In this segment, you will explore the various paradigms of artificial intelligence. These paradigms refer specifically to the technical approaches, and consequently, the kinds of tasks that can be accomplished using different approaches to artificial intelligence. This segment will also help you develop a strong a strong vocabulary related to artificial intelligence. This segment will also help you develop a strong vocabulary related to artificial intelligence concepts, equipping you with a solid understanding of technical terms such as artificial intelligence, machine learning, deep learning, and more.
In the next video, Kshitij will discuss artificial intelligence and machine learning.
Kshitij began by providing the following definition of artificial intelligence:
“Artificial Intelligence is intelligence demonstrated by machines where machines try to mimic the way humans think.”
He then used the following diagram to illustrate the relationship between artificial intelligence and machine learning.
Note that this diagram is an oversimplification of the relationship between AI and ML. But as shown in the shown in the diagram, machine learning can be considered a subset of artificial intelligence. While there are other approaches to AI that differ from machine learning and have their own specific uses, our focus will primarily be on machine learning and its subfields.
Kshitij defined machine learning as follows:
“Machine learning is a field devoted to understanding and building methods that let machines ‘learn’. These methods leverage data to enhance computer performance on specific tasks.”
Examples of tasks in which machine learning models are useful include spam detection, fraud detection, and customer segmentation. In general, machine learning models learn the specific relationships between input and output for a given task. Kshitij also discussed the training data used for training ML models and the sizes of typical machine learning models. Specifically, he mentioned that ML models usually 1k-100k points in the data sets over which they are trained. Specifically, he menti oned that ML models usually 1k-100k data points in the data sets over which they are trained. Sizes of these models are specified using the number of parameters and ML models generally have no more than a thousand parameters.
Before proceeding, answer the following questions to evaluate your understanding of the topics covered in this segment.
Before proceeding to learn more about deep learning let’s explore some factors that impact the performance of ML models, In the next video, Kshitij will discuss several factors that influence a model’s performance.
Kshitij outlined three factors that affect the performance of ML models:
- Training data
- Model size
- Model architecture
A more representative and lager data set can lead to better performance in a machine learning model. However, performance is also constrained by the model’s size. Kshitij used the analogy of the brain to explain how the size and architecture of the network contribute to improved performance on more complex tasks.
In the next video, Kshitij will explain deep learning models.
Kshitij began by updating the diagram that describes the different paradigms in artificial intelligence.
As seen in the diagram, deep learning is a subset of machine learning. Kshitij used image classification as an example of a task where deep learning models excel. Certain types of deep learning models, such as recurrent neural networks, are designed to handle sequential data, making them well-suited for language modelling tasks like sentence completion and translation. However, their performance on these language tasks was still lacking in terms of the length of the output.
You have now learned about machine learning and deep learning, as well as the trends in training data, model size, and architectures associated with these models. In the next segment, we will continue exploring AI paradigms and focus on generative AI models.