Reliance of Machine Learning on Feedback Loops
Crucial to the improvement of machine learning (ML) algorithms are feedback loops. Different research studies in AI have established that neural network models with at least a single feedback loop often outperform those devoid of one. Yet, there are ethical concerns resulting from the incorporation of these loops into intelligent systems.
Commonly utilized particularly in neural networks is the system known as closed-loop machine learning or simply, machine learning feedback loops. This method significantly enhances the labeling accuracy. The principle is simple: the more feedback loops the neural network comprises, the more precise the output. In essence, enhancing the robustness of a neural network model simply necessitates adding more feedback loops.
Neural network feedback loops, emulating the human brain, are engaged in models for review purposes, allowing the model to revisit known data as a learning tool for more accurate future performance. Its similarities to a student's study routine is notable. One of their critical roles is to ensure that AI does not reach a plateau in its development. They allow for the data collected during the delivery of the user-desired product to be used for the training of newer model versions. Without feedback loops, AI tends towards the easiest path, even when it's incorrect, resulting in decreased performance. By incorporating a feedback loop, learning in models can be amplified, thereby fueling their steady evolution over time.
Limitations and Misuse of Feedback Loops
However, some misuse of feedback data by renowned social media companies has elicited public outcry. Internet mammoths like Facebook and YouTube use intricate recommendation systems, leveraging feedback data to manipulate online user behavior. They analyze user's online behavior patterns - frequently searched topics, preferred content, search history - and use these to deliver content that aligns closely with the user's interests. Consequently, user engagement on their platforms increases, ramping up their visibility through ads. But this approach has its downsides. For instance, harmful or illegal content such as videos endorsing terrorism or explicit content is often promoted more easily by these recommendation algorithms. This subsequently entraps the user in a consumption pattern of detrimental content, prompting some users to boycott such platforms.
The development of self-driving vehicles serves as a more tangible illustration of the problem. Amongst all its applications, feedback loops are indispensable in the object identification technology space. For a self-driving car, it is paramount to accurately identify traffic signals, road signs, humans, other vehicles, and diverse object categories –a feat made possible by feedback loops. But the narrative does not end there. Some car manufacturers also recur to feedback loops for vehicle decision making, leading to raised eyebrows. In times of emergency or impending car accidents, the autonomous car must make split-second decisions that could result in harm to passengers, collision with pedestrians, or damage to property. Proving to be something of a double-edged sword, feedback loops thus have their downsides too.
Applications of Feedback Loops
Feedback loops are versatile and find applications in diverse domains including:
- In Software Development: Feedback loops are utilised to identify potential code errors or defects.
- Economics domain: Corporations leverage feedback loops as a mechanism to reinvest profits to generate even more revenue.
- In the business Sphere: Loops are employed as a tool where consumer feedback informs future product development strategies.
- Biology: Organisms use feedback loops to maintain balance throughout their life cycles. Humans example include internal temperature regulation and healing processes.