Not-so-common machine learning examples that challenge your knowledge

Machine learning refers to the process by which a computer learns and changes its operations based on patterns identified in large amounts of data. When we think of machine learning, we think of a few well-known examples. For example, the way Amazon recommends products is remarkably similar to the Google searches you’ve done. The scope of machine learning is much wider than what we know and observe in our daily lives.

Because machine learning is such a young science, the limits of its applicability are continually being pushed out. Virtual personal assistants were once the stuff of fantasies, but now they can be found in every other household. While some cases are obvious, there are several other ways that machine learning affects our lives that you may not have considered.


Machine learning has recently been used to detect seismic waves and evaluate the models of a million hand-labeled seismograms using the collected data and machine learning. More than twice as many earthquakes are detected by the algorithm as by scientists. Scientists will be able to identify earthquakes in near real time when a significant amount of data has been collected and trends established.

Due to the nature of machine learning, it may soon be possible to predict earthquakes in advance. This has a huge influence on the readiness and readiness of disaster management forces, health care, firefighters and other emergency responders.


Underwater noise pollution is a serious danger to marine mammals such as whales. It was recently discovered that machine learning was able to process acoustic data and reliably recognize whales. This is because of the training data that is fed into the machine learning algorithms. Quality, accurate training data is crucial for algorithms, and companies like Cogito and Analytics are companies that can provide high quality training data. Participants were invited to submit their best machine learning algorithms for detecting whale calls from audio recordings in a competition hosted by Marinexplore and Cornell University. Cargo carriers, agents and other interested parties can use the information to plan shipping routes and prevent accidents if a whale is discovered. The sound footprint of whales would be reduced if ships were diverted from them.


Cook Inlet’s belugas are endangered and machine learning may be the key to saving them. Underwater sounds disturb marine life and the resulting disturbance can cause behavioral changes and physiological damage in creatures. Scientists can plan the recovery of this whale population by using algorithms to accurately and quickly understand the characteristics of whales.


A 9-year-old competition led to the idea that psychopathic traits can be detected in people based on their language patterns and social behavior, both of which can be analyzed using Twitter. A survey of Reddit users was recently conducted in a similar fashion. The algorithm learned to identify people with mental health issues by accumulating information from user posts on specific forums.

Since the outbreak made face-to-face contact impossible, the use of social media has exploded in recent years. In addition to the increased use of social media, the ongoing epidemic has resulted in an increase in the number of people suffering from poor mental health. It would be extremely beneficial to develop a method of identifying people struggling with their mental health. Researchers who use machine learning to assess indications of mental health disorders can share their findings with businesses, organizations, and individuals.

There are existing apps that interact human-like to help users overcome loneliness and improve mental health.


Machine learning is frequently mentioned as a way for consumers to assess how products look on their skin from the comfort of their own homes. Producers, too, use this technology to evaluate products while they are still in the creation stage. This means that the training data that is fed into the machine learning algorithms is the reason. The algorithms require precise, high-quality training data, which can be provided by companies like Cogito and Analytics.

Machine learning is increasingly used in the beauty industry to produce more profitable and faster products. The same technology is now used to “objectively” classify people based on their attractiveness. Apps that perform this assessment require the person to submit a photo of their face without makeup and compare it to other people’s photos. Aspects such as symmetry, wrinkles, dark circles and facial imperfections are all taken into account. This is a disturbing thought, not only because many people are sensitive to their appearance, but also because of the mental health consequences of body dysmorphia and other associated disorders.

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The question arises: why do we have to classify people based on their physical appearance? If we truly believe that “beauty is in the eyes of the beholder,” then an algorithm that compares and analyzes people and how they look is just unsettling. This flies in the face of the concept that beauty is subjective and could have dire consequences on people’s self-perception.


In the sports industry, a large amount of data is accessible to analyze player performance, success rate and other statistics. One of the most exciting applications of machine learning in this industry is how it can be used by sports teams and coaches to anticipate injuries. The system analyzes muscle movement data and detects recurring patterns, allowing teams and coaches to be notified when default patterns are disturbed.

This information is useful in the preventive care of the player. The sports team will save millions of dollars in medical expenses, missed income and recovery costs. However, while every professional athlete is aware of the danger of injury, having an algorithm predicting that you would be injured within that time frame (assuming high accuracy) could have a severe impact on a player’s mental health and performance.


Data is needed for machine learning models to work. Even the best performing algorithms can be rendered worthless without a high quality training database. This is because when machine learning models are trained on insufficient, incorrect, or irrelevant data at first, they can be handicapped. When it comes to training data for machine learning, the old adage “garbage in, garbage out” still holds true.

This blog demonstrates that machine learning is both dynamic and adaptable. Its applications cover industries and even time. Machine learning is becoming increasingly important due to the huge amounts of data accessible and the fact that ML successfully collects, analyzes and interprets this data. At the same time, the challenges that the IT industry can solve through machine learning are becoming increasingly worrying. It is essential that we use our heads to find innovative, relevant and ethical applications for the amazing instrument that is machine learning.

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Roger max

Hello, my name is Roger Max. I am a technology writer specializing in understanding and addressing training data requirements for companies across various industries and industries that use machine learning, AI or NLP. At Cogito, I manage highly motivated teams of data annotators, taggers and content moderators in processing many datasets during the day and night, I write about my experiences and offer ideas and insights. solutions.

About Shirley L. Kreger

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