Three of the most important concepts in data science are artificial intelligence (AI), machine learning (ML), and deep learning (DL). While these terms are frequently used interchangeably, they are not synonymous. Each term in AI systems represents a different complexity and functionality level. In this blog post, I’ll define and differentiate these three concepts to help you understand their roles in data science.
The value of AI models in business cannot be overstated. AI models can help businesses automate tasks, optimize operations, and create personalized customer experiences. In fact, according to a study by IDC, worldwide spending on AI systems is expected to reach $97.9 billion by 2023. This post will also highlight the most popular models and their business applications.
Definition and Differences
Artificial Intelligence (AI)
The simulation of human intelligence in machines that are programmed to think and act like humans are called artificial intelligence (AI). Artificial intelligence (AI) aims to create intelligent machines that can learn from their surroundings and make decisions based on that knowledge. Narrow AI, also known as Weak AI and General AI, are the two types of AI systems.
Narrow AI is intended to perform a single or limited set of tasks, such as image recognition, language translation, or voice recognition. Narrow AI systems are trained on specific data sets and cannot perform tasks that are not part of their training.
Some examples of Narrow AI models include:
- Apple’s Siri and Amazon’s Alexa for speech recognition and natural language processing
- Google Translate for language translation
- Tesla’s Autopilot for self-driving cars
In contrast, general AI is intended to perform any intellectual task that a human can perform. General AI does not yet exist and is only a theoretical concept. Many researchers, however, are working to create general AI by building on the success of narrow AI systems.
Machine Learning (ML) is a subset of AI that focuses on building algorithms to learn from data and make predictions or decisions based on that data. ML algorithms are designed to find patterns and relationships in data without being explicitly programmed. Three types of Machine Learning algorithms exist supervised, unsupervised, and reinforcement learning.
Algorithms used in supervised learning are trained on labeled data, which involves providing the algorithm with a set of inputs and their corresponding outputs. The algorithm develops the ability to predict outcomes by mapping inputs to outputs. Examples comprise:
- Spam filters that classify emails as spam or not spam
- Fraud detection algorithms that detect fraudulent transactions
- Credit risk models that predict whether a person is likely to default on a loan
When there is no labeled data, unsupervised learning algorithms are used. These algorithms cluster or group together similar data points to discover patterns and relationships in the data. Here are some examples:
- Market segmentation algorithms that group customers based on their preferences
- Anomaly detection algorithms that identify unusual patterns in data
- Recommendation engines that suggest products or services based on users’ preferences
When teaching a system to make decisions based on rewards and punishments, reinforcement learning algorithms are used. In this scenario, the system learns to act in a way that increases reward and decreases punishment. Examples comprise:
- Game-playing algorithms that learn to win games through rewards and punishments
- Robotics algorithms that learn to perform specific tasks, such as walking or grasping objects, through trial and error
Deep Learning (DL)
Building artificial neural networks that can learn and make decisions similarly to the human brain is the goal of deep learning (DL), a subset of machine learning. Data representations can be learned at different levels, with each level building on the one before it, using deep learning (DL) algorithms.
Natural language processing, autonomous driving, image and speech recognition, and other processes all use deep learning algorithms. When it’s necessary to accurately predict the future while processing a lot of complex data, deep learning algorithms are frequently used. Examples of DL models are as follows:
- Convolutional Neural Networks (CNNs) that recognize objects in images and videos
- Recurrent Neural Networks (RNNs) that process sequences of data, such as speech or text
- Generative Adversarial Networks (GANs) that create realistic images or videos
The key difference between ML and DL is that ML algorithms require human input to identify relevant features in the data, while DL algorithms can automatically identify relevant features through the process of feature learning.
Driving Business Innovation and Growth with AI
The value of AI models in business cannot be overstated. AI models can help businesses automate tasks, optimize operations, and create personalized customer experiences. In fact, according to a study by IDC, worldwide spending on AI systems is expected to reach $97.9 billion by 2023.
Here are some of the most popular AI models and their business applications:
- Natural Language Processing (NLP)
NLP is a branch of AI that aids computers in comprehending and interpreting human language. NLP models can be applied to a variety of tasks, including language translation, sentiment analysis, and chatbots. For instance, businesses can use chatbots to offer 24/7 customer support, and sentiment analysis can help them learn what their clients think of their goods or services. The global market for NLP is anticipated to reach $80.7 billion by 2027, per a Grand View Research study.
- Computer Vision
AI’s subfield of computer vision enables machines to comprehend and analyze visual information from their environment. Applications for computer vision models include object detection, autonomous driving, and facial recognition. For instance, object detection can be used to identify and track items in a warehouse, while facial recognition can be used for security purposes. The global market for computer vision is anticipated to reach $19.1 billion by 2023, according to a MarketsandMarkets study.
- Recommender Systems
AI models called recommender systems make recommendations to users for products or content based on their preferences and actions. Applications for recommender systems include personalized marketing, content recommendations, and product recommendations. Product recommendations, for instance, can be used to suggest products that a customer might be interested in purchasing. In contrast, personalized marketing can be used to create targeted ads for particular audiences. According to a Grand View Research study, the global market for recommender systems is anticipated to reach $6.5 billion by 2025.
- Predictive Analytics
Predictive analytics is an AI subfield that uses statistical algorithms to forecast future events. Predictive analytics models have many applications, including fraud detection, risk assessment, and demand forecasting. Fraud detection, for example, can be used to detect fraudulent transactions before they occur, whereas demand forecasting can be used to predict future sales and inventory requirements. According to MarketsandMarkets, the global predictive analytics market is expected to reach $23.6 billion by 2025.
Estimating the ROI of AI
The applications and industries affect AI models’ returns on investment (ROI). However, a McKinsey report claims that organizations investing in AI can anticipate a 20–30% increase in cash flow over time. As an illustration, the software company Adobe increased revenue by 10% by implementing AI to personalize their customers’ experiences. Another illustration is Shell, a company that produces energy. Shell used AI to optimize their oil and gas production, which increased revenue by $1 billion.
In conclusion, data science relies heavily on AI, ML, and DL concepts. AI models have a lot to offer companies in many different sectors. Among the most popular are NLP, computer vision, recommender systems, and predictive analytics.