Author: Gaurav Sharma Machine Learning Photo by Nature.com Machine Learning is the method through which computers learn and modify their operations using patterns found in large amounts of data. There are a handful of well-known examples when we talk about machine learning. Amazon’s recommendation system for products looks very similar to what you have done with Google. The reach of machine learning is much wider than the ones we’re used to and see in our everyday lives. Machine learning is still a relatively young field of science. This means that the limits of machine learning’s applicability continue to be pushed beyond. While virtual personal assistants used to be a fantasy, they are now available in all homes. Although some of the effects are obvious, machine learning can have a significant impact on our daily lives in many other ways. PREDICTION AND DETECTION EARTHQUAKES Machine Learning has been recently used to predict earthquakes and analyze patterns from over a million seismograms. This was done using machine learning and data. The algorithm detects more earthquakes than scientists. When a large amount of data is collected and trend patterns established, scientists will soon be able identify earthquakes almost immediately. Machine learning makes it possible to predict earthquakes in advance. This is a significant influence on emergency response and preparedness, including healthcare and firemen as well as disaster management and medical services. WHALES IN OCEAN IDENTIFICATION Marine mammals such as whales are at risk from underwater noise pollution. Recent research has shown that machine learning can recognize whales by processing their acoustic signals. Marinexplore invited participants to submit the best machine-learning algorithm they could use to detect whale sounds using audio recordings. This competition was hosted by Cornell University and Marinexplore. If a whale is found, cargo carriers and agents may use the information to design maritime routes or avoid collisions. Ships could be diverted away from whales to reduce their noise footprints. https://www.youtube.com/watch?v=tSVHDzOZ_-Q The beluga whales of Cook Inlet are endangered, and machine learning might be the key to saving them. Submarine sounds can disrupt marine life and cause physiological and behavioral problems. The algorithms used by scientists to quickly and precisely understand patterns in whale behavior can be used to plan for the restoration of this population. MENTAL HEALTH – USE OF SOCIAL MEDIA A nine-year-old contest led to the idea that psychopathic traits can be identified in individuals based on language and their social behavior. Both of these patterns can be analyzed with Twitter. Similar methods were used in an earlier Reddit survey. By aggregating information from specific forum posts, the algorithm was able to determine who has mental illness. Social media use has increased dramatically since the outbreak made it impossible to have in-person contact. The ongoing epidemic has led to an increase in people suffering from mental illness. A method to identify people who have problems with their mental health would be very beneficial. Machine learning can be used by researchers to identify signs of mental disorders. These findings may then be shared with people, businesses and organizations. Replika is one example of an existing application that can converse with users in a human-like way to help them overcome loneliness and improve their mental health. A platform could be created that uses social media to identify the most vulnerable and then directs them towards Replika, which allows for quick intervention. INDUSTRY OF BEAUTY Machine Learning is often mentioned by consumers as a method for them to assess how products might feel on their skin, from their home. This technology is also used by producers to assess goods as they’re still being created. https://www.youtube.com/watch?v=2tmJw1_XPA0 Machine learning is increasingly being used in the beauty sector to produce goods that are more cost-effective and timely. This same technology can now be used to “objectively” rank individuals based upon their beauty. This rating requires that the person submits a picture of their face, without any makeup. Then they can compare it with other photos. All aspects such as facial symmetry, wrinkles and dark circles are taken into consideration. It is a worrying thought because so many people are very sensitive to their appearances. This raises the question: Why do we have to rate people on the basis of their appearance? An algorithm that analyzes the appearance of people is unsettling if we really believe that beauty is subjective. This algorithm is against beauty’s subjective nature and could have devastating consequences on people’s self-perceptions. PREDICTIONS OF SPORTS INJURY There is a lot of information available in the sports industry for analysis and statistics such as player success rates, performance, and other stats. Machine learning can be applied to sports coaching and teams in order to predict injuries. This is one of the best uses of machine-learning. This system analyses muscle movements and detects patterns that are consistent, so coaches and teams can be alerted when these patterns change. The information can be used to help the player with their preventative care. This will result in a savings of millions of dollars for the sports team, including missed income and medical expenses. While professional athletes are aware of the risk of injury, an algorithm that predicts you will be hurt within a certain timeframe (assuming high accuracy), could have serious consequences for the athlete’s mental health as well as their performance. Machine learning models can only work with data. MACHINE LEARNING FOR THE FUTURE Without high-quality data training, even the most efficient algorithms could be useless. In fact, machine learning models that are trained using insufficient, inaccurate, or inappropriate data during the initial stages of development might become handicapped. The old saying “garbage out, garbage in” applies to machine learning data training. Machine learning can be dynamic as well as adaptable, and this blog shows it. Machine learning has many applications across industries, and can even be used to extend time. Because of the large amount of data available and the ease with which ML analyzes and interprets it, machine learning has become more important. Machine learning is also a solution to many problems that IT can solve. We must use our brains to discover ethical, relevant and innovative uses for machine learning’s amazing tool. This article, Not so common machine learning examples that challenge your knowledge was first published on Medium. People are responding and highlighting this story on Medium. Published via
