How Is Machine Learning Helping Us?
As discussed earlier, artificial intelligence is a wide variety of innovative techniques and processes used to perform complex tasks with minimal human intervention. One of the major branches of AI is called ‘machine learning’, which operates on the principle that a machine can be trained to effectively read large amounts of data and infer the appropriate information and conclusions from it, for the benefit of man. The more data is fed into a system, the more it can learn and store results for future use.
In this article, we are going to learn about the main applications of machine learning, and how it is intertwined with our everyday life.
Machine Learning in Emotion Detection:
Emotion detection is the ability of a machine to interpret human emotion and tone. This covers everything from alterations in facial expressions, to hand gestures, speech gestures, body language, posture and more. After studying what each of these mean, and how they change in relation to each human emotion; a machine learning application can apply that knowledge onto new input or data, and come up with faultlessly accurate information about user sentiments that a company may need.
Emotions or non-verbal context can be understood by the help of parameters that a machine is trained to detect and pinpoint- the main one being facial expressions. After learning the parts of a human face, and how each muscle moves in relation to another; as well as determining baseline features based on a targeted population- a machine is able to understand how the face changes when, for example, a customer is unhappy, a child cries, or even psychological states of unrest and worry.
Real time measurements of blood pressure, heart rate, pulse changes, temperature, sweating, etc., are some commonly used physiological signals incorporated into ML based emotion detection. By the heaps of information given to the machine about what each bio signal is signifying, it can assess the new measurements and reach conclusions about what a certain person is feeling.
A machine learning system can listen to spoken language and understand what is being said. Not just that, but it can dive into the context including sociocultural norms and undertones used in a spoken piece of data. Learning a language- mainly based on techniques like NLP- including its contextual, metaphorical, and idiomatic twists and turns, is an art difficult to master for even humans, but with big data and machine learning algorithms, this task is achievable.
Body Gestures and Movements:
Our musculoskeletal system reacts to any change in our mental state and these responses, for instance, fidgeting of legs, shrugging, jumping up and down in joy, are observed by machine learning systems and interpreted accordingly.
Targeted Advertisements and Machine Learning:
The term targeted advertisement has garnered a lot of attention in the past decade or two. The principle behind it is that with the growing demographic diversity and varied socio-cultural trends, comes the need to micro-target customers; as it is impossible to have one single product campaign and wish to create the desired impact on all the different consumer groups.
Machine learning obtains data from social media sites or search engines, etc. and uses it to categorize the entire consumer base into sub-groups. The way it is able to achieve this is by studying and analyzing user behavior and tendencies. Sites like Facebook, for example, track even mouse movements of their users and are able to collect this level of extensive and specified data and sell it to companies so that they can understand their target market and run different versions of the same campaign, designed for each group.
Load Forecasting Using Machine Learning:
Load forecasting is the estimation of electric power required to meet the demands of a certain grid, used by electricity providing companies today. The creation of “smart grids” has enabled companies to predict how much power they are going to need to produce in the foreseeable future. The three categories they divide it into are short-term load forecasting (STLF), medium-term load forecasting (MTLF), and long-term load forecasting (LTLF). Machine learning systems are also able to factor in variables like weather, seasonal changes, demand trends, time of the day, and electricity prices etc.
This usage of machine learning and artificial intelligence has proven to save time and cost for companies, while also benefiting the consumers when their power demands are accurately estimated and catered to, leaving almost no room for power outages or supply issues.
Much like load forecasting, traffic prediction also uses different variables and their historical data values to come up with an estimated amount of traffic that can be expected on a particular road at a particular time. This helps transportation companies plan their routes and schedule deliveries, while also aiding the general public in avoiding possible traffic jams, road maintenance activities and detours of any kind. An example of this can be Google Maps, which suggests the shortest route to any location depending on, not just the distance, but also traffic prediction algorithms.
As we all know, self-driving or autonomous cars are said to be ‘the future’ of vehicles. Equipped with sonar, lidar, radar sensors, and cameras; an autonomous car can see, hear and perceive a lot of data all at once. Machine learning comes in here and processes all this information in real time, as well as deciding the next best course of action. The whole process takes only a few seconds and this astounding level of accuracy and functionality makes self-driving cars, according to some, even better than human drivers.
The list of time and money saving applications of machine learning does not end here, and the ever-evolving research around it signifies that we have just scratched the surface of how revolutionary it can be to human life. In just a few decades, we have come from only rapid mathematical calculations to human-like intelligence and mannerisms. The road ahead is exciting, to say the least.