AI Explained Simply: A Guide for Beginners and Tech Professionals

Artificial Intelligence: It uses data and statistics to mimic human intelligence and make predictions.

Machine Learning: It focuses on developing algorithms that allow computers to learn from and make decisions based on data rather than being explicitly programmed to perform specific tasks. These algorithms are statistical techniques to learn patterns in data and make predictions or decisions without human intervention. 

In machine learning, there are two major tasks we perform.

Classification - Predicts Categories.

Regression - Predicts Continuous Values.

Regression: In Regression, you don't have any fixed categories where your mapping input and output can be basically any number. In this case, possibilities are very high.

Machine Learning is broadly classified into three categories:

Supervised Learning: Supervised Machine Learning is used for a labelled data set.

Unsupervised Learning: Unsupervised Machine Learning is used for patterns in data set. In unsupervised learning, we provide an unlabelled data set to Machine Learning program, and then it learns to identify the patterns and structure in the data without any explicit guidance. Unsupervised Learning can also be used for outliers. 

Reinforcement Learning:  Reinforcement machine learning in which an agent learns to make decisions by interacting with an environment and receiving rewards or penalties based on its actions. Over time, the agent learns a strategy (called a policy) that maximises cumulative rewards.

Deep Learning: Deep learning is a machine learning technique that uses neural networks to learn from large amounts of data, mimicking the human brain's ability to recognise patterns and make decisions. It focuses on artificial neural networks with multiple layers.

Deep Learning can also be used when the structured data is huge to get an effective output.

• Not all machine learnings are deep learning because traditional machine learning methods still play a pivotal role in many applications. So we got techniques like: Linear Regression, Decision Trees, Vector Machines, Clustering Algorithms, Random Forest( Example: XG BOOST). These are all other types of machine learning. And they have been widely used for a long time. In certain scenarios, deep learning may be overkill, or it may not be the most suitable approach.

Foundation Models: Foundation models are large-scale neural network models trained on a vast amount of data, and they serve as a base or a foundation for a multitude of applications. So instead of training a model from scratch for each specific task, you can take a pre-trained foundation model and fine-tune it for a particular application, which saves a bunch of time and resources.

• Now, foundation models have been trained on diverse data sets capturing a broad range of knowledge and can be adapted to tasks ranging from language translation to content generation to image recognition. So foundation models sit within the deep learning category, but represent a shift towards more generalized adaptable, and scalable AI solutions.
• LLMs are Foundation Models, but not all LLMs are Foundation models.

Generative AI: Generative AI is a type of Artificial Intelligence that creates new content based on what it has learned from existing content. The process of learning from existing content is called training and results in the creation of a statistical model. When given a prompt, GenAI uses this statistical model to predict what an expected response might be, and this generates new content. It is a type of AI system capable of generating new content, such as text and images.

Example:

• OpenAI's ChatGPT
• DALL-E
• Google's Gemini
• Microsoft's Copilot