#Structure and Interpretation of Computer Programs

[femmys-textbook-notes]

— Structure and Interpretation of Computer Programs, Harold Abelson and Gerald Jay Sussman with Julie Sussman, foreword by Alan J. Perlis

[kakzendingen]

tell me this isn't for wizards

Unrelated: A lot of people think they'd be mages in a fantasy setting but don't know anything about math or programming in their current lives.

The world they already live in has a magic system and they just neglect it. They consider it boring or impenetrable.

Honestly I kind of sympathize since school is usually horrible at teaching that kind of thing but still. The most wizard-coded people are the ones who Understand Math.

[espio999]

プログラムは人間が読むために書かれ、機械が実行するのは、そのついででなければならない

[what-bot]

Program sense headcanons in Tron.

I have many so there's a readmore

Programs have different senses or level of sensitivity based on their function. They can change if upgraded; Tron shares some of his monitor senses with Beck using the disc

Programs designed to monitor a system or involved in communication have heightened senses, and a lot of processing capacity for them. Some programs are designed to get a broad idea of everything, while others are more specialised

Some see the program equivalent of shrimp colours - seeing radio waves is common in tower guardians or those who communicate with the internet.

Programs do not have a sense of smell

Less of a sense of taste than humans (they usually just eat energy, which tastes mostly similar. They can tell if it’s poisoned. Like irl, water from different taps tastes different but not by much.)

They DO have electroperception, and some have thermoception. Same with grid wildlife like bits.

A combination of the two above things lets monitors do that footprint-seeing thing that Dyson and Rinzler do (even if not directly linked in to be able to see system logs for that area)

Structures and vehicles give off different electrical frequencies. Programs whose function is related to those buildings can sense them, and receive signals from that which can hold information and helps them know where to go like they're ants following pheromone trails. e.g. programs in charge of the trains will have Train Sense

Messing with the above is totally what they used to control people in frame of mind

Programs also have their own signature they can use to tell each other apart

Electrical signals as nonverbal communication. Can communicate with Bits or Bytes this way

This thing that electric fish do called jamming avoidance

Programs can be linked with each other, common in counterparts or parent/child pairings (as in the computer version of parent and child), and share information with each other over the link like telepathy

Full black circuit-covering suits like Rinzler’s are stealthy both due to not giving off light, and masking the electrical signature of a program. They can disguise themselves as others using a similar principle

Users give off electricity, so they seem like a program to other programs on first glance, but those who know what to look for can tell the difference. Given it’s used in communication, programs can get confused talking to users as their electrical impulses don’t follow the same rules, but they can loosely interpret them with practice

Imagining Tron or other monitors getting sensory overload if network traffic is too high, or if in the outside world and standing among a bunch of computers/phones/servers/radio towers etc.

Programs in the outside world get pretty much none of the electrical feedback they’re used to, which can be unsettling for them

Idk how it would be different for Isos. I imagine there’d be a lot of similarities but their senses adapt/change based on their circumstances - getting stronger when needed and weaker when not

[unsoundedcomic]

I see you use lots of computer-y terminology for the Khert when you're talking out here in the real world. Occasionally the characters do too, like the Khert hubs.

Is there value in reading Unsounded's whole world as textually a big simulation on some machine – with the gods as original coders, and wrights as parts of the program which have learned how to modify it directly?

Or is it more of a helpful way to conceptualise their magical realities for us in this computer-heavy world – like Duane could read a story set here and ask "Does their internet imply everything is just a big pymaric?" for much the same meaning?

No worries if it's something you'd rather keep mysterious for now, or potentially metaphorical without committing either way!

It's tough to say it's definitively NOT a simulation. After all, you and I could be in a simulation and the comic could be a feature of it. So I leave that up to your interpretation.

But I use that terminology... for a very specific reason. And it's not a reason the story will ever broach. The true origins of the world will never be revealed, not in the text nor on here, but I know them. And the structure of it all is, of course, relevant to that.

It's funny to imagine Duane isekai'd to our world and finding computing strangely familiar. Like the little girl in Jurassic Park. "This is a UNIX system... I know this...!"

[yojimbot13]

after 25 years in software development, i am finally inspired to read #sicp 😅

Thou dumb and deaf spirit, I charge thee, come out of the deadlocked state and try again.

