source of humour will be different for each image. But the AI is capable of doing it even for memes not included in the training set.

Well, other than Observable, reactive notebooks are not that common and well known (precisely because Jupyter, which is the most famous, didn't support that model before).

So maybe today is the first day that you are exposed to that model and you learn about it? There's always a first time.

An always-reactive notebook is essentially a "literate spreadsheet", where you have data cells in between multimedia descriptions. In this model, all computed data is always up to date with whatever changes you make to the input parameters, including things like graphics connected to interactive sliders and text boxes. You can prototype the logic of an application very fast with real data and interactions.

Reactive notebooks are what change the workflow from command line-like to spreadsheet-like.

It may not matter much of you use the notebook as a glorified terminal, but it is a godsend if your workflow involves data analysis with heavy dependencies between filtered subsets.

On the other hand, the link description alone was enough to convince me that this is something I want.

Having a very specific target makes it easier to reach that target in writing, I guess, and harder for people outside the target to understand what it's about.

It's up to the people of a democratic country to decide if they want to try. Overthrowing the government because it may potentially go that way it's not what the West nominally defend as their values.

Chile of Allende may actually have had a chance to avoid that destiny had not it been for US actively destroying their economy; they were for once trying modern social and economic projects based on scientific models that might have made a difference, but that was thrown away with the dictatorship.