Artificial Intelligence vs Machine Learning: 9 Key Differences

Last updated by Rishabh Dev Choudhary on Aug 30, 2024 at 06:05 PM | Reading time: 7 minutes

What is Artificial Intelligence?

In simple terms, Artificial Intelligence is the ability of computers and machines to mimic human intelligence and perform human cognitive functions like learning and problem-solving.

Some experts define AI as computer software that impersonates the cognitive abilities of humans to perform different complex tasks like strategic decision-making, data analysis, language translation, and more.

Today, AI is pervasive and present in almost everything related to technology such as mobile phones, social media, banking, and more. Companies are incorporating AI techniques such as natural language processing to automate tasks, improve decision-making, and engage with customers through chatbots.

What is Machine Learning?

‍

Machine Learning is a subset of AI and uses algorithms to gain insights and identify patterns from data and apply that information to make predictions & better decisions.

AI trains on different ML algorithms and data sets to develop machine learning models that can perform complex activities like forecasting sales, analyzing data, and more.

Simply speaking, ML is the training that a machine or a system receives whereby it can learn independently without needing to be explicitly programmed. It is a complicated and hyper-intelligent process capable of continuously learning from extracted data.

Artificial Intelligence vs Machine Learning: Exploring the Differences

Artificial Intelligence is computer software capable of mimicking human intelligence to perform complex tasks and make decisions. In comparison, AI trains Machine Learning on data to develop models that can carry out complex tasks like data analysis, reasoning, and more.

Mostly AI is used for machine learning and due to this, both the terms are often used interchangeably. But they are very different from each other.

While AI is used to develop and mimic human cognition using computer software, ML is only one such method to do so. Take for instance, data scientists use ML to study patterns in data sets to improve AI.

When used together, they help in better & faster decision-making, increased operational efficiency, and more.

Now, let’s look at the key differences between the two technologies:

Particulars	Artificial Intelligence	Machine Learning
Focus	AI focuses on developing machines that can perform complex human tasks efficiently	ML analyzes large volumes of data using statistical models to identify patterns in data
Methods	It includes a wide array of methods such as genetic algorithms, deep learning (DL), ML, and more	It is divided into two parts - supervised and unsupervised learning. Supervised ML algorithms learn problem-solving using data values that are labeled as input and output. While unsupervised ML is more exploratory and focuses on finding hidden patterns in unlabelled data
Implementation	Building AI is complex because a lot of research is required to develop models and software that can mimic human intelligence	Building ML models has two steps: selecting and preparing a training dataset; & choosing a pre-existing ML strategy or model
Requirements	Developing AI models does not require too many infrastructural resources to begin with	It requires data sets with several hundred data points for training and enough computational power to run
Objectives	The goal is to simulate natural intelligence to solve complex problems	It learns from data to maximize performance on certain tasks
Classification	Divided into three broad categories - Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI)	It is divided into three categories - Supervised, Unsupervised, and Reinforcement Learning
Type of data	AI can work with structures, semi-structured, and unstructured data	ML works only with structured and semi-structured data
Human involvement	AI systems can work with minimal human intervention and autonomously; and make decisions, and take actions based on data	Human involvement is necessary in ML algorithms to set up, train, and optimize the systems
Target	Its systems are focused more on maximizing the chances of success	ML is more concerned with enhancing the pace of identifying trends and improving accuracy

AI vs ML: Application and Use Cases

Many experts believe AI and ML to be two different sides of the same coin. They can be applied together in several ways to enhance the overall efficiency of the organization and help achieve targets quickly and efficiently.

Implementing AI and ML can help companies use available data and other resources to drive productivity and efficiency to make data-driven decisions.

Let’s look at some of the common uses of AI and ML:

Banking

AI and machine learning are proving to be of great value for the banking sector by helping quickly detect fraud, prevent risks, and provide proactive financial advice to customers.

Banks are using AI and ML to learn the transaction patterns of the users and make relevant recommendations that can improve their overall banking experience.

Retail

The use of AI and ML in the retail sector is increasing at a rapid pace. Several e-commerce sites such as Amazon are using these technologies to give personalized recommendations to the customers.

Chatbots are using natural language processing to interact better with customers and engage with them to improve their overall shopping experience. Some retail companies are using advanced AI and ML systems to enhance the self-checkout process.

Healthcare

The healthcare industry produces a vast amount of big data such as patient records, medical tests, and more.

