AI, in general, refers to the development of intelligent systems

 

For those who are new to the field of artificial intelligence, grasping the many complex terms associated with it can prove to be quite overwhelming. Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLMs), and Generative AI are all related concepts in the field of computer science, but there are important distinctions between them. They have significant differences in their functionality and applications. We will take a closer look at these concepts and gain a better understanding of their distinctions further.

Artificial Intelligence

AI belongs to the field of computer science that deals with the development of computer systems that can perform tasks that typically require human intelligence, such as speech recognition, natural language processing (NLP), text generation and translation, video, sound, and image generation, decision making, planning, and more.

AI, in general, refers to the development of intelligent systems that can mimic human behavior and decision-making processes. It encompasses techniques and approaches enabling machines to perform tasks, analyze visual and textual data, and respond or adapt to their environment. One of the key advantages of artificial intelligence is its ability to process large amounts of data and find patterns in it. AI tools are designed to make decisions or take actions based on that knowledge.

Applications and synergies:

AI, machine learning and generative AI find applications across various domains. AI techniques are employed in natural language processing, virtual assistants, robotics, autonomous vehicles and recommendation systems. Machine learning algorithms power personalized recommendations, fraud detection, medical diagnoses and speech recognition. Generative AI has gained prominence in areas such as image synthesis, text generation, summarization and video production.

While machine learning is a subset of AI, generative AI is a subset of machine learning . Generative models leverage the power of machine learning to create new content that exhibits characteristics learned from the training data. The interplay between the three fields allows for advancements and innovations that propel AI forward.

How to choose and build the right machine learning model

Developing the right ML model to solve a problem requires diligence, experimentation and creativity. Although the process can be complex, it can be summarized into a seven-step plan for building an ML model.

1. Understand the business problem and define success criteria. Convert the group's knowledge of the business problem and project objectives into a suitable ML problem definition. Consider why the project requires machine learning, the best type of algorithm for the problem, any requirements for transparency and bias reduction, and expected inputs and outputs.

2. Understand and identify data needs. Determine what data is necessary to build the model and assess its readiness for model ingestion. Consider how much data is needed, how it will be split into test and training sets, and whether a pretrained ML model can be used.

3. Collect and prepare the data for model training. Clean and label the data, including replacing incorrect or missing data, reducing noise and removing ambiguity. This stage can also include enhancing and augmenting data and anonymizing personal data, depending on the data set. Finally, split the data into training, test and validation sets.

By - Aaradhay Sharma