Difference between revisions of "Loop"

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[http://www.google.com/search?q=loop+feedback+machine+learning+reinforcement ...Google search]
 
[http://www.google.com/search?q=loop+feedback+machine+learning+reinforcement ...Google search]
  
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* [http://en.wikipedia.org/wiki/Feedback Feedback | Wikipedia]
 
* [http://www.ultimate.ai/blog/ultimate-knowledge/so-what-actually-is-a-feedback-loop So, What Actually Is a Feedback Loop? | Tina Nord - Ultimate Knowledge]  ...With feedback loops, a system is constantly in dialogue with itself.
 
* [http://www.ultimate.ai/blog/ultimate-knowledge/so-what-actually-is-a-feedback-loop So, What Actually Is a Feedback Loop? | Tina Nord - Ultimate Knowledge]  ...With feedback loops, a system is constantly in dialogue with itself.
 
* [http://www.sciencedirect.com/science/article/pii/S1474667017437190 AI in the Feedback Loop: A Survey of Alternative Approaches | Karl-ErikÅrzén - ScienceDirect]  ...paper gives special attention to fuzzy control and expert control.  
 
* [http://www.sciencedirect.com/science/article/pii/S1474667017437190 AI in the Feedback Loop: A Survey of Alternative Approaches | Karl-ErikÅrzén - ScienceDirect]  ...paper gives special attention to fuzzy control and expert control.  

Revision as of 05:12, 26 September 2020

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Feedback Loop

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any process where the outputs of a system are plugged back in and used as iterative inputs. Feedback loops exist just about everywhere. In nature, the evolutionary "arms race" between predators and prey is a classic example. In business, the practice of taking customer feedback (the output of a product or service) and using it to improve future processes is another commonly used feedback loop. Today, rapid advances in artificial intelligence (AI) and machine learning are helping businesses do more with data. These systems — and their ability to analyze an inhuman amount of data — allow businesses to adjust algorithms, workflows and processes on the fly. Get More Out Of Feedback Loops With AI | Arka Dhar - Forbes

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Feedback loops: How nature gets its rhythms - Anje-Margriet Neutel
While feedback loops are a bummer at band practice, they are essential in nature. What does nature’s feedback look like, and how does it build the resilience of our world? Anje-Margriet Neutel describes some common positive and negative feedback loops, examining how an ecosystem’s many loops come together to make its ‘trademark sound.’ Lesson by Anje-Margriet Neutel, animation by Brad Purnell.

How Games Use Feedback Loops | Game Maker’s Toolkit
Playing Pyre over Christmas got me thinking about feedback loops: the reward structures in games that can reinforce or balance out winners and losers. In this episode I’ll explain what this all means, and talk about the design of Pyre’s positive and negative loops.

Automated Feedback with AI

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Future of feedback: Automated Feedback with AI
Speaker: Jan-Hein Gooszen (Technology Enhanced Learning at FeedbackFruits)

FeedbackFruits Official Video: The Reason Behind Our Journey
FeedbackFruits mission: Improve Learning. Find out more about our initiative to nurture innovation in higher education on a global scale on https://edtech-consortium.com or check out our official homepage at http://feedbackfruits.com !

Feedback Loops are Creating Consciousness

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Michio Kaku: Feedback loops are creating consciousness | Big Think
One of the great questions in all of science is where consciousness comes from. When it comes to consciousness, Kaku believes different species have different levels of consciousness, based on their feedback loops needed to survive in space, society, and time. According to the theoretical physicist, human beings' ability to use past experiences, memories, to predict the future makes us distinct among animals — and even robots (they're currently unable to understand, or operate within, a social hierarchy). Dr. Michio Kaku is the co-founder of string field theory, and is one of the most widely recognized scientists in the world today. He has written 4 New York Times Best Sellers, is the science correspondent for CBS This Morning and has hosted numerous science specials for BBC-TV, the Discovery/Science Channel. His radio show broadcasts to 100 radio stations every week. Dr. Kaku holds the Henry Semat Chair and Professorship in theoretical physics at the City College of New York (CUNY), where he has taught for over 25 years. He has also been a visiting professor at the Institute for Advanced Study at Princeton, as well as New York University (NYU).

Feedback Loop - Stock Market Predictions

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Why you can beat the market, even when it does not seem so. The importance of loops, patterns, and predictable events. Random events are don’t measure risks, and should not affect your decision making. Some traders follow the trend, and some go against it. At I Know First we work on algorithmic strategies which are neither, we simply try an assess where the next opportunity is and provide stock market predictions. If this means to do what everyone else does, than why not. If it means going against when everyone else does, this is also fine. The tricky part is determining where this opportunities are, this article will discuss how to find opportunities in what can seem as total randomness. Markets are Complex, but not Unpredictable! There are two major misconceptions about the stock market. The first one is connected to the classical economic theory which claims markets to be efficient, and as such unpredictable. In this case trying to select one stock over another becomes useless, as no opportunity is ever better than the other. Both stocks are perfectly priced according to their opportunity and risk, with everyone having all information. However, the truth of the matter is that some people profit trading stocks while others lose – this by itself proves the market to be inefficient, and thus exploitable. While US markets are very efficient, and most information is available, not everyone interprets this information the same. Stock Market Predictions: Where In The Feedback Loop Is Your Portfolio? | I Know First


