AI & Machine learning

What is AI and ML?

Artificial Intelligence (AI) is an umbrella term for computer software that mimics human cognition in order to perform complex tasks and learn from them. Machine learning (ML) is a subfield of AI that uses algorithms trained on data to produce adaptable models that can perform a variety of complex tasks.

What's the difference between ML and AI?

In simplest terms, AI is computer software that mimics the ways that humans think in order to perform complex tasks, such as analyzing, reasoning, and learning. Machine learning, meanwhile, is a subset of AI that uses algorithms trained on data to produce models that can perform such complex tasks.

Most AI is performed using machine learning, so the two terms are often used synonymously, but AI actually refers to the general concept of creating human-like cognition using computer software, while ML only one method of doing so.

Benefits and the future of AI.

AI and machine learning provide a wide variety of benefits to both businesses and consumers. While consumers can expect more personalized services, businesses can expect reduced costs and higher operational efficiency.

It’s little surprise that the global market for AI is expected to increase exponentially in the coming years. According to Grand View Research (GVR), the global market size for artificial intelligence is projected to expand from $136.6 billion in 2022 to a whopping $1.8 trillion in 2030 [2].

 Some common benefits for businesses using AI and machine learning in the real world include:

AI & Machine learning

How are AI and ML connected?

While AI and ML are not quite the same thing, they are closely connected. The simplest way to understand how AI and ML relate to each other is:

AI is the broader concept of enabling a machine or system to sense, reason, act, or adapt like a human.
ML is an application of AI that allows machines to extract knowledge from data and learn from it autonomously.
One helpful way to remember the difference between machine learning and artificial intelligence is to imagine them as umbrella categories. Artificial intelligence is the overarching term that covers a wide variety of specific approaches and algorithms. Machine learning sits under that umbrella, but so do other major subfields, such as deep learning, robotics, expert systems, and natural language processing.

AI & Machine learning

Advantages of Artificial Intelligence.

1. AI allows a machine to simulate human intelligence to solve problems.

2. The goal is to develop an intelligent system that can perform complex tasks.

3. We build systems that can solve complex tasks like a human.

4. AI has a wide scope of applications.

5. AI uses technologies in a system so that it mimics human decision-making.

6. AI works with all types of data: structured, semi-structured, and unstructured.

7. AI systems use logic and decision trees to learn, reason, and self-correct.

Advantages of Machine Learning.

1. ML allows a machine to learn autonomously from past data.

2. The goal is to build machines that can learn from data to increase the accuracy of the output.

3. We train machines with data to perform specific tasks and deliver accurate results.

4. Machine learning has a limited scope of applications.

5. ML uses self-learning algorithms to produce predictive models.

6. ML can only use structured and semi-structured data.

7. ML systems rely on statistical models to learn and can self-correct when provided with new data.

AI & Machine learning

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