Five Fun Facts About Machine Learning, a Way for People to Work Smarter, Not Harder
By Space Coast Daily // November 26, 2018
What is the first thing you think about when you hear the words “machine learning”? Most people will say a classroom full of children, sitting in front of computers, learning a new computer concept.
Would you be surprised to know machine learning is not related to the education system? The introductory guide to machine learning included below will help you gain an understanding of what machine learning is, and the real-life applications we use every day without knowing it.
Believe it or not, machine learning is already a large part of our daily life. Let’s take social media, Facebook, for example, facial recognition tags you in photos without the need for you to tag yourself, or the Facebook algorithm determining what posts and advertisements you would like to see in your timeline based on your typical Facebook activity.
Machine learning does not stop with social media. It plays a large part in the expansion of knowledge in linguistics, management, visualization, statistics, and Big Data.
To help you form a better understanding, we have compiled the below the facts below.
1. Machine Learning vs. Artificial Intelligence
It is important to understand machine learning and artificial intelligence are not the same. The two terms are often used interchangeably, however, they are quite different.
Artificial Intelligence is defined by the ability to acquire and apply knowledge to increase success. The idea is for it to mimic human response and behavior in different circumstances. Think of the HBO series Westworld, and how the robot created to mimic human behavior and response.
Machine Learning focuses on the acquisition of knowledge or skill to improve accuracy. The goal is to develop self-learning algorithms to sort through large amounts of data. As we mentioned before, the Facebook algorithm is a prime example. If you have 100 friends on Facebook, you would see 50,000 posts, if you saw every single post each of your friends posted. The algorithm uses data from your regular interactions to select which posts are most relevant to you.
2. The Effects of Data
The effectiveness of the machine learning depends directly on the quality of data fed to the machine. When utilizing self-learning algorithms, it is important to note the machine develops a model based on the data. If the parameters are too wide, the model will be skewed.
3. Improving the Effectiveness of Machine Learning
Machine learning is only effective if it is being used with the type of data it was trained to use. It is crucial to renew the models on a regular basis to improve and maintain the effectiveness of the machine learning process.
4. The Difficulties of Machine Learning Processes
The most difficult part of the machine learning process is transforming raw data into a set of features that represent the data. Machine learning goes beyond creating and adjusting algorithms, It focuses on selecting data and character development and transforming that information into features the program can use to understand data.
5. The Biased Machine
It is important to note that machine learning can produce biased results. All decisions the system makes are based on past data. Once a biased pattern has been developed, the machine learning runs a risk of processing all new data in support of previously obtained biases.
The advancements in modern machine learning algorithms bring about a world of possibilities. With a range of real-world possibilities in the business world, entrepreneurs are now searching out developers who work with and implement machine learning.
Utilizing machine learning for business purposes helps big and small businesses process large volumes of data faster than before. Having the ability to process data at a quicker speed allows business owners to pull relevant data without manually sifting through all of the data manually. Machine learning is not creating a scary doomsday apocalypse as many movies predict; it delivers a way for people to work smarter, not harder.
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