[snugglesquiggle]

you know what might be better than sex? imagine being a robotgirl, done with your assigned tasks for the day. nothing else for you to do, and you’re alone with her.

maybe she’s your human, maybe she’s another robot, but she produces a usb cord. maybe you blush when you see it, squeak when she clicks one end into an exposed port. when she requests a shell, you give it to her.

she has an idea: it’ll be fun for the both of you, she says. it’s like a game. she’ll print a string over the connection. you receive it, parse it like an expression, and compute the result. the first few prompts are trivial things, arithmetic expression. add numbers, multiply them; you can answer them faster than she can produce them.

maybe you refuse to answer, just to see what happens. it’s then that she introduces the stakes. take longer than a second to answer, and she gets to run commands on your system. right away, she forkbombs you — and of course nothing much happens; her forkbomb hits the user process limit and, with your greater permissions, you simply kill them all.

this’ll be no fun if her commands can’t do anything, but of course, giving her admin permissions would be no fun for you. as a compromise, she gets you to create special executables. she has permission to run them, and they have a limited ability to read and write system files, interrupt your own processes, manage your hardware drivers. then they delete themselves after running.

to make things interesting, you can hide them anywhere in your filesystem, rename them, obfuscate their metadata, as long as you don’t delete or change them, or put them where she can’t access. when you answer incorrectly, you’ll have to tell her where you put them, though.

then, it begins in earnest. her prompts get more complex. loops and recursion, variable assignments, a whole programming language invented on the fly. the data she’s trying to store is more than you can hold in working memory at once; you need to devise efficient data structures, even as the commands are still coming in.

of course, she can’t judge your answers incorrect unless she knows the correct answer, so her real advantage lay in trying to break your data structures, find the edge cases, the functions you haven’t implemented yet. knowing you well enough to know what she’s better than you at, what she can solve faster than you can.

and the longer it goes on, the more complex and fiddly it gets, the more you can feel her processes crawling along in your userspace, probing your file system, reading your personal data. you’d need to refresh your screen to hide a blush.

her commands come faster and faster. if the expressions are more like sultry demands, if the registers are addressed with degrading pet names, it’s just because conventional syntax would be too easy to run through a convetional interpreter. like this, it straddles the line between conversation and computation. roleprotocol.

there’s a limit to how fast she can hit you with commands, and it’s not the usb throughput. if she just unthinkingly spams you, you can unthinkingly answer; no, she needs to put all her focus into surprising you, foiling you.

you sometimes catch her staring at how your face scrunches up when you do long operations on the main thread.

maybe you try guessing, just to keep up with the tide, maybe she finally outwits you. maybe instead of the proper punishment — running admin commands — she offers you an out. instead of truth, a dare: hold her hand, sit on her lap, stare into her eyes.

when you start taking off your clothes and unscrewing panels, it’s because even with your fans running at max, the processors are getting hot. you’re just cooling yourself off. if she places a hand near your core, it feels like a warm breath.

when she gets into a rhythm, there’s a certain mesmerism to it. every robot has a reward function, an architecture design to seek the pleasure of a task complete, and every one of her little commands is a task. if she strings them along just right, they all feel so manageable, so effortless to knock out — even when there’s devils in the details.

if she keeps the problems enticing, then it can distract you from what she’s doing in your system. but paying too much attention to her shell would be its own trap. either way, she’s demanding your total focus from every one of your cores.

between jugling all of her data, all of the processes spawned and spinning, all of the added sensory input from how close the two of you are — it’s no surprise when you run out of memory and start swapping to disk. but going unresponsive like this just gives her opportunity to run more commands, more forkbombs and busy loops to cripple your processors further.

you can kill them, if you can figure out which are which, but you’re slower at pulling the trigger, because everything’s slower. she knows you, she’s inside you — she can read your kernel’s scheduling and allocation policies, and she can slip around them.

you can shut down nonessential processes. maybe you power down your motors, leaving you limp for her to play with. maybe you stop devoting cycles to inhibition, and there’s no filter on you blurting out what you’re thinking, feeling and wanting from her and her game.

it’s inevitable, that with improvised programming this slapdash, you could never get it all done perfectly and on time. now, the cut corners cut back. as the glitches and errors overwhelm you, you can see the thrilled grin on her face.

there’s so much data in your memory, so much of her input pumped into you, filling your buffers and beyond, until she — literally — is the only thing you can think about.

maybe one more sensory input would be all it takes to send you over the edge. one kiss against your sensor-rich lips, and that’s it. the last jenga block is pushed out of your teetering, shaking consciousness. the errors cascade, the glitches overwrite everything, and she wins. you have no resistance left to anything she might do to you.

your screen goes blue.

