How to get started with AI: Learning Pathways for Tech and Beyond

The AI revolution is no longer on the horizon—it's here, transforming industries and daily life.

Understanding how to navigate this new landscape is crucial, whether you're a tech professional or someone simply curious about AI's impact.

This guide is a humble attempt to define AI learning pathways in 2025, offering some clarity and directions for both tech-focused individuals and those from non-technical backgrounds, ensuring everyone can unlock their AI potential.

Let's explore AI learning pathways in 2025, particularly for the tech industry. We can define two primary pathways: Core R&D Learning and AI as a Framework.

Of course, for non-technical individuals, learning pathways involve understanding Generative AI (GenAI) in general, focusing on text and image generation, and developing the ability to interact with GenAI tools. The goal is to learn how to use these tools to improve daily life.

Diving into the Tech Pathways

Even if you consider yourself non-technical, please don't skip this section. You might be surprised by the valuable insights and learning opportunities it offers.

Core R&D Learning

Many in the tech industry, whether newcomers or experienced professionals, are considering learning AI through Machine Learning, Model Tuning, Deep Learning, Neural Networks, and similar fields. This is a perfectly valid approach for those aiming to become core AI/ML engineers.

This path can lead to high-paying jobs, but it's important to acknowledge that the market is highly competitive.

AI as a Framework (AIaaF)

It's likely that more people than they admit find the deep dive into AI/ML less appealing. Concerns about being left behind and the notion of AI taking over jobs are valid in this rapidly evolving tech landscape. However, there are ways to remain relevant.

This is where learning AI as a framework becomes valuable. Others may use different terminology, but for this article, this is how we will define it.

Let's look at the AI as a Framework pathway in more detail and explore how it can be leveraged in various scenarios.

AI as a Library or Package for Problem Solving

Think of learning AI as similar to learning a new software library for integration into existing projects. Typically, the learning process involves reviewing documentation, understanding the library's purpose and functionality, and then, based on project needs, adopting the library's APIs.

When leveraging AI as a library, initial confusion often arises: What can it actually be used for?

Calculator? - Using AI for basic calculations would be inefficient and overly complex.

Scheduler Service? - Similarly, implementing a scheduler service with AI would likely be too expensive and complex.

Sentiment Analysis? - This is where AI starts to show its potential.

Consider sentiment analysis:

1. A traditional approach involves using natural language toolkits, which require data pre-processing, tokenization, stop word removal, stemming, lemmatization, and more. (If these terms are unfamiliar, don't worry! The aim is to explore an alternative approach.)
2. A modern approach utilizes GenAI. You can ask GenAI for its sentiment analysis directly. For more control, you can provide examples of classifications and observe the results. If a JSON output format is desired, specifying the structure will guide the AI. This aspect of GenAI learning is known as Prompt Engineering.

Let's consider another example where a GenAI-based solution might be superior to building from scratch or using numerous tools: Text or Image classification.

1. A standard library approach would involve data pre-processing, tagging, using example datasets, and applying regression algorithms.
2. With GenAI, classification can be achieved by simply asking a question and providing the text or image context. This is Zero-Shot Prompting, where no examples are given. For increased control, you can ask it to classify within specific categories (Prompt Engineering). For even finer control, providing examples of text and their corresponding classification categories allows for more tailored results. This is Few-Shot Prompting, where you demonstrate the desired task to the AI through examples.

AI as Agentic AI

So far, we've viewed GenAI through the lens of a library analogy. However, GenAI models are capable of tackling more than just single, isolated problem statements.

Imagine building an AI-powered alarm clock with these features:

1. Personalized greetings by name upon wake-up.
2. Providing local weather details.
3. Delivering the latest news.

Simply prompting GenAI to perform these steps directly might lead to hallucinations or a declaration of inability.

A basic prompt might look like this:

text
Greet me {name} good morning. Give me the weather details and today's news.

In reality, you would solve this by executing each step sequentially, passing context between them. In programming, this involves chaining functions, structuring output, and potentially integrating with voice assistants like Alexa.

The limitation of GenAI in this scenario stems from its lack of access to real-time information like your location, weather data, and current news. Try asking GenAI for today's date to see its limitations.

The solution lies in augmenting GenAI with capabilities, which we can call "functions" or "tools." These could include a tool to get location (contextual information), a weather tool for a given location, and a news retrieval tool.

By instructing AI to utilize these tools when information is lacking, we unlock its potential. This concept is AI Agent or Agentic AI.

A revised prompt incorporating tools might be:

text
Greet me {name} good morning. Give me the weather details and today's news.
 
Context:
User's name is <PERSONS NAME>
Today's date is <CURRENT DATE>
User's location is <CURRENT LOCATION>
 
Available tools:
1. getWeather - parameter: Location
2. getNews - parameter: Location (optional for international news)

Agentic AI empowers AI models to go beyond basic text generation by interacting with external data and functionalities.

Further enhancements to this AI alarm clock could include:

1. Summarizing unread emails.
2. Providing a daily schedule overview from your calendar.
3. And many other possibilities.

