#quantification

[suliqyre]

We put enormous emphasis on optimizing quantities. Our attention is shackled to screens where we make certain numbers go up and other numbers go down. We want to measure everything so that we can control and optimize it. The benefit of this is that we are able to fulfill our material needs more efficiently and with less effort.

But by doing so, we begin to think only about quantities. We create incentives to sacrifice quality in order to obtain better quantitative results. We become so focused on measuring and optimizing, on increasing efficiency and productivity, that we stop thinking about what is happening to us in our day-to-day lives.

It is perhaps not surprising that we then feel there is something missing — as though life is not going as well as it ought to be, even though all of the numbers are moving in the right direction.

Read more...

[geopolicraticus]

Aristotelian Science and Modern Science

The Displacement of Abstraction.—Before the rise of modern quantitative science, the quantitative aspects of science were treated as being of little relevance, while the anthropocentric impacts of phenomena, including the projection of anthropic properties onto natural phenomena, were treated as that which demanded explanation. The explanatory power of Aristotelian science, if it possessed any, derived from its abstractions and reductions as applied to anthropic properties of the world. The rise of modern science in its quantitative form neatly reversed this distribution of what is to be explained, what is to be abstract, and what is to be concrete. Now the anthropic properties of the world are treated as being of little relevance, while the focus of abstraction and reductive explanation is on the quantitative features of the world.  

[quotelr]

Learning can never be quantified with a flawed educational unit.

Louise Philippe Dulay

[amiscellany]

A look at how data, and the process of collecting it, can't capture everything. A couple of examples:

The wider the user base for the data, the more decontextualized the data needs to be. Theodore Porter’s landmark book, Trust in Numbers, gives a lovely example drawn from a history of land measurement compiled by Witold Kula, the early twentieth-century Polish economist. Older measures of land often were keyed to their productivity. For example, a “hide” of land was the amount required to sustain the average family. Such measures are incredibly rich in functional information. But they required a lot of on-the-ground, highly contextual expertise. The land assessor needs to understand the fertility of the soil, how many fish are in the rivers and deer are in the woods, and how much all that might change in a drought year. These measures are not usable and assessable by distant bureaucrats and managers. Societies tend to abandon such measures and switch from hides to acres when they shift from local distributed governance to large, centralized bureaucracies. The demands of data—and certainly data at scale—are in tension with the opacity of highly local expertise and sensitivity. This kind of local awareness is typically replaced with mechanically repeatable measures in the movement to larger-scaled bureaucracy.

And:

Here is the second principle: every classification system represents some group’s interests. Those interests are often revealed by where a classification system has fine resolution and where it doesn’t. For example, the International Classification of Disease (ICD) is a worldwide, standardized system for classifying diseases that’s used in collecting mortality statistics, among other things. Without a centralized, standardized system, the data collected by various offices won’t aggregate. But the ICD has highly variable granularity. It has separate categories for accidents involving falling from playground equipment, falling from a chair, falling from a wheelchair, falling from a bed, and falling from a commode. But it only has two categories for falls in the natural world: fall from a cliff, and an “other fall” category that lumps together all the other falls—including, in its example, falls from embankments, haystacks, and trees. The ICD is obviously much more interested in recording the kinds of accidents that might befall people in an urban industrial environment than a rural environment, note Bowker and Star. The ICD’s classification system serves some people’s interests over others.

[sparklyshoesharkthing-blog]

Un nouvel algorithme promet des modèles d’intelligence artificielle moins coûteux, plus précis et plus légers

L’intelligence artificielle (IA) est en constante évolution, et l’un des principaux défis actuels est de créer des modèles puissants tout en maîtrisant les coûts énergétiques et financiers associés à leur entraînement et déploiement. Une récente avancée algorithmique pourrait bien révolutionner ce secteur en permettant de concevoir des modèles d’IA à la fois plus précis, plus légers et moins…

[majokko120]

The debate about Intelligence is much like the debate about Art: people who can't define it want to judge about it, and in their ignorance, numbers come to the rescue.

[librarianrafia]

"It is easy to take for granted the value of data. It has come to seem self-evidently useful, as necessary and natural as water. It doesn’t even matter what has been measured and datafied; data in the abstract, as an idea, is taken to be a good thing, and of course there should be more of it, to enrich our knowledge of the world and to make anything that is “data-driven” work better. If data is being collected but not leveraged, why bother? Why have an archive of implosion images if not to simulate any implosion image imaginable?