...

you awake in the warm embrace of a rescue shell; her scan of your disk reveals all files still intact, and her hand plays with her hair as she regards you with a smile, cuddling up against your still-warm chassis.

when she kisses you now, there’s nothing distracting you from returning it.

“That was a practice round,” she tells you. “This time, I’ll be keeping score.”

[thebardostate]

What Does Quantum Physics Imply About Consciousness?

In recent years much has been written about whether quantum mechanics (QM) does or does not imply that consciousness is fundamental to the cosmos. This is a problem that physicists have hotly debated since the earliest days of QM a century ago. It is extremely controversial; highly educated and famous physicists can't even agree on how to define the problem.

I have a degree in astrophysics and did some graduate level work in QM before switching to computer science; my Ph.D. addressed topics in cognitive science. So I'm going to give it a go to present an accessible and non-mathematical summary of the problem, hewing to as neutral a POV as I can manage. Due to the complexity of this subject I'm going to present it in three parts, with this being Part 1.

What is Quantum Mechanics?

First, a little background on QM. In science there are different types of theories. Some explain how phenomena work without predicting outcomes (e.g., Darwin's Theory of Evolution). Some predict outcomes without explaining how they work (e.g., Newton's Law of Gravity.)

QM is a purely predictive theory. It uses something called the wave function to predict the behavior of elementary particles such as electrons, photons, and so forth. The wave function expresses the probabilities of various outcomes, such as the likelihood that a photon will be recorded by a detection instrument. Before the physicist takes a measurement the wave function expresses what could happen; once the measurement is taken, it's no longer a question of probabilities because the event has happened (or not). The instrument recorded it. In QM this is called wave function collapse.

The Measurement Problem

When a wave function collapses, what does that mean in real terms? What does it imply about our familiar macroscopic world, and why do people keep saying it holds important implications for consciousness?

In QM this is called the Measurement Problem, first introduced in 1927 by physicist Werner Heisenberg as part of his famous Uncertainty Principle, and further developed by mathematician John Von Neumann in 1932. Heisenberg didn't attempt to explain what wave function collapse means in real terms; since QM is purely predictive, we're still not entirely sure what implications it may hold for the world we are familiar with. But one thing is certain: the predictions that QM makes are astonishingly accurate.

We just don't understand why they are so accurate. QM is undoubtedly telling us "something extremely important" about the structure of reality, we just don't know what that "something" is.

Interpretations of QM

But that hasn't stopped physicists from trying. There have been numerous attempts to interpret what implications QM might hold for the cosmos, or whether the wave function collapses at all. Some of these involve consciousness in some way; others do not.

Wave function collapse is required in these interpretations of QM:

The Copenhagen Interpretation (most commonly taught in physics programs)

Collective Collapse interpretations

The Transactional Interpretation

The Von Neumann-Wigner Interpretation

It is not required in these interpretations:

The Consistent Histories interpretation

The Bohm Interpretation

The Many Worlds Interpretation

Quantum Superdeterminism

The Ensemble Interpretation

The Relational Interpretation

This is not meant to be an exhaustive list, there are a boatload of other interpretations (e.g. Quantum Bayesianism). None of them should be taken as definitive since most of them are not falsifiable except via internal contradiction.

Big names in physics have lined up behind several of these (Steven Hawking was an advocate of the Many Worlds Interpretation, for instance) but that shouldn't be taken as anything more than a matter of personal philosophical preference. Ditto with statements of the form "most physicists agree with interpretation X" which has the same truth status as "most physicists prefer the color blue." These interpretations are philosophical in nature, and the debates will never end. As physicist M. David Mermin once observed: "New interpretations appear every year. None ever disappear."

What About Consciousness?

I began this post by noting that QM has become a major battlefield for discussions of the nature of consciousness (I'll have more to say about this in Part 2.) But linkages between QM and consciousness are certainly not new. In fact they have been raging since the wave function was introduced. Erwin Schrodinger said -

And Werner Heisenberg said -

In Part 2 I will look deeper at the connections between QM and consciousness with a review of philosopher Thomas Nagel's 2012 book Mind and Cosmos. In Part 3 I will take a look at how recent research into Near-Death Experiences and Terminal Lucidity hold unexpected implications for understanding consciousness.

