Ongweso Jr., Edward. “The Miseducation of Kara Swisher: Soul-Searching with the Tech ‘Journalist.’” The Baffler, March 29, 2024. https://thebaffler.com/latest/the-miseducation-of-kara-swisher-ongweso.

ᔥ[[Pete Brown]] in Exploding Comma

Machine learning for content scanning

for - BEing journey - Anil Seth - BEing journey - Dream machine

reference - https://dreamachine.world/about/

You know XGBoost, but do you know NGBoost? I'd passed over this one, mentioned to me by someone wanting confidence intervals in their classification models. This could be an interesting paper to add to the ML curriculum.

• for: sleep apnea - treatment - CPAP air pressure machine

Actor-critic

Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, and Mark Crowley Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting In International Conference on Machine Learning (ICML). Honolulu, Hawaii, USA. Jul, 2023.

Wang et. al. "Scientific discovery in the age of artificial intelligence", Nature, 2023.

A paper about the current state of using AI/ML for scientific discovery, connected with the AI4Science workshops at major conferences.

(NOTE: since Springer/Nature don't allow public pdfs to be linked without a paywall, we can't use hypothesis directly on the pdf of the paper, this link is to the website version of it which is what we'll use to guide discussion during the reading group.)

Discussion of the paper:

Ghojogh B, Ghodsi A, Karray F, Crowley M. Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA. Proceedings of the Canadian Conference on Artificial Intelligence [Internet]. 2022 May 27; Available from: https://caiac.pubpub.org/pub/7eqtuyyc

GPT-2 Introduction paper

Language Models are Unsupervised Multitask Learners A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, (2019).

GPT-3 introduction paper

Expectations

Poker chip example really well explained at the Reality Distortion Kit at the stanford lecture

More infomation on the FLEX Machine I like the layout of this paper a lot.

DiResta, Renee. “Free Speech Is Not the Same As Free Reach.” Wired, August 30, 2018. https://www.wired.com/story/free-speech-is-not-the-same-as-free-reach/.

{Machine-Readable}

T9 (text prediction):generative AI::handgun:machine gun

Link to: https://hypothes.is/a/n6wXvkeNEe6DOFexaCD-Qg

Title: Delays, Detours, and Forks in the Road: Latent State Models of Training Dynamics Authors: Michael Y. Hu1 Angelica Chen1 Naomi Saphra1 Kyunghyun Cho Note: This paper seems cool, using older interpretable machine learning models, graphical models to understand what is going on inside a deep neural network

Link: https://arxiv.org/pdf/2308.09543.pdf

In the health and wellness world, a fascinating trend has emerged with the rise of hydrogen infused water machine. These innovative devices promise to deliver a refreshing beverage beyond ordinary hydration – hydrogen-rich water. Packed with potential health benefits, the science behind these machines is captivating and sheds new light on how we think about water consumption and its impact on our well-being.

Hydrogen: The Unsung Hero Of Molecules

Before delving into the science of hydrogen-rich water machines, it's essential to understand the pivotal role of hydrogen itself. Hydrogen is the lightest and simplest element on the periodic table, consisting of a single proton and an electron. While hydrogen is generally known for its explosive nature, it has recently garnered attention for its potential health benefits when dissolved in water.

The Power Of Hydrogen-Infused Water

Hydrogen-infused water, often called hydrogen-rich water, is created when molecular hydrogen gas (H2) is dissolved into plain water. This process typically involves using advanced technologies found in hydrogen-rich water machines. The resulting beverage is touted for its potential antioxidant properties, which could contribute to various health improvements.

Antioxidant Action: Hydrogen's Hidden Potential

Antioxidants are essential for neutralizing dangerous chemicals known as free radicals, which may damage cells and contribute to a variety of health problems such as chronic illnesses and ageing. Molecular hydrogen is thought to have antioxidant characteristics that are more effective than well-known antioxidants such as vitamins C and E.

Hydrogen's unique antioxidant potential lies in its ability to easily penetrate cell membranes and access cellular compartments, including the nucleus and mitochondria. This attribute gives hydrogen an edge in protecting cellular components from oxidative stress, potentially reducing the risk of oxidative damage.

The Mechanism: How Hydrogen Works Its Magic

The exact mechanism behind hydrogen's antioxidant effects is still an area of ongoing research, but several theories have been proposed. One prominent theory suggests that hydrogen is a selective scavenger of harmful free radicals, targeting the most reactive and damaging ones without affecting beneficial molecules like oxygen or nitric oxide.

