Basics of AI, ML and Generative AI - Definitions, Types, Use Cases and much more

Learn through the podcast and article

Welcome to the Fifth edition of my newsletter. 

In this newsletter, 

I share a  summary of my podcast with  Ranjani Mani, Director and Country Head of Generative AI at Microsoft India. 

We get into some  fundamentals of Generative AI and different types of algorithms. 

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Summary of the Episode with Ranjani - Product Talk with Malthi 

In my latest episode with Ranjani Mani we talked about AI Integration in Product Management.  

A recognized leader in AI, Ranjani brings over two decades of expertise in AI product platforms and data sciences from Microsoft, VMWare, Dell, and Atlassian. She mentors startups and champions women in AI leadership, speaks at TEDx and industry panels, and hosts the podcast "Generative AI & I." Discover her journey and deep industry insights on staying ahead in the evolving world of AI. Here’s a quick summary of our conversation Watch the full video here.

Common Use Cases for Gen AI 

Heavily Regulated Industries: Highly regulated sectors like Banking, Financial Services, and Insurance (BFSI) might prioritize low-risk applications of Gen AI, focusing on tasks that minimize potential disruptions or errors.

Digital Native Companies: Businesses with a strong digital presence (e-commerce companies) might be more comfortable exploring cutting-edge applications of Gen AI, potentially incorporating hybrid LLMs for complex tasks.

How should leaders strategies GenAI adoption

Implementing Gen AI should begin with a top-down strategy, focusing on business outcomes. Key considerations include:

1. Are you creating a new revenue stream?

2. Are you delivering a differentiated customer experience?

3. Are you modernizing internal processes?

As a business leader, one should select use cases that align with these desired outcomes to ensure the most effective application of Gen AI.

Common Reasons for Project Failures

Many companies struggle to transition Gen AI projects into production and generate revenue. The costs associated with infrastructure and talent are significant. Rajini shares her observations on this matter:

Traditional AI projects have a success rate of only 30-40%. Common challenges include:

• Issues with datasets, misaligned questions, consumption plans, and AI strategies not aligned with business strategies.

• Recognizing that some companies are seeing tangible benefits, leading to increased investment in Gen AI. Funding for these initiatives often comes from IT budgets rather than innovation budgets.

It is essential to have a proper data strategy and the right technical talent to implement and scale Gen AI. 

Guidelines for Product Managers Starting with AI Products

When building Gen AI solutions, it’s crucial to prioritize the problem over the technology. Instead of seeking problems to fit the technology, understand the business needs and pain points first. Engage stakeholders, like data scientists, to gain comprehensive insights.

Use Case Prioritization:  Gen AI is rapidly evolving, and new developments can emerge quickly. 

1. When selecting a tech stack, decide between open-source and closed-source models.

2. Plan for scalability and determine the appropriate time to introduce SLMs.

3. Continuously optimize and manage changes, as consumption is often where projects fail.

The PM role is evolving to address these challenges. Unlike traditional PDMC cycles, Gen AI projects must start with data, as it is the core component. 

Measuring Metrics and Outcomes for AI Products

When Product Managers (PMs) introduce an AI-first product, the approach to measuring metrics and outcomes differs from that of a SaaS product.

In AI projects, the focus should shift from merely considering the output of the model to evaluating the outcomes that drive impact. This involves assessing whether the AI solution translates into cost efficiency and revenue generation. Mature organizations often implement processes such as A/B testing to ensure a more outcome-oriented approach.

Guiding People Through Learning Gen AI

To effectively navigate the learning journey in Gen AI, Rajini recommends the following approach:

1. Stay close to what you are passionate about. Most learning happens on the job, so immerse yourself in practical experiences.

2. Productive work occurs when you delve deeply into a subject. Always start with the fundamentals to build a strong foundation.

3. Understand that processes drive outcomes, not just plans. For example, setting a goal to lose weight is important, but the process of running 30 minutes daily is what achieves it.

4. Engage directly with customers and get hands-on experience. Attend meetups and aim to advance your knowledge from basic (Level 100) to more advanced levels (Level 200 to Level 300).

