Every day, the impact of new technology affects industries, cultures and people. Digital technology has the potential to change the world more than many other industries. Out of the various new inventions and ideas emerging from the IT sector, perhaps none are as impactful and important as machine learning.
But what exactly is machine learning, how does it work and why is it so crucial for the future?
Machine learning explained
Machine learning is a kind of artificial intelligence technology. It leverages vast quantities of data and specialized algorithms to imitate human behavior and, more specifically, the way that humans learn skills. Over time, machine learning tools and technologies improve their accuracy or efficacy at certain tasks.
Think of machine learning technology as software that enables computers to become “smarter” or better at certain things. For example, through machine learning, cybersecurity technology can gradually become better at detecting malware and other computer viruses, including those that change their appearances or shapes (e.g., polymorphic viruses).
Machine learning has far more potential applications beyond digital security, however. With machine learning, industries and businesses of all kinds can see potential benefits and improvements in their bottom lines.
How does machine learning work?
Although the details can vary heavily depending on the software or tools at hand, machine learning primarily works through three main steps.
First, machine learning algorithms perform a decision process. They receive input data from a user or another system, and the algorithm then makes an estimate or guess about a pattern in the data. For instance, a marketing firm may use machine learning technology to better guess the prices of online ads for the customers of its client.
Next, the machine learning algorithm is subjected to an error function. An error function is an evaluation that checks the prediction accuracy of the model reduced by the machine learning algorithm. If there are any examples already in use, the error function may compare the algorithm’s success or accuracy against those real-world examples.
Finally, if the machine learning (or ML) model can fit data points in a specific set of limitations better, it adjusts its “weights” or elements in its algorithms. In theory, this will allow it to make progressively better and more accurate predictions going forward. The algorithm repeats these three steps over and over to evaluate and optimize its processes, becoming a little better each time.
As ML technology improves, these three steps each become more effective and efficient, resulting in faster improvements.
Methods of machine learning
Practically all major ML algorithms and technologies use four primary approaches or methods.
- Supervised learning, in which data scientists give algorithms carefully labeled training data and specifically define variables that they want the algorithm to assess or evaluate for. Through supervised learning, ML algorithms can become better at guessing or predicting specific things more quickly.
- Unsupervised learning, in which ML algorithms train themselves on unlabeled data. Algorithms may scan through vast data sets and look for meaningful connections. However, this training model still has predictions and output recommendations that are predetermined by testers.
- Semi-supervised learning, in which the two preceding types of learning are mixed. Data scientists might give a machine learning algorithm primarily training data, but also allow the algorithm to explore other non-training data to develop a better understanding of the data set/type.
- Reinforcement learning, in which data scientists teach machines to complete multistep processes with very clearly defined rules. Data scientists may program a specific algorithm to complete a certain task, then give it positive and/or negative cues as it figures out how to complete the task most efficiently or correctly. The algorithm is left to determine which steps to take to maximize its functionality.
Common algorithms and tools
Machine learning algorithms use different tools and processes to accomplish their tasks and to become gradually better at their jobs over time. These include:
- Neural networks: Effectively simulate how the human brain operates. They link many processing nodes together to enable algorithms and AI software to recognize patterns. They’re important in ML technologies like image recognition, language translation and speech recognition.
- Linear regression algorithms: Used to predict numerical values based on linear relationships between unique values. Linear regression algorithms are oftentimes used for stock market analyses and similar purposes.
- Logistic regression algorithms: Make predictions for response variables like yes or no questions. Such algorithms may be utilized for classifying spam emails, detecting malware and other viruses, or ensuring quality control for a manufacturing plant’s production line.
- Decision trees: Can be used to predict values or classify data into different categories. Decision trees use branching sequences of connected or linked decisions, which are then represented using tree diagrams.
- Random forests: Where ML algorithms predict certain values or categories by combining the outcomes from different decision trees.
- Clustering algorithms: Identify data patterns so the data points can be categorized. Computers with clustering algorithms may help data scientists by quickly identifying differences between different data items that humans can miss or overlook by accident.
The importance of machine learning
Machine learning is highly important in a variety of industries and sectors. For example, cybersecurity firms and tools use it to better analyze and detect malware and other computer viruses. As it becomes more adept at detecting viruses, the risk of malware intrusions becomes less likely for businesses, government servers and other important data centers.
Similarly, ML can be used to improve cloud computing technology and other business technologies. For instance, machine learning can help maximize data transmission between servers, streamline cloud operations and further enhance cloud security.
Cloud computing, artificial intelligence and ML are all interconnected, and they’ll likely become even more so in the years to come. But machine learning can go beyond even these basic functions. In the real world, businesses already use ML applications for things like chatbots. By being trained on recorded customer service conversations, ML algorithms can produce near-human responses for customers who need answers to basic questions, such as how to contact certain people or how to fix simple problems.
In another instance, ML technology is used for speech recognition software. Self-driving cars, while still in the early stages, leverage the technology to drive more safely than even the most responsive and attentive human as well.
In time, ML will become so effective in so many areas that it will replace human workers in a variety of professions. Data-driven jobs, especially those about data analysis or organization, may be taken over by ML algorithms, which will be able to do the same work faster and with fewer errors than human counterparts.
Should we rage against the machine, or embrace it?
Machine learning is already vitally important many industries, and it will likely become more crucial as time goes on. Odds are your business has already been impacted, but we’re not all out of a job just yet! For now, there are definitely ways for technology providers to leverage ML tools for greater business success, whether through cybersecurity applications or other means.
Looking for a partner to help your business take advantage of emerging technology, among other IT trends? Check out our partner guide for ways Sherweb can help you achieve your goals.
