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Feedback Loop

Feedback Loop AI

Most ML algorithms rely heavily on feedback loops to improve their performance. Many AI research indicates that neural network models with at minimum one feedback loop perform much better than those without. However, some debate the ethical implications of implementing feedback loops in intelligent systems.

  • Machine learning feedback loops (also known as closed-loop machine learning) are commonly utilized, particularly neural networks, to enhance labeling accuracy.

The further feedback loops the NN contains, the more accurate the output. Simply, if we want a stronger neural network model, we merely need to add more feedback loops.

Neural network feedback loops are used in models to try to simulate the human brain. A feedback loop allows your model to review what everyone already knows so that it may continue to learn from this data to perform better the next time, much like a student studies.

Feedback loops guarantee that AI advances do not stall. This also has the considerable benefit of using data from the same delivering the product that the client is interested in forecasting over to train fresh versions of the model. Without them, AI will take the path of least obstacle, even if it is the wrong path, leading its performance to suffer. You may boost your models’ learning and keep them developing over time by implementing a feed loop.

The dark side of Feedback Loops

On the other hand, there has been an increase in public criticism of well-known social media companies that exploit feedback data to control their users’ online behavior. To increase the frequency with which people use their online platforms, internet giants such as Facebook, YouTube, and others use sophisticated recommendation systems to study users’ online behavior information such as often searched topics, most viewed content and search history so that they can deliver contents that are more accurately matched to the user’s preferences. As a response, consumers will spend more time on such platforms, and those companies will gain visibility through adverts. However, there are a lot of extreme or illegal items on their platforms, such as terrorism or pornography, where recommendation algorithms assist disseminate that information more readily. For example, if a viewer has viewed a video clip or two about terrorism, the algorithm would propose more movies on the subject to keep the individual viewing, thus creating a cycle in which the viewer consumes horrible stuff. As a result, some people are boycotting those sites.

The creation of a self-automobile is another illustration of this issue. Without the need for a question, feedback loops are essential in this sector of object identification technology. While driving autonomously, a vehicle must be able to detect traffic signals, road signs, people, automobiles, and all other types of objects, where feedback loops can aid enhance accuracy. However, this is not the entire tale. Some automakers also employ feedback loops to manage the vehicle’s decision-making component, which leaves many people scratching their heads. In the event of a crisis or an occurring automobile catastrophe, the automated car would have to make an instant choice for the passengers within, which might hurt the passengers, strike pedestrians on the street, or ruin other people’s belongings. After all, feedback loops appear to be a double-edged sword.

Positive Feedback Loop examples

Feedback loops may be used in a variety of disciplines, including:

  • Feedback loops are used in software development to discover possible faults or errors in the code.
  • In economics, a loop is a corporation that reinvests sales money to produce even more profits.
  • In business, a loop is used as a technique in which product developers utilize consumer input to influence future actions.
  • Feedback loops in biology aid organisms in maintaining equilibrium throughout their life cycles. Internal temperature control and healing are two examples of feedback loops in humans.