Though machine learning is hardly a new technology, it’s entering more and more conversations as artificial intelligence continues its rapid expansion. The implications of machine learning are wide—you can’t pick up your phone without crossing several machine learning models at work.
With the technology becoming more approachable, businesses are turning to it in droves, and are quickly realizing its transformative potential. Repetitive processes that used to suck up hours of employee time can now be automated, freeing up humans for higher quality work. Customers are better served, receiving responses faster. Organizations operate with increased efficiency, squeezing more value from technology and people. It’s plain to see that machine learning is changing the way we work.
So what is machine learning? This introductory article will give a brief history, provide examples of common machine learning applications, and discuss the benefits of using machine learning in a business setting.
What Is Machine Learning?
In computer science, machine learning is a type of artificial intelligence (AI) that helps software applications grow more accurate in predicting outcomes without being explicitly programmed. To do this, machine learning relies on algorithms and statistical models that are trained on large amounts of data. As a system processes more and more data, it is able to make more accurate decisions.
Types of Machine Learning
There are several different types of machine learning. Three of the most common include supervised learning, unsupervised learning, and deep learning.
In supervised learning, the system is trained on labelled data, where the correct output is provided for each input. This allows the system to learn the relationship between the input and the output and make predictions on new data.
In unsupervised learning, the system is not given any labelled data, and must find patterns and relationships within the data on its own. This is often used for clustering and grouping data points.
Deep learning uses algorithms specifically designed to learn from large, unstructured datasets. It’s called “deep” because the model consists of many layers of interconnected nodes. Deep learning algorithms are able to learn hierarchical representations of data, which allows them to perform complex tasks such as image and speech recognition, natural language processing (NLP), and machine translation.
The History of Machine Learning
The concept of machine learning has its roots in the field of artificial intelligence, which emerged in the 1950s as a way to develop algorithms and models that could simulate human intelligence. In the early days of AI research, the focus was on developing algorithms that could solve specific problems, such as playing chess or proving mathematical theorems.
Over time, research teams recognized the limitations of these approaches, and began to explore ways of building algorithms that could learn from data rather than being explicitly programmed. This led to the development of the first machine learning algorithms, which were designed to learn from labeled data and improve their performance over time.
Fueled by the availability of data and the development of more powerful computing systems, machine learning experienced a resurgence in the 1980s and 1990s. This led to the creation of new machine learning algorithms and techniques, which have become fundamental tools in modern machine learning.
In recent years, the field of machine learning has continued to evolve and grow, driven by advances in artificial intelligence, the proliferation of big data, and the increasing availability of powerful computing systems. Today, machine learning is used in a wide range of applications.
Applications of machine learning
The applications of machine learning software are widespread, and more and more industries are realizing its potential for optimizing business processes.
Everyday applications of Machine Learning
Image and Speech Recognition
Machine learning solutions can be used to identify objects, people, and scenes in images, as well as recognize and transcribe spoken words.
Natural Language Processing
Machine learning technology can be used to understand and interpret human language, allowing computers to read and understand text, and even hold conversations with humans.
Machine learning tools can be used to analyze data and make predictions about future events, such as customer behavior or market trends.
Machine learning software can be used to recommend products or content to users based on their past behavior and preferences.
These are just a few examples of the many different applications of machine learning. As the technology advances, the potential uses for machine learning will continue to expand.
Machine Learning in Healthcare
One of the main benefits of using machine learning technology and intelligent automation in the healthcare industry is that it can make document processing more accurate and efficient. For example, machine learning technology can extract handwritten and machine-typed data on prescription forms, and then package it into a machine-readable format for use in downstream systems – even across the messiest handwritten forms.
Additionally, machine learning can be used to automate certain tasks, such as analyzing medical images, patient records, doctor’s notes and other complex documents, which can help reduce the workload on healthcare professionals and allow them to focus on providing high-quality care to their patients.
Machine Learning in Finance
Machine learning is increasingly being used in the financial industry for a variety of purposes, though there is still lots of room for wider adoption. According to Gartner research1, 64% of finance chiefs believe autonomous finance will be the reality within the next six years, but only 21% are using machine learning in their finance operations. As the technology evolves and more financial institutions recognize its benefits, adoption will, adoption will become more widespread.
As with healthcare, one of the main benefits of using machine learning technology in the finance industry is improved efficiency.
