Machine learning applications came out of the field of artificial intelligence (AI). It is the process of computers having the ability to learn from experience. For instance, the software is coded with a generic algorithm that it can build upon. As technology continues to evolve, machines can now learn from input. We need machine learning to filter through, and analyze, massive sets of complex data.
Specific types of machine learning algorithms include:
- Unsupervised learning (Discovering the rules, and groupings of data, without the corresponding output)
- Supervised learning (Learning from previous examples)
- Reinforcement learning (Learning through a series of rewards and punishments)
In addition, examples of machine learning applications include:
- Facial recognition
- Predicting the weather
- Medical patient diagnosis
- Filtering emails
Only five decades ago, machine learning was considered a technology of science fiction. Famous writers, from Jules Verne to George Lucas, had the bright imagination to humanize artificial entities.
In 1952, Arthur Samuel at IBM created a program to play checkers. Samuel played with the program so often that it was able to improve with each consecutive game. It was Samuel who first coined the term, “machine learning.” [Rosenblatt, Frank. “The perceptron: a probabilistic model for information storage and organization in the brain.” Psychological review 65.6 (1958): p. 386.]
Today, machine learning has become an embedded technology many of us take for granted in our daily lives. There are many industries which currently use it to help improve the efficiency of their processes.
Through machine learning, businesses can move ahead of descriptive and predictive analytics to prescriptive analytics without a hitch. Keep reading to learn more.
Whether marketers work in the B2C or B2B fields, they are now utilizing it to grow their audiences and personalize their messages to make them much more relevant.
To start, many marketers are realizing the gold mine machine learning provides in terms of competitor analysis. Through software, marketers can track the conversions made through the experiences their competitors provide.
Some say competitors are the best friends you can have. As a result, it is critical to keep an eye on what your competitors are doing to ensure you will always have a place in your industry.
With machine learning applications, marketers are given an in-depth view of what works for their competitors. Even with mounds of data, it is helping marketers to get through it all.
Another way marketers are currently using machine learning is through social listening. Today’s markets are consumer-driven.
These customers want to be seen, heard, and understood. Naturally, there just aren’t enough hours in the day to try to understand every single prospect or customer on social media. Instead of spending thousands of hours combing various platforms, marketers are using machine learning instead.
In addition, marketers are using it to decipher complex algorithms when deciding how to optimize their marketing content. To illustrate, machine learning software can help marketers figure out how to set the tone for their messages and which specific words will best resonate with their target audience.
Agriculture is another industry moving fast into the future with the help of machine learning algorithms. For instance, it is being used to forecast the weather for chosen locations. It is also being used to figure out if machinery can access remote fields.
Then, there are options to find out whether pests are prominent based on the environmental surroundings. In agriculture, machine learning is also being used with a combination of historical data to provide accurate data and analysis for a variety of farm/agricultural environments. So, farmers can make much better decisions.
Since the construction industry is vital to the growth of any nation, they inherently aren’t going to get left out of the machine learning revolution. On any given day, a construction project can be embedded with hundreds of change orders and thousands of issues.
But machine learning applications now including being used to help teams determine the most critical tasks such as the top 10 priorities that need attention ASAP. It is also being used for safety
monitoring, finding safety hazards, and identifying issues such as missing materials or equipment.
There isn’t any doubt that machine learning is transforming many industries, including manufacturing. Certainly, manufacturing is process work which drives innovation. During the process, companies are always trying to determine the specific factors – such as quality and efficiency – which will drive commercial success. Well, now the hours needed for that process has been cut significantly.
According to this study by McKinsey & Company, machine learning has helped to reduce annual maintenance costs of industrial equipment by 10 percent through predictive maintenance.
Machine learning has also helped to reduce scrap rates in the semiconductor manufacturing industry. Furthermore, according to PwC, machine learning will contribute to a 31 percent growth rate for connected factories over the next five years. [p. 48, PDF]
Plus, manufacturers are using it to improve product availability while decreasing any errors made in supply chain forecasting.
Machine learning applications are driven by data consumption. And, there are just so many manual processes used in healthcare. The good news is machine learning is helping to drastically improve workflows and the data compiled into electronic medical records.
With this technology, healthcare practitioners can use the power of data-driven analytics. As a result, they are able to improve the accuracy of patient diagnoses, treatment options, and medical resource efficiency.
In our modern times, we store most of our data on computers and various devices. Sensitive data could be saved on disks, local servers, or on the cloud. Moreover, massive amounts of data are transmitted every day across networks to other devices.
For these reasons, and more, cyber security is critical in an age where cyber attacks are growing by the second. It is welcome news that machine learning is helping to improve the cyber security industry.
Despite many advancements in cyber security technologies, there are still challenges to overcome. Well, machine learning is providing the answers to those obstacles. For example, machine learning algorithms can use models of behavior to make predictions about future security threats.
Just read the daily news feeds, and you’ll see new cyber attacks every day – companies are truly overwhelmed. Yet, machine learning is now being used to provide warnings of potential/emerging attacks. Also, it is being utilized to monitor network traffic while learning about the norms of these types of systems.
As you can see, there are so many fantastic machine learning applications used behind the scenes for multiple industries. If you notice an industry becoming much more efficient, then it’s a safe bet they’re using machine learninng.