For many years now, Artificial Intelligence (AI) has been redefining the way organizations conduct their day-to-day operations. However, as it mimics human cognitive abilities, such as learning and decision-making, the phrase is often wrongly used to describe any computer software that performs a task independently. This can lead to confusion and misconceptions, plenty of which can be found online.
That said, to understand ‘what is AI used for’ and where its capabilities and limitations lie, it’s important to familiarize oneself with its top disciplines. In this article, we’re going to break down the common buzzwords and mention the top AI business benefits and techniques.
Understanding AI disciplines and techniques
Below, we're going to take you through the top 7 AI areas, along with their respective subdivisions and common use cases. Let's start with Machine Learning.
Machine Learning (ML)
Machine Learning is a branch of AI that focuses on allowing systems to learn and improve over time. The system achieves this by observing data gathered from past experiences and finding common patterns. Machine Learning intends for systems to make precise decisions using collected data without intervention or programming from humans.
Machine Learning also includes Deep Learning under its umbrella.
Deep Learning: Deep Learning is a subfield of Machine Learning methods based on Artificial Neural Networks with representation learning. Learning can take one of the following forms:
- Supervised learning, i.e., the system learns from experience by providing labeled data that is already classified and applied to future events.
- Semi-supervised learning, i.e., the system learns from partially labeled datasets.
- Unsupervised learning, i.e., the system must learn by itself without prior data or experience.
What problems can Machine Learning solve? Examples in business
Some of the AI business benefits of Machine Learning include:
Providing product recommendations
One of the most well-known applications of Machine Learning is recommendation systems. Many places around the web use recommendation systems to encourage additional purchases and upsell to customers. This is commonly seen on websites like Amazon and eBay, YouTube’s suggested videos, Spotify's made-for-you playlists, and Google’s suggested form filler.
Here's an example of a very familiar recommendation system:
Product recommendations have proven to be successful in driving business. In fact, 35% of Amazon’s revenue stems from product recommendations. The primary function of recommendation systems is to provide personalized suggestions to the user based on parameters such as:
- Purchase history
- Item or page views
- Watch and listen to the history
- Previous search queries
- The behavior of similar users
Sentiment Analysis
Sentiment Analysis is a Machine Learning application that can detect positive, negative, or neutral emotions in data sets such as text. This application of ML is beneficial to business by giving insight into:
- Brand perception
- Product analysis
- Social media monitoring
- Customer feedback
The most common use is analyzing the sentiment of feedback provided on products or services. But a sentiment analyzer can gather information about the thought and tone of the text in any document. For example, Sentiment Analysis is used in social media applications to collect information about the emotion of posts and whether the sentiment is too violent or inappropriate for the platform.
Image & video recognition
The above-mentioned subset of Machine Learning known as Deep Learning has advanced over the last few years. This has led to remarkable advancements in image and video recognition methods. Object identification, face identification, word detection, visual search, logo and landmark detection, and picture creation are just a few applications of this kind of technology.
The human mind is excellent at recognizing and classifying images. But Machine Learning algorithms can train Deep Learning frameworks to detect and categorize photos in the dataset with more accuracy than people.
Deep Learning also makes video recognition possible by converting video frames into several digital images. These images are then classified and organized as the system would do during image recognition processes. This process is seen when designers look for the right stock images and videos on websites such as Shutterstock, or when eBay users search for objects using visual search options.
Natural Language Processing
Natural Language Processing (NLP) is a branch of AI that focuses on computers’ ability to understand and process natural human language. Without this technology, computers cannot interpret the true meaning and intention behind words.
NLP uses concepts from many disciplines, such as computer science and computational linguistics, to close the gap in understanding. NLP enables machines to extract data and classify content, as well as generate text on their own.
What problems can Natural Language Processing solve? Examples in business
Here are a few AI business benefits NLP offers to businesses:
- Answering customer queries with chatbots, voice bots, and virtual assistants. This relieves customer agents from answering repetitive questions. Call identification and call steering are also sophisticated uses for NLP in customer queries.
- Filtering emails and labeling based on content. This is demonstrated by Google’s Gmail service, as it uses NLP to filter into three categories: primary, promotions, and social.
- Improving search engines by focusing on the user’s intent and similar search behaviors, making it easier to find results with minimal searching.
Expert systems
An expert system is a program that uses AI technologies to solve problems within a specific domain, like that of a human expert. An expert system typically includes:
- A Knowledge Base, which contains information about a particular domain. The greater the amount of data in the knowledge base, the more exact the expert system will be.
- The Inference Engine (Rules of Engine), helps extract information from the knowledge base. It uses inference rules to generate an error-free conclusion or deduce new information from the knowledge base.
There are three main components of an expert system:
- Data Acquisition – using data collection to "train" a system and create the knowledge base.
- Reasoning – the primary mode in which an expert system operates.
- User Interface – how the expert system communicates and presents output to the user.
What problems can expert systems solve? Examples in business
Medical diagnosis and predictions
Expert systems in healthcare aim to diagnose medical issues with the same precision as human experts. Expert systems can use data such as patient demographic and billing data to derive new knowledge and make predictions to improve diagnosis. Traditional expert systems are being strengthened by the combination of data mining techniques and algorithmic advancements.