Here are some recommended sources for learning about Gen AI:

• Microsoft’s Gen AI 101 Fundamentals: A great starting point for foundational knowledge.

• LinkedIn Learning: Offers a variety of courses on AI and related topics.

• AI Canon by AI 16Z: Provides in-depth content on AI advancements.

Additionally, “A Brief History of Intelligence” is an insightful read that I’m currently exploring.

How AI is Transforming the Role of Product Management

. Here are some key points Ranjani shares

1. PMs can use AI to streamline and optimize various tasks, allowing them to focus on more strategic aspects of their role. For e.g. whether it’s conducting research, building user stories, or making data-driven decisions. The linear/exponential framework helps While tasks may be automated, the overall job remains intact

2. There are PMs who focus on AI core functionality products and those who integrate AI into broader product strategies. Both roles benefit from AI’s ability to handle complex data analysis and provide actionable insights.

3. AI provides the analytical power needed to identify trends, predict outcomes, and optimize product development processes.

4. In summary, AI is not replacing PMs but rather enhancing their roles by automating tasks, providing deeper insights, and enabling more strategic decision-making.

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Fundamentals of Gen AI

Are you curious about how machines can create entirely new content? Today, we'll delve into the world of Generative AI (Gen AI) and explore the algorithms that make it tick. But before we jump in, let's revisit some foundational concepts.

1. Artificial Intelligence (AI): AI is the broad field of computer science focused on creating machines that can think and act like humans. It encompasses a wide range of techniques and approaches.

Examples: Self-driving cars, playing chess at a super-human level, medical diagnosis assistance.

2. Machine Learning (ML): ML is a subset of AI.  ML focuses on enabling machines to learn from data without explicit programming. ML algorithms learn from patterns in data to make predictions or decisions on new data.

Examples: Recommender systems, spam filtering, image recognition.

3. Deep Learning: Deep Learning is a subset of ML based on artificial neural networks, where algorithms learn from large amounts of data to identify patterns and make decisions. DL models are inspired by the structure and function of the human brain.

Examples:  Deep learning is particularly successful in tasks like image and speech recognition, natural language processing (understanding and generating human language).

4. Generative AI: Generative AI is a subset of AI technologies that can generate new content, ideas, or data across various formats (text, images, music, code, etc.) 

Examples:  Generating realistic images, creating new music pieces, writing different creative text formats, and more.

Foundation Models and Gen AI

Foundation Models (FMs) are adaptable models trained on vast amounts of text, images, code, and more. Gen AI models use these pre-trained FMs as a base and fine-tune them for specific tasks like writing poetry, creating realistic images, or composing music. However, not all Gen AI models rely on FMs; some use simpler tools or are built from scratch for specific purposes.

Key Takeaways:

• FMs are powerful pre-trained models used as a base for Gen AI.

• Gen AI leverages FMs for specific tasks like text generation and image creation.

• Not all Gen AI models rely on FMs; some use simpler architectures or are built from scratch.

Large Language Models (LLMs)

Large Language Models (LLMs) excel in various text-based tasks, including:

• Natural Language Understanding: Interpreting the meaning behind words.

• Text Generation: Creating poems, scripts, and stories.

• Translation: Converting languages accurately.

• Summarization: Condensing lengthy texts into summaries.

• Question Answering: Providing informative answers from vast text data.

However, LLMs are limited to text and cannot generate images or music.

Key Differences Between FMs and LLMs:

Differentiation between ML, DL and   Gen AI?

 The amount of data needed. 

• Machine Learning (ML): Effective with relatively small datasets.

• Deep Learning (DL): Requires large datasets to learn complex patterns.

• Generative AI (Gen AI): Needs massive amounts of data, to fuel their creative abilities.

Types of Gen AI models

• Generative Language Models: Learn language patterns from training data and predict subsequent text based on given input.

• Generative Image Models: Use techniques like diffusion to create new images from random noise or prompts, transforming them into coherent visuals.