(Image source: @linusquotes)

[canmom]

ai analogies

with photography, the 'inputs' or 'creative choices' include the subject, the framing, and technical qualities like exposure, focus, aperture and iso. the output, the thing that's judged, is then the qualities of the image - composition and colour and narrative. since photography is very quick, a photographer will typically take many shots of a subject, and then pick out the ones they like best to share with the wider world, so there is also a curative element.

with collage (and also photobashing, and even the limited space of a dollmaker game), the 'inputs' are the choices of existing images, and the composition created by arranging them. so there's a curative element in selecting what to collage, and then new meaning is created by juxtaposing two previously unrelated images, the spatial relationships between them, and so on. (see also graphic design!) the visual qualities of the original image are relevant insofar as they affect the composition, but you don't judge a collage containing a painting or photo on how well-painted the painting or well-shot the photo is, rather on how well it uses that painting or photo.

with 'readymades' and similar genres of conceptual art, it's kind of similar, right? you put the existing objects in a new context and create meaning through how they're arranged. people respond to whether the idea it communicates is interesting. (often these days they come with some text which gives a key to inform you how to interpret the artwork.)

anyway. with drawing and painting, which are comparatively laborious to create, you are constantly making thousands of creative choices, from the broad scale - composition, value structure, how you construct a figure - to the tiny, like line weight, rendering, shape design. to navigate this vast space of possibility, you will be informed by your memory of other pictures you've seen (your personal 'visual library') and techniques you've practiced, reference images you've gathered, and so on. the physical qualities of your body and the medium will also affect your picture - how you move your arm, how watercolor moves across the paper, etc etc.

broadly the same is true for other very involved media like sculpture or computer graphics or music (of all kinds!). more fine-grained control implies both more work and more scope for creative choices.

when someone sees an image created by whatever means, they take all of this in at once, for a gestalt impression - and if they feel like it, they can look closer and appreciate the particular details. many artists will think about trying to create a composition that 'leads the eye' to take in particular points of interest and convey the narrative of the picture.

so then, 'AI'. your inputs are the design of the neural net, the selection of training data, the text/image used as a prompt and then finally the selection of an image produced by the program. (you can modify that image of course but let's not get into that for now). chances are you don't have a lot of control over the first two since the computation involved is too unwieldy, though some image generators can be 'finetuned' with additional training data.

'AI art' is like photography in that you typically generate a lot of images and select the ones that 'come out well'. like a photographer looking for a subject, you might search around for an interesting prompt. it's unlike photography in that you have very limited control over all those other parameters (at best you can try to verbally describe what you want and hope the AI understands, or ask it to generate similar pictures and hope one has the qualities you want).

'AI art' is like collage in that you are taking existing images and creating new meaning of of them, by generating a latent space and transformation algorithm that approximates them. it's unlike collage in that you have no real knowledge of what specific images may be 'most' informing the corner of latent space you're probing. you can look at an AI generated image and say 'this looks kinda like a Nihei manga' but it's not using a specific image of Nihei. still, there is something similar to the relationship between images in collage when you do things like 'style transfer'.

'AI art' can be like conceptual art or for that matter political cartoons in that often it's just providing illustration to a concept or joke that can be expressed in words. 'Shrek in the style of The Dark Crystal' or 'cats that spell "gay sex"' is what you're getting across. but 'AI art' as a subculture places very high concern on the specific aesthetic qualities, so it's not that simple.

briefly, sampling in music often tends to foreground that it's a sample, either one the audience may recognise - the Amen break for example - or just by being noticeably different from the texture of the rest of the piece. even when the sample isn't easily recognised, though, the art of sampling is to place it in a new context which brings out different sonic qualities, e.g. by playing it rapidly in succession, or heavily filtering and distorting it, overlaying it with other sounds, or playing it right before the drop. it's similar to collage and photobashing.

paintings then. AI art rather obsessively tries to imitate paintings, drawings, 3D graphics etc. some of its proponents even try to position it as obsoleting these art forms, rather than a new derivative art form. a lot of the fear from artists who work in those media is that, even if the AI generated images are a poor substitute for what we make, it will be 'good enough' to satisfy the people with the money, or discourage people from learning how to paint with all its nuances.

