Javier Antich concluded the AI/ML in Networking webinar with the ugly challenges of using AI/ML in networking. I won’t spoil the fun, you REALLY SHOULD watch the video (keeping in mind he was trying to stay polite and diplomatic).
Russ White’s Weekend Reads are full of gems, including a recent pointer to the AI Illusion – State-of-the-Art Chatbots Aren’t What They Seem article. It starts with “Artificial intelligence is an oxymoron. Despite all the incredible things computers can do, they are still not intelligent in any meaningful sense of the word.” and it only gets better.
While the article focuses on natural language processing (GPT-3 model), I see no reason why we should expect better performance from AI in networking (see also: AI/ML in Networking – The Good, the Bad, and the Ugly).
Erik Hoel published a wonderful article describing how he’s fighting the algorithm that is deciding whether to approve a charge on his credit card.
My credit card now has a kami. Such new technological kamis are, just like the ancient ones, fickle; sometimes blessing us, sometimes hindering us, and all we as unwilling animists can do is a modern ritual to the inarticulate fey creatures that control our inboxes and our mortgages and our insurance rates.
There are networking vendors unleashing similar “spirits” on our networks. Welcome to the brave new world ;)
In the second part of the webinar, he described “The Good, The Bad and The Ugly”, starting with the good parts: where does AI/ML make sense in networking?
- Machine learning techniques, including unsupervised learning (clustering and anomaly detection), supervised learning (regression, classification and generation) and reinforced learning
- Machine learning implementations, including neural networks, deep neural networks and convolutional neural networks.
In May 2021, Javier Antich ran a great webinar explaining the principles of Artificial Intelligence and Machine learning and how they apply (or not) to networking.
He started with a brief overview of AI/ML hype that should help you understand why there’s a bit of a difference between self-driving cars (not that we got there) and self-driving networks.
I hope you're familiar with Clarke's third law (and leave it to your imagination to explain how it relates to SDN ;). In case you want to look beyond the Machine Learning curtain, you might find the Machine Learning Explained article highly interesting. Spoiler: it all started in 1960s with over 2000 matchboxes.