AI Mastery: Discover Tools That Will Skyrocket Your Learning (Part 5 of 5)

If you haven't checked the previous articles of my "Getting started with AI" series, I would highly recommend reading them to gain an understanding of the basic concepts of generative AI.

Generative AI Unleashed: The Artistry of Transformers (Part 1 of 5) | LinkedIn

Chatbots, Poetry, and More: Inside the Minds of Large Language Models (Part 2 of 5) | LinkedIn

Mastering the Art of Prompting: A Creative Guide to Generative AI (Part 3 of 5) | LinkedIn

Embracing Responsible AI: A Step Towards Ethical Innovation (Part 4 of 5) | LinkedIn

In the ever-evolving landscape of technology, Artificial Intelligence (AI) has emerged as a cornerstone of innovation, driving efficiency and productivity across various domains. From simplifying complex tasks to enhancing decision-making processes, AI tools are reshaping the way we approach our daily routines. Although, using the chat interface is fairly straight-forward for everyday usage, below tools will help take your learning to the next level and help you power up as a superuser.

AI Tools to supercharge learning

Microsoft Copilot

Microsoft Copilot is an AI-powered tool designed to boost productivity, unlock creativity, and enhance understanding of information through a simple chat experience. Its flavors are integrated with several frequently used Microsoft applications, such as Windows, Edge, Word, Excel, PowerPoint, and Teams. Copilot can help you decide what to keep, amend, or dismiss, and its main functions are to free up creativity, boost productivity, and sharpen user skills.

Copilot can be a powerful tool for boosting your AI learning. It leverages machine learning models trained on a vast amount of publicly available code to offer intelligent code generation capabilities. By integrating it into your code editors or Integrated Development Environments (IDEs), you can receive real-time suggestions and completions as you write code. This hands-on experience with AI models can provide a practical understanding of how these models work.

Microsoft Designer

Microsoft Designer is an AI-powered graphic design tool that has revolutionized the way we create and visualize ideas. Launched as a web app, it offers a unique blend of creativity and technology. With Microsoft Designer, you can generate images and designs using AI, creating unique content like signs, invitations, logos, social media posts, website banners, and much more.

Microsoft Designer is not just a design tool; it's also a fantastic platform for learning about AI. Interacting with system's components, such as the text-understanding model and the image generator, provide a hands-on way to explore AI concepts. This makes Microsoft Designer an excellent tool for a practical, engaging, and intuitive way to delve into the world of AI.

Ollama

It is an open-source platform designed to run large language models (LLMs) locally. It streamlines model weights, configurations, and datasets into a unified package managed by a Modelfile. This makes it easy for users to run various models such as Llama 3, Phi 3, Mistral, Gemma, and others. Ollama is designed to be efficient, scalable, and easy to use, making it an attractive option for developers and organizations looking to deploy their AI models into production.

Ollama can be a powerful tool for boosting your AI learning. You can customize and create your own models, offering a practical understanding of how these models work. Ollama's command-line interface allows you to interact directly with these models, providing immediate feedback and facilitating a deeper understanding of AI.

Example - it can be used to run an AI assistant that has sound philosophical knowledge. You can run a model like Phi-3 on Ollama and then create a model file that will contain instructions for customizing the model. So in this case we set the system message to “You are a helpful assistant with sound philosophical knowledge.”

FROM microsoft/Phi-3-mini-4k-instruct
# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
# set the system message
SYSTEM "You are a helpful assistant with sound philosophical knowledge."        

Then the model is executed with following commands:

ollama create philosophy -f ./Modelfile
ollama run philosophy        

Once the model is running, you can start interacting with it. You can ask it philosophical questions or have it generate philosophical content.

• Advantages: Ollama's strengths lie in its ability to run LLMs locally, making it a powerful tool for developers and researchers. It's designed to be efficient and scalable, allowing it to handle large models and complex tasks. Ollama's command-line interface provides a direct and flexible way to interact with these models.
• Disadvantages: While Ollama is a powerful tool, it does have some limitations. It requires a GPU to deliver the best performance. Also, its native dialogue interface is conducted in the command line, which might be inconvenient for some users.

AutoGen Studio

Autogen offers an intuitive environment where users can experiment with AI models, gain hands-on experience, and understand the intricacies of machine learning. By providing access to a suite of tools for automating code generation, data analysis, and model training, Autogen Studio simplifies the learning curve for AI enthusiasts.

Whether you’re a beginner or an advanced learner, the platform’s guided tutorials and interactive sessions ensure a comprehensive understanding of AI concepts. Moreover, Autogen Studio’s collaborative features enable learners to connect with experts and peers, fostering a community-driven approach to AI education. Embrace Autogen Studio to accelerate your AI learning journey and transform theoretical knowledge into practical expertise.

