IBM’s AI model explained

Explore IBM technology resources that explore fundamental principles of artificial intelligence and business applications, with a focus on large-scale language models (LLM) and machine learning (ML).

Although available as a YouTube playlist, this is not a formal course, but rather a comprehensive educational series that covers a wide range of topics in artificial intelligence (AI) and machine learning (ML), deftly moving from fundamental concepts in neural networks to cutting-edge developments in generative AI.

Here’s a breakdown of the series’ content:

Generative AI and Large-Scale Language Models (LLM)
Describes the core concepts of LLM and the Transformer architecture that powers it. We also distinguish between different model sizes, such as LLM (Large Language Model), SLM (Small Language Model), and Frontier models.
That’s great, but how do you interact with them? To that end, techniques used to interact with them are used, such as prompt engineering (zero-shot, few-shot, and chain-of-thought prompts) and search augmentation generation (RAG).

Machine learning fundamentals and architecture
Learn more about how machines actually “learn” by explaining the mathematics behind learning, such as gradient descent and backpropagation. It then goes through learning types such as supervised learning, unsupervised learning, and federated learning.

Types of neural networks (deep learning)
Let’s take a look at the application of NN. Visual recognition using convolutional neural networks (CNNs) for image processing and pattern recognition, long short-term memory (LSTM) networks and recurrent neural networks (RNNs) for processing sequential data such as text and time series, and generative adversarial networks (GANs) for the generation of new data.

Actual implementation and MLOps
This material goes beyond theory and explains how to build and deploy AI in the real world. This is achieved by introducing PyTorch as a standard framework for building deep learning models, considering the model lifecycle (from data preparation to deployment), and introducing MLOps (DevOps for Machine Learning) to automate and stabilize this process.

Specialized areas of AI
Finally, this article discusses specific subfields of data science.

• Natural Language Processing (NLP): Converts unstructured text into structured data using techniques such as tokenization and named entity recognition.

• Knowledge graph: Map relationships between entities (nodes and edges) to infer new information.

• Time series analysis: Analyze time-stamped data to predict future trends, such as sales or weather.

• Monte Carlo simulation: Use random sampling to estimate the outcome of uncertain events.

The list of 37 videos included is as follows from top to bottom:

1. 5 steps to create a new AI model
2. How large language models work
3. Why are there so many foundation models?
4. What is a Convolutional Neural Network (CNN)?
5. What is GAN (Generative Adversarial Network)?
6. What is a transformer (machine learning model)?
7. What is LSTM (Long Short-Term Memory)?
8. What is NLP (natural language processing)?
9. NLP vs. NLU vs. NLG
10. What is Random Forest?
11. What is an autoencoder?
12. What is Knowledge Graph?
13. What is Monte Carlo simulation?
14. Overfitting, underfitting, and bad data are ruining predictive models
15. Gradient descent explained
16. What is RBM (Restricted Boltzmann Machine)?
17. Fluid intelligence and crystallized intelligence
18. Edge AI and distributed AI
19. Speed ​​up automated claims processing with AI-powered automation
20. Supervised vs. unsupervised learning
21. What is time series analysis?
22. What are MLOps?
23. Large-scale language models are zero-shot reasoners
24. What is backpropagation?
25. Training AI models with Federated Learning
26. What is PyTorch? (Machine/Deep Learning)
27. Efficiently scale AI model training and inference using PyTorch
28. How to quickly add AI to your apps using built-in AI
29. Build large language model AI chatbots using search augmented generation
30. Open source practices with watsonx
31. What is an AI agent?
32. LLM as an auditor: Expanding your AI evaluation strategy
33. Can you trust AI to judge fairly? LLM Bias Investigation
34. What is a large-scale reasoning model (LRM)? Smarter AI beyond LLM
35. LLM Explained: Build Next-Generation AI with LLM, RAG, and Kubernetes
36. LLM vs. SLM vs. FM: Choosing the right AI model
37. What is prompt caching? Optimize LLM latency using AI Transformers

If I were to highlight two videos, the first is “How Large Language Models Work.” This is because it serves as a foundational lecture that demystifies the technology behind large-scale language models (LLMs) by breaking them down into three important components: data, architecture, and training. The other is “Building a Large-Scale Language Model AI Chatbot with Search Augmented Generation.” We’ll show you how to go beyond abstractions and code functional AI applications that can “chat with your own data.”

In summary, Explaining AI Models covers a wide range of AI topics and provides an overall overview suitable for a wide range of readers. Everyone will find it equally useful: software developers and engineers, data scientists and AI practitioners, business leaders and decision makers.

Detailed information

AI model description

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