The knowledge base of a medical expert system contains an array of information about illnesses and diseases. Furthermore, it contains knowledge pertaining to the symptoms, lab results, and history of a disease and how they correlate with each other. For example, a diagnosis of the flu would show relationships with symptoms like fever and coughing.
Automatic speech recognition
Expert systems make speech recognition possible by identifying human speech and transcribing it into a written format.
Here are a few examples of automatic speech recognition uses in business:
- Faster note-taking allows employees to complete small tasks more quicker.
- Decreased document turnaround times.
- Accessibility options for employees who struggle with traditional input methods.
- Ability to use chatbots and virtual assistants, freeing up employee time.
- Quicker results when scanning large documents and datasets.
- Task execution for calling, messaging, and other menial tasks.
AI Vision
AI Vision (or Computer Vision) is a discipline of Artificial Intelligence that teaches computers how to process and interpret images. The goal of AI vision is to replicate the human ability to interpret the visual world. The process uses a combination of object recognition, object tracking, and picture categorization. With digital pictures and deep learning models, machines can effectively detect and categorize objects in the image and then have a reaction to that image.
What problems can AI Vision solve? Examples in business
Here are a few examples of what AI Vision enables:
- Image moderation and classification - AI Vision can categorize or tag images depicting various items. Social media platforms take advantage of this feature to find and delete content that goes against their terms of service.
- Detecting patterns & shapes - AI Vision is used in smartphones for fingerprint reading and facial recognition.
- Medical recognition - AI Vision can spot common illnesses and facilitate diagnosis, allowing doctors more time to focus on their patients.
Speech recognition
Speech recognition is a division of AI that can translate voice communications to text. It is also capable of recognizing a person based on their voice command. Voice recognition, a subtype of speech recognition, is the technique for identifying a person based on their voice.
There are two components of speech recognition in AI:
- Speech-to-text - this software transcribes spoken words or audio into written content in a text document or other display interface.
- Text-to-speech (TTS) - this software creates audible units of speech from text-based content.
What problems can speech recognition solve? Examples in business
Several common devices use speech recognition to perform simple tasks for users. One of the most prevalent uses of speech recognition occurs in smartphones. Examples include iPhone’s Siri and Google Assistant on Android devices.
Speech recognition has the ability to improve productivity. By 2024, the market for speech recognition is expected to have grown exponentially, and it’s predicted that 70% of white-collar workers will use speech recognition platforms daily. Speech recognition offers numerous possibilities and benefits, including:
- Increased accessibility for customers who have visual impairments.
- Improves customer experience with online shopping by incorporating voice technologies, such as Alexa.
- Allows doctors and other medical staff to input patient notes quickly.
AI planning
AI planning, also known as AI scheduling, is a domain of Artificial Intelligence that deals with solving, scheduling, and planning problems in which there is an initial beginning state that needs to change into the desired target state. AI Planning solves this issue by performing a sequence of activities.
What problems can AI planning solve? Examples in business
AI Planning systems can support a number of business objectives, such as:
- Plan an initial schedule for a project
- Assess risk for each potential plan (i.e., risk management)
- Engage in rapid prototyping
- Work on projects with minor changes on a repetitive basis, which requires cognitive skills (AI can be trained to decide how to proceed with an obstacle or recognize when it requires human decision-making to be cleared).
Robotics
Robotics is a subsection of AI that deals with creating intelligent robots by combining computer science, mechanical engineering, and electrical engineering. Robotics differ from other AI programs in the following ways:
- Robots operate in the physical world
- Input comes in the form of speech waveforms or images rather than symbols and rules
- Instead of computers, robots need hardware with sensors and effectors
What problems can Robotics solve? Examples in business
Enhanced manufacturing process
Industrial businesses have used robotics for a long time. Co-bots, also known as robots that work alongside people, have worked in assembly lines in manufacturing plants to efficiently test and assemble products in several industries, such as the automotive industry. It’s estimated that three million robots are currently being used for industrial purposes.
Improved logistics
The high expectation of fast shipping times has led robotics to become essential to retailers and logistic companies to maximize efficiency in both the warehouse and on the road. Robotics assist with shipping, handling, and quality control. In the future, it’s expected that logistics robots, or last-mile robots, will play a significant role in package delivery.
Assisting in surgeries
In the healthcare industry, robot-assisted surgeries have allowed doctors to perform with higher levels of precision. Here are four examples of robots being used in healthcare:
- The Xenex Germ-Zapping Robot disinfects hospital rooms
- The TUG helps deliver medicines to hospitals
- The PARO Therapeutic Robot provides patients with a therapeutic experience.
- Robots are being used during COVID-19 for PPE manufacturing and sealing testing swabs.
Summary
Unfortunately, there are many buzzwords in the field of Artificial Intelligence that are used interchangeably (and, often, erroneously). This makes it difficult for non-technical people to understand what AI is used for. Partially, this comes down to the fact that many businesses offer AI without fully explaining what it means.
Hopefully, this article has helped better define and showcase the many differences between the fields. AI is an ever-expanding industry that continues to add and develop new and exciting features. It continues to revolutionize the way business is conducted, the productivity achieved, and the capabilities of countless platforms and systems across the world.
If you’d like to learn more about the various subsets of AI, be sure to take a look at other articles. For more examples of AI use cases and how this technology can be used not just by enterprises but also by SMEs, we also recommend looking at our dedicated resource page.