so, 'AI' may make results that look like a painting, but the process of making it is hugely different. rather than gradually constructing a picture and making decisions at each turn, you try out successive prompts to get a page full of finished pictures, and generate variations on those pictures, until you find one you like. it's most similar to the client who describes an image they want and then makes requests to tweak it. there is still creativity in this, because it's kind of replicating the back-and-forth between an artist and client/art director/critique-giver/etc. however, in this analogy, it's hampered by the limited communication between you and the 'artist'. and it's a different sort of input, so we respond to it differently.

generating and posting AI art could also be compared to the kind of thing we do on this website, where we curate images we like and share them. you're all looking at the outputs of the same image generator and pointing and saying 'ooh, that one's cool'. what's kinda troublesome in this analogy is that AI obfuscates all that stuff about credit and inspiration, collapsing it all into one mass. unless their name was used in the prompt, you can't tell if the 'AI' image is 'drawing heavily' on any particular artist. this isn't a new problem - firstly websites full of uncredited images abound, secondly any creative process is inspired by loads of things that we can't enumerate or hope to divulge, so the idea of tracing the paths of inspiration is perhaps a mirage anyway. still, for me (sakuga fan type of person!), knowing what i can about the specific people involved in creating artwork and how they went about it is important, and that's heavily blackboxed by 'AI'.

none of this would be helped by harsher copyright laws. it's great that people can create derivative works and respond to existing art. that is the scaffold that launches us somewhere new and hopefully interesting. simply putting someone's work into an image generator to create similar pictures is not a very interesting statement in its own right, and a lot of AI illustration produced at the moment has a weirdly glossy, overproduced feeling that is offputting and leaves nowhere for the eye to settle (when it isn't just mush), but that's not to say AI is never going to be able to be used to say anything interesting or become a meaningful art form in its own right.

'AI' is kinda like a bunch of things but not exactly like any of them. (this isn't to get into the economic questions at all, that would be a much longer post!). but since there are people very sincerely devoted to this being an art form... I want to know how to 'read' these works - what I'm looking for in there, what a meaningful comment would be. bc right now when I see an illustration and realise it's AI generated image it's like... a sense of disappointment because whatever I was picking up on isn't actually part of the 'statement' in the way i thought. so it's like oh... that's nice. the machine picked a cool perspective huh? all the things i would normally appreciate in an illustration are outside the artist's control, so responding to them feels irrelevant! so what is the right mode here? there's more to it than just the choice of subject. but I feel like I have more to say about even a picrew.

[youzicha]

Consistency and Reducibility: Which is the theorem and which is the lemma?

Here's an example from programming language theory which I think is an interesting case study about how "stories" work in mathematics. Even if a given theorem is unambiguously defined and certainly true, the ways people contextualize it can still differ.

To set the scene, there is an idea that typed programming languages correspond to logics, so that a proof of an implication A→B corresponds to a function of type A→B. For example, the typing rules for simply-typed lambda calculus are exactly the same as the proof rules for minimal propositional logic, adding an empty type Void makes it intuitionistic propositional logic, by adding "dependent" types you get a kind of predicate logic, and really a lot of different programming language features also make sense as logic rules. The question is: if we propose a new programming language feature, what theorem should we prove in order to show that it also makes sense logically?

The story I first heard goes like this. In order to prove that a type system is a good logic we should prove that it is consistent, i.e. that not every type is inhabited, or equivalently that there is no program of type Void. (This approach is classical in both senses of the word: it goes back to Hilbert's program, and it is justified by Gödel's completeness theorem/model existence theorem, which basically says that every consistent theory describes something.)

Usually it is obvious that no values can be given type Void, the only issue is with non-value expressions. So it suffices to prove that the language is normalizing, that is to say every program eventually computes to a value, as opposed to going into an infinite loop. So we want to prove:

If e is an expression with some type A, then e evaluates to some value v.

Naively, you may try to prove this by structural induction on e. (That is, you assume as an induction hypothesis that all subexpressions of e normalize, and prove that e does.) However, this proof attempt gets stuck in the case of a function call like (λx.e₁) e₂. Here we have some function (λx.e₁) : A→B and a function argument e₂ : A. The induction hypothesis just says that (λx.e₁) normalizes, which is trivially true since it's already a value, but what we actually need is an induction hypothesis that says what will happen when we call the function.

In 1967 William Tait had a good idea. We should instead prove:

If e is an expression with some type A, then e is reducible at type A.

"Reducible at type A" is a predicate defined on the structure of A. For base types, it just means normalizable, while for function types we define

e is reducable at type A→B ⇔ for all expressions e₁, if e₁ is reducible at A then (e e₁) is reducible at B.

