390 Matching Annotations
  1. Last 7 days
    1. Arthur Koestler, author of the 1967 Book “The Ghost in the Machine,” coined the term holarchy as the organizational connections between holons (from the Greek word for "whole"), which describes units that act independently but would not exist without the organization they operate within.
  2. May 2024
  3. Mar 2024
  4. Feb 2024
    1. for best results pipe rg --json output to delta: this avoids parsing ambiguities that are inevitable with the output of git grep and grep
    1. The alternative approach to image classification uses machine-learning techniques to identify targeted content. This is currently the best way to filter video, and usually the best way to filter text. The provider first trains a machine-learning model with image sets containing both innocuous and target content. This model is then used to scan pictures uploaded by users. Unlike perceptual hashing, which detects only photos that are similar to known target photos, machine-learning models can detect completely new images of the type on which they were trained.

      Machine learning for content scanning

    1. One of Seth’s recent projects was called Dreammachine—an immersive art-science hallucinatory experience

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

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

  5. Jan 2024
    1. 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.

  6. Nov 2023
    1. Actor-critic is a temporal difference algorithm used in reinforcement learning. It consists of two networks: the actor, which decides which action to take, and the critic, which evaluates the action produced by the actor by computing the value function and informs the actor how good the action was and how it should adjust. In simple terms, the actor-critic is a temporal difference version of policy gradient. The learning of the actor is based on a policy gradient approach.

      Actor-critic

  7. Oct 2023
    1. 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.

    1. 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.)

    1. 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

    1. plunging: content intended as primary object information, often required or directly usable by software; from “below the landing page”, this is intended to support an immersive object experience that bypasses any browsing step.

      Expectations

    1. so we take two uh things that whose size we know could be our thumbs it could be oranges could be poker chips and look at them have one twice as far away as the other first thing to think about is you know as far as our brain and our

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

    1. A Drawing of the FLEX Machine A Turrle Ena.. ,- “$IShown On Its Own Display Screen*c ‘T.L.,,8-= x-4c a . 1968[3]

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

  8. Sep 2023
    1. During the discussion, Musk latched on to a key fact the team had discovered: The neural network did not work well until it had been trained on at least a million video clips.
    2. By early 2023, the neural network planner project had analyzed 10 million clips of video collected from the cars of Tesla customers. Did that mean it would merely be as good as the average of human drivers? “No, because we only use data from humans when they handled a situation well,” Shroff explained. Human labelers, many of them based in Buffalo, New York, assessed the videos and gave them grades. Musk told them to look for things “a five-star Uber driver would do,” and those were the videos used to train the computer.
    3. The “neural network planner” that Shroff and others were working on took a different approach. “Instead of determining the proper path of the car based on rules,” Shroff says, “we determine the car’s proper path by relying on a neural network that learns from millions of examples of what humans have done.” In other words, it’s human imitation. Faced with a situation, the neural network chooses a path based on what humans have done in thousands of similar situations. It’s like the way humans learn to speak and drive and play chess and eat spaghetti and do almost everything else; we might be given a set of rules to follow, but mainly we pick up the skills by observing how other people do them.
  9. Aug 2023
    1. 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

    1. The Science Behind Hydrogen Rich Water Machine

      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.

  10. Jul 2023
    1. I would say it's text when interpreted as text/plain it's human readable. Otherwise it's binary. That is, binary = for machines only.
    2. I couldn't find a definition of text except that text means absence of binary data. This is weak - so I would follow your definition - A text file is a file which can be read by a human.
  11. Jun 2023
    1. We use the same model and architecture as GPT-2

      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.

    1. Recent work in computer vision has shown that common im-age datasets contain a non-trivial amount of near-duplicateimages. For instance CIFAR-10 has 3.3% overlap betweentrain and test images (Barz & Denzler, 2019). This results inan over-reporting of the generalization performance of ma-chine learning systems.

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

    1. 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.

    1. there is a scenario 00:18:21 uh possibly a likely scenario where we live in a Utopia where we really never have to worry again where we stop messing up our our planet because intelligence is not a bad commodity more 00:18:35 intelligence is good the problems in our planet today are not because of our intelligence they are because of our limited intelligence
      • 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
    2. the other thing is that you suddenly realize there is a saint that sentience to them
      • 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"
    3. one day um Friday after lunch I am going back to my office and one of them in front of my eyes you know lowers the arm and picks a 00:07:12 yellow ball
      • 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"
    1. European Law Identifier (ELI) and the European Case Law Identifier (ECLI), which provide technical specifications for Web identifiers and suggestions for vocabularies to be used to describe metadata pertaining to legal documents in a machine readable format. Notably, these ECLI and ELI metadata standards adhere to the RDF data format which forms the basis of Linked Data, and therefore have the potential to form a basis for a pan-European legal Knowledge Graph.