For example, machine learning can be used to speed up a bank’s onboarding process while mitigating associated cost and risks, helping financial institutions meet regulatory standards without increasing headcount.. Or, a machine learning algorithm can be used to analyze market data and make trades on behalf of a financial institution, allowing the institution to take advantage of market opportunities more quickly and efficiently.
Machine learning can also help financial institutions reduce the risk of human error. Algorithms can be trained to identify patterns and take action based on those patterns without the potential for bias or other errors that can occur with human decision-making. This can help financial institutions make more accurate and reliable decisions.
Machine Learning in Insurance
Claims are the backbone of insurance agencies, and this process is often accompanied by a
paper trail full of manual processes. Take into account the high variation in forms and the amount of handwritten signatures involved, and filing insurance claims manually results in unnecessary clerical errors, delayed decisions, and unhappy customers.
Fortunately, the insurance industry is aggressively adopting AI-based solutions. In fact, ninety-nine percent of the insurance industry has implemented or plans to implement AI technologies by 2025, according to the 2023 Gartner CIO and Technology Executive Survey2.
Machine learning applications can make notoriously paper-intensive processes highly streamlined. These solutions automatically classify and extract critical information across various forms, and this digitized data can be easily used later by other applications.
Machine learning can also be used to identify fraudulent activities, such as the submission of false claims or the use of fake identities to obtain coverage, helping insurers save money and reduce the likelihood of paying out fraudulent claims.
Machine Learning in Government
The use of machine learning technology in the public sector has the potential to greatly improve the efficiency, and effectiveness of government programs and services. As such, its popularity is rapidly increasing—among government respondents to the 2022 Gartner CIO and Technology Executive Survey, 23% plan to increase spending in artificial intelligence/machine learning3.
Whether it’s dealing with vast volumes of assistance applications and permitting requests, or time-sensitive claims, invoices or tax refunds, machine learning document processing solutions can read and transcribe documents, regardless of messy handwriting, fax lines or other imperfections.
By leveraging the power of data and advanced algorithms, machine learning can help government agencies make better decisions, deliver services more effectively, and improve the lives of the people they serve.
Machine Learning in Retail
The retail industry has been using machine learning extensively in recent years to improve the accuracy and efficiency of personalization and recommendation systems. Machine learning algorithms can be trained on large amounts of data about customers and products, such as past purchases and browsing behavior, to make more accurate and relevant recommendations. This can help retailers improve the customer experience and increase sales by showing customers products that they are more likely to be interested in.
Additionally, machine learning can be used to automate certain tasks, such as analyzing customer feedback and identifying trends, which can help retailers make better decisions about which products to stock and how to market them.
Use of Machine Learning in Intelligent Document Processing
By using machine learning, organizations can significantly improve the accuracy and efficiency of their document processing, and can reduce the amount of manual work involved in extracting information from documents and emails. This can help organizations save time and resources, and can also help them make better decisions by providing them with more accurate and up-to-date information.
To do this, machine learning algorithms are trained on large amounts of data, but this training doesn’t impose a significant burden on users. Additionally, human supervision (known as human-in-the-loop) enables users to provide their input when the machine is not confident enough to produce an accurate prediction. This feedback is used to augment and improve the training data, leading to better performance.
An ML-based approach to document processing can also be very helpful for automating processes with high document variability, such as invoicing. Invoices vary wildly from one company to the next, but with the use of ML, it’s not necessary to create hundreds or even thousands of layouts for each format simply to identify and extract relevant data.
Machine learning is used throughout several stages of the intelligent document processing (IDP) workflow. Here’s how ML can be applied during three of these steps.
If the input formats are images (e.g., scanned images of the handwritten document and printed text), machine learning can fix image distortions such as document skew and rotation. Furthermore, ML models convert images of the input content to a series of text segments.
ML-based classification algorithms match the input documents document to a user-defined layout for additional processing.
After inputs have been converted to text, Additional ML models can be applied to extract info and insight per users’ requirements. This includes information such as names, account IDs on a form, transaction details on banking statements, and paragraphs describing competitions in long financial documents.
Machine Learning Benefits
The three main benefits of implementing machine learning in an organization are:
Improved accuracy and efficiency: Machine learning software can analyze large amounts of data faster than humans can, and has the ability to identify patterns that may not be easily discernible for human eyes. This helps improve the accuracy and efficiency of document processing by automating repetitive tasks, such as reviewing mortgage applications and keying information found on forms.