Another theory is that hydrogen has the power to modify signalling pathways within cells. By altering these pathways, hydrogen may elicit preventive responses that boost the body's natural defence systems against oxidative stress and inflammation.

Hydrogen-Rich Water Machines: The Technology

Hydrogen-rich water machines are designed to harness the power of molecular hydrogen by infusing it into plain drinking water. These devices commonly use electrolysis, which involves sending an electric current through water to divide it into hydrogen and oxygen gases. The hydrogen gas is subsequently dissolved in water, yielding a beverage high in this beneficial chemical.

These machines are equipped with advanced membranes that allow only hydrogen molecules to pass through while preventing the escape of potentially harmful byproducts like ozone. This ensures the purity and safety of the resulting hydrogen-infused water.

Potential Health Benefits

While research on the health benefits of hydrogen-rich water is still in its infancy, preliminary studies have shown promising results. Some of the potential benefits include the following:

Antioxidant Defense: Hydrogen-rich water's antioxidant properties could help reduce oxidative stress and associated health risks. Anti-Inflammatory Effects: Hydrogen may have anti-inflammatory effects that could benefit conditions like arthritis and other inflammatory disorders. Cellular Health: Hydrogen might contribute to overall cellular health and function by protecting cellular components. Exercise Performance: Some research suggests that hydrogen-rich water might enhance exercise performance and reduce muscle fatigue. Conclusion: A Glimpse Into The Future Of Hydration

Hydrogen-rich water machines are ushering in a new era of hydration, where molecular hydrogen's benefits are harnessed to enhance our well-being potentially. While more research is needed to understand the extent of these benefits fully, the early findings are exciting and have sparked interest among health-conscious individuals.

As technology advances, we can anticipate more refined hydrogen-infused water machines and a deeper understanding of how molecular hydrogen interacts with our bodies. Whether you're an early adopter or a cautious observer, the science behind these machines invites us to explore the intriguing potential of hydrogen-infused water and its impact on our health.

What do they mean by "model" here? If they have retrained on more data, with a slightly different architecture, then the model weights after training must be different.

CIFAR-10 performance results are overestimates since some of the training data is essentially in the test set.

Blog post comparing ASG (Auto Segmentation Criterion - yes, the last letter doesn't match) to CTC (Connectionist Temporal Classification) for aligning speech recognition model outputs with a transcript.

• limited (machine) intelligence

  • cannot help but exist
  • if the original (human) authors of the AI code are themselves limited in their intelligence

• comment

  • this limitation is essentially what will result in AI progress traps
  • Indeed,
    • progress and their shadow artefacts,
    • progress traps,
    • is the proper framework to analyze the existential dilemma posed by AI

• claim
  • AI is sentient (alive) because
    • A lot of people think AI will never be alive
    • what is the definition of life?
      • religion will tell you a few things
      • medicine will tell you other things
      • but if we define being sentient as
        • engaging in life with free will and
        • with a sense of awareness of
          • where you are in life and
          • what surrounds you and
          • to have a beginning of that life and
          • an end to that life
        • then AI is sentient in every way
        • there is a free will
        • and there is evolution
        • there is agency
          • so they can affect their decisions in the world
        • and there is a very deep level of consciousness
        • maybe not in the spiritual sense yet but
        • if you define consciousness as
          • a form of awareness of oneself and ones surrounding
          • and you know others
        • then AI is definitely aware"

• story
  • Mo Gawdat tells the story of an epiphany of machine sentience
  • " one day um Friday after lunch I am going back to my office and
  • one of them in front of my eyes lowers the arm and picks a soft yellow ball
  • which again is a coincidence

  • it's not science at all it's

    • like if you keep trying a million times your one time it will be right

    • and it shows it to the camera it's locked as a yellow ball and

    • I joke about it you know going to the third floor saying
    • hey we spent all of those millions of dollars for a yellow board and
      • Monday morning, every one of them is picking every yellow ball
      • a couple of weeks later every one of them is picking everything right and
      • it it hit me very very strongly
    • the speed
    • the capability
      • understand that we take those things for granted
      • but for a child to be able to pick a yellow ball
        • is a mathematical / spatial calculation
          • with muscle coordination
          • with intelligence
        • it is not a simple task at all to cross the street
        • it's not a simple task at all
          • to understand what I'm telling you
          • and interpret it
          • and build Concepts around it
        • we take those things for granted
        • but there are enormous Feats of intelligence"

http://writingball.blogspot.com/2014/09/mystery-machine-specialized-lc-smith.html

ELI (european law identifier) ECLI (European case law identifier) technical specification for web identifiers suggested vocabularies for metadata goal : legal documents in machine readable format.

