AI & You #2: How to speak about AI vs ML

Hey Subscriber,

We hope you enjoy this issue of “AI & You.”

As executives at your company and peers begin to talk about Artificial Intelligence more often this year, here is our guide to sounding smart while discussing AI and ML.

AI vs. ML

Artificial Intelligence (AI) is a vast concept that encompasses a broad range of technologies and ideas, while Machine Learning (ML) is a distinct subset of AI. AI refers to the development of computer systems that can perform tasks that require human intelligence, while ML is concerned with the development of algorithms that empower computers to learn and enhance their performance based on data inputs.

The Synergy Between AI and ML

AI and ML are not mutually exclusive, and they often work together to create powerful, intelligent systems, as seen in natural language processing and computer vision, where ML techniques are incorporated to improve performance and accuracy.

Rule-Based vs. Learning From Data

In contrast to traditional rule-based systems, which necessitate explicit programming to perform specific tasks, ML algorithms are designed to automatically analyze extensive datasets, identify patterns, and make predictions or decisions. Consequently, ML-driven systems continually improve in terms of accuracy and efficiency as they process more data, effectively “learning” from their experiences.

To learn more about the differences between these two important concepts, check out our blog on “AI vs. ML.”

4 Types of Machine Learning

Machine learning is a rapidly evolving field that has the potential to transform many industries, from healthcare to finance to manufacturing. At the core of machine learning are four main types of learning techniques: supervised learning, unsupervised learning, semi-supervised learning,
 and reinforcement learning.

To learn more about the different types of machine learning, check out our article on “Different Types of Machine Learning.”

What is Deep Learning?

Deep learning (DL) is a subset of ML that primarily focuses on mimicking the human brain’s ability to learn and process information. In the rapidly evolving world of AI), deep learning has emerged as a groundbreaking technology that is impacting virtually every field, from healthcare to autonomous systems.

The Building Blocks of Deep Learning

The foundation of deep learning is built upon the concept of artificial neural networks, or ANNs, which are inspired by the structure and function of the human brain. ANN’s consist of three main layers: input layer, hidden layer, and output layer.

Challenges of Deep Learning

Despite the remarkable successes of deep learning, there remain several challenges and areas for future research that warrant further exploration to advance the field and ensure responsible deployment of these technologies. These include interpretability and explainability, data and computational requirements, and robustness and security.

Applications of DL

Deep learning has transformed various industries and applications, such as image recognition and computer vision, natural language processing, speech recognition and generation, reinforcement learning, generative models, and healthcare. With advancements in deep learning techniques, applications like facial recognition, autonomous vehicles, sentiment analysis, and drug discovery have been made possible, making deep learning a powerful tool for solving complex problems.

To learn more about deep learning, make sure to check out our article on “What is Deep Learning?

Thank you for taking the time to read AI & You!

Skim AI is a Machine Learning and Artificial Intelligence consultancy that educates executives, performs due-diligence, advises, architects, builds, deploys, maintains, updates and upgrades enterprise AI across language (NLP), vision (CV) and automation based solutions.

Chat with me about Enterprise AI

Follow Skim AI on Twitter