Machine learning (ML) is defined as the discipline of artificial intelligence (AI) that enables machines to automatically learn from data and past experience to recognize patterns and make predictions with minimal human intervention. This article covers the basics of machine learning, its types, and five of its most important applications. It also shared the top 10 trends in machine learning for 2022.
Table of contents
How does machine learning work?
1. Supervised machine learning
2. Unsupervised machine learning
Top 5 Machine Learning Applications
Top 10 machine learning trends for 2022
1. Blockchain meets machine learning
2. AI-based self-service tools
3. Personalized AI assistants and search engines
4. All-inclusive intelligent assistance
7. Advances in the automotive industry
9. Generative Adversarial Network (GAN)
What is machine learning?
Machine learning (ML) is the discipline of artificial intelligence (AI) that enables machines to automatically learn from data and past experience, while recognizing patterns to make predictions with minimal human intervention.
Machine learning methods allow computers to function autonomously without being explicitly programmed. ML applications receive new data and they can learn, grow, develop and adapt independently.
Machine learning derives insightful information from large amounts of data by using algorithms to identify patterns and learn in an iterative process. ML algorithms use computational methods to learn directly from data, rather than relying on any predetermined equations that can be used as models.
The performance of ML algorithms improves adaptively as the number of samples available increases during the "learning" process. For example, deep learning is a subfield of machine learning that trains computers to mimic natural human characteristics, such as learning from examples. It provides better performance parameters than traditional ML algorithms.
While machine learning is not a new concept -- dating back to the Enigma machines used in World War II -- the ability to automatically apply complex mathematical calculations to the ever-growing volume and variety of data available is a relatively new one. development of.
Today, with the rise of big data, the Internet of Things, and ubiquitous computing, machine learning has become the key to solving problems in many fields, such as
- Computational Finance (credit scoring, algorithmic trading)
- Computer Vision (Facial Recognition, Motion Tracking, Object Detection)
- Computational biology (DNA sequencing, brain tumor detection, drug discovery)
- Automotive, Aerospace and Manufacturing (Predictive Maintenance)
- Natural Language Processing (Speech Recognition)
How does machine learning work?
Machine learning algorithms are trained on training datasets to create models. When new input data is introduced to a trained ML algorithm, it uses the developed model to make predictions.
How Machine Learning Works
Note : The diagram above exposes an advanced use case scenario. However, a typical machine learning example may involve many other factors, variables, and steps.
Also, check the accuracy of the forecast. Depending on its accuracy, repeatedly deploy or train an ML algorithm using augmented training datasets until the desired accuracy is achieved.
Types of Machine Learning
Machine learning algorithms can be trained in a variety of ways, and each method has its advantages and disadvantages. Based on these methods and learning styles, machine learning can be roughly divided into four main types:
Types of Machine Learning
1. Supervised machine learning
This type of ML involves supervision, where a machine is trained on a labeled dataset and is able to predict an output based on the training provided. A labeled dataset specifies that some input and output parameters are already mapped. Thus, machines are trained with inputs and corresponding outputs. A device that predicts outcomes using the test dataset in subsequent stages.
For example, consider an input dataset of images of parrots and crows. Initially, the machine was trained to understand the pictures, including the color, eyes, shape and size of parrots and crows. After training, input a picture of a parrot and expect the machine to recognize the object and predict the output. A trained machine examines various features of objects in input images, such as color, eyes, shape, etc., to make a final prediction. This is the process of object recognition in supervised machine learning.
The main goal of supervised learning techniques is to map input variables (a) to output variables (b). Supervised machine learning is further divided into two broad categories:
- Classification : These refer to algorithms that solve classification problems where the output variable is categorical; for example, yes or no, true or false, male or female, etc. Practical applications of this class are evident in spam detection and email filtering.
Some known classification algorithms include random forests, decision trees, logistic regression, and support vector machines.
- Regression : Regression algorithms deal with regression problems where the input and output variables have a linear relationship. These are known to predict continuous output variables. Examples include weather forecasting, market trend analysis, etc.
Popular regression algorithms include simple linear regression, multiple regression, decision tree, and lasso.
2. Unsupervised machine learning
Unsupervised learning refers to a learning technique that is not supervised. Here, the machine is trained using an unlabeled dataset and is able to predict an output without any supervision. Unsupervised learning algorithms aim to group unsorted datasets based on similarities, differences, and patterns in the inputs.
