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Advice for non-technical people on building their AI knowledge

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Summary

This article provides advice for non-technical people on how to build their AI knowledge. It suggests taking three free courses to build a strong understanding of the fundamentals, and then diving into real-world applications of AI and experimenting with AI tools. It also previews upcoming content for Year 2049, which will include more case studies and tutorials, plus a video series.

Q&As

What advice is given to non-technical people who want to build their AI knowledge?
The advice given to non-technical people who want to build their AI knowledge is to start with first principles and fundamentals, dive deep into real-world applications of AI, and get their hands dirty and have some fun.

What are the three free courses recommended to build the base of the AI knowledge pyramid?
The three free courses recommended to build the base of the AI knowledge pyramid are AI For Everyone by Andrew Ng, Making Friends with Machine Learning by Cassie Kozyrkov, and Generative AI Learning Path by Google Cloud.

What are some questions to ask when analyzing real-world applications of AI?
Some questions to ask when analyzing real-world applications of AI are what are the specific ways AI is being applied in an industry or a specific workflow, did this company train their own AI system or use an out-of-the-box solution from a provider like OpenAI, and are they measuring the AI’s performance for those specific tasks?

What tools are recommended for experimenting with AI?
The tools recommended for experimenting with AI are ChatGPT, Midjourney, and any other AI tools.

What is the ambitious video series mentioned in the article that will be posted to Instagram and TikTok?
The ambitious video series mentioned in the article that will be posted to Instagram and TikTok is not specified.

AI Comments

👍 This article is extremely well-written and provides a great starting point for non-technical people interested in learning about AI. The free courses and resources recommended are incredibly useful and the author's enthusiasm for AI is inspiring.

👎 The article is overly long and could have been more succinct. The author's recommendations are quite general and don't provide much guidance for those who have already gained some knowledge of AI.

AI Discussion

Me: It's about advice for non-technical people on building their AI knowledge, with a free learning plan so you don't feel "left behind". It suggests three free courses to help you understand AI fundamentals and then dives into real-world applications of AI, with specific case studies to help you understand how AI is being used in different industries. Finally, it encourages people to experiment with AI tools and have fun.

Friend: Wow, that's really helpful! It sounds like a great way to get up to speed on AI without feeling overwhelmed.

Me: Absolutely. The article highlights the importance of having a strong base of knowledge in order to have meaningful conversations about AI and recognize its potential applications. It also emphasizes the need to stay informed and experiment with AI tools in order to have an intuitive understanding of its capabilities and limitations.

Action items

Take the three free courses recommended in the article to gain a strong understanding of how AI systems are trained, how they work, and how they generate outputs.
Read the case studies provided in the article to gain deeper insight into the real-world applications of AI.
Experiment with AI tools such as ChatGPT, Midjourney, and Code Interpreter to build your intuition of what AI can and can't do.

Technical terms

AI: Artificial Intelligence - a branch of computer science that deals with the development of computer systems that can think and act like humans.
Machine Learning: A type of artificial intelligence that uses algorithms to learn from data and make predictions.
AI Literacy: The ability to understand and use AI technologies.
First Principles: A set of basic assumptions or axioms that are used to build a logical argument or system.
Fundamentals: The basic principles or concepts of a subject.
Fine-Tune: To make small adjustments to a system or process to improve its performance.
Human in the Loop: A system in which humans are involved in the decision-making process.
Generative AI: A type of AI that can generate new data or content from existing data.