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- AI 101 — What Mid-Career Professionals Need to Know in 2025
AI 101 — What Mid-Career Professionals Need to Know in 2025
Brett Stark
May 22, 2025
🧭 THIS WEEK AT AI SECOND ACT
Howdy, welcome to the 100th+ subscribers 🎉!! This is the third edition, and I’d love your help shaping it. You’ll find a quick feedback poll at the end, and I always welcome direct replies.
👉 Just hit "Reply" and let me know what you want more (or less) of 💬. My goal is to make this as valuable and practical as possible for mid-career professionals like you navigating the new AI era. 🚀
What’s going on:
Quick guide to AI fundamentals.
What machine learning, neural networks, and large language models actually are.
Why your experience gives you a unique advantage in the AI era.
How to set up a smarter AI chatbot.
You can read past issues and share them easily at www.aisecondact.com.
[Photo by Gunnar Ridderström]
🧰 AI NEWS + LEARNING
Here’s a few things I found recently:
Google announced a bunch of new features, including ‘AI Mode’ making the search feature a lot more ‘chatbot oriented’.
Google also released Firebase Studio recently, a tool to build complete full stack AI apps. Looks incredible and I’ll have to cover this in full in a future edition.
Perplexity now has templates which all you to get started quickly with multiple types of chat uses - e.g. ‘tutor’, ‘brainstorm buddy’. Seems like OpenAI’s ChatGPT custom GPTs. Quick fave: ‘YC Startup Advisor’!
OpenAI acquired the AI device firm, io from ex Apple Designer. This means typical iPhone and Android devices are likely to get some new competition.
WordPress, a super popular blogging platform released an AI website builder. Great for personal blogs or even business websites.
Lazy AI, a tool to help you build apps using prompts and generative AI. A great way of trying out what this technology can do.
🗺️ FEATURED INSIGHT
The AI Revolution Is Here
The rapid rise of AI might feel overwhelming, particularly if you’re not fresh out of your graduation ceremony. You're not alone but we can simplify it.
The good news: You don't need a machine learning degree to understand AI fundamentals or to start using them to your advantage. Just know this diagram:
AI Landscape
🤖 Machine Learning: Learning from Data
Machine learning (ML) is the technology behind most of today's AI applications. Unlike traditional software that follows explicit instructions, machine learning systems:
Learn patterns from data rather than following programmed rules
Improve their performance over time without being explicitly reprogrammed
Make predictions or decisions based on what they've learned
Traditional programming of branching, decision and looping statements give detailed directions. Machine learning, however, has the application learning behavior from data.
🧠 Neural Networks: The Brain-Inspired Architecture
When you hear about "deep learning" or "neural networks," you're dealing with a subset of machine learning inspired by how human brains work:
Imagine a neural network as a big team of tiny decision-makers called “neurons” (like brain cells).
These neurons are organized in layers:
The input layer gets information.
The hidden layer(s) process this information, looking for patterns and connections.
The output layer gives the final answer.
This architecture is what powers features like image recognition, natural language processing.
💬 Large Language Models: Conversation / Mega Auto Complete
The most visible AI tools today, like ChatGPT, Claude, and Gemini are built on large language models (LLMs). I liked the analogy from last edition, ‘auto complete’:
These are neural networks trained on huge datasets
They learn patterns from the data
They can generate text, images video, translate languages, summarise content, etc.
There are other important definitions and I put those just on AI Second Act to keep it brief here.
🗺️ AI Definitions
Term | Definition | Use Case(s) |
|---|---|---|
NLP (Natural Language Processing) | AI focused on the interaction between computers and human language. | Chatbots, language translation |
Computer Vision | A field of AI that trains computers to interpret and understand visual data. | Facial recognition, medical image analysis |
Supervised Learning | Type of ML where models learn from labeled data. | Email spam detection, image classification |
Unsupervised Learning | ML where models identify patterns in unlabeled data. | Customer segmentation, anomaly detection |
Reinforcement Learning | A type of ML where agents learn by trial and error, guided by rewards and penalties. | Robotics, game playing |
Generative AI | AI models designed to create new content, such as text, images, code, or music. | Writing assistants, image and video generation |
Fine-tuning | A process of adapting a pre-trained model to perform well on a specific task or domain. | Legal document review, financial forecasting |
RAG (Retrieval-Augmented Generation) | An architecture combining LLMs with external information retrieval for more accurate responses. | Research assistants, domain knowledge base search tools |
Prompt Engineering | The art of crafting inputs to guide LLMs and generative models toward desired outputs. | Improving chatbot outputs, building custom assistants |
🗺️ FEATURED INSIGHT
🏔️ What Gives You a Mid-Career Edge?
Your years of experience aren’t obsolete, they’re your greatest advantage. That’s because the most common tools depend on context and clear prompts to work effectively.
Here’s your advantage:
- You know the real-world. Invaluable to judge AI output.
- You bring domain expertise. Giving the ability to inject additional context into LLMs and get better results.
- You can ‘multiply your impact’. By using AI in unique and valuable ways within your profession and domain.
Think of it as your strategic copilot, not your replacement.
AI Is Changing the Definition of "Expertise"
AI can instantly access and process vast amounts of specialized information
Generate content that previously required significant expertise and time
Identify patterns in data with incredible speed & precision
Therefore, the professionals who will thrive aren't those who know the most about how AI works technically, but those who best understand how to apply it to add the most value.
The best way to do this is to use AI, get proficient with the tools, learn what works, where and when:
Three Actionable Steps to Start Using AI Today
Step 1: Experience AI Firsthand with a Large Language Model
What to do: If you haven’t already (we explored this in Edition 2), set up a free account with ChatGPT, Claude, or Gemini and spend 30 minutes experimenting.
Try these prompts:
"Summarize the key points from this meeting agenda: [paste text]"
"Explain [industry concept] as if I'm new to the field"
"Generate 5 potential solutions to [problem you're facing]"
Step 2: Identify One Repetitive Task to Automate
What to do: List your weekly tasks and pick one repetitive, time-consuming activity that involves information processing. Use your favorite AI chatbot to explore how you might automate or streamline the effort involved.
Examples:
Summarizing long reports or articles
Creating first drafts of standard communications
Generating presentation outlines
Step 3: Build Your AI Knowledge
What to do: Allocate 20 minutes weekly to stay informed about AI developments relevant to your field.
Resources:
Subscribe to AI Second Act newsletter (you're already here—good job!)
Follow 2-3 industry leaders who discuss AI applications in your field on LinkedIn
🛠️ Final Tip: Make Your Chatbot Smarter
Take 2 minutes to upgrade your AI assistant.
🧠 In ChatGPT: Head to Settings > Personalization . Tell it who you are, what you do, what you want to do (goals etc), and how you want it to talk. [there are similar settings in all other tools].
💾 Enable memory: In ChatGPT or other supporting tools like Gemini and Grok. Helps your bot remember your context over time.
That's it. You’ll get more value when the chatbot "knows" your role, goals, and preferences. Memory, over time will increase the efficiency of your chats with better outputs.