For example, consider an input dataset of images of containers full of fruit. Here, the machine learning model has no knowledge of these images. When we feed a dataset into an ML model, the model's task is to recognize patterns in objects, such as colors, shapes, or differences seen in the input images, and classify them. After classification, the machine predicts the output when tested with a test dataset.
Unsupervised machine learning is further divided into two categories:
- Clustering : Clustering technique refers to the grouping of objects into clusters based on parameters such as similarity or difference between objects. For example, group customers by customer product.
Some known clustering algorithms include K-Means clustering algorithm, Mean-Shift algorithm, DBSCAN algorithm, principal component analysis, and independent component analysis.
- Association: Association learning refers to identifying typical relationships between variables in large datasets. It determines dependencies of various data items and maps related variables. Typical applications include web usage mining and market data analysis.
Popular algorithms that obey association rules include Apriori's algorithm, Eclat's algorithm, and FP-Growth's algorithm.
3. Semi-supervised learning
Semi-supervised learning includes features of both supervised and unsupervised machine learning. It uses a combination of labeled and unlabeled datasets to train its algorithms. Using these two types of datasets, semi-supervised learning overcomes the disadvantages of the above options.
Consider the example of a college student. In college, students learning a concept under the supervision of a teacher is called supervised learning. In unsupervised learning, students teach themselves the same concepts at home without the guidance of a teacher. At the same time, it is a semi-supervised form of learning for students to revise concepts after learning under the guidance of university teachers.
4. Reinforcement Learning
Reinforcement learning is a feedback-based process. Here, the AI component automatically assesses its surroundings through a hit-and-trial approach, takes action, learns from experience, and improves performance. This component is rewarded for every good move and penalized for every bad move. Therefore, the reinforcement learning component aims to maximize the reward by performing well-performed actions.
Unlike supervised learning, reinforcement learning lacks labeled data, and agents can only learn through experience. Consider video games. Here, the game specifies the environment, and each action of the reinforcement agent defines its state. The agent is empowered to receive feedback through punishments and rewards, thereby affecting the overall game score. The agent's ultimate goal is to get a high score.
Reinforcement learning is applied in different fields such as game theory, information theory, and multi-agent systems. Reinforcement learning is further divided into two categories of methods or algorithms:
- Positive reinforcement learning : This refers to adding a reinforcement stimulus after a specific behavior of the agent to make the behavior more likely to happen again in the future, for example, adding a reward after a behavior.
- Negative Reinforcement Learning : Negative reinforcement learning refers to reinforcing specific behaviors to avoid negative outcomes.
Top 5 Machine Learning Applications
Vertical industries that deal with large amounts of data have realized the importance and value of machine learning technology. As machine learning derives insights from real-time data, organizations using it can work efficiently and gain an edge over their competitors.
In this fast-paced digital world, every industry vertical can greatly benefit from machine learning technology. Here, we take a look at the top five ML application domains.
1. Healthcare industry
Machine learning is increasingly being adopted in the healthcare industry thanks to wearable devices and sensors such as wearable fitness trackers, smart health watches, and more. All such devices monitor the user's health data to assess their health in real time.
Additionally, the technology is helping physicians analyze trends or flag events that may help improve patient diagnosis and treatment. ML algorithms are even allowing medical experts to more accurately predict how long patients with deadly diseases will live.
Furthermore, machine learning has made significant contributions in two areas:
- Drug Discovery : Making or discovering a new drug is expensive and involves a lengthy process. Machine learning helps speed up the steps involved in this multi-step process. For example, Pfizer uses IBM's Watson to analyze large volumes of disparate data for drug discovery.
- Personalized Therapy : Drug manufacturers face the daunting challenge of validating the effectiveness of specific drugs in large populations. This is because the drug only works in a small number of people in clinical trials and may cause side effects in some subjects.
To address these issues, companies such as Genentech have partnered with GNS Healthcare to leverage machine learning and simulated AI platforms to innovate biomedical therapies to address these issues. Machine learning technology provides targeted treatment to patients by analyzing individual genes to find markers of the patient's response.
2. Bank
Today, some finance institutions and banks use machine learning techniques to combat fraudulent activities and extract important insights from massive amounts of data. ML-derived insights help identify opportunities, allowing investors to decide when to trade.
