AI Guide

There is so much to AI, so let’s break it down here.

Types of AI

1. Machine Learning (ML)

  • AI learns from data without being explicitly programmed.

  • Types: Supervised, Unsupervised, Reinforcement Learning.

  • Example: Fraud detection, recommendation engines.

2. Deep Learning (DL)

  • A subset of ML using neural networks with many layers.

  • Example: Image recognition, speech processing.

3. Natural Language Processing (NLP)

  • AI understands and generates human language.

  • Example: Chatbots, virtual assistants.

4. Expert Systems

  • AI programmed with expert-level knowledge for decision-making.

  • Example: Medical diagnosis systems.

5. Computer Vision

  • AI that processes and understands images or videos.

  • Example: Facial recognition, autonomous vehicles.

6. Robotics

  • AI-powered machines that interact with the physical world.

  • Example: Boston Dynamics robots, industrial robots.

Conversational AI

This is the area of AI I’m most familiar with and see the most broad application. This is a system that enable human-like interactions between computers and users through natural language. These systems use a combination of machine learning (ML), natural language processing (NLP), and deep learning to understand, process, and respond to text or voice inputs.

Key Components of Conversational AI:

  1. Natural Language Processing (NLP) – Helps AI understand and interpret human language.

  2. Machine Learning (ML) – Allows AI to improve responses over time by learning from interactions.

  3. Speech Recognition (for Voice AI) – Converts spoken words into text (e.g., Alexa, Google Assistant).

  4. Dialog Management – Maintains conversation flow and context.

  5. Response Generation – Provides appropriate and meaningful replies.

Examples of Conversational AI:

  • Chatbots: Customer service bots on websites (e.g., Intercom, Drift).

  • Virtual Assistants: AI-powered voice assistants (e.g., Siri, Alexa, Google Assistant).

  • Voice Bots: Used in call centers to handle customer inquiries.

  • AI-Powered Messaging Apps: WhatsApp or Facebook Messenger bots for automated interactions.

Use Cases of Conversational AI:

  • Customer Support: Automating FAQs, ticketing, and troubleshooting.

  • E-Commerce: Assisting shoppers with product recommendations.

  • Healthcare: Virtual health assistants for patient queries.

  • Banking & Finance: AI chatbots for account inquiries and fraud detection.

Generative AI

The craze that started back in November 2022 is here to stay. Generative AI refers to artificial intelligence systems that create new content—such as text, images, music, code, and even videos—by learning from existing data patterns. Instead of just analyzing and processing information, Generative AI generates new outputs that resemble human-created content.

How Generative AI Works

  1. Training on Large Datasets

    • AI models are trained on vast amounts of data (e.g., text, images, or code) to learn structures and relationships.

  2. Using Deep Learning & Neural Networks

    • Techniques like Transformers (e.g., GPT models) and Generative Adversarial Networks (GANs) help the AI create high-quality, human-like content.

  3. Generating Content Based on Prompts

    • The AI takes user input (a prompt) and generates content based on learned patterns.

Examples of Generative AI

  • Text Generation:

    • ChatGPT, Google Gemini, Claude (AI chatbots)

    • AI-generated articles, blogs, summaries

  • Image Generation:

    • DALL·E, Midjourney, Stable Diffusion (AI art)

  • Video Generation:

    • Runway ML, Pika Labs (AI-created videos)

  • Music & Audio Generation:

    • AIVA, Jukebox (AI-generated music)

  • Code Generation:

    • GitHub Copilot, ChatGPT (AI-assisted coding)

Use Cases of Generative AI

  • Marketing & Content Creation: Automating blog posts, social media captions, and ad copies.

  • Customer Experience (CX): AI-powered chatbots for personalized responses.

  • Design & Creativity: Generating logos, product designs, and art.

  • Software Development: Writing and debugging code with AI assistance.

  • Healthcare & Science: AI-driven drug discovery, medical imaging analysis.

Agentic AI

Agentic AI refers to AI systems that can autonomously take actions, make decisions, and pursue goals with minimal human intervention. Unlike traditional AI models that respond passively to prompts, Agentic AI acts independently, plans tasks, and adapts dynamically to achieve objectives.

