Meta Leadership Primer: Machine Learning
In our world today, the computational systems we rely on are growing more intelligent by the day. In the quest to make the computer more human than machine, researchers and developers have imbibed some super abilities in the computer and machine learning is one of those special computer abilities.
Machine Learning or ML is a branch of Artificial Intelligence that is based on the idea that machines can learn from data, experiences, identify patterns, and make decisions on its own just like humans would. Through Machine Learning, computers learn automatically and improve their experience from data gathered without being programmed by humans at all time. In a nutshell, they program themselves spontaneously.
Machine learning has grown from its initial simple concept of common pattern recognition to becoming a process that is of importance to the world of today; making recommendations, and predicting events as they learn continuously through exposure to data. It is a lot simpler to say that Machine Learning has brought computers closer to humans intellectually.
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Machine Learning Methods
Machines are learning faster today because of their ability to process a vast array of data in a short period, and this learning can happen through four methods: supervised learning algorithm, unsupervised learning algorithm, semi-supervised learning algorithm, and Reinforcement Learning algorithm.
Supervised Learning Algorithm: This Machine Learning method makes it possible for machines to learn under supervision from programmers by processing specifically labeled data which is the traditionally accepted type of data that computers processed. What this means is that they learn when data is inputted where and how they are supposed to; e.g., adding + or — in columns where they request for a charge or stuff like that. Anything outside labeled data is as good as useless for machines learning with this algorithm.
Unsupervised Learning Algorithm: This the opposite of supervised learning and in this method, the machine learns from roughly placed data by trying to make sense out of the data cluster. This method is best when a machine is meant to learn from humans of various kinds as humans communicate in unlabeled data. In this case, the machine has to learn from unstable human communication like sarcasm, ironies, and others making it difficult for the machine to predict future events accurately.
Semi-supervised Learning Algorithm: This method is more effective as the machine has to learn from both labeled and unlabeled data (more from unlabeled because it has to relate more with random humans). This method allows for a more accurate prediction of events and a more natural classification of data. It is mostly adopted by e-commerce stores to recommend products to customers based on the customer’s shopping records (unlabeled) and the sizes of the customer’s previously purchased products (labeled).
Reinforcement Learning Algorithm: This is more like a trial and error kind of learning where the machine interacts with its environment, learn from it, perform actions and produce results and when it doesn’t work out well, it continues to work on improving further outcomes. It learns as the trial and error process goes through environmental feedbacks.
Applications of Machine Learning
Machine Learning has grown so relevant that it is considered a valuable asset in different sectors of the economy including:
Financial Services: Banks and other financial services has employed machine learning to help prevent frauds by spam blocking as the machine learns to block emails, files, and software that share a certain level of similarities to prevent fraudulent acts. Also, the finance industry uses ML to identify essential insights on data which helps identify investment opportunities and help investors know when to trade.
Healthcare: Machine Learning has created chances for improved medical diagnoses by the invention of wearable devices and sensors to keep track of a patient’s health progression.
Government Agencies: Because of the extensive amount of data available to the government, they have turned to Machine Learning to help manage these data and help improve services and track progressive changes in democratic countries.
Oil and Gas: This sector being one of the most significant sectors of the world economy requires Machine Learning analyze data about minerals in the ground and predicting refinery sensor failures.
Transportation: This is one of the major sectors where Machine Learning has made a wave with the invention of self-driven Google cars which gets to learn from the way it is driven over time and then it takes over the driving from there over time.
Retail: This is another sector where Machine Learning has a significant impact as we can see it evidently in retail store recommender systems where the recommendation machine learns from past experiences to accurately predict what a particular customer would like and recommend them.
Machine Learning is a promising advancement in Artificial Intelligence and technological advancements generally. If you have any hopes for organizational long term Viability, this is definitely a technology domain that resources in your entity should assigned to investigating further.
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