Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and Predictive maintenance.

How Does Machine Learning Work?

Similar to how the human brain gains knowledge and understanding, machine learning relies on input, such as training data or knowledge graphs, to understand entities, domains and the connections between them. With entities defined, deep learning can begin.
The machine learning process begins with observations or data, such as examples, direct experience or instruction. It looks for patterns in data so it can later make inferences based on the examples provided. The primary aim of ML is to allow computers to learn autonomously without human intervention or assistance and adjust actions accordingly.

Why Is Machine Learning Important?

Machine learning as a concept has been around for quite some time. The term “machine learning” was coined by Arthur Samuel, a computer scientist at IBM and a pioneer in AI and computer gaming. Samuel designed a computer program for playing checkers. The more the program played, the more it learned from experience, using algorithms to make predictions.
As a discipline, machine learning explores the analysis and construction of algorithms that can learn from and make predictions on data.
ML has proven valuable because it can solve problems at a speed and scale that cannot be duplicated by the human mind alone. With massive amounts of computational ability behind a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes.

Machine Learning Is Widely Adopted

Machine learning is not science fiction. It is already widely used by businesses across all sectors to advance innovation and increase process efficiency. In 2021, 41% of companies accelerated their rollout of AI as a result of the pandemic. These newcomers are joining the 31% of companies that already have AI in production or are actively piloting AI technologies.
As a discipline, machine learning explores the analysis and construction of algorithms that can learn from and make predictions on data.

• Data security: Machine learning models can identify data security vulnerabilities before they can turn into breaches. By looking at past experiences, machine learning models can predict future high-risk activities so risk can be proactively mitigated.
• Finance: Banks, trading brokerages and fintech firms use machine learning algorithms to automate trading and to provide financial advisory services to investors. Bank of America is using a chatbot, Erica, to automate customer support.
• Healthcare: ML is used to analyze massive healthcare data sets to accelerate discovery of treatments and cures, improve patient outcomes, and automate routine processes to prevent human error. For example, IBM’s Watson uses data mining to provide physicians data they can use to personalize patient treatment.
• Fraud detection: AI is being used in the financial and banking sector to autonomously analyze large numbers of transactions to uncover fraudulent activity in real time. Technology services firm Capgemini claims that fraud detection systems using machine learning and analytics minimize fraud investigation time by 70% and improve detection accuracy by 90%.
• Retail: AI researchers and developers are using ML algorithms to develop AI recommendation engines that offer relevant product suggestions based on buyers’ past choices, as well as historical, geographic and demographic data.

Training Methods for Machine Learning Differ

Machine learning offers clear benefits for AI technologies. But which machine learning approach is right for your organization? There are many to ML training methods to choose from including:

• supervised learning
• unsupervised learning
• semi-supervised learning

Let’s see what each has to offer.

Training Methods for Machine Learning Differ

Supervised machine learning algorithms apply what has been learned in the past to new data using labeled examples to predict future events. By analyzing a known training dataset, the learning algorithm produces an inferred function to predict output values. The system can provide targets for any new input after sufficient training. It can also compare its output with the correct, intended output to find errors and modify the model accordingly.

Supervised Learning: More Control, Less Bias

Supervised machine learning algorithms apply what has been learned in the past to new data using labeled examples to predict future events. By analyzing a known training dataset, the learning algorithm produces an inferred function to predict output values. The system can provide targets for any new input after sufficient training. It can also compare its output with the correct, intended output to find errors and modify the model accordingly.

Unsupervised Learning: Speed and Scale

Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. At no point does the system know the correct output with certainty. Instead, it draws inferences from datasets as to what the output should be.

Reinforcement Learning: Rewards Outcomes

Reinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovering errors or rewards. The most relevant characteristics of reinforcement learning are trial and error search and delayed reward. This method allows machines and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Simple reward feedback — known as the reinforcement signal — is required for the agent to learn which action is best.

Machine Learning Is Not Perfect

It is important to understand what machine learning can and cannot do. As useful as it is in automating the transfer of human intelligence to machines, it is far from a perfect solution to your data-related issues. Consider the following shortcomings before you dive too deep into the ML pool:

• Machine learning is not based in knowledge. Contrary to popular belief, machine learning cannot attain human-level intelligence. Machines are driven by data, not human knowledge. As a result, “intelligence” is dictated by the volume of data you have to train it with.
• Machine learning models are difficult to train. Eighty-one percent of data scientists admit that training AI with data is more difficult than expected. It takes time and resources to train machines. Massive data sets are needed to create data models, and the process involves manually pre-tagging and categorizing data sets. This resource drain can create latency and bottlenecks in advancing ML initiatives.
• Machine learning is prone to data issues. Ninety-six percent of companies have experienced training-related problems with data quality, data labeling and building model confidence. Those training-related problems are a key reason why seventy-eight percent of ML projects stall prior to deployment. This has created an extraordinarily high threshold for ML success.
• Machine learning is often biased. Machine learning systems are known for operating in a black box, meaning you have no visibility into how the machine learns and makes decisions. Thus, if you identify an instance of bias, there is no way to identify what caused it. Your only recourse is to retrain the algorithm with additional data, but that is no guarantee to resolve the issue.

The Future of Machine Learning: Hybrid AI

For all of its shortcomings, machine learning is still critical to the success of AI. This success, however, will be contingent upon another approach to AI that counters its weaknesses, like the “black box” issue that occurs when machines learn unsupervised. That approach is symbolic AI, or a rule-based methodology toward processing data. A symbolic approach uses a knowledge graph, which is an open box, to define concepts and semantic relationships.
Together, ML and symbolic AI form hybrid AI, an approach that helps AI understand language, not just data. With more insight into what was learned and why, this powerful approach is transforming how data is used across the enterprise.