Key Characteristics of Agentic AI

  1. Autonomous Decision-Making – Can assess situations and make decisions without needing constant human input.

  2. Long-Term Planning – Able to strategize and execute multi-step tasks over time.

  3. Adaptive Learning – Continuously improves by learning from interactions and experiences.

  4. Context Awareness – Maintains memory and understands the broader context of its actions.

  5. Self-Executing Tasks – Can take action in software, websites, or digital systems to complete tasks automatically.

How Agentic AI Works

Agentic AI leverages:

  • Large Language Models (LLMs) for reasoning (e.g., GPT-4, Gemini)

  • Reinforcement Learning (RL) for adaptive improvement

  • Autonomous Agents that interact with APIs, databases, and systems

  • Memory & Retrieval-Augmented Generation (RAG) to store and recall past interactions

Examples of Agentic AI

  1. AutoGPT & BabyAGI – AI agents that autonomously break down and execute tasks.

  2. AI Customer Support Agents – Proactively solving customer issues without human oversight.

  3. AI Code Agents (e.g., Devin by Cognition AI) – Independently writing, testing, and deploying code.

  4. AI Personal Assistants – Managing emails, scheduling meetings, and making reservations.

  5. AI-Powered Finance Bots – Optimizing investments and executing trades without manual input.

Use Cases of Agentic AI

  • Customer Experience (CX): AI agents handling support tickets end-to-end.

  • Marketing Automation: AI running ad campaigns and optimizing content.

  • Software Development: AI autonomously debugging and improving code.

  • E-commerce: AI agents managing inventory, pricing, and personalized shopping.

  • Finance & Trading: AI making investment decisions based on real-time market data.

Resources

Blogs:

  1. Machine Learning Mastery: A blog by Jason Brownlee, focused on helping professional developers apply machine learning to complex problems.

  2. MarkTechPost: Provides easy-to-consume updates on machine learning, deep learning, and data science research.

  3. BAIR Blog: The Berkeley Artificial Intelligence Research Lab's blog, offering accessible discussions on research findings and perspectives in AI.

  4. DeepMind News & Blog: Shares insights from the world leader in AI research, covering breakthroughs and applications of artificial intelligence.

  5. MIT Technology Review - Artificial Intelligence: Offers in-depth analysis and coverage of the latest advances in AI, including machine learning, neural networks, and robotics.

YouTube Channels:

  1. Lex Fridman: A popular podcast featuring in-depth interviews with AI experts, scientists, and thought leaders, exploring cutting-edge topics in artificial intelligence, technology, and their societal impacts.

  2. The Next Wave: A podcast focused on AI and the future of technology, offering fresh takes, industry insights, and practical perspectives on implementing AI for business growth.

  3. Marketing AI Institute: A channel dedicated to helping marketers understand and leverage AI in their work, providing evaluations of AI tools, best practices, and strategies for integrating AI into marketing processes.

  4. IBM Technology: The official IBM technology channel, offering a wide range of content on AI and other tech topics, with a focus on business applications and ethical considerations of AI technologies.

  5. Ravit Show: Semi-weekly podcast and live show hosted by Ravit Jain, featuring interviews with industry leaders in data science and AI to explore trends, technologies, and insights in these fields.

Podcasts:

  1. High Agency: Focused on AI builders and featuring interviews with leaders in AI product development.

  2. Latent Space: Dedicated to AI engineers, covering the latest in AI engineering and research.

  3. No Priors: Explores the AI revolution with discussions on AGI, market disruption, and societal impacts.

  4. Cognitive Revolution: Biweekly podcast featuring interviews with AI innovators and researchers.

  5. DeepMind: The Podcast: Award-winning series exploring how AI is reshaping our world.

Newsletters:

  1. The Neuron: A daily newsletter trusted by over 450,000 professionals from leading tech companies, offering quick, engaging breakdowns of important AI trends, tools, and industry news.

  2. The Rundown AI: A comprehensive daily newsletter reaching 750,000+ subscribers, summarizing the latest AI advancements, trends, and practical applications.

  3. The Batch: A weekly newsletter featuring four deep, thoughtful analyses of important AI developments, along with commentary from renowned AI researcher Andrew Ng.

  4. Ben's Bites: A daily newsletter providing insights, trends, and updates on AI with a humorous touch, making it both informative and entertaining.

  5. Import AI: A weekly newsletter that covers the latest in AI research, industry developments, and ethical considerations, popular among AI professionals and enthusiasts.

Experts:

  1. Andrew Ng: Founder of deeplearning.ai, co-founder of Coursera, and former VP and chief scientist at Baidu. Known for his work on the Google Brain project and autonomous robotics.

  2. Fei-Fei Li: Professor at Stanford University, co-director of the Stanford Institute for Human-Centered AI, and founder of AI4ALL. Renowned for her contributions to ImageNet and computer vision.

  3. Geoffrey Hinton: Professor at the University of Toronto and research scientist. Known as one of the "Godfathers of AI" for his pioneering work on neural networks.

  4. Demis Hassabis: Co-founder and CEO of DeepMind, known for achievements like AlphaGo and advancements in protein folding.

  5. Jeff Dean: Head of Google AI, overseeing significant AI research and applications.

Tools

I use these everyday:

  1. Google NoteboookLM for interacting with your own personal documents.

  2. Perplexity for research with real-time, cited summaries.

  3. ChatGPT for content creation, personalized with my tone of voice using it’s memory and customization features.