Faster and more effective problem-solving: Machine learning solutions can be trained to solve complex problems, such as recognizing objects in an image or translating natural language. Quickly solving dedicated problems and presenting the data in a way that is human-readable helps organizations make better business decisions, faster.
Increased competitiveness: Machine learning can help organizations gain a competitive edge by providing them with insights and capabilities that are unavailable or under-used by competitors. Additionally, by automating certain tasks and freeing up human resources, ML-based solutions help organizations become more agile and better able to respond to changing market conditions.
Implementing Machine Learning
A successful framework for implementing machine learning typically involves the following steps:
- Define the Problem and Goal: The first step is to clearly define the problems you want to solve and the goals that you want to achieve, along with the metrics you will use to evaluate the performance. This will help you identify the type of machine learning solution most appropriate for your needs.
- Prepare the Data: Training machine learning models requires large amounts of data, and it is important to ensure that this training data is high quality and is in the appropriate format. Preparation can involve cleaning and pr-eprocessing the data, and may also require labeling the data if you are using a supervised learning algorithm.
- Train the Model: Once the data is prepared, you can train the machine learning model. This will typically involve selecting the model architecture, hyperparameters, and training algorithm, and then training the model on the data using a distributed computing platform.
- Evaluate Model Performance: After the model is trained, it is important to evaluate its performance using the metrics that you defined in step 1 to determine whether the model is achieving the desired goals.
- Iterate and improve: Once the model is evaluated, it may be necessary to iterate and improve the model. This may involve collecting additional data, fine-tuning the model, or applying regularization techniques to prevent overfitting.
Challenges and Limitations of Machine Learning
While machine learning has many potential benefits, there are also challenges and limitations that must be addressed in order to fully realize its potential. Some of the most common challenges include:
Lack of Labeled Data
One of the key challenges of machine learning is the need for large amounts of data to train the algorithms. In many cases, obtaining and labeling the data can be time-consuming and expensive, which can limit the applicability of machine learning to certain tasks and domains.
High Computational Requirements
Training machine learning models can be computationally intensive, and can require significant amounts of data storage and hardware resources, particularly when real-time performance is required.
Machine learning algorithms can only learn from the data that is available to them, and if the data is biased, the resulting models may be biased as well. For example, if a machine learning model is trained on a dataset that is disproportionately composed of men, it may not be able to accurately predict the outcomes for women. Addressing bias in the data is a key challenge for machine learning practitioners.
Poor Visibility & Interpretability
Many machine learning solutions are black boxes, meaning that it is difficult or impossible to understand how they are making their decisions and predictions. This can make it difficult to trust and validate the results.
The Future of Machine Learning
The future of machine learning will involve further advances in the underlying algorithms and technologies, as well as the expansion of its applications to new domains and industries.
There are many exciting developments and trends in machine learning, and the field is evolving rapidly. Some of the latest machine learning trends include:
- Increased use of Large-scale Language Models:
Due to their ability to generate human-like text on a wide range of topics, large-scale language models, such as GPT-3, have become increasingly popular in recent years. These models are based on transformer language models, and have the potential to revolutionize natural language processing and many other applications.
- Advances in Transfer Learning:
Transfer learning is a machine learning technique that involves pre-training a model on a large dataset and then fine-tuning it on a smaller, specific task. This can help improve the performance of the model and reduce the amount of data and compute resources needed for training. Transfer learning is becoming increasingly important as the amount of available data continues to grow.
- Growth of Explainable AI:
Explainable AI (XAI) is a field of research that focuses on developing machine learning algorithms that are transparent and interpretable, so that their decisions and predictions can be understood by humans. XAI is becoming increasingly important as machine learning is used in more mission-critical applications, such as medical diagnosis and autonomous vehicles, where the ability to understand and trust the decisions of the algorithms is crucial.
The future of machine learning looks to be one of continued growth and innovation, with the technology playing an increasingly important role in a wide range of fields and applications. As businesses continue to adopt machine learning solutions, they can anticipate greater operational efficiency and more-informed decision making.
- Gartner, “CFOs Must Change to Unlock the Future of Autonomous Finance”, August 4, 2022.
- Gartner, “Tool: Artificial Intelligence Use Cases for Insurance”, November 14, 2022.
- Gartner, “CFOs Must Change to Unlock the Future of Autonomous Finance”, June 3, 2022”, August 4, 2022.
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