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GenAI Overview | AI ML Basics

Updated: Feb 3


Relationship Between AI ML DL GenAI | Gen AI for Project managers

AI (Artificial Intelligence)

Definition: Artificial Intelligence refers to the simulation of human intelligence in machines programmed to mimic human thought processes and behavior.


Use Case: Chatbots in customer service utilize AI to interpret and respond to customer inquiries, improving efficiency and user experience.


ML (Machine Learning)

Definition: Machine Learning is a subset of AI focusing on the development of algorithms that enable machines to learn and improve from experience.


Use Case: Recommendation systems in online platforms, like Netflix, use ML to analyze viewing habits and suggest personalized content to users.


Deep Learning

Definition: Deep Learning is a subset of ML based on artificial neural networks with representation learning. It learns from vast amounts of unstructured data.


Use Case: Voice assistants like Siri or Google Assistant use deep learning to understand and process natural language queries.


GenAI (Generative AI)

Definition: Generative AI is an advanced area within AI, where algorithms create new and original outputs, ranging from text to images, based on learned data.

It is an extension of deep learning and ML, leveraging complex models to generate novel outputs instead of just interpreting or classifying data. Its a key for a PM to understand these AL ML Basics.


GenAI Types and Use Cases

Generative AI, characterized by its ability to generate new content, encompasses various types and has a wide range of applications. Understanding these types and their potential use cases is crucial for program managers overseeing projects in this domain.


Text Generation Models

These models generate textual content, which can range from simple responses to complex narrative texts.

Use Cases

  • Content creation for blogs, articles, and social media posts.

  • Automated report generation.

  • Scriptwriting and narrative design in gaming.


Image Generation Models

Capable of creating visual content, these models generate new images or modify existing ones.

Use Cases:

  • Generating artwork and designs.

  • Visual data augmentation for training other AI models.

  • Concept art creation for media and entertainment industries.


Music and Sound Generation Models

These models create musical or audio content, either by composing new pieces or altering existing tracks.

Use Cases:

  • Composing background scores for games and films.

  • Creating unique sound effects.

  • Assisting in music production by generating melodies and harmonies.


Data Generation Models

Description: Focused on generating synthetic data, these models are used to create datasets that mimic real-world data.

Use Cases:

  • Data augmentation for training machine learning models.

  • Generating test data for software testing and validation.

  • Research in fields where data privacy is a concern, like healthcare.


Video Generation Models

These models can generate or alter video content, creating new footage or modifying existing videos.

Use Cases:

  • Creating training videos for educational purposes.

  • Generating visual effects in the film industry.

  • Developing marketing and promotional content.


3D Model Generation

Capable of generating three-dimensional models, these are used in various design and simulation applications.

Use Cases:

  • Architectural design and visualization.

  • Creation of 3D models for video games and virtual reality.

  • Prototyping in product design and manufacturing.

 

For program managers, understanding these types and their applications is vital. Each type of Generative AI brings unique project challenges, such as varying computational requirements, data privacy considerations, ethical implications, and the necessity for specialized talent.

 

Differences between AI, ML, DL and GenAI

Aspect

Artificial Intelligence (AI)

Machine Learning (ML)

Deep Learning

Generative AI

Definition

A broad field of computer science aimed at building smart machines capable of performing tasks that typically require human intelligence.

A subset of AI focused on the development of algorithms that can learn and make predictions or decisions based on data.

A subset of ML that uses neural networks with many layers (deep networks) to learn from data.

A type of AI focused on creating new content, data, or information based on training data.

Approach

Uses algorithms based on logic and rules.

Uses statistical methods to enable machines to improve with experience.

Uses complex neural networks for pattern recognition and learning from large amounts of data.

Uses advanced algorithms, often based on deep learning, to generate new data similar to the training set.

Applications

Virtual assistants, game playing, language translation, etc.

Recommendation systems, spam filtering, fraud detection, etc.

Image and speech recognition, natural language processing, etc.

Art generation, text generation, music composition, data augmentation, etc.

Data Dependency

Varies widely; rule-based systems require less data, learning-based systems need more.

Requires substantial data for training and improving accuracy.

Needs large volumes of data for effective learning and accuracy.

Requires a large and diverse dataset to effectively generate high-quality, novel outputs.

Complexity

Ranges from simple, rule-based systems to complex learning and problem-solving.

Generally more complex than traditional algorithms but simpler than deep learning.

High complexity due to deep neural networks and large-scale data processing.

Very high, as it needs to understand and replicate the nuances of the input data to generate new content.

 

Additional Reading: Check out this wonderful video by Henrik Kniberg - Generative AI in a Nutshell - how to survive and thrive in the age of AI.



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