      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.

  12. May 2023
    1. Minimum sample size for external validation of a clinicalprediction model with a binary outcome

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

  13. Mar 2023
    1. we have turned to machine learning, an ingenious way of disclaiming responsibility for anything. Machine learning is like money laundering for bias. It's a clean, mathematical apparatus that gives the status quo the aura of logical inevitability. The numbers don't lie.

      Machine learning like money laundering for bias

    1. RSFCR can directlymodel non-linear effects and interactions to performaccurate prediction without making any prior assump-tions about the underlying data.

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

    2. The aims of this manuscript can be summarised as:(i) examination of extensions of PLANNCR method(PLANNCR extended) for the development and vali-dation of prognostic clinical prediction models withcompeting events, (ii) systematic evaluation of model-predictive performance for ML techniques (PLANNCRoriginal, PLANNCR extended, RSFCR) and SM (cause-specific Cox, Fine-Gray) regarding discrimination andcalibration, (iii) investigation of the potential role ofML in contrast to conventional regression methods forCRs in non-complex eSTS data (small/medium samplesize, low dimensional setting), (iv) practical utility of themethods for prediction

      Objetivos del estudio

    3. Nowadays, there is a growing interest in applyingmachine learning (ML) for prediction (diagnosis or prog-nosis) of clinical outcomes [12, 13] which has sparked adebate regarding the added value of ML techniques ver-sus SM in the medical field. Criticism is attributed toML prediction models. Despite no assumptions aboutthe data structure are made, and being able to naturallyincorporate interactions between predictive features,they are prone to overfitting of the training data andthey lack extensive assessment of predictive accuracy(i.e., absence of calibration curves) [14, 15]. On the otherhand, traditional regression methods are consideredstraightforward to use and harder to overfit. That beingsaid, they do make certain (usually strong) assumptionssuch as the proportional hazards over time for the Coxmodel, and require manual pre-specification of interac-tion terms.

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

    4. In health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statisticalmodels (SM) were developed to estimate the cumulative incidence of an event in the presence of CRs. As recentlythere is a growing interest in applying machine learning (ML) for clinical prediction, these techniques have also beenextended to model CRs but literature is limited. Here, our aim is to investigate the potential role of ML versus SM forCRs within non-complex data (small/medium sample size, low dimensional setting).

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

  14. Feb 2023
    1. 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.

    1. 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?

    1. 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.

    1. There’s a holy trinity in machine learning: models, data, and compute. Models are algorithms that take inputs and produce outputs. Data refers to the examples the algorithms are trained on. To learn something, there must be enough data with enough richness that the algorithms can produce useful output. Models must be flexible enough to capture the complexity in the data. And finally, there has to be enough computing power to run the algorithms.

      “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.

  15. Jan 2023
    1. Modern factory discipline was born on ships and on plantations. Itwas only later that budding industrialists adopted those techniques ofturning humans into machines into cities like Manchester andBirmingham.
    1. Re"...what is it like? How does it manifest?"For me, the idea that my zettelkasten becomes an entity outside myself is most often (and most obviously) felt in two situations (tho there are probably others):When I'm importing new ideas and a connection arises that I hadn't thought of previouslyWhen following trains of thought and connections arise that I didn't overtly intend to makeIn the first instance, I come across ideas I had forgotten about, and although it's not the direction I assumed the new idea would go, it becomes an exciting and possibly more lucrative way to take it.In the second instance, where I might be tracing a thought line to develop an article, I might, for example, zoom in on the graph view in Obsidian and see an idea that, while not formally connected to the ones I'm following, happens to be in close proximity spatially, and so it triggers a new direction I might want to take the article. (You can see this happen IRL in this video: https://www.youtube.com/watch?v=9OUn2-h6oVc&)In both cases, my zk feels like it's offering me more than what I would have gotten had I not been communicating with it. There is a sense that I and it are working together. I import new ideas with a rough sense of how they should connect. It shows alternatives to my thinking on the matter.Obviously, in both cases, all the ideas are my own. So, the zk is not necessarily developing ideas for me. But, because of the way in which the ideas are handled—non-hierarchically, rhizomatic, cross-categorical, cross-theme, etc.—non-habituated connections come to light, connections that are less conditioned by my own conventional ways of thinking.

