Ok, I accept that 80% of you will stop reading in the next 17 seconds.

It will feel too basic and not "guru-like". But for the other 20% like me...

Someone recently asked me - "Marco so what specifically do you talk about - AI or Generative AI? What's the difference?". Here's how I explain it in my talks:

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AI - The ability of machines to perform tasks that normally require human intelligence. Example: A virtual assistant like Siri or Alexa that can understand and respond to voice commands.

Machine Learning - A type of AI where machines learn from data to improve their performance over time without being explicitly programmed. Example: A recommendation system that suggests movies based on your Netflix viewing history on Netflix.

Deep Learning - A subset of Machine Learning that uses neural networks (similar to a human brain) to analyze data in complex ways. Example: An image recognition system that can identify objects in photos.

Generative AI - A specific application of Deep Learning that can generate new content, such as text, images, or music. These models can produce creative and coherent outputs that mimic human-like creativity. Example: An AI that creates digital art like Midjourney.

LLMs (Large Language Models) - Generative AI models that understand and generate human language based on massive amounts of text data. Example: ChatGPT - it can carry on conversations by generating human-like text based on the input it receives.

GPT (Generative Pre-trained Transformer) - A specific type of Large Language Model developed by OpenAI (and introduced initially by Google) that excels at generating human-like text. Example: GPT-4, used in applications like Chat-GPT.

Pfffff that was a LOT, I know. But hopefully helps us all 20 percenters understand a bit more about the world of AI).

A big thank you to my friend Alyssa Fenoglio who explained this incredibly well in a recent conference, and that inspired me to write this edition.

And as always if I didn't get anything right, feel free to give feedback and clarify for others.

We are all (literally) learning.

See you next week.