Deciphering AI: A Simplified Guide for Clarity in the AI jungle.

What does it all mean ?? ?

When I started working in the startup ecosystem, I was overwhelmed with the jargon. There was a flood of buzzwords that everyone used, and I understood only half of them. Furthermore, I realized that people hardly asked if they had the same understanding. The same thing happened when I entered the AI space. Therefore, this article is about clarity and enabling a common understanding.

Gen AI and General AI (AGI) are different things

This is just one example that can get very confusing, or at least it happened to me in the beginning. They sound similar but are quite different. To help you, the Data-driven VC made a great framework upon which I built this article.

Understanding AI Terminology

Here's a short overview of the most important terminologies around Gen AI and the basics of AI.

1. Artificial Intelligence (AI):AI refers to machines or software that simulate human intelligence, including reasoning, learning, and inference. It encompasses a wide range of applications, from chatbots to self-driving cars.
2. Machine Learning (ML):ML is a subset of AI that uses data and algorithms to enable computers to learn from experience. It improves accuracy over time by mimicking human learning processes.
3. Deep Learning (DL):DL is a subset of ML that employs layered neural networks (such as the transformer architecture) for complex tasks. It enables autonomous decision-making, similar to how the human brain processes information.
4. Generative AI (GenAI):GenAI generates high-quality content (text, images, videos) based on learned patterns. It’s a branch of DL that creates novel, human-like outputs.
5. General AI (AGI):AGI aims to mimic human cognition across a wide range of tasks. It remains an aspiration, as no system fully achieves AGI yet.
6. Generative Pre-Trained Transformers (GPT):GPT models are advanced GenAI systems based on the transformer architecture. They learn from large amounts of unlabelled text data and generate contextually relevant content.
7. Large Language Model (LLM):LLMs, like GPT, perform tasks such as summarization, translation, and text generation. They’re powerful tools for natural language understanding and production.
8. GPT-4:The latest iteration of the GPT model, succeeding versions like GPT-3 and GPT-3.5 Turbo. It continues to improve language understanding and generation capabilities.
9. ChatGPT:A user-friendly chatbot interface powered by GPT models. It enables natural language interaction with AI systems.

Why Should I Care?

Working with companies, speaking the same language is half the success. I experienced in lots of projects how crucial "understanding the same thing" can be. Terminology can help, but do we actually understand the same thing for a term. I had exactly the discussion about GenAI and AGI where one spoke about AGI where the other person understood GenAI. We solved it at some point. But it led to a rather confusing conversation.

Three Things to Remember

1. Clear Communication: In a world inundated with buzzwords and jargon, clarity is key. By grasping the basics of AI terminology, you can communicate more effectively with peers, stakeholders, and clients, ensuring everyone is on the same page.

2. Informed Decision-Making: Whether you're a startup founder, investor, or industry professional, having a solid understanding of AI fundamentals empowers you to make informed decisions.

3. Better Ask Twice: AI is a rapidly evolving field with lots of jargon. If you don't understand it, just ask.

If you want to dive in deeper - Check out the Data-driven VC.