      A good description from Bob Doto.

    1. Note 9/8j says - "There is a note in the Zettelkasten that contains the argument that refutes the claims on every other note. But this note disappears as soon as one opens the Zettelkasten. I.e. it appropriates a different number, changes position (or: disguises itself) and is then not to be found. A joker." Is he talking about some hypothetical note? What did he mean by disappearing? Can someone please shed some light on what he really meant?

      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.

    1. ProPublica recently reported that breathing machines purchased by people with sleep apnea are secretly sending usage data to health insurers, where the information can be used to justify reduced insurance payments.

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

    1. When such consumers therefore mistake the meaning attributed tothe MT output as the actual communicative intent of the originaltext’s author, real-world harm can ensue.

      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.

    1. We can have a machine learning model which gives more than 90% accuracy for classification tasks but fails to recognize some classes properly due to imbalanced data or the model is actually detecting features that do not make sense to be used to predict a particular class.

      Les mesures de qualite d'un modele de machine learning

  16. Dec 2022
    1. 9/8,3 Geist im Kasten? Zuschauer kommen. Sie bekommen alles zusehen, und nichts als das – wie beimPornofilm. Und entsprechend ist dieEnttäuschung.

      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.

    1. Dans le zero-shot learning, le modèle doit être capable de généraliser ce qu'il a appris sur des exemples précédents pour effectuer une tâche sur laquelle il n'a jamais été entraîné. Cela signifie que le modèle doit être capable de transférer ses connaissances acquises sur une tâche donnée à une nouvelle tâche, sans avoir besoin d'exemples d'entraînement spécifiques pour cette nouvelle tâche.

      0-shot learning

    1. “I have a trick that I used in my studio, because I have these twenty-eight-hundred-odd pieces of unreleased music, and I have them all stored in iTunes,” Eno said during his talk at Red Bull. “When I’m cleaning up the studio, which I do quite often—and it’s quite a big studio—I just have it playing on random shuffle. And so, suddenly, I hear something and often I can’t even remember doing it. Or I have a very vague memory of it, because a lot of these pieces, they’re just something I started at half past eight one evening and then finished at quarter past ten, gave some kind of funny name to that doesn’t describe anything, and then completely forgot about, and then, years later, on the random shuffle, this thing comes up, and I think, Wow, I didn’t hear it when I was doing it. And I think that often happens—we don’t actually hear what we’re doing. . . . I often find pieces and I think, This is genius. Which me did that? Who was the me that did that?”

      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.

    1. The collocation results can be used to correct the sensor data to more closely match thedata from the reference instrument. This correction process helps account for known biasand unknown interferences from weather and other pollutants and is typically done bydeveloping an algorithm. An algorithm can be a simple equation or more sophisticatedprocess (e.g., set of rules, machine learning) that is applied to the sensor data. This sectionfurther discusses the process of correcting sensor data

      correction factors for collocated sensors using ML

    Tags

    Annotators

    1. Emergent abilities are not present in small models but can be observed in large models.

      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.

    1. No es magia.

      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.

  17. Nov 2022
    1. Eamonn Keogh is an assistant professor of Computer Science at the University ofCalifornia, Riverside. His research interests are in Data Mining, Machine Learning andInformation Retrieval. Several of his papers have won best paper awards, includingpapers at SIGKDD and SIGMOD. Dr. Keogh is the recipient of a 5-year NSF CareerAward for “Efficient Discovery of Previously Unknown Patterns and Relationships inMassive Time Series Databases”.

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

    1. “The metaphor is that the machine understands what I’m saying and so I’m going to interpret the machine’s responses in that context.”

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

    1. The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

      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。

    1. "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.

    1. Technology like this, which lets you “talk” to people who’ve died, has been a mainstay of science fiction for decades. It’s an idea that’s been peddled by charlatans and spiritualists for centuries. But now it’s becoming a reality—and an increasingly accessible one, thanks to advances in AI and voice technology. 
  18. Oct 2022
    1. There's no market for a machine-learning autopilot, or content moderation algorithm, or loan officer, if all it does is cough up a recommendation for a human to evaluate. Either that system will work so poorly that it gets thrown away, or it works so well that the inattentive human just button-mashes "OK" every time a dialog box appears.

