AI vs. Machine Learning: Key Differences and Business Applications

Artificial intelligence (AI) and machine learning (ML) are revolutionizing industries, transforming the way businesses operate and driving unprecedented efficiency and innovation.

Despite their prevalence in everyday activities, these two distinct technologies are often misunderstood and many people use these terms interchangeably.

This article aims to clarify what sets AI and ML apart, delve into their respective use cases, and explore how they can benefit the supply chain and other business operations.

What is Artificial Intelligence?

AI refers to the development of computer systems that can perform tasks typically requiring human intelligence and discernment. These tasks include problem-solving, decision-making, language understanding, and visual perception.

Key AI Capabilities:

Problem-Solving: AI systems can analyze complex problems and find solutions, mimicking human cognitive functions.
Decision-Making: AI can make autonomous decisions based on data analysis.
Language Understanding: Natural language processing (NLP) allows AI to understand and replicate or "speak" human language.
Visual Perception: Computer vision enables AI to interpret and process tables, charts, maps, and other visual data.

What is Machine Learning?

Machine learning (ML) is a subset of AI. It involves training algorithms to learn from and make predictions and forecasts based on large sets of data.

Unlike traditional programming, where specific instructions are coded, ML algorithms are "trained" to improve their performance as they are exposed to more and more data. This ability to learn and adapt makes ML particularly powerful for identifying trends and patterns to make data-driven decisions.

Core ML Components:

Algorithms: Mathematical models that learn from data. Think of them as a set of step-by-step rules to perform a specific task or solve a problem using a methodical, logical sequence of actions or instructions that a computer (or sometimes a human) performs to achieve a specific outcome. Examples of algorithms in action include GPS navigation, Google's search results, or even a recipe.
Training Data: The datasets used to teach models how to make informed predictions or decisions. The process consists of providing input-output pairs where the input is the data used to "train" the ML model and the output shows the expected result. With enough of these pairs, the model learns to recognize patterns, relationships, and other features and can then apply these learnings to new, unseen data.

Further Differences Between AI and Machine Learning

While AI is a much broader field that relates to the creation of intelligent machines, ML focuses specifically on "teaching" machines to learn from data.

In simpler terms, all machine learning is AI, but not all AI involves machine learning.

AI Applications

AI has a multitude of applications across different sectors:

Healthcare: AI systems improve patient outcomes by analyzing vast amounts of medical data to assist in diagnostics.
Manufacturing: AI optimizes supply chain management and predictive maintenance. This includes price forecasting, waste minimization, raw material sourcing, inventory management, and predictive equipment maintenance.
Customer Service: AI powers chatbots and virtual assistants, enhancing customer interactions.
Finance: AI helps in fraud detection, risk management, and personalized financial planning. AI algorithms analyze transaction patterns to identify suspicious activities and predict market trends.

Explore how Epicor’s AI solutions can transform your business operations.

Machine Learning Applications

Machine learning is integral to applications such as:

Fraud Detection: Identifies suspicious patterns in financial transactions.
Recommendation Systems: Powers suggestions on platforms like Netflix and Amazon.
Sentiment Analysis: Analyzes social media, reader comments, and customer feedback for insights.
Customer Segmentation: Categorizes customers into distinct groups based on purchasing behavior and demographics for targeted marketing.
Supply Chain Optimization: Enhances logistics and inventory management by predicting demand and optimizing routes.
Image and Voice Recognition: Powers applications like facial recognition, speech-to-text, and virtual assistants.
Personalized Marketing: Tailors marketing campaigns based on individual customer preferences and behavior.
Healthcare Diagnostics: Assists in diagnosing diseases and recommending treatments by analyzing medical data.
Natural Language Processing (NLP): Powers chatbots, language translation, and document summarization.

By learning from historical data, ML models can predict future trends and automate decision-making processes, reducing human error and increasing efficiency.

As you can see, there is overlap in the types of tasks and processes that ML and AI can complete, and highlights how ML is a subset of the broader AI domain.

Deep Learning: A Subfield of Machine Learning

Deep learning is an advanced form of ML that uses artificial neural networks to model highly complex patterns in data. These networks are inspired by the human brain’s structure and are particularly effective at tasks such as image and speech recognition.

Deep Learning Highlights:

Neural Networks: Layers of interconnected nodes (units) that process data similar to the human brain.
High Accuracy: Particularly useful for tasks requiring high precision, such as medical diagnostics and autonomous driving.

Natural Language Processing and Understanding

Natural language processing (NLP) and natural language understanding (NLU) enable machines to understand and respond to human language.

These AI technologies are used in chatbots and virtual assistants like Chat GPT and Siri, providing more natural and intuitive user interactions.

NLP Capabilities:

Text Analysis: Read and extract meaningful information from text.
Speech Recognition: Convert spoken language into written text.
Language Generation: Create human-like text based on input data or prompts.

Real-World Use Cases of AI and ML

AI and ML are being applied in various real-world scenarios.

Self-Driving Cars

Navigation: ML models process data from sensors to navigate and make decisions on the road.

Finance

Market Analysis: AI systems analyze market trends and optimize trading strategies.

Healthcare

Diagnostics: AI algorithms analyze medical images and data to assist in diagnosing diseases and recommending treatments.

Retail

Personalized Marketing: ML models analyze customer behaviors to tailor marketing campaigns and recommend products.