AutoGen Studio is powered by AutoGen, a leading framework for orchestrating the power of agents.

Installation and Configuration

Before diving into the transformative world of AutoGen Studio, it’s essential to ensure that your system is primed for a smooth installation and operation. Here’s what you need to get started:

System Requirements

- Python 3.11: AutoGen Studio UI 2.0 operates on Python 3.11.

- Conda Environment: Using a Conda environment is highly recommended.

- LLM Providers and API Keys: AutoGen Studio leverages Language Learning Models (LLMs) to power its AI capabilities.

Setting Up Your Environment

# Step 1: Create and activate a Python virtual environment
# Open your terminal and run the following commands:

# Install virtualenv if not already installed
# pip install virtualenv

# Create a new virtual environment
# virtualenv myenv

# Activate the virtual environment
# On Windows, use:
# myenv\\Scripts\\activate

# On Unix or MacOS, use:
# source myenv/bin/activate

# Step 2: Install AutoGen Studio using pip
# Ensure that your virtual environment is activated and then run:
# pip install autogen-studio

# Step 3: Configure your environment with either OPENAI_API_KEY or AZURE_OPENAI_API_KEY
# You can do this by setting an environment variable in your terminal session. 
# Replace 'your-api-key' with your actual API key.

# On Windows, use:
# set OPENAI_API_KEY=your-api-key
# or
# set AZURE_OPENAI_API_KEY=your-api-key

# On Unix or MacOS, use:
# export OPENAI_API_KEY=your-api-key
# or
# export AZURE_OPENAI_API_KEY=your-api-key        

An Interesting Use Case:

One of the practical real-world applications of AutoGen Studio is in the realm of content creation. For instance, you can build a multi-agent team to convert a YouTube transcript into a blog post and a tweet thread.

Stable Diffusion

Stable Diffusion is an innovative AI-powered image processing software that has been making waves in the world of artificial intelligence. Launched in August 2022, it is a versatile generative AI model that can create or modify images based on text prompts. The ability to generate striking visuals from text descriptions has a magical quality to it and points clearly to a shift in how humans create art.

Stable Diffusion is not just about creating images; it's also a powerful tool for learning about AI. By experimenting with Stable Diffusion, users can gain a deeper understanding of how AI works. The system's components, such as the text-understanding component and the image generator, provide a hands-on way to explore AI concepts. This makes Stable Diffusion an excellent tool for boosting AI learning, providing a practical, engaging, and intuitive way to delve into the world of AI.

Installation and Configuration

Setting up Stable Diffusion involves a few steps:

1. Install Python & Git: Python 3.10.6 is required to run Stable Diffusion. Git is used to manage repositories on your PC.

2. Clone Stable Diffusion Web-UI to your PC: You can clone the Stable Diffusion Web-UI repository from GitHub using Git Bash.

3. Download the Stable Diffusion model file: You can download the Stable Diffusion model file from HuggingFace and place it in the models folder.

4. Setup the Web-UI: After setting up the above, you can run the web ui-user.bat file to install the dependencies and launch the web interface.

5. Run Stable Diffusion: Finally, you can generate images using different settings and models in the web interface.

Note: This is a GPU intensive program and will need a graphic card with 4 GB of VRAM to run. Although, a card like Nvidia RTX 3080 is needed to generate quality results.

Expanding the AI Toolkit beyond the aforementioned tools, the AI ecosystem is brimming with innovative solutions designed to tackle specific challenges. Mentioning some other ones that may be advanced for an everyday user:

Hugging Face Transformers: This is a Python library for Natural Language Processing (NLP) and machine learning, which provides general-purpose architectures for several tasks such as Named Entity Recognition (NER), Masked Language Modeling (MLM), etc. It supports a wide range of models including BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5, etc.

TensorFlow Model Garden: It is a repository with implementations of state-of-the-art (SOTA) models to help advance machine learning research. It not only provides the implementation of models in TensorFlow but also provides pre-trained weights.

PyTorch-Transformers: It is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP). The library currently contains PyTorch implementations, pre-trained model weights, usage scripts, and conversion utilities for models such as BERT, GPT-2, RoBERTa, etc.

DeepSpeed: It is a deep learning optimization library that makes distributed training easy, efficient, and effective. It includes a powerful training engine, large model support, memory optimization, model parallelism, and more.

Fairseq: It is a general-purpose sequence-to-sequence library for training custom models using PyTorch. It was developed by the Facebook AI Research (FAIR) team.

Hope you found this series useful. Happy Learning!