Intro

Working at as a Software Engineer and Consultant, I have plenty of opportunities to see how AI can be impactful and solve real-world problems. However, as you can imagine, with all the hype around AI, there's also a lot of noise that comes along with it.

I decided to take a step back and learn more about AI and Machine Learning starting with the basics. To do this, I'll be following along with a few courses from Datacamp and eventually applying my knowledge to a real project for us to learn together.

The end goal is to have a solid foundation to build our own Models and deploy them through platforms like Hugging Face or potentially taking the deeper dive into Microsoft Azure's AI services.

So What is AI?

AI is a broad field with many applications, at its core it is about creating machines that can learn and make decisions equal to or greater than humans. This looks similar to how Computer Science and Software work today. We take an input, process it with a known algorithm of some kind, and return an output. However, not all Algorithms are created equal nor are they meant for AI. So what's the difference?

In traditional software algorithms, we define the process explicitly to calculate the output. An AI system, on the other hand, uses algorithms that can learn from data and experience to make decisions or predictions. Remember the point of AI is to make decisions that are human-like in nature. AI encompasses several subfields and technologies, including:

• Machine Learning: Algorithms that improve through experience
  • Deep Learning: A subset of machine learning using neural networks
• Natural Language Processing: Enabling computers to understand and generate human language
• Computer Vision: Allowing machines to interpret and analyze visual information
• Robotics: Designing and creating intelligent machines that can interact with the physical world

These components often work together in AI systems to solve complex problems and make human-like decisions.

Disciplines such as Data Science, Mathematics, and Statistics play crucial roles in developing and working with AI systems. While AI and Machine Learning were once primarily the domain of specialists, they have become increasingly accessible to a wider audience. One of the most common and widely-used applications of AI today is Natural Language Processing (NLP), with chatbots like ChatGPT and Claude serving as prime examples.

Why AI is important?

AI is important for several reasons:

• Automation: AI can automate routine tasks, freeing up human workers for more complex and creative tasks.
• Efficiency: AI can process large amounts of data quickly, leading to more efficient operations.
• Innovation: AI can help us discover new insights and solve complex problems that would be difficult or impossible for humans to tackle alone.
• Personalization: AI can tailor experiences to individual users, making them more relevant and engaging.
• Cost Reduction: AI can help reduce costs by optimizing processes and reducing waste.

How does AI work?

AI systems typically follow a similar process:

1. Data Collection: AI systems start by collecting data from various sources. This data can include text, images, audio, or other forms of information.
2. Data Preprocessing: The collected data is then preprocessed to clean and prepare it for analysis. This includes tasks like removing duplicates, normalizing data, and splitting it into training and testing sets.
3. Feature Extraction: AI systems use feature extraction to identify important patterns and relationships in the data. This involves selecting the most relevant features and transforming them into a format that can be used to train a model.
4. Model Training: Once the data is prepared and features are extracted, the AI system uses a machine learning algorithm to train a model. This model is then used to make predictions or decisions.
5. Model Evaluation: After the model is trained, it is evaluated to ensure that it is accurate and effective. This involves testing the model on a separate dataset and checking its performance.

Up Next

That's a lot of terms and concepts to take in, so let's take a break. In the next post, I'll go over Machine Learning and its relationship to AI. We'll break this down into structured and unstructured learning styles, and cover supervised and unsupervised learning.