But to accept that at face value would be to neglect the vast infrastructure involved not merely in collecting it and making it useful and tradable, but also establishing its reputation for objectivity. Measurement is an ideology; among its central tenets is that there is no such thing as datafication but just data itself, naturally given by the things in themselves. It presents itself as a form of representation that transcends representation: Data is no longer about the world but is instead taken to be the world itself, as though materiality were a matter not of atoms but of information. The image of an implosion is an implosion.

Likewise, this ideology would persuade us to ignore the market for data, which shapes what is measured and how, and have us believe it is more like a natural resource, a found material waiting for refinement rather than a structured informational good without any natural status at all. Implosions just happen.

Calls to measure everything and collect as much data as possible are offered as efficient strategies to better grasp the world as it is. But measurement is an act of power, not observation. Datafication always reifies an existing distribution of power that grants the measurers the ability to decide which aspects of the world count and which ones don’t. Having measurements taken as objective — having representations be treated as realities — requires power and recurrent processes of legitimation."

...

Restating that in the terms outlined above, an archive recognizes the power relations intrinsic to measurement (and representation in general) whereas a dataset suppresses them (helping entrench the power relations that underwrite the data it assembles). An archive attempts to retain how and why representations were made, and a dataset disregards all that to allow representations to masquerade as universal facts. When representations become data, they reinforce the utility of the infrastructures (algorithmic decision-making systems, AI models, etc.) developed to exploit them. And that infrastructure in turn reinforces the power relations authorizing the data.

[floortile34]

not sure if this is a hot take but school is extremely traumatic and needs huge reform. it causes immense damage to everyone who goes through it. no large-scale school system is exempt from this. this happens in all countries, not just the ones where it's worst. this problem's also inextricably tied to capitalism and neither can be fixed without also fixing the other

school genuinely breaks people (and others) with even worse being done to those with intellectual disabilities and neurodivergents and those with other psychiatric disorders

[jessicawwcaww]

I go out of my way to use this NanoDrop (a device to quantify DNA) even though there’s one closer to my lab, and it’s because this laptop cracks me up so much. Literally academic science in a nutshell: a dinosaur running Windows 95 that we cannot ever turn off because the retired professor it belongs to refuses to tell anyone the password.

[frank-olivier]

Bayesian Active Exploration: A New Frontier in Artificial Intelligence

The field of artificial intelligence has seen tremendous growth and advancements in recent years, with various techniques and paradigms emerging to tackle complex problems in the field of machine learning, computer vision, and natural language processing. Two of these concepts that have attracted a lot of attention are active inference and Bayesian mechanics. Although both techniques have been researched separately, their synergy has the potential to revolutionize AI by creating more efficient, accurate, and effective systems.

Traditional machine learning algorithms rely on a passive approach, where the system receives data and updates its parameters without actively influencing the data collection process. However, this approach can have limitations, especially in complex and dynamic environments. Active interference, on the other hand, allows AI systems to take an active role in selecting the most informative data points or actions to collect more relevant information. In this way, active inference allows systems to adapt to changing environments, reducing the need for labeled data and improving the efficiency of learning and decision-making.

One of the first milestones in active inference was the development of the "query by committee" algorithm by Freund et al. in 1997. This algorithm used a committee of models to determine the most meaningful data points to capture, laying the foundation for future active learning techniques. Another important milestone was the introduction of "uncertainty sampling" by Lewis and Gale in 1994, which selected data points with the highest uncertainty or ambiguity to capture more information.

Bayesian mechanics, on the other hand, provides a probabilistic framework for reasoning and decision-making under uncertainty. By modeling complex systems using probability distributions, Bayesian mechanics enables AI systems to quantify uncertainty and ambiguity, thereby making more informed decisions when faced with incomplete or noisy data. Bayesian inference, the process of updating the prior distribution using new data, is a powerful tool for learning and decision-making.

One of the first milestones in Bayesian mechanics was the development of Bayes' theorem by Thomas Bayes in 1763. This theorem provided a mathematical framework for updating the probability of a hypothesis based on new evidence. Another important milestone was the introduction of Bayesian networks by Pearl in 1988, which provided a structured approach to modeling complex systems using probability distributions.

While active inference and Bayesian mechanics each have their strengths, combining them has the potential to create a new generation of AI systems that can actively collect informative data and update their probabilistic models to make more informed decisions. The combination of active inference and Bayesian mechanics has numerous applications in AI, including robotics, computer vision, and natural language processing. In robotics, for example, active inference can be used to actively explore the environment, collect more informative data, and improve navigation and decision-making. In computer vision, active inference can be used to actively select the most informative images or viewpoints, improving object recognition or scene understanding.

