Artificial Intelligence (AI) and Machine Learning (ML) are the two most popular technologies in the computer science field. AI focuses on developing intelligent machines and systems that can think and perform tasks like a human such as speech recognition, natural language processing, etc.
In comparison, ML is a sub-field of AI and involves teaching machines to learn from data to identify patterns and trends to make better decisions.
AI and ML terms are being used interchangeably but there are vast differences between them.
While ML is particularly suited for tasks like predictive analysis, recommendations systems, AI covers a broader range of applications, including natural language processing.
In this article, we explain how Artificial Intelligence and Machine Learning are different from one another.
We also delve into discussing the applications, use cases, and some of the lucrative careers these technologies give rise to.
Also Read: Machine Learning vs. Data Science — Which Has a Better Future?
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?
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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:
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:
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.
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.Â
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.Â
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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