Supply Chain Management

Demand Forecasting: AI predicts future product demand to optimize inventory levels and reduce waste.

Business Benefits of AI and ML

Integrating AI and ML into your business can lead to significant benefits, including:

Enhanced Data Analysis: AI and ML provide deep, unbiased insights into data, enabling informed decision-making.
Improved Efficiency: Automation of routine tasks frees up human workers for strategic initiatives.
Cost Reduction: Streamlining operations through AI-driven processes saves significant time and lowers operational costs.

AI and ML play a critical role in planning, analysis, and data management solutions. Read about Epicor’s data management solutions.

Challenges and Ethical Considerations

Despite their immense benefits, AI and ML pose many challenges such as data privacy concerns, algorithmic bias, and potential human job displacement.

It is essential to address these issues and ensure the responsible and ethical use of these technologies.

Key Challenges:

Data Privacy: Ensuring that data used for AI and ML is protected and secure.
Bias in Algorithms: Addressing and mitigating biases in ML models to ensure fair outcomes.
Job Displacement: Managing the impact of automation on the workforce. While AI will displace some workers, it is also creating new jobs as well.

The Future of AI and Machine Learning

The future of AI and ML shines bright, with advancements in generative AI, artificial general intelligence (AGI), and artificial superintelligence (ASI) on the horizon. These developments promise further to transform business practices, industries, and society overall, offering new possibilities and ethical challenges.

Future Trends

Generative AI: Creating new content, such as text, images, and music, from existing data. This form of AI involves using algorithms and models to produce new, original content through analyzing and learning from patterns in existing databases. The technology can be applied to natural language processing (NLP), image synthesis, audio generation, and similar tasks to create human-like text or even compose original music scores.
Artificial General Intelligence (AGI): Artificial intelligence (AI) that can perform any intellectual task that a human can. Unlike narrow AI, which is designed for specific functions, AGI has flexible and adaptive intelligence that can apply to a wide range of tasks to solve diverse problems with novel (never-seen-before) situations.
Artificial Superintelligence (AS): A hypothetical form of AI that surpasses human intelligence instead of merely striving to equal it. ASI represents a level of cognitive ability that goes far beyond even the smartest of humans, leading to revolutionary advancements in science and technology. ASI, however,r raises significant ethical concerns and existential risks.

Real-World Applications of AI and Machine Learning

Generative AI: Creating new content, such as text, images, and music, from existing data. This form of AI involves using algorithms and models to produce new, original content through analyzing and learning from patterns in existing databases. The technology can be applied to natural language processing (NLP), image synthesis, audio generation, and similar tasks to create human-like text or even compose original music scores.

Epicor Customer Success Stories

Many companies have successfully integrated Epicor’s AI and ML solutions for a remarkable transformation in their business operations.

Carvana

Carvana, a leading tech-driven car retailer known for its multi-story car vending machines, has significantly improved its operations using Epicor's AI and ML technologies.

Through integrating the Epicor Catalog--a comprehensive, cloud-based database with access to over 17 million SKUs from 9,500+ manufacturers-- Carvana has dramatically increased productivity and cut the cost per unit for parts by more than 50%.

Additionally, Carvana workers enthusiastically took to Epicor. Adoption rates reached 98% within two weeks, highlighting its usefulness and user-friendliness.

Franklin Foods

Franklin Foods, a renowned cheese manufacturer in business for over 120 years, has significantly enhanced its operations using Epicor's AI and ML technologies.

By integrating Epicor Enterprise Content Management (ECM) and Intelligent Data Capture (IDC), Franklin Foods automated numerous manual document processes in their AP department.

Epicorm ECM's AI-powered features improve productivity and dramatically reduce errors. The ECM excels at making smart user recommendations for content fields based on patterns learned in previous entries. This machine learning (ML) functionality saves time and limits data entry errors--and performance continually improves. As it continuously learns from user interactions, the ECM makes better, smarter recommendations.

Success breeds success. The success of Franklin Foods' AP automation led to a total overhaul of its credit memo process.

Previously disorganized and inefficient, the credit memo process now provides clear insight into all credit statuses and who has signing approval. This has sped up the approval process and eliminated questionable approvals in a streamlined, three-level process.

Summary

AI and machine learning are powerful technologies transforming businesses everywhere. Even more traditional businesses, like the 125-year-old Franklin Foods, are seeing major business and revenue wins to ensure their business that's thrived since the 19th century continues to thrive in the 21st.

Across all industries, AI and machine learning can update, automate, enhance, and continue to "learn" as users integrate and interact with these technologies.

While AI encompasses a vast range of intelligent systems that perform human-like tasks, ML focuses specifically on learning from past data to make better predictions and forecasts and improve recommendations over time.

Businesses everywhere are adopting these technologies to enhance data management, automate processes, improve decision-making, improve productivity, and increase business revenue. These organizations, like Franklin Foods and Carvana, have a significant competitive edge over competitors who are reluctant or slow to realize the benefits of AI and machine learning.

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Mark Jensen

Director of Product Marketing

Mark is a Product Management and Marketing leader with over 25 years of experience in ERP software. He started his ERP career at Great Plains Software and worked at Microsoft after they acquired Great Plains. Mark has been at Epicor for over 8 years and leads a global, cross-functional Product Marketing team bringing Epicor products to market.

AI vs. Machine Learning