Timeline:

1763: Bayes' theorem

1988: Bayesian networks

1994: Uncertainty Sampling

1997: Query by Committee algorithm

2017: Deep Bayesian Active Learning

2019: Bayesian Active Exploration

2020: Active Bayesian Inference for Deep Learning

2020: Bayesian Active Learning for Computer Vision

The synergy of active inference and Bayesian mechanics is expected to play a crucial role in shaping the next generation of AI systems. Some possible future developments in this area include:

- Combining active inference and Bayesian mechanics with other AI techniques, such as reinforcement learning and transfer learning, to create more powerful and flexible AI systems.

- Applying the synergy of active inference and Bayesian mechanics to new areas, such as healthcare, finance, and education, to improve decision-making and outcomes.

- Developing new algorithms and techniques that integrate active inference and Bayesian mechanics, such as Bayesian active learning for deep learning and Bayesian active exploration for robotics.

Dr. Sanjeev Namjosh: The Hidden Math Behind All Living Systems - On Active Inference, the Free Energy Principle, and Bayesian Mechanics (Machine Learning Street Talk, October 2024)

Saturday, October 26, 2024

[suliqyre]

We want to be successful, so we are always thinking about our goals... We measure and evaluate and plan everything we do for the purpose of fulfilling our goals. These goals might be significant projects that will take months or years to complete, but they can also be so small that we don't even realize they are goals. Our compulsion towards measurement and progress is so strong that we might even go so far as to plan out our spare time to achieve the best possible outcome, even if it is just to maximize the pleasure we experience.

Our focus on goals can also creep into our efforts to become more aware of ourselves and the world around us... But in doing so, we work against ourselves.

Read more...

[softcuddledrone]

I may not be particularly good at simulation based math but

Based on the facts that Mistress is fat (additive modifier), made of dense metal/synthetic materials (multiplicative modifier) and also about three times as tall as an average human (exponential modifier), Manna’s weight probably sits somewhere in the mid-four-digit range

Which isn’t particularly Necessary math but I think adds to the gravity (ha) of giant synth dragon lady being so so gentle with you despite the strength required to exist at that scale

[nihiltism]

i always get mildly shocked when a friend of mine mentions they have a sibling. this is absolutely not rare in fact i think im the odd one out however i being an only child just kind of assume everybodys an only child unless stated otherwise

[ontonix]

Complexity Science is Dead.

The so-called Complexity Science has been around for a few decades now. Chaos, as well as complexity, were buzzwords in the 1980s, pretty much like Artificial Intelligence is today. There are Complexity Institutes in many countries. They speak of Complexity Theory. In a beautiful and recent video called “The End of Science”, Sabine Hossenfelder, speaks of Complexity Science and how it has not…

[sharanje]

i keep making myself laugh with the stuff i write in my diary

but i love it so far and i've managed to switch to croatian even

i have also realized that i'm missing some good conversations in my life and i have realized i want to share my life too

this is my first ever actual attempt at keeping a diary and so far so good! i keep having thoughts and feelings i want to write down

i think i fell in love with writing when i was very young. i remember making a comic when i was in kindergarten about little creatures who live in our teeth and i remember writing an essay on peter pan in middle school which got me a prize and i remember all the essays i did in high school which i was always made to read in front of the class

[futurefind]

//idt i'll make a giant formal announcement but i think its safe to say its hiatus/semi-hiatus season (as you may have noticed from my activity atm anyway) — Not wrt Rp, just Tumblr

My primary motivation on returning to tumblr was for sake of, tldr, 'networking' and making new rp peeps and friends, but my preferred rp method has always been (multipara theads) via discord. while tumblr is still very useful for consolidating muse info and headcanons (and inbox memes!! chefs kiss abt those tbh), I just... don't have the energy to blindly yell and run around and hope it gets people interested :')

I'll still be around (probably), but I'll probably stick even moreso than usual to just... vibing in my corner and answering any threads/asks/prompts rather than working on 'outreach', so to speak. With my brain's two speeds being 'vibrate silently and anxiously wish for a sign to babble at them at all' and 'gush freely and have periodic anxiety you're overwhelming them/Doing It Wrong' it's Not Great to try and juggle. (Edit:) Everyone's more than welcome to poke in and ask for babbling/overtly give permission to gush about muses or thread ideas or what have you but I won't be initiating much idt.

Maybe it's a phase and I'll be clawing at the walls within a week, maybe I won't feel the urge 'til 2-3 months from now when my dad's recovered from his leg surgery. who knows!

Either way, for those interested (for multi-platform rp chatter, non-rp chatter, or even discord rp): have a discord drop:

nethernor_i