As a result, the role of AI and ML is crucial to understanding this data and making better decisions with increased chances of achieving the desired results.

Using AI and ML can help in faster diagnosis, expanding access to healthcare offerings, discovering new drugs, and doing efficient clinical research.

AI in healthcare is commonly applied to develop ML models that can scan X-rays to detect any cancerous growths. develop programs to create personalized treatment plans and more.

Manufacturing

Manufacturing is another sector where AI and ML have prominent applications and use cases. Today, manufacturers are using industrial robots to reduce the burden of repetitive and dangerous tasks on human workers.

In addition, these technologies are also being used for better managing the supply chains to manage the inventories better and reduce wastage.

AI and ML Jobs in the US

Today, AI and ML are some of the most demanded professions. There are several job roles that you can look for if you are looking to start your career in these fileds or if you are looking to move to higher positions in the two domains.

Let’s take a look at different types of jobs in these fields:

Artificial intelligence	Machine Learning
Position	Description	Position	Description
Big Data Analyst	Find patterns in data to help make predictions and decision	ML Engineer	Design and implement ML models, expand and optimize data pipelines
Natural Language Processing Engineer	Explore and develop connection between human language and computers or machines	Data Scientist	Analyze, process, model, and interpret data to create actionable plans to guide decision-making
AI Engineer	Build AI models from the start and help product managers and stakeholders understand the results	Software Developer	Design and build applications and predict user reactions
Big Data Engineer	Develop such systems that facilitate communication and collect data	Computer Vision Engineer	Perform image based work for image detection and face recognition

AI and ML Salaries in the US

Salaries of AI and ML experts in the US are dependent on several factors, even though they are some of the most demanded job profiles out there today.

The two most popular job roles in these fields are AI Engineer and ML Engineer. The average salary of an AI Engineer $114,672, while the average salary of an ML Engineer is $104,000.

Let’s take a look at salaries earned by AI and ML professionals in the US.

Artificial Intelligence	Machine Learning
Position	Average Salary in the US ($)	Positions	Average Salary in the US ($)
Entry-Level	114,652	Entry-Level	108,307
Mid-Level	146,220	Mid-Level	155,000
Senior-Level	204,380	Senior-Level	213,572

FAQs: Artificial Intelligence vs Machine Learning

1. Can AI make decisions on its own without any human intervention?
While AI systems can analyze data and make decisions autonomously, they often require human oversight and intervention, especially in critical or complex scenarios to ensure accuracy and ethical considerations.

2. Are there any ethical concerns associated with the use of AI and ML in various industries?
Yes, the widespread adoption of AI and ML raises ethical concerns such as data privacy, algorithmic bias, job displacement, and the potential for misuse of technology.
Organizations need to address these concerns and implement responsible AI practices.

3. What are some limitations of AI and ML technologies?
Despite their advancements, AI and ML technologies have limitations, including the need for large amounts of data for training, susceptibility to bias in data sets, and challenges in interpreting complex decision-making processes.

4. How do AI and ML contribute to sustainability and environmental conservation?
AI and ML technologies can contribute to sustainability efforts by optimizing energy usage, predicting environmental patterns, and enhancing resource management practices.
For example, they can optimize transportation routes to reduce fuel consumption and greenhouse gas emissions.

5. Can AI and ML be used in creative fields such as art and music?
Yes, AI and ML have applications in creative fields, including generating art, music, and literature. Generative AI is one such field using which creators and tech professionals can unleash their creativity and bring their ideas to life.

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AUTHOR

Rishabh Dev Choudhary

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Rishabh Dev Choudhary

Attend our Free Webinar on How to Nail Your Next Technical Interview

How to Nail your next Technical Interview

Worried About Failing Tech Interviews?

Tools to Enhance Full Stack Development with AI

The Future of Data Science: Emerging Trends and Opportunities

Data-Driven Decision Making: Your Roadmap to Business Success

The Business Impact of Machine Learning: Real-world Case Studies

What is OpenAI? Everything You Need to Know

Mock Interviews for Generative AI: Essential Practice to Land Top AI Roles

Top Python Scripting Interview Questions and Answers You Should Practice

Complex SQL Interview Questions for Interview Preparation

Zoox Software Engineer Interview Questions to Crack Your Tech Interview

Rubrik Interview Questions for Software Engineers

Top Advanced SQL Interview Questions and Answers

Twilio Interview Questions

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