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GenAI Overview | AI ML Basics

Updated: Feb 25


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

The Genesis of AI

The formal foundation for AI as a scientific discipline began in the mid-20th century.


The 1950s: Laying the Groundwork
  • Alan Turing and the Turing Test: Often considered the father of theoretical computer science and artificial intelligence, Alan Turing proposed the ‘Turing Test’ in 1950 as a criterion of intelligence. This test examines a machine's ability to exhibit intelligent behavior indistinguishable from that of a human. ‘Eugene Goostman’ is a chatbot that some regard as having passed the Turing test in 2014, a test of a computer's ability to communicate indistinguishably from a human.

  • The Dartmouth Conference (1956): This conference is widely acknowledged as the birthplace of AI as a field. John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon penned a proposal asserting that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."


1960s-1970s: Early Enthusiasm and AI Winters

  • The initial decades saw significant advancements, such as the development of the first AI programs, including ELIZA (a natural language processing computer program) and SHRDLU (a program that could answer questions about objects in a block world). However, the field also experienced its first "AI Winter," a period marked by reduced funding and interest in AI research, due to unmet expectations.


1980s: A Resurgence with Expert Systems

  • The 1980s witnessed a resurgence of interest in AI, fueled by the success of expert systems, which are computer programs that emulate the decision-making abilities of a human expert. This period also saw significant advancements in machine learning algorithms.


The 1990s to Early 2000s: The Internet Era and Machine Learning

  • The expansion of the internet and increases in computational power led to significant advancements in machine learning and data processing capabilities, setting the stage for modern AI.


The 2010s: Breakthroughs in Deep Learning and Generative AI

  • The 2010s were marked by breakthroughs in deep learning, a subset of machine learning based on artificial neural networks. This period saw the development of generative models like Generative Adversarial Networks (GANs) and transformers, leading to the emergence of Generative AI, capable of creating content that is increasingly indistinguishable from that created by humans.


The 2020s: A New Era of Generative AI, Powered by Technological Advancements

  • In the 2020s, the field of artificial intelligence witnessed a significant milestone with the launch of ChatGPT by OpenAI. Released in November 2022, ChatGPT represents a quantum leap in AI's capability to understand and generate human-like text, making it a cornerstone for applications requiring natural language processing and generation. This period underscored the maturation of Generative AI, with ChatGPT becoming a symbol of AI's growing influence in everyday life, demonstrating how machines can engage in meaningful, context-aware conversations with humans.


Drivers of this AI Revolution

  • Surge in Computing Power: The 2020s have seen an exponential increase in computing power, enabling researchers and developers to train more complex AI models than ever before.

  • Availability of Large Datasets: Large datasets are indispensable for training AI models, and the abundance of data in the 2020s has facilitated the training of models, which require extensive data to understand and mimic human language patterns accurately.

  • Advancements in Chip Technology: Companies like NVIDIA have led the charge, producing chips specifically designed for AI tasks, which have been instrumental in advancing the capabilities of AI models.

  • Expansion of Cloud Computing: Cloud computing has democratized access to powerful computing resources, allowing individuals and organizations to train and deploy AI models without the need for significant hardware investments.

  • Collaborative Open-Source Ecosystems: The AI community has benefited from a culture of open-source collaboration, with researchers and developers sharing tools, algorithms, and datasets.


Notable People in AI's Evolution

  • Geoffrey Hinton, Yann LeCun, and Yoshua Bengio: Often referred to as the "Godfathers of AI," for their pioneering work in deep learning.

  • Ian Goodfellow: Known for inventing Generative Adversarial Networks (GANs).

  • Demis Hassabis: Co-founder of DeepMind, which has been at the forefront of advancing AI technologies.


Notable Companies in Generative AI

  1. OpenAI: Known for its GPT series, including GPT-3 and GPT-4, OpenAI has been pivotal in advancing natural language processing and generation technologies. Their projects, such as ChatGPT and DALL·E, demonstrate the breadth of GenAI applications, from conversational agents to image generation.

  2. DeepMind: A subsidiary of Alphabet Inc., DeepMind's achievements in AI are numerous, including AlphaGo, which defeated the world champion in the complex board game Go. DeepMind continues to push the boundaries of AI in areas like protein folding with AlphaFold.

  3. Stability AI: The company behind Stable Diffusion, a deep learning model capable of generating high-quality images from textual descriptions. Stability AI has democratized access to powerful image generation tools, fostering creativity and innovation.


Notable Projects in AI/GenAI

  1. AI-Driven Drug Discovery (Insilico Medicine): Utilizing AI to accelerate the process of identifying potential drug candidates, Insilico Medicine has showcased how GenAI can revolutionize the pharmaceutical industry by reducing the time and cost associated with drug development.

  2. Content Creation (Jasper): Jasper, formerly known as Jarvis, leverages AI to assist with content creation, offering tools for writing marketing copy, blog posts, and more. This application of GenAI demonstrates its capacity to enhance productivity and creativity in content marketing.

  3. AI in Fashion (Stitch Fix): Stitch Fix uses AI to personalize clothing recommendations for its customers. By analyzing customer preferences and trends, Stitch Fix's algorithms can curate personalized fashion selections, showcasing the potential of GenAI in retail.

  4. Legal Document Analysis (ROSS Intelligence): ROSS Intelligence employs AI to sift through legal documents, helping lawyers find relevant case law faster than traditional methods. This application of GenAI streamlines legal research, demonstrating its potential to improve efficiency in legal professions.

  5. AI in Art and Design (Artbreeder): Artbreeder uses GenAI to allow users to create unique artworks by blending and modifying existing images. This platform exemplifies how GenAI can be used as a tool for artistic exploration and creativity.


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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