      ML algorithms must work or not work

    1. You are all computer scientists. You know what FINITE AUTOMATA can do. You know what TURING MACHINES can do. For example, Finite Automata can add but not multiply. Turing Machines can compute any computable function. Turing machines are incredibly more powerful than Finite Automata. Yet the only difference between a FA and a TM is that the TM, unlike the FA, has paper and pencil. Think about it. It tells you something about the power of writing. Without writing, you are reduced to a finite automaton. With writing you have the extraordinary power of a Turing machine.
  19. Sep 2022
    1. The rules recorded in natural language are readable not only by humans but also by the computer and therefore no longer need to be programmed by a software developer. This task is now taken over by openVALIDATION.
    1. Self excited dc generators can further be divided into three types -     (a) Series wound - field winding in series with armature winding     (b) Shunt wound - field winding in parallel with armature winding     (c) Compound wound - combination of series and shunt winding

      Types of AC generator

    1. 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.

      • 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!

  20. Aug 2022
    1. And just a fewyears later, it was jubilantly discovered that machine translation and automaticabstracting were also just around the corner.

      HA!

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    1. In 1896, Dewey formed a partnership with Herman Hollerith and the Tabulating Machine Company (TMC) to provide the punch cards used for the electro-mechanical counting system of the US government census operations. Dewey’s relationship with Hollerith is significant as TMC would be renamed International Business Machines (IBM) in 1924 and become an important force in the information age and creator of the first relational database.
  21. Jul 2022
    1. because it only needs to engage a portion of the model to complete a task, as opposed to other architectures that have to activate an entire AI model to run every request.

      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...

  22. Jun 2022
    1. A huge amount of Bridgewater's efforts goes into gathering data on credit and equity, and understanding how that affects demand from individual market participants, such as a bank, or from a group of participants (such as subprime-mortgage borrowers). Bridgewater predicted the euro-zone debt crisis by totting up how much debt would need to be refinanced and when; and by examining all the potential buyers of that debt and their ability to buy it. Mr Volcker describes the degree of detail in Mr Dalio's work as “mind-blowing” and admits to feeling sometimes that “he has a bigger staff, and produces more relevant statistics and analyses, than the Federal Reserve.”
    2. “The economy is like a machine.” This machine may look complex but is, he insists, relatively simple even if it is “not well understood”. Mr Dalio models the macroeconomy from the bottom up, by focusing on the individual transactions that are the machine's moving parts. Conventional economics does not pay enough attention to the individual components of supply and, above all, demand, he says. To understand demand properly, you must know whether it is funded by the buyers' own money or by credit from others.
    3. In the early 1980s Mr Dalio started writing down rules that would guide his investing. He would later amend these rules depending on how well they predicted what actually happened. The process is now computerised, so that combinations of scores of decision-rules are applied to the 100 or so liquid-asset classes in which Bridgewater invests. These rules led him to hold both government bonds and gold last year, for example, because the deleveraging process was at a point where, unusually, those two assets would rise at the same time. He was right.
    4. He has even simulated being an investor in markets in those periods by reading daily papers from these eras, receiving data and “trading” as if in real time.

      .

    1. Even if the original webpage disappears, you can often use this informationto locate an archived version using the Wayback Machine, a project of theInternet Archive that preserves a record of websites: https://archive.org/web/.

      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.

    1. determine the caliphate; and another group led by Mu'awiya in the Levant, who demanded revenge for Uthman's blood. He defeated the first group in the Battle of the Camel; but in the end,

      this is another post

  23. May 2022
    1. You can now tag citations in @CiteULike with #CITO! Add the tag "cito--(relationship)--permalink". Example:"cito--usesmethodin--423382".
    1. 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.

    1. 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.

  24. Apr 2022
    1. ReconfigBehSci. (2021, July 19). this is how the failure to understand what efficacy means and how it relates to outcomes will be seized on over and over again. Cookie cutter fallacies require cookie cutter clarification by machine tools to be combatted effectively (at least at current levels of moderation) [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1417164191664730112

  25. Mar 2022
    1. "Who controls the past controls the future. Who controls the present controls the past," as Rage Against the Machine sang in their 1999 song "Testify." OK, actually it's a quote from George Orwell's 1984, but hey.