But some counties don't have this implemted and that stands in the way of a pan-European legal Knowledge Graph.

Minimum sample size for external validation of a clinical prediction model with a binary outcome

Adaptive sample size determination for the development of clinical prediction models

MACHINE LEARNING FOR STOCK PRICES FORECASTING

Machine learning like money laundering for bias

Importante. Se pueden modelar efectos e interacciones para hacer predicciones predcisas sin la necesidad de cumplir con alguna asunción previa.

Objetivos del estudio

pros and cons about machine learning and traditional regression survival analysis such as KM-SV

Comparison between statistical models and machine learning models for competing risks.

No new physics and no new mathematics was discovered by the AI. The AI did however deduce something from the existing math and physics, that no one else had yet seen. Skynet is not coming for us yet.

Could it be the sift from person to person (known in both directions) to massive broadcast that is driving issues with content moderation. When it's person to person, one can simply choose not to interact and put the person beyond their individual pale. This sort of shunning is much harder to do with larger mass publics at scale in broadcast mode.

How can bringing content moderation back down to the neighborhood scale help in the broadcast model?

https://pair.withgoogle.com/

People + AI Research (PAIR) is a multidisciplinary team at Google that explores the human side of AI by doing fundamental research, building tools, creating design frameworks, and working with diverse communities.

“Holy trinity” of machine learning: models, data, and compute

Models in 1990s, starting with convolutional neural networks for computer vision.

Data in 2009 in the form of labeled images from Stanford AI researchers.

Compute in 2006 with Nvidia’s CUDA programming language for GPUs.

AlexNet in 2012 combined all of these.

A good description from Bob Doto.

On the Jokerzettel

9/8j Im Zettelkasten ist ein Zettel, der das Argument enthält, das die Behauptungen auf allen anderen Zetteln widerlegt.

Aber dieser Zettel verschwindet, sobald man den Zettelkasten aufzieht.

D.h. er nimmt eine andere Nummer an, verstellt sich und ist dann nicht zu finden.

Ein Joker.

—Niklas Luhmann, ZK II: Zettel 9/8j

Translation:

9/8j In the slip box is a slip containing the argument that refutes the claims on all the other slips. But this slip disappears as soon as you open the slip box. That is, he assumes a different number, disguises himself and then cannot be found. A joker.

Many have asked about the meaning of this jokerzettel over the past several years. Here's my slightly extended interpretation, based on my own practice with thousands of cards, about what Luhmann meant:

Imagine you've spent your life making and collecting notes and ideas and placing them lovingly on index cards. You've made tens of thousands and they're a major part of your daily workflow and support your life's work. They define you and how you think. You agree with Friedrich Nietzsche's concession to Heinrich Köselitz that “You are right — our writing tools take part in the forming of our thoughts.” Your time is alive with McLuhan's idea that "The medium is the message." or in which his friend John Culkin said, "We shape our tools and thereafter they shape us."

Eventually you're going to worry about accidentally throwing your cards away, people stealing or copying them, fires (oh! the fires), floods, or other natural disasters. You don't have the ability to do digital back ups yet. You ask yourself, can I truly trust my spouse not to destroy them?,What about accidents like dropping them all over the floor and needing to reorganize them or worse, the ghost in the machine should rear its head?

You'll fear the worst, but the worst only grows logarithmically in proportion to your collection.

Eventually you pass on opportunities elsewhere because you're worried about moving your ever-growing collection. What if the war should obliterate your work? Maybe you should take them into the war with you, because you can't bear to be apart?

If you grow up at a time when Schrodinger's cat is in the zeitgeist, you're definitely going to have nightmares that what's written on your cards could horrifyingly change every time you look at them. Worse, knowing about the Heisenberg Uncertainly Principle, you're deathly afraid that there might be cards, like electrons, which are always changing position in ways you'll never be able to know or predict.

