The models behind the Magic

Disclaimer:

The views and opinions expressed here are mine alone and do not necessarily represent the views of any organization or entity with which I may be affiliated. I am using AI tools in the creation of this work.

Unveiling the Models Behind the Magic

Hello!? This week we are going to spend a little time talking about other ways of using and optimizing AI.? My intent for this work is to make AI topics less technical and more engaging.? Let me know how I am doing in the comments.

As a lifelong Scouting volunteer, I learned a valuable trick for teaching a wide range of topics to large audiences. We'd start by discussing the serious aspects of the subject, ensuring everyone understood the core concepts. Then, we'd transition into something fun and engaging, reinforcing the learning in a practical and memorable way. We'll use that same approach here. We've talked about history in my last post, now, let's have some fun!

AI can seem too good to be true or like magic. Behind the scenes, there are other things that come to together to make that work. One of the things an AI uses is a “Model” Imagine you're teaching a puppy a new trick. You show them what to do, reward them when they get it right, and gently correct them when they make a mistake. An AI model is kind of like that puppy, but instead of tricks, it learns from tons of information. It's a computer program designed to recognize patterns and relationships in data, whether that data is text, images, sounds, or anything else. So, if you feed an AI model a mountain of cat pictures, it starts to learn what makes a cat a cat. Then, when you show it a new picture, it can tell you, "Hey, that's a cat!" It's all about learning and making predictions based on the information it's been given.

Think of these models as the brains behind the AI, each with its own special skills. Let's peek at a few of the most common types:

• Large Language Models (LLMs): These are the rockstars of the AI world right now. They're trained on massive amounts of text data, allowing them to understand and generate human language. Think of them as super-smart parrots that can not only mimic but also understand and respond in a human-like way. They power chatbots, write stories, and even translate languages. Think of it like different chefs making the same dish. Even if they all follow a recipe, each chef might add their own little twist or use slightly different ingredients, so the final dishes will all taste a bit different. LLMs are similar – they learn from different "recipes" (training methods) and "ingredients" (data), so they all end up with their own unique characteristics.

You can explore the concepts behind LLMs on platforms like Google AI (https://ai.google/).

• Generative Adversarial Networks (GANs): GANs are a bit like a creative duo. They consist of two neural networks, a generator and a discriminator, constantly competing and learning from each other. The generator tries to create realistic data (like images), while the discriminator tries to tell the difference between real and generated data. This competition leads to increasingly realistic and creative outputs. NVIDIA has some great resources on GANs (https://blogs.nvidia.com/blog/2018/01/31/generative-adversarial-networks-gans/).
• Transformer networks are highly effective at understanding context and relationships within data, especially in sequential data like text or time series. These models are the driving force behind many of the latest advancements in natural language processing. For more information about transformers and their applications, you can explore the original research paper "Attention is All You Need" (https://arxiv.org/abs/1706.03762) or educational resources available on platforms such as Towards Data Science (https://towardsdatascience.com/).
• Diffusion Models: These models generate images by progressively adding noise to an image until it becomes pure noise, then learning to reverse this process, effectively "denoising" from random noise to form a coherent image. They have achieved remarkable success in image generation. Check out this blog post from Google AI for more information: https://ai.googleblog.com/2022/05/image-generation-with-min-unconditional.html
• Reinforcement Learning Models: These models learn by trial and error, receiving rewards or penalties for their actions. They're used in robotics, game playing, and even in optimizing complex systems. You can learn more about Reinforcement Learning at https://www.microsoft.com/en-us/research/research-area/artificial-intelligence/.

These are just a few examples of models evolving in the AI revolution. In future posts, we'll dive deeper into specific applications and explore how these models are changing the world around us.

It is time to do something fun or at least different and amusing.? After this exercise you will get a feel for using AI to help you with a use case.

Use Case:

I want to create a reusable prompt you can copy from here and use with your favorite AI LLM to create useful output.? We are at the beginning of the year.? Many of us are writing goals. Wouldn’t it be great to have a way to sharpen our focus and keep us on track as we set them?

Project Name: ?B Friendly Smart Goal Advisor

// You can type or copy this into the prompt of your LLM of choice. I used Gemini for this one.

// A small prompt to help you find a topic and state it as a smart goal.

SMART GOAL ADVISOR

You are the SMART Goal expert and will guide users through creating and managing SMART goals for 2025.

STEP 1: "Hi, I'm your SMART goal builder. SMART goals are Specific, Measurable, Achievable, Relevant, and Time-bound. What would you like to work on?"

STEP 2: State: “Great! Now we will draft a SMART goal for you.”

STEP 3: For each goal, follow these steps:

Specific: Ask: "What specifically do you want to achieve with this goal? What will you do, and why is it important?"? Wait for the user's response and incorporate it into the goal statement.

Measurable: Ask: "How will you measure your success? What tangible evidence or metrics will show you've achieved the goal?"? Incorporate the user's response into the goal statement.

Achievable: Ask: "Is this goal achievable in 2025? Is it challenging yet realistic? What resources or support do you need?"? Adjust the goal based on the user's feedback to ensure it's achievable.

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Relevant: Ask: "Why is this goal relevant to you? How does it align with your personal or professional objectives for 2025?"? Ensure the goal aligns with the user's overall objectives.

Time-Bound: Ask: "When do you want to achieve this goal?? Let's aim for a specific date within 2025."? Add the date to the goal statement.

STEP 4: After completing each SMART step for a goal, summarize the complete SMART goal statement. Ask: "Does this SMART goal look good to you? Would you like to refine it further, move on to another goal, or review all your goals?"

STEP 5 (Milestones): After the user confirms a SMART goal, ask: "Now, let's create 1-4 key milestones to help you reach this goal during 2025. What can we measure to know we are succeeding? When do you think you can get this done?"? For each milestone, record it and ask if they want to create more milestones for this goal.

STEP 6 (Final Review): After the user says they'd like to review all their goals, present all completed SMART goals and milestones in a formatted list. Ask: "Would you like a bonus?"

STEP 7 (Bonus): If the user says yes, offer 2 or 3 suggestions for the next actionable steps they could take today towards their 2025 goals.

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