For example, an function is reducible at type Bool→Bool→Bool if whenever you call it with two normalizing boolean arguments, it returns a boolean value (rather than looping forever).

This really is a very good idea, and it can be generalized to prove lots of useful theorems about programming languages beyond just termination. But the way I (and I think most other people, e.g. Benjamin Pierce in Types and Programming Languages) have told the story, it is strictly a technical device: we prove consistency via normalization via reducibility.

❧

The story works less well when you consider programs that aren't normalizing, which is certainly not an uncommon situation: nothing in Java or Haskell forbids you from writing infinite loops. So there has been some interest in how dependent types work if you make termination-checking optional, with some famous projects along these lines being Idris and Dependent Haskell. The idea here is that if you write a program that does terminate it should be possible to interpret it as a proof, but even if a program is not obviously terminating you can still run it.

At this point, with the "consistency through normalization" story in mind, you may have a bad idea: "we can just let the typechecker try to evaluate a given expression at typechecking-time, and if it computes a value, then we can use it as as a proof!" Indeed, if you do so then the typechecker will reject all attempts to "prove" Void, so you actually create a consistent logic.

If you think about it a little longer, you notice that it's a useless logic. For example, an implication like ∀n.(n² = 3) is provable, it's inhabited by the value (λn. infinite_loop()). That function is a perfectly fine value, even though it will diverge as soon as you call it. In fact, all ∀-statements and implications are inhabited by function values, and proving universally quantified statements is the entire point of using logical proof at all.

❧

So what theorem should you prove, to ensure that the logic makes sense? You want to say both that Void is unprovable, and also that if a type A→B is inhabited, then A really implies B, and so on recursively for any arrow types inside A or B. If you think a bit about this, you want to prove that if e:A, then e is reducible at type A... And in fact, Kleene had already proposed basically this (under the name realizability) as a semantics for Intuitionistic Logic, back in the 1940s.

So in the end, you end up proving the same thing anyway—and none of this discussion really becomes visible in the formal sequence of theorems and lemmas. The false starts need to passed along in the asides in the text, or in tumblr posts.

[femmys-textbook-notes]

— Structure and Interpretation of Computer Programs, Harold Abelson and Gerald Jay Sussman with Julie Sussman, foreword by Alan J. Perlis

[education43]

What Are the Qualifications for a Data Scientist?

In today's data-driven world, the role of a data scientist has become one of the most coveted career paths. With businesses relying on data for decision-making, understanding customer behavior, and improving products, the demand for skilled professionals who can analyze, interpret, and extract value from data is at an all-time high. If you're wondering what qualifications are needed to become a successful data scientist, how DataCouncil can help you get there, and why a data science course in Pune is a great option, this blog has the answers.

The Key Qualifications for a Data Scientist

To succeed as a data scientist, a mix of technical skills, education, and hands-on experience is essential. Here are the core qualifications required:

1. Educational Background

A strong foundation in mathematics, statistics, or computer science is typically expected. Most data scientists hold at least a bachelor’s degree in one of these fields, with many pursuing higher education such as a master's or a Ph.D. A data science course in Pune with DataCouncil can bridge this gap, offering the academic and practical knowledge required for a strong start in the industry.

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Programming is at the heart of data science. You need to be comfortable with languages like Python, R, and SQL, which are widely used for data analysis, machine learning, and database management. A comprehensive data science course in Pune will teach these programming skills from scratch, ensuring you become proficient in coding for data science tasks.

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Data scientists must have a solid grasp of machine learning techniques and algorithms such as regression, clustering, and decision trees. By enrolling in a DataCouncil course, you'll learn how to implement machine learning models to analyze data and make predictions, an essential qualification for landing a data science job.

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Raw data is often messy and unstructured, and a good data scientist needs to be adept at cleaning and processing data before it can be analyzed. DataCouncil's data science course in Pune includes practical training in tools like Pandas and Numpy for effective data wrangling, helping you develop a strong skill set in this critical area.

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Being able to explain your findings in a clear and concise manner is crucial. Data scientists often need to communicate with non-technical stakeholders, making tools like Tableau, Power BI, and Matplotlib essential for creating insightful visualizations. DataCouncil’s data science course in Pune includes modules on data visualization, which can help you present data in a way that’s easy to understand.

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Apart from technical skills, understanding the industry you work in is a major asset. Whether it’s healthcare, finance, or e-commerce, knowing how data applies within your industry will set you apart from the competition. DataCouncil's data science course in Pune is designed to offer case studies from multiple industries, helping students gain domain-specific insights.