Additionally, data mining methods help network monitoring systems zero in on warning signs of fraudulent activity and subsequently eliminate them. Several finance institutions are already partnering with tech companies to take advantage of machine learning.
3. Retail
Retail websites make extensive use of machine learning to recommend items based on a user's history. Retailers use ML techniques to capture data, analyze it, and provide customers with a personalized shopping experience. They also implement machine learning for marketing campaigns, customer insights, customer merchandising, and price optimization.
According to a September 2021 report by Grand View Research, Inc., the global recommendation engine market is expected to reach a valuation of $17.3 billion by 2028. Common everyday examples of recommender systems include:
- When you browse items on Amazon, the product recommendations you see on the home page come from machine learning algorithms. Amazon uses artificial neural networks (ANNs) to provide intelligent, personalized recommendations relevant to customers based on their recent history, reviews, bookmarks and other online activity.
- Netflix and YouTube rely heavily on recommendation systems to recommend shows and videos to users based on their viewing history.
Additionally, retail websites are equipped with virtual assistants or conversational chatbots that leverage ML, natural language processing (NLP) and natural language understanding (NLU) to automate the customer shopping experience.
4. Tourism industry
Machine learning is playing a pivotal role in expanding the reach of the travel industry. Uber, Ola, and even self-driving car ride-hailing services have powerful machine learning backends.
Consider Uber's machine learning algorithm that handles the dynamic pricing of its rides. Uber uses a machine learning model called "Geosurge" to manage dynamic pricing parameters. It uses real-time predictive models for traffic patterns, supply and demand. If you're late for a meeting and need to hail an Uber in a crowded place, the dynamic pricing model kicks in and you can get an Uber instantly, but pay twice the normal fare.
Additionally, the travel industry uses machine learning to analyze user reviews. Classify user reviews with sentiment analysis based on positive or negative scores. This is used by companies in the travel industry for event monitoring, brand monitoring, compliance monitoring, etc.
5. Social Media
With the help of machine learning, billions of users can effectively participate in social media networks. Machine learning is playing a key role in driving social media platforms from personalizing news feeds to serving user-specific content. For example, Facebook's auto-tagging feature uses image recognition to recognize your friends' faces and automatically tags them. The social network uses ANNs to identify familiar faces in a user's contact list and facilitate automatic tagging.
Likewise, LinkedIn knows when you should apply for your next job, who you need to contact, and how your skills rank compared to your peers. All of these features are enabled by machine learning.
Top 10 machine learning trends for 2022
Machine learning has had a major impact on all industry verticals across the globe, from startups to Fortune 500 companies. According to a 2021 report by Fortune Business Insights, the global machine learning market size was valued at USD 15.5 billion in 2021 and is expected to grow to a staggering USD 152.24 billion by 2028, at a CAGR of 38.6%.
A similar trajectory is expected in 2022, judging by the increasing adoption of machine learning. Here, we look at the top 10 machine learning trends for 2022.
1. Blockchain meets machine learning
Blockchain, the technology behind cryptocurrencies like Bitcoin, benefits numerous businesses. The technology uses a decentralized ledger to record each transaction, thereby increasing transparency between the parties involved without any intermediaries. Additionally, blockchain trades are irreversible, meaning that once the ledger is updated, they can never be deleted or changed.
Blockchain is expected to merge with machine learning and artificial intelligence, as some of the capabilities of the two technologies complement each other. This includes decentralized ledgers, transparency and immutability.
For example, blockchain -driven projects such as Barclays Bank and HSBC Bank are committed to providing customers with interest-free loans . Additionally, the bank employs machine learning to determine a potential loan
2. AI-based self-service tools
Some businesses are already adopting AI-based solutions or self-service tools to streamline their operations. Big tech companies like Google, Microsoft, and Facebook use bots on messaging platforms like Messenger and Skype to efficiently perform self-service tasks.
For example, when you search for a location on a search engine or on Google Maps, the "Get Directions" option pops up automatically. This saves you valuable time by telling you the exact route to your desired destination. If this trend continues, machine learning will eventually be able to provide a fully automated experience for customers who are looking for enterprise products and services.
3. Personalized AI assistants and search engines
Today, everyone knows everything about artificial intelligence assistants like Siri and Alexa. These voice assistants perform various tasks such as booking flights, paying bills, playing a user's favorite song, and even sending messages to colleagues.
