AI and ML in Networking
Artificial Intelligence (AI) and Machine Learning (ML) are the next big hype in networking following Software-Defined Everything and Intent-Based Everything. Like with the previous hype bubbles it’s worth figuring out
- How much of the hype is real (TL&DR: not much)?
- Whether the technology is ready to be used in production networks (TL&DR: some of it)
- How you could use the technology to make your life easier
How Real Is It?
Like with the previous hype tsunamis I’ll do my best to help you figure out the answers to the above questions with a hefty dose of skepticism and snark1, starting with:
I also decided to “kick the tires” and document my (often less-than-stellar) experience with the most-overhyped products:
- Real-Life Not-Exactly-Networking AI Use Case
- ChatGPT on BGP Routing Security
- Kicking the Tires of GitHub Copilot
- Building a Small Network with ChatGPT
- ChatGPT Explaining the Need for iSCSI CRC
- Source IP Address in Multicast Packets
AI/ML in Networking: The Good, the Bad and the Ugly
Javier Antich created a wonderful AI/ML in Networking in 2021. If you know nothing about AI/ML and wonder whether you should care about it, you MUST watch these videos from his webinar:
- Introduction to AI/ML Hype
- Machine Learning 101
- Machine Learning Techniques
- Use Cases for AI/ML in Networking
- The Long Tail of AI/ML Problems
- Ugly Challenges of Using AI/ML in Networking
- Language Models in AI/ML Landscape
In 2023, Javier published a book covering the same set of topics in way more details. I would highly recommend you read it if you want to know more.
What Others Are Saying
I keep collecting interesting articles talking about AI in general and (lately) ChatGPT. I found these interesting enough to mention them in worth reading blog posts:
- Machine Learning Explained (2020)
- AI Makes Animists of Us All (2022)
- The AI Illusion (2022)
- Collections: On ChatGPT (a Historian Perspective) (2023)
- Putting Large Language Models in Context (2023)
- The Dangers of Knowing Everything (2023)
- Building Trustworthy AI (2023)
- Cargo Cult AI (2023)
- Building Stuff with Large Language Models Is Hard (2023)
- Worth Reading: AI Does Not Help Programmers (2023)
- Eyes that glaze over. Eyes like saucers. Eyes that narrow. (2023)
- Networking for AI Workloads (2023)
- Looking Inside Large Language Models (2023)
- Where Are the Self-Driving Cars? (2023)
- AI Risks (2023)
These are not bad either:
- What Is ChatGPT Doing … and Why Does It Work?
- We Can’t Build a Hut to the Moon
- The Delusion at the Center of the A.I. Boom (aka AI Solutionism)
- ChatGPT and Chemistry
- Cal Newport on ChatGPT
- Ruby Development with ChatGPT
- ChatGPT Is Your New Intern
- Using ChatGPT as a Technical Writing Assistant
- Why OpenAI is the new AWS
- Overemployed Hustlers Exploit ChatGPT To Take On Even More Full-Time Jobs
Finally, a few real-life uses of large language models:
- An Exploration of Embeddings and Vector Databases
- How GPT and LLMs will affect documentation
- I Built an AWS Well-Architected Chatbot with ChatGPT
- Building Boba AI – how to build a custom user interface in front of a large language model.
- Using Langchain to interact with ChatGPT
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Please don’t blame me for pointing out the ever-lasting validity of Sturgeon’s law. Contrary to what some people think, I’m not trying hard to pick up dismal examples of AI failures, I’m just good at looking in the wrong places. Also, I’m too old to be wearing rosy glasses and drinking Kool-Aid. ↩︎