Difference between revisions of "Loop"
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* [http://www.wired.com/story/the-toxic-potential-of-youtubes-feedback-loop/ The Toxic Potential of YouTube’s Feedback Loop | Guillaume Chaslot - Wired] | * [http://www.wired.com/story/the-toxic-potential-of-youtubes-feedback-loop/ The Toxic Potential of YouTube’s Feedback Loop | Guillaume Chaslot - Wired] | ||
* [http://www.kdnuggets.com/2017/06/unintended-consequences-machine-learning.html The Unintended Consequences of Machine Learning | Frank Kane - Sundog Education - KDnuggets] | * [http://www.kdnuggets.com/2017/06/unintended-consequences-machine-learning.html The Unintended Consequences of Machine Learning | Frank Kane - Sundog Education - KDnuggets] | ||
+ | * [http://towardsdatascience.com/dangerous-feedback-loops-in-ml-e9394f2e8f43 Dangerous Feedback Loops in ML | David Blaszka - Towards Data Science] | ||
+ | http://miro.medium.com/max/400/1*2-mGCxjRYINaExTLwH-Bhw.gif | ||
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... [http://medium.com/@rchang/getting-better-at-machine-learning-16b4dd913a1f Getting Better at Machine Learning | Robert Chang - Medium] | 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... [http://medium.com/@rchang/getting-better-at-machine-learning-16b4dd913a1f Getting Better at Machine Learning | Robert Chang - Medium] |
Revision as of 23:37, 25 September 2020
YouTube search... ...Google search
- Human-in-the-Loop (HITL) Learning
- Recommendation
- Reinforcement Learning (RL)
- Closing the Loop: How Feedback Loops Help to Maintain Quality Long-Term AI Results | Natalie Fletcher - Clarifai
Contents
Feedback Loop
YouTube search... ...Google search
- So, What Actually Is a Feedback Loop? | Tina Nord - Ultimate Knowledge ...With feedback loops, a system is constantly in dialogue with itself.
- AI in the Feedback Loop: A Survey of Alternative Approaches | Karl-ErikÅrzén - ScienceDirect ...paper gives special attention to fuzzy control and expert control.
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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Automated Feedback with AI
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Feedback Loops are Creating Consciousness
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Stock Market Predictions - Feedback Loop
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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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Synthetic Feedback Loop
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Recursion
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Unintended Feedback Loop
YouTube search... ...Google search
- The Toxic Potential of YouTube’s Feedback Loop | Guillaume Chaslot - Wired
- The Unintended Consequences of Machine Learning | Frank Kane - Sundog Education - KDnuggets
- Dangerous Feedback Loops in ML | David Blaszka - Towards Data Science
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
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Unintended Feedback Loop - Filter Bubbles
YouTube search... ...Google search
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
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