However, as you increase the complexity of tasks and the number of tools, you may encounter context loss or token limit errors.

Context Window refers to the limitation of an AI model's memory. As interactions become lengthy, the model might lose track of earlier steps. Think of it as a memory constraint.

The question of increasing the context window is valid, but it involves architectural and cost considerations, which are topics for another discussion.

AI as a Copilot

This explores AI's capabilities further. Let's examine real-world problem-solving with AI assistance.

Summarization Use Case

Often, we want to avoid reading lengthy research papers, books, or meeting minutes. Our first instinct is to search for summaries in video or text format. While summaries exist for popular content, niche content or specific meeting notes often lack readily available summaries, leading to time-consuming manual summarization.

GenAI offers a solution. You can ask it to summarize content. However, there's a possibility of Hallucinations, where the AI adds information from its training data that is not present in the provided content, which may or may not be relevant.

To mitigate this and gain better control, you can instruct the AI to only use the provided article's content for summarization. This is called Grounding.

GenAI summarization is often less biased than human summarization.

Other Use Cases

1. Code Writer & Reviewer
2. Content Creation & Reviewer
3. Microsoft Copilot for PPT creation and other MS Office suite tasks.
4. Web Search - Enhanced search results without needing to click every link, as seen with Google and Perplexity.

AI as a Service

Industries are actively building service solutions using AI to address sub-problems, targeting verticals like Healthcare, Finance, Customer Support, and IT Operations.

This often involves developing multi-agent systems orchestrated by a master AI agent or human oversight.

Delving deeper into this area would significantly expand this article. If you're interested in case studies, please reach out! I'm always eager to learn and share knowledge.

Non-Tech Learning Path

For those who consider themselves less technical, here's a starting point for your GenAI journey. However, reading the "Tech Pathways" section is still recommended to familiarize yourself with GenAI terminology, which will be helpful in understanding technical discussions.

Basic Tools Understanding and Usage

Familiarize yourself with fundamental AI tools. Even if you don't have immediate product applications, experimenting with these tools is crucial to gain familiarity and avoid feeling left behind.

1. ChatGPT or Gemini - Chat-based interfaces. Many are already familiar with these. If not, explore the links and start experimenting.
   • When unsure about models, begin with a basic model and a single prompt to observe how different models respond to the same input.
   • These tools are now often voice-enabled on mobile devices.
2. Explore Microsoft Edge's built-in Copilot.
3. Midjourney for AI image generation. Try prompts like "generate a nice image of pet dogs or cats playing football."
4. Sora or Runway for generative AI video. Experiment with prompts like "generate a cyberpunk-styled model walking in the rain" or let your imagination guide you.

After getting acquainted with these tools, start integrating them into your daily routines.

Example Use Cases:

1. Summarize or simplify content.
2. Generate images for blog articles.
3. Create marketing videos for platforms like Instagram.
4. Perform data analysis (consider company policies regarding data sharing with online tools).
5. And many more.

Custom AI Solutions

Numerous AI startups are emerging, offering diverse solutions for various problem statements.

Depending on your specific needs, consider adopting existing AI solutions and tailoring them to your use cases.

1. Otter.ai - A transcription tool for converting speech to text.
   • Useful for students, especially when combined with tools like NotebookLM.
   • Can be integrated into meetings to automatically take and summarize notes.
2. Canva Magic Studio - A creative design suite with AI-powered tools for marketing flyers, social media posts, and content creation, including voice enhancement, image generation, and image enhancement.
   • Some are even using Canva to automate the generation of content for platforms like Instagram and YouTube Shorts.
3. Microsoft's Copilot for work - Microsoft is integrating AI across its product suite, including Excel and Word, depending on company policies.
   • For example, you could ask AI to create a specific grouped table with formulas in Excel, potentially receiving a ready-made solution or a solid starting point.
4. Google also offers similar AI-powered features for Google Workspace.
5. And many more solutions difficult to put them all here and others which are continually emerging.

Some AI solutions are evolving into comprehensive platforms, becoming one-stop shops for various AI needs.

Core AI companies like OpenAI, Google, and X (formerly Twitter), among others, are continuously developing foundational models that power these AI solutions. These advancements aim to improve accuracy, precision, relevance, and overall performance. This rapid evolution might even render some AI startups obsolete, reflecting the dynamic nature of the tech industry.

Conclusion

Instead of fearing job redundancy due to AI, consider the broader perspective: in today's world, lack of upskilling in your domain is a greater threat to job security. AI is a transformative skill impacting nearly every field. Upskilling in AI, particularly in effectively utilizing AI tools, will be a crucial factor for professional survival in the coming years. Even while writing this, I feel a sense of urgency, but I'm choosing to move forward with a positive, learning-oriented attitude. Keep learning!

As AI continues to evolve at a rapid pace, continuous learning and adaptation are key. Whether you choose to delve into the core of AI development or leverage AI as a powerful framework and set of tools, the opportunities are vast and transformative. Embrace the journey, explore these pathways, and empower yourself to thrive in the age of AI. Keep learning!