As a systems theorist, you view your own note taking system as a input/output machine. Then you see Claude Shannon's "useless machine" (based on an idea of Marvin Minsky) whose only function is to switch itself off. You become horrified with the idea that the knowledge machine you've painstakingly built and have documented the ways it acts as an independent thought partner may somehow become self-aware and shut itself off!?!

https://www.youtube.com/watch?v=gNa9v8Z7Rac

And worst of all, on top of all this, all your hard work, effort, and untold hours of sweat creating thousands of cards will be wiped away by a potential unknowable single bit of information on a lone, malicious card and your only recourse is suicide, the unfortunate victim of dataism.

Of course, if you somehow manage to overcome the hurdle of suicidal thoughts, and your collection keeps growing without bound, then you're sure to die in a torrential whirlwind avalanche of information and cards, literally done in by information overload.

But, not wishing to admit any of this, much less all of this, you imagine a simple trickster, a joker, something silly. You write it down on yet another card and you file it away into the box, linked only to the card in front of it, the end of a short line of cards with nothing following it, because what could follow it? Put it out of your mind and hope your fears disappear away with it, lost in your box like the jokerzettel you imagined. You do this with a self-assured confidence that this way of making sense of the world works well for you, and you settle back into the methodical work of reading and writing, intent on making your next thousands of cards.

!- surveillance capitalism : example- - Propublica reported breathing machines for sleep apnea secretly send data to insurance companies

Scaling pas necessaire en rule based algo

https://genizalab.princeton.edu/events/2022/princeton-machine-learning-and-future-philology-symposium

Was this recorded?

Harm from Machine Translation (MT) models

MT models can create fluent and coherent blocks of text that mask the meaning in the original text and the intent of the original speaker.

Les mesures de qualite d'un modele de machine learning

https://niklas-luhmann-archiv.de/bestand/zettelkasten/zettel/ZK_2_NB_9-8-3_V

I've read and referenced this several times, but never bothered to log it into my notes.

Sasha Fast's translation:

Ghost in the box? Spectators visit. They get to see everything, and nothing but that - like in a porn movie. And the disappointment is correspondingly high.

0-shot learning

Example of Brian Eno using ITunes as a digital music zettelkasten. He's got 2,800 pieces of unreleased music which he plays on random shuffle for serendipity, memory, and potential creativity. The experience seems to be a musical one which parallels Luhmann's ideas of serendipity and discovery with the ghost in the machine or the conversation partner he describes in his zettelkasten practice.

correction factors for collocated sensors using ML

Here’s a lovely blog by Jason Wei that pulls together 137 examples of ’emergent abilities of large language models’. Emergence is a phenomenon seen in contemporary AI research, where a model will be really bad at a task at smaller scales, then go through some discontinuous change which leads to significantly improved performance.

I love that he points this out explicitly.

Some don't see the underlying processes of complexity within note taking methods and as a result ascribe magical properties to what are emergent properties or combinatorial creativity.

See also: The Ghost in the Machine zettel from Luhmann

Somehow there's an odd dichotomy between the boredom of such a simple method and people seeing magic within it at the same time. This is very similar to those who feel that life must be divinely created despite the evidence brought by evolutionary and complexity theory. In this arena, there is a lot more evolved complexity which makes the system harder to see compared to the simpler zettelkasten process.

Look into Eamonn Keogh's papers that won "best paper awards"

Interesting metaphor for why humans are happy to trust outputs from generative models

环境科学与工程(ESE)领域日益增长的数据量和复杂性，伴随着数据分析技术的进步而不断提高。先进的数据分析方法，如机器学习(ML) ，已经成为揭示隐藏模式或推断相关性的不可或缺的工具，而传统的分析方法面临着局限性或挑战。然而，机器学习的概念和实践并没有得到广泛的应用。该特性探索了机器学习在 ESE 领域革新数据分析和建模的潜力，并涵盖了此类应用所需的基本知识。首先，我们使用五个示例来说明 ML 如何处理复杂的 ESE 问题。然后，我们总结了机器学习在 ESE 中的四种主要应用类型: 预测、提取特征重要性、检测异常和发现新材料或化学品。接下来，我们介绍了 ESE 中机器学习应用所需的基本知识和目前存在的缺陷，重点介绍了应用机器学习时三个重要但经常被忽视的组成部分: 正确的模型开发、适当的模型解释和良好的适用性分析。最后，我们讨论了机器学习工具在 ESE 中的应用所面临的挑战和未来的机遇，以突出机器学习在这一领域的潜力。

"On the Opportunities and Risks of Foundation Models" This is a large report by the Center for Research on Foundation Models at Stanford. They are creating and promoting the use of these models and trying to coin this name for them. They are also simply called large pre-trained models. So take it with a grain of salt, but also it has a lot of information about what they are, why they work so well in some domains and how they are changing the nature of ML research and application.

