Let’s cut through the noise and get straight to the heart of Artificial Intelligence (AI).

Forget the jargon and hype—AI is about creating software that emulates human intelligence, recognizing patterns, processing language, and making decisions.

Quick heads up: I'm going to simplify these concepts to keep things short and to the point. If you're deep into the field, bear with me—there will be some generalizations here. The goal is clarity, not a deep dive.

🤖Artificial Intelligence (AI)

AI is a branch of computer science focused on building machines that can perform tasks traditionally requiring human intelligence.

It’s everywhere, from recommending what show to watch next on Netflix to handling complex conversations via chatbots.

At the heart of AI is Machine Learning (ML), a powerful subset that trains machines to learn from data instead of explicit programming.

It’s like giving a computer a massive pile of data and letting it find patterns to make informed predictions. For example, Netflix analyzes your viewing history to recommend what you might enjoy next.

💻Machine Learning

A key player in machine learning is supervised learning. Here, models are trained on labeled data, where the answers are known. Imagine training a system to predict house prices based on historical data using techniques like regression. The machine learns from examples to accurately forecast outcomes.

From supervised learning, we dive into deep learning, a more advanced area of ML that handles complex data patterns beyond simple algorithms. Deep learning uses multi-layered neural networks to process intricate relationships in data. It powers systems like GPT—the tech behind advanced chatbots and language models.

Generative AI is where deep learning truly shines.

Large language models (LLMs) like GPT can summarize text, answer questions, and even convert speech to text. These models are used in everything from customer service bots to translating languages and processing forms and invoices.

AI thrives on data: knowledge mining—extracting valuable information from data to make it searchable and actionable.

This allows AI pulling text from images or documents, enriching it, and providing insights to support better decision-making.

📊AI thrives on data

Customer searches, purchases, and social media.

AI taps into all of this to drive automation and enhance decision-making.

AI literacy isn’t just about knowing what AI is; it’s about understanding its strengths and weaknesses. You need to critically evaluate claims about AI’s potential benefits and risks to stay ahead.

Artificial Intelligence (AI) MindMap

• Machine Learning (ML)

  • Supervised Learning

    • Classification

    • Regression

  • Unsupervised Learning

    • Clustering

    • Dimensionality Reduction

  • Reinforcement Learning

• Deep Learning

  • Neural Networks

  • Convolutional Neural Networks (CNN)

  • Recurrent Neural Networks (RNN)

  • Transformers

• Generative AI

  • Generative Adversarial Networks (GANs)

  • Variational Autoencoders (VAEs)

  • Diffusion Models

🌎The world is changing fast, we're at a crucial moment where understanding how AI can transform businesses is essential.

🧠Incorporating AI: It's a Mindset

• By raising your AI awareness, you can demystify the technology and see it for what it is—a powerful tool for innovation.

The real question is, will you keep up, or will you be left behind?

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📚Reference

AI &Data Literacy by Bill Schmarzo: https://youtu.be/ICSdFD5ViIQ?si=q58G8HyZ1KClpgaL

Netflix PRS 2024 - Applying LLMs to Recommendation Experiences: https://eugeneyan.com/speaking/netflix-prs/

Knowledge Mining: https://azure.microsoft.com/en-us/solutions/knowledge-mining#:~:text=What%20is%20knowledge%20mining%3F,from%20vast%20amounts%20of%20information.

Supervised learning: https://c3.ai/introduction-what-is-machine-learning/supervised-learning/

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