Over time, these chatbots are expected to provide a more personalized experience, such as providing legal advice on various issues, making key business decisions, providing personalized medicine, and more.
On the other hand, search engines like Google and Bing crawl multiple data sources to provide the right content. With increased personalization, today's search engines can scrape personal data to provide users with personalized results.
For example, when you search Google for 'sneakers', the next time you visit Google, you'll see related to your previous search. As a result, search engines become more personal as they can provide specific results based on your data.
4. All-inclusive intelligent assistance
With personalization taking center stage, smart assistants are ready to help in every way by performing tasks on our behalf, such as driving, cooking, and even groceries. These will include advanced services that we normally receive through human agents, such as arranging travel or seeing a doctor if we feel unwell.
For example, if you are sick, all you need to do is call your assistant. Based on your data, it will book appointments with the top doctors in your area. An assistant will then follow up with the hospital and book an Uber to pick you up on time.
5. Personal medical devices
Today, wearable medical devices have become part of our daily lives. These devices measure health data, including heart rate, blood sugar levels, salt water, and more. However, with the widespread implementation of machine learning and artificial intelligence, such devices will have more data available to users in the future.
Wearable devices will be able to analyze health data in real time and provide personalized diagnosis and treatment according to individual needs. In critical situations, wearable sensors will also be able to suggest a series of health tests based on health data. They can even make an appointment with a nearby specialist.
6. Augmented reality (AR)
Although augmented reality has been around for a few years, we are now witnessing the true potential of the technology. Microsoft's HoloLens is a popular example. These AR glasses project a digital overlay on the physical environment and allow users to interact with the virtual world using voice commands or gestures.
However, an advanced version of AR will be in the news in the coming months. Such devices will continue to improve through 2022, as they may allow face-to-face interactions and conversations with friends and family from any location. This is one of the reasons why augmented reality developers are in high demand today.
7. Advances in the automotive industry
Self-driving cars are already being tested on the streets. They are able to drive in complex urban environments without any human intervention. While there are big questions about when they will be allowed on the road, 2022 is expected to drive the debate.
By 2022, self-driving cars may even allow drivers to take short naps during the journey. This is not limited to self-driving cars, but could change the transportation industry. For example, self-driving buses could make a big push, carrying multiple passengers to their destinations without human intervention.
8. Full-stack deep learning
Today, deep learning is taking root in image recognition, self-driving cars, voice interaction, and many other applications. Additionally, games such as DeepMind's AlphaGo explore deep learning, making it easy to reach expert levels.
By 2022, deep learning will be applied in medical imaging, allowing doctors to use image recognition to more accurately diagnose conditions. In addition, deep learning will lead to major advances in the development of programming languages that can understand code and write programs themselves based on the input data provided .
For example, consider an Excel spreadsheet that contains multiple entries of financial data. Here, the machine learning system will use deep learning-based programming to learn which numbers are good data and which are bad data based on previous examples.
9. Generative Adversarial Network (GAN)
Generative adversarial networks are an important machine learning breakthrough in recent years. It can generate valuable data, usually images or music, from scratch or random noise. In short, instead of training a single neural network with millions of data points, we can have two neural networks compete against each other and find the best path.
For example, when you feed an image of a horse into a GAN, it can generate an image of a zebra.
10. TinyML
TinyML revolutionized machine learning. Inspired by the Internet of Things, it allows IoT edge devices to run machine learning-driven processes. For example, smartphone wake-up commands such as "Hey Siri" or "Hey Google" belong to tinyML.
Also, web requests sent to the server take time to generate a response. First, a request sends data to a server, processed by a machine learning algorithm, and a response is received. Instead, a time-saving process might be to use ML programs on edge devices. This approach has several advantages such as lower latency, lower power consumption, less bandwidth usage while ensuring user privacy.
With the proliferation of connected devices, tinyML will continue to grow in complexity and gain popularity in 2022.
Summarize
Computers can learn, remember and generate accurate output through machine learning. It enables companies to make informed decisions critical to streamlining business operations. This kind of data-driven decision-making helps companies across industries, from manufacturing, retail, healthcare, energy and financial services, optimize their current operations while seeking new ways to reduce their overall workload.
As computer algorithms get smarter, we can expect an upward trajectory of machine learning in 2022 and beyond.