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How Do Stock Trading Algorithms Work?
The stock market can be a foracious beast to those that don't understand it, but nowadays, you don't even need to understand it to make money. The rise of the digital information age and AI has brought about a new way of stock trading called algorithmic trading. Sometimes referred to as automated trading or black-box trading, this is essentially a program that can trade stocks at high speeds and frequencies perfectly in line with the market. These programs are given constraints and instructions like timing, price, amount, etc. and a user can fine tune how they exactly work. So how does this all work then... let's take a look. All images courtesy of Creative Commons or protected under Fair Use. For questions or concerns about the use of any media, please contact the page directly.

Synthetic Feedback Loop

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The Wandering Dreamer: An Synthetic Feedback Loop
This experiment uses four machine learning models to create a feedback loop between synthesized images and text. All of the images you see here are fabricated, as is the text that describes each image. Made by Brannon Dorsey using Runway. Source code

1. The first row of images are produced from a class label using BigGAN. 2. The text below is an auto-generated caption of the BigGAN image using Im2Text. 3. The next set of images are synthesized by an Attentional GAN using the auto-generated captions. 4. The text at the bottom classifies the image above it using MobileNet. This class label is then sent back to BigGAN as input to create an infinite loop.

Recursion

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Recursion for Beginners: A Beginner's Guide to Recursion
Recursion has an intimidating reputation for being the advanced skill of coding sorcerers. But in this tutorial we look behind the curtain of this formidable technique to discover the simple ideas under it. Through live coding demos in the interactive shell, we'll answer the following questions:

  • What is recursion, and when is it a good idea and bad idea to use it?
  • What's a stack, the call stack, and a stack overflow?
  • What are all the confusing ways that recursion is commonly taught?
  • Do some problems require recursion? Can recursion do anything a loop can't?
  • What is memoization, and how does functools.lru_cache work?
  • How do I draw that cool-looking recursive fractal artwork with Python's turtle module?

Beginners will be able to follow this talk. All that is required is a willingness to learn, and a willingness to have the willingness to learn, and a willingness to have the willingness to have the willingness to learn, and... so on. A Python conference north of the Golden Gate

Thinking Recursively | Microsoft Interview Question | Software Engineer UI/Frontend
Thinking Recursively is very important if you're a Software Engineer. Be it Microsoft Interview, or even while in day to day job, Recursion plays a very important role in Software Engineer's life. In this video, I'll solve a Microsoft Interview Question, just like how candidates do in the interview. Will also tell you how to create a mental model of solution and think while approaching such questions. It's not necessary that you solve the question in the first attempt. Most of the time we have to think step by step and gradually come up to the final solution.

Can you solve the Towers of Hanoi problem in Python using recursion? SOLUTION INCLUDED
This is a complete explanation of recursion. Recursion is a very useful tool in computer science and data science. Here I show you what recursion is and how to use recursion to solve the Towers of Hanoi problem using Python. I also use recursion to calculate factorial. Want to learn Python? You can buy my course here: http://bit.ly/2OwUA09 Want to ace the Data Science Interview? Over 1000 Data Science Practice Questions with model solutions: http://bit.ly/30ul0nX

Python Sudoku Solver - Computerphile
Fun comes in many forms - playing puzzles, or writing programs that solve the puzzles for you. Professor Thorsten Altenkirch on a recursive Sudoku solver. http://www.facebook.com/computerphile http://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: https://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

Unintended Feedback Loop

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Models that are an integrated part of a product experience, or what we referred to as data products, often involve feedback loops. When done right, feedback loops can help us to create better experiences. However, feedback loops can also create unintended negative consequences, such as bias or inaccurate model performance measurements... Getting Better at Machine Learning | Robert Chang - Medium

Leaders hoping to shift their posture from hindsight to foresight need to better understand the types of risks they are taking on, their interdependencies, and their underlying causes... Confronting the risks of artificial intelligence | B. Cheatham, K. Javanmardian, and H. Samandari - Mckinsey & Company

One of the key features of live ML systems is that they often end up influencing their own behavior if they update over time. This leads to a form of analysis debt, in which it is difficult to predict the behavior of a given model before it is released. These feedback loops can take different forms, but they are all more difficult to detect and address if they occur gradually over time, as may be the case when models are updated infrequently.