Algorithms

Algorithms are pieces of code that help explore, analyse, and find meaning in complex data sets. The algorithms used in ML or Gen-AI are different.  Let’s explore them here

Machine Learning (ML):

Here are some of the algorithms that are used in ML

1. Supervised Learning Algorithms: These algorithms learn from labelled data (data with corresponding labels or outputs). 

2. Linear Regression: Predicts a continuous value (like house price) based on one or more input features (like square footage).

3. Logistic Regression: Classifies data into discrete categories (like spam/not spam email).

4. Decision Trees: Make decisions based on a series of questions about the data, leading to a final classification or prediction.

5. Support Vector Machines (SVMs): Find the best hyperplane (decision boundary) to separate different classes in data.

6. Unsupervised Learning Algorithms: These algorithms find patterns in unlabelled data (data without predefined labels).

7. Q-learning: An agent learns to take actions that maximize future rewards based on past experiences.

8. K-Means Clustering: Groups data points into a specific number of clusters based on similarities.

9. Principal Component Analysis (PCA): Reduces the dimensionality of data by identifying the most important features.

10. Reinforcement Learning Algorithms: These algorithms learn through trial and error, interacting with an environment and receiving rewards for desired behaviours. 

Deep Learning (DL):

Here are some of the algorithms that are used in DL.

1. Deep Neural Networks (DNNs): These are artificial neural networks with multiple hidden layers, inspired by the structure and function of the human brain. 

2. Convolutional Neural Networks (CNNs): Excellent at image recognition and other tasks involving grid-like data.

3. Recurrent Neural Networks (RNNs): Designed to handle sequential data like text or speech, where the order of elements matters.

4. Long Short-Term Memory (LSTM): A type of RNN that can handle long-term dependencies in sequential data.

Generative AI (Gen AI):

Gen AI often leverages algorithms from both ML and DL, particularly those suited for generating new content. Here are some prominent algorithms used:

Generative Adversarial Networks (GANs): These are used to generate realistic images and can be applied to tasks like image-to-image translation and artwork generation. There are two types.

1. Generator: Creates new data (like images or text).

2. Discriminator: Evaluates the realism of the generated data.

Variational Autoencoders (VAEs): these are used to create a new data points like training data, often applied in image and video generation.

Transformer Models: These include models like GPT-4, which are used for text generation, translation, and other language-related tasks.

Additional Considerations:

Hybrid Approaches: Integrating algorithms from Machine Learning (ML), Deep Learning (DL), and Generative AI (Gen AI) can be highly effective for specific tasks.

Evolutionary Algorithms: Inspired by natural selection, these algorithms are used in Gen AI for creative content generation, selecting the “fittest” outputs.

Attention Mechanisms: This technique within neural networks, especially transformers, enables models to focus on relevant parts of the data. It’s essential for understanding relationships in text and other sequential data.

Choosing the Right Algorithm:

The choice of algorithm depends on the specific task and data type. 

Here's a general guideline:

1. For structured data and classification tasks: Supervised learning algorithms like decision trees or SVMs might be suitable.

2. For image recognition: Convolutional Neural Networks (CNNs) from Deep Learning are a popular choice.

3. For text generation: Recurrent Neural Networks (RNNs) or transformers with attention mechanisms might be used.

4. For generating new content: Generative algorithms like GANs or VAEs can be powerful tools.

Small Language model

SLMs are type of large language model (LLM) with a smaller size and computational complexity compared to its larger counterparts like GPT-3

SLMs are suitable for situations where high performance isn't crucial, but efficiency and accessibility are important. Examples include:

Chatbots: SLMs can power basic chatbots for customer service or information retrieval.

On-device assistants: They can be used to create lightweight virtual assistants on mobile devices.

Personalization: SLMs can personalize content or recommendations based on user interactions.

Conclusion

The boundaries between human and machine capabilities are blurring thanks to the continuous progress in generative AI, machine learning algorithms, and deep learning networks. As these fields continue to evolve, the possibilities for innovation and advancement in various aspects of society are truly limitless.

Hope you found this edition useful. Do share your comments and share if you like.