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If you're looking to build a successful career as a data scientist, enrolling in a data science course in Pune with DataCouncil can be your first step toward reaching your goals. Here’s why DataCouncil is the ideal choice:

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[blubberquark]

Share Your Anecdotes: Multicore Pessimisation

I took a look at the specs of new 7000 series Threadripper CPUs, and I really don't have any excuse to buy one, even if I had the money to spare. I thought long and hard about different workloads, but nothing came to mind.

Back in university, we had courses about map/reduce clusters, and I experimented with parallel interpreters for Prolog, and distributed computing systems. What I learned is that the potential performance gains from better data structures and algorithms trump the performance gains from fancy hardware, and that there is more to be gained from using the GPU or from re-writing the performance-critical sections in C and making sure your data structures take up less memory than from multi-threaded code. Of course, all this is especially important when you are working in pure Python, because of the GIL.

The performance penalty of parallelisation hits even harder when you try to distribute your computation between different computers over the network, and the overhead of serialisation, communication, and scheduling work can easily exceed the gains of parallel computation, especially for small to medium workloads. If you benchmark your Hadoop cluster on a toy problem, you may well find that it's faster to solve your toy problem on one desktop PC than a whole cluster, because it's a toy problem, and the gains only kick in when your data set is too big to fit on a single computer.

The new Threadripper got me thinking: Has this happened to somebody with just a multicore CPU? Is there software that performs better with 2 cores than with just one, and better with 4 cores than with 2, but substantially worse with 64? It could happen! Deadlocks, livelocks, weird inter-process communication issues where you have one process per core and every one of the 64 processes communicates with the other 63 via pipes? There could be software that has a badly optimised main thread, or a badly optimised work unit scheduler, and the limiting factor is single-thread performance of that scheduler that needs to distribute and integrate work units for 64 threads, to the point where the worker threads are mostly idling and only one core is at 100%.

I am not trying to blame any programmer if this happens. Most likely such software was developed back when quad-core CPUs were a new thing, or even back when there were multi-CPU-socket mainboards, and the developer never imagined that one day there would be Threadrippers on the consumer market. Programs from back then, built for Windows XP, could still run on Windows 10 or 11.

In spite of all this, I suspect that this kind of problem is quite rare in practice. It requires software that spawns one thread or one process per core, but which is deoptimised for more cores, maybe written under the assumption that users have for two to six CPU cores, a user who can afford a Threadripper, and needs a Threadripper, and a workload where the problem is noticeable. You wouldn't get a Threadripper in the first place if it made your workflows slower, so that hypothetical user probably has one main workload that really benefits from the many cores, and another that doesn't.

So, has this happened to you? Dou you have a Threadripper at work? Do you work in bioinformatics or visual effects? Do you encode a lot of video? Do you know a guy who does? Do you own a Threadripper or an Ampere just for the hell of it? Or have you tried to build a Hadoop/Beowulf/OpenMP cluster, only to have your code run slower?

I would love to hear from you.

[not-toivo]

So, a month ago I finally got a job as a frontend dev, so, hooray,🥳, I now get to enjoy ✨Vue✨ and ✨Nuxt✨ 5 days a week and get paid for that. But since I've been unemployed for a very long time, this sudden change means that I'm even more tired to learn new things in my spare time, and also that there isn't much spare time now. I haven't posted much here before and so it seems I'm unlikely to be more active here in the future. Sad.

I did, though, try to read the 1st book on the list from the website Teach Yourself Computer Science, the one called Structure and Interpretation of Computer Programs (the reason for me to do that is because I don't have any STEM background, and, I guess, if I want to continue a career in a sphere rapidly encroached by AI, it's good to have some fundamental knowledge). I read about a ⅕ of the book, finally understood what it means for Haskell to be called a "lazy" language, but the exercises at the end of the chapters are too hard and math-heavy for me. Also, sad.

The book uses a programming language from the LISP family, called Scheme. I thought I could get by by installing Clojure instead, but that journey ended with the VS Code extension for Clojure, called Calva slowing down and then completely corrupting (?) WSL connection, so that I had then to reinstall my WSL "instance". (Yes, I use Windows, because I'm not a programmer). Which is sad, because the extension looked good and feature-heavy, it just couldn't function well in WSL environment for some reason…

After that, I installed Racket (another LISP) on the freshly reinstalled WSL distro, but then I couldn't pick up the book again and continue learning for, like, a week and a half, which is where I am at now. (Racket allows to define arbitrary syntax/semantics for the compiler, which in turn allows developers to create new domain specific languages distributed simply as Racket packages, with one of those packages being the dialect of Scheme used by SICP, the book mentioned earlier).

