How ChatGPT could replace IT network engineers

Fashionable IT networks are advanced mixtures of firewalls, routers, switches, servers, workstations and different gadgets. What’s extra, almost all environments are actually on-premise/cloud hybrids and are continually below assault by menace actors. The intrepid souls that design, implement and handle these technical monstrosities are referred to as community engineers, and I’m one.

Though different passions have taken me from that world into one other as a start-up founder, a relentless stream of breathless predictions of a world with out the necessity for people within the age of AI prompted me to research, at the least cursorily, whether or not ChatGPT might be used an efficient instrument to both help or finally change these like me. 

Right here’s what I came upon.

I began by getting the opinion of the perfect supply I might consider about how ChatGPT might add worth to community engineers: ChatGPT. It didn’t disappoint and generated a listing of three areas it decided it might assist:

• Configuration administration
• Troubleshooting
• Documentation

I then developed a set of prompts — admittedly not optimized — to find out whether or not or not the instrument might, in actual fact, be an asset to community engineers in a number of of those areas.

Configuration administration

To check ChatGPT’s capacity so as to add worth in configuration administration, I submitted the next prompts:

• Are you able to generate an entire instance configuration for a Cisco router with the aim of beginning an web alternate from scratch?

• Are you able to create a Jinja template for every vendor?

The ChatGPT outcomes are intensive, so area — and my respect for the boredom limits of these studying this — limits an exhaustive copy of them right here, however I’ve posted the entire transcript of the entire ChatGPT prompts and outcomes on GitHub for these trying to find a non-pharmaceutical substitute for Ambien.

So, within the case of configuration administration, ChatGPT carried out pretty effectively on primary configuration duties, and I concluded that it’s conscious of vendor-specific syntax and might generate configurations. Nevertheless, the configurations generated by the system needs to be rigorously inspected for accuracy. The generic prompts I examined could be akin to constructing a fast lab, a activity most younger networking engineers discover tiresome at a minimal and clearly a chore that may be dealt with by the know-how (with, once more, some human oversight).

Troubleshooting

To check ChatGPT’s prowess at troubleshooting community engineering challenges, I turned to Reddit, and particularly the /r/networking subreddit to search out real-world questions posed by community engineers to their friends. I pulled just a few questions from the thread and proposed them to ChatGPT with out optimizing the immediate, and the chatbot dealt with the simpler questions effectively, whereas it struggled with the harder challenges.

Notably, I particularly requested a query that required information of STP, or the Spanning Tree Protocol, a swap functionality answerable for figuring out redundant hyperlinks that would lead to undesirable loops. Frankly, my opinion is that ChatGPT understands STP higher than many networking professionals I’ve interviewed over time.

At current, ChatGPT can’t change skilled networking professionals for even barely advanced points, but it surely wouldn’t be alarmist to recommend that it’d outcome within the obsolescence of many subreddits and Stack Overflow threads within the coming years.

Automating documentation

This was the realm of highest deficiency for ChatGPT. The chatbot initially assured me that it might generate networking diagrams. Realizing it’s a text-based instrument, I used to be clearly skeptical, a prejudice that was confirmed once I requested it to generate a diagram and it defined to me that it doesn’t have graphical functionality.

Additional prompting for community documentation led to the belief — confirmed by ChatGPT — that I wanted to offer an in depth community description for it to offer a community description, clearly not a price add. Thus, within the case of automating documentation, the chatbot not solely failed, however was responsible of producing lies and deception (so maybe it’s nearer to demonstrating human traits than we expect). In equity to AI basically, there are AI functions able to producing photographs, and it’s very potential a type of could also be able to producing a usable community diagram.

I then requested ChatGPT if it might generate a community description based mostly on a router configuration file, and it offered an honest abstract of what’s configured till it apparently reached the bounds of its computational dedication to my immediate, a restrict doubtless carried out by its designers. It’s, in spite of everything, a free instrument, and assets are costly, particularly for a company burning significant money as of late.

Conclusions

A number of of the challenges I encountered in my temporary experiment when utilizing ChatGPT for community engineering embody:

• Making certain accuracy and consistency
• Dealing with edge instances and exceptions
• Integration with present techniques and processes

My guess is these points should not distinctive both to ChatGPT or AI functions typically, and a few cursory analysis could clarify why. Cornell researchers have been learning massive language fashions (LLMs) for a while and “draw a distinction between formal competence — the knowledge of linguistic rules and patterns — and useful competence, a set of expertise required to make use of language in real-world conditions.”

Additionally from a few of their analysis summaries: “Too usually, folks mistake coherent textual content era for thought and even sentience. We name this a “good at language = good at thought” fallacy. Equally, criticisms directed at LLMs middle on their incapability to suppose (or do math or preserve a coherent worldview) and generally overlook their spectacular advances in language studying. We name this a “unhealthy at thought = unhealthy at language” fallacy.

This evaluation is according to my expertise making ready this text: Specificity reigns supreme in terms of placing ChatGPT to work. Giant, open-ended prompts on advanced subjects spotlight an absence of “useful competence” within the chatbot, however that actuality doesn’t neutralize its spectacular capabilities when employed for particular duties by a person expert in utilizing it correctly.

So, can ChatGPT change community engineers?

Not but.

Mike Starr is the CEO and founding father of trackd.