  • Direct Feedback Loops. A model may directly influence the selection of its own future training data. It is common practice to use standard supervised algorithms, although the theoretically correct solution would be to use bandit algorithms. The problem here is that bandit algorithms (such as contextual bandits) do not necessarily scale well to the size of action spaces typically required for real-world problems. It is possible to mitigate these effects by using some amount of randomization, or by isolating certain parts of data from being influenced by a given model.
  • Hidden Feedback Loops. Direct feedback loops are costly to analyze, but at least they pose a statistical challenge that ML researchers may find natural to investigate. A more difficult case is hidden feedback loops, in which two systems influence each other indirectly through the world. One example of this may be if two systems independently determine facets of a web page, such as

one selecting products to show and another selecting related reviews. Improving one system may lead to changes in behavior in the other, as users begin clicking more or less on the other components in reaction to the changes. Note that these hidden loops may exist between completely disjoint systems. Consider the case of two stock-market prediction models from two different investment companies. Improvements (or, more scarily, bugs) in one may influence the bidding and buying behavior of the other. Hidden Technical Debt in Machine Learning Systems | D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, M. Young, J. Crespo, and D. Dennison - Google

When the system is retrained on future data, it may become not less but more detrimental to historically disadvantaged groups. In order to build AI systems that are aligned with desirable long-term societal outcomes, we need to understand when and why such negative feedback loops occur, and we need to learn how to prevent them. When bias begets bias: A source of negative feedback loops in AI systems | Lydia T. Liu - University of California, Berkeley - Microsoft Research Blog

Weapons of Math Destruction | Cathy O'Neil | Talks at Google
Cathy O'Neil is a data scientist and author of the blog mathbabe.org. She earned a Ph.D. in mathematics from Harvard and taught at Barnard College before moving to the private sector and working for the hedge fund D. E. Shaw. O'Neil started the Lede Program in Data Journalism at Columbia and is the author of "Doing Data Science." She appears weekly on the "Slate Money" podcast. In this talk, O'Neil sounds an alarm on the mathematical models that pervade modern life and threaten to rip apart our social fabric. Get the book ... mathbabe

Building Trust in Your AI | Veritone
AI can deliver compelling business results, but do you know for a fact you are using the best available AI model for your data? Do you know what to expect after deploying? Is there risk of performance degradation or bias? Many AI projects fall short of expectations due to poor model performance or the unintended consequences of inaccurate AI decisions. What if there was a universal way for ML Ops / AI Ops to evaluate and monitor the performance and behavior of AI models, both pre-deployment and ongoing, no matter the vendor or features used? In this session we will review the pitfalls of opaque AI models, and discover how to evaluate, compare, and monitor performance and behavior across AI models, for better AI model trust and explainability. We will also demonstrate the Veritone Clarity product, showing how you can easily select the best AI model for the job, detect drift and correct it to achieve better business outcomes. For more details, visit us at http://Veritone.com

Unintended Feedback Loop - Filter Bubbles

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In December 2009, Google began customizing its search results for all users, and we entered a new era of personalization. With little notice or fanfare, our online experience is changing, as the websites we visit are increasingly tailoring themselves to us. In this engaging and visionary book, MoveOn.org board president Eli Pariser lays bare the personalization that is already taking place on every major website, from Facebook to AOL to ABC News. As Pariser reveals, this new trend is nothing short of an invisible revolution in how we consume information, one that will shape how we learn, what we know, and even how our democracy works. The Filter Bubble | Eli Pariser



In news media, echo chamber is a metaphorical description of a situation in which beliefs are amplified or reinforced by communication and repetition inside a closed system. Filter Bubble | Wikipedia



How Filter Bubbles Isolate You
In this video, you’ll learn more about how filter bubbles work to automatically curate content for you when you're online. Our text-based lesson We hope you enjoy!

Filter Bubbles and Echo Chambers
You've probably heard the term Filter Bubble and/or Echo Chamber at least once or twice in the past few months. It's a term that has been circling the media for some time about Facebook and the 2016 U.S. Presidential Election. But what do they mean exactly? How does it relate to the internet or more specifically Facebook and Google? How does it affect you? Watch the video to find out! Don't forget to leave us a comment about what you think and how Filter Bubble or Echo Chamber relates to you! Scene 1 “Filter Bubble” is a theory that the algorithms from companies like Facebook and Google bases the information given to you on data acquired from things like, your search history, your past click behavior, the type of your computer and your location. Therefore, limiting the topics that reach you to a bubble of only your own formulated interests and personalized search subjects. Scene 2 The term was coined by Eli Pariser who wrote a book on this subject explaining that these algorithms are “closing us off to new ideas, new subjects and important information ”. What he means is that you are not given information outside your own political views, religious views or even other data like for example updates on women's rights and animal rights.

Beware online "filter bubbles" | Eli Pariser
http://www.ted.com As web companies strive to tailor their services (including news and search results) to our personal tastes, there's a dangerous unintended consequence: We get trapped in a "filter bubble" and don't get exposed to information that could challenge or broaden our worldview. Eli Pariser argues powerfully that this will ultimately prove to be bad for us and bad for democracy. Read our community Q&A with Eli (featuring 10 ways to turn off the filter bubble): http://on.ted.com/PariserQA

Feedback loops in data systems - Matthieu Ranger
When 'filter bubbles' came to public attention, it became pressing that systems that consume their own recommendations as data can be subject to noxious feedback loops. In this talk, we go over several examples of feedback loops, then discuss the technical and management issues related. Montréal-Python 74: Virtual Echo http://montrealpython.org/2019/03/mp74/