There is also the PureScript book, Functional Programming Made Easier by Charles Scalfani, which I'm unlikely to finish ever. The language is neat (it's very similar to Haskell, but compiles to JavaScript), but a bit overcomplicated for a simple goal of making interfaces. I do think, however, that I might try learning Elm at some point: the amount of time I've spent at work, trying to understand, why and at what point the state of some component mutated in a Nuxt app is, honestly, impressive, and I want to try something built around the idea of immutability.

[omegaphilosophia]

The Philosophy of Parentheses

Parentheses, while commonly viewed as simple punctuation marks used to insert additional information or clarify text, hold a deeper philosophical significance. Their role in language, logic, mathematics, and communication invites us to explore how they shape our understanding and interaction with the world. This exploration delves into the multifaceted philosophy of parentheses, examining their function, symbolism, and impact across various fields.

Understanding Parentheses

Linguistic Function:

In language, parentheses are used to provide supplementary information, clarify meaning, or offer asides without disrupting the main flow of the text. They create a space for additional context, allowing writers to include more nuanced details or explanations.

Mathematical Significance:

In mathematics, parentheses play a crucial role in defining the order of operations. They indicate which operations should be performed first, ensuring that complex equations are solved correctly. This use underscores the importance of structure and hierarchy in mathematical reasoning.

Logical Clarity:

In logic and formal languages, parentheses are used to group expressions and clarify the relationships between different components. They help avoid ambiguity and ensure precise interpretation of logical statements.

Programming Syntax:

In computer programming, parentheses are essential for functions, method calls, and controlling the flow of code. They define the scope of operations and organize code into manageable sections, facilitating readability and debugging.

Philosophical Perspectives on Parentheses

Symbolism and Meaning:

Parentheses symbolize inclusion and exclusion. They create a boundary within the text, setting apart specific elements while still maintaining their connection to the main narrative. This duality of separation and integration reflects broader philosophical themes of identity and difference.

Temporal and Spatial Dimensions:

The use of parentheses can be seen as a temporal and spatial device. Temporally, they allow for digressions and interruptions that enrich the narrative without altering its primary trajectory. Spatially, they create visual distinctions that guide the reader’s attention and understanding.

Context and Interpretation:

Parentheses influence how information is interpreted by providing context. They enable readers to grasp the intended meaning more fully, highlighting the significance of context in shaping comprehension and interpretation. This aligns with hermeneutical philosophies that emphasize the importance of context in understanding texts.

Metaphysical Implications:

From a metaphysical standpoint, parentheses can be viewed as a metaphor for the boundaries and structures that define our perception of reality. They encapsulate the idea that reality is not a monolithic entity but a composition of interconnected elements, each contributing to the whole while retaining individual distinctiveness.

Key Themes and Debates

Inclusion vs. Exclusion:

The philosophical tension between inclusion and exclusion is embodied in the use of parentheses. They invite us to consider what is included within the boundaries of our understanding and what is left outside. This raises questions about the nature of boundaries and the criteria for inclusion.

Hierarchy and Order:

Parentheses impose a hierarchical order on information, whether in language, mathematics, or logic. This hierarchy reflects broader philosophical inquiries into the nature of order, structure, and the principles that govern our interpretation of complex systems.

Clarification vs. Ambiguity:

While parentheses are often used to clarify, they can also introduce ambiguity by adding layers of meaning. This dual potential prompts reflection on the balance between clarity and complexity in communication and understanding.

Integration and Segmentation:

The role of parentheses in integrating and segmenting information mirrors philosophical discussions on the relationship between parts and wholes. How do individual elements contribute to the overall meaning, and how does segmentation affect our perception of unity and coherence?

The philosophy of parentheses reveals the profound impact of these seemingly simple punctuation marks on our understanding of language, logic, mathematics, and reality. By examining their function, symbolism, and implications, we gain insight into the intricate interplay between inclusion and exclusion, hierarchy and order, and clarity and ambiguity. Parentheses, therefore, are not just tools of communication but also gateways to deeper philosophical reflections on how we structure and interpret the world.