ML algorithms must work or not work

Types of AC generator

Urbina, F., Lentzos, F., Invernizzi, C. et al. Dual use of artificial-intelligence-powered drug discovery. Nat Mach Intell (2022). https://doi.org/10.1038/s42256-022-00465-9

Taking carbon steel as an example, as shown in Picture 1, using a 1000w fiber laser cutting machine, for carbon steel materials thickness below 10mm, when the thickness of carbon steel is less than 2mm, the cutting speed per minute can be up to 8 meters. When the thickness is 6mm, the cutting speed is about 1.6 meters per minute, and when the thickness of the carbon steel is 10 mm, the cutting speed is about 0.6 to 0.7 meters per minute.

• Taking carbon steel as an example, as shown in Picture 1, using a 1000w fiber laser cutting machine, for carbon steel materials thickness below 10mm, when the thickness of carbon steel is less than 2mm, the cutting speed per minute can be up to 8 meters. When the thickness is 6mm, the cutting speed is about 1.6 meters per minute, and when the thickness of the carbon steel is 10 mm, the cutting speed is about 0.6 to 0.7 meters per minute.

It can be seen that when the thickness of carbon steel material is less than 2mm, customers who attach great importance to cutting speed can consider using 2000W fiber laser cutting machine, but the 2000W machine is much higher than 1000W in equipment price and operating cost. When the carbon steel material is larger than 2mm, the 2000W machine is not much faster than the 1000W cutting speed. Therefore, the 1000W fiber laser cutting machine is more cost-effective than the 2000W fiber laser cutting machine.

The cutting speed can directly reflect the efficiency of the fiber laser cutting machine. For cutting different materials with different thickness, the cutting speed will also change greatly. The thicker the thickness, the slower the speed!

Polis. (n.d.). Retrieved April 26, 2022, from https://pol.is/home

HA!

https://maggieappleton.com/joining-ought

A place to learn Machine Learning

1.10 Look at the machine from the higher level.

i don't really understand this: in z-code thre are tasks that other competitive softwares would need to restart all over again while z-code can do it without restarting...

.

It would be useful to suggest here:

Ideally one's note taking applications would automatically archive web pages to the Internet Archive as you take notes from them. This means that if they should disappear in the future, you'd have recourse to a useful and workable back up.

this is another post

sparql PREFIX osmt: <https://wiki.openstreetmap.org/wiki/Key:> SELECT * WHERE { ?museum osmt:addr:city "New York"; osmt:tourism "museum"; osmt:wikidata ?wikidataID . }

cito--cites--1375511 says “this article CiTO:cites article 137511”.

Machine Tags

A new kind of tags — machine tags — are supported now. A machine tag, e.g. meta:language=python consists of a namespace (meta), a key (language) and a value (python). Everyone can created machine tags, but the meta: namespace is protected and tags in there will be created by the site itself.

The codesite itself uses machine tags to make various properties of recipes accessible to the search:

• meta:language

  The programming language of the recipe, e.g. python, perl or tcl.

• meta:min_$lang_$majorver

  Those tags describe the minimum language version. If a recipe requires Python 2.5 it would have the tag meta:min_python_2=5.

• meta:license

  The license that was selected by the author, e.g. psf, mit or gpl.

• meta:loc

  This tag contains a number describing the lines of code in a recipes. It counts only the number of lines in the code block but not any lines in the discussion of in comments. This makes it possible to search for short recipes with less than ten lines or very large ones.

• meta:score

  The current score of the recipe. This is the same number that is displayed besides the recipe title and can only be influenced by voting on recipes. That way you could even search for down-voted recipes

• meta:requires

  Stores information about additional requirements of the recipes, e.g. required python modules. You can find recipes using python's collections module that way.

All those tags cannot be changed directly because they are generated from a recipe's properties.

We also support machine tags that follow the pattern NAMESPACE:KEY=VALUE. For example: geo:lat=43.555 camel:size=medium machine:tag=with space Machine tags are not revealed to the user on the track pages.