How ChatGPT could replace IT network engineers

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

Although 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 analyze, a minimum of cursorily, whether or not ChatGPT could be used an efficient device to both help or ultimately replace these like me. 

Here’s what I came upon.

I began by getting the opinion of the very best supply I could consider about how ChatGPT could add worth to network engineers: ChatGPT. It didn’t disappoint and generated an inventory of three areas it decided it could assist:

• Configuration administration
• Troubleshooting
• Documentation

I then developed a set of prompts — admittedly not optimized — to find out whether or not or not the device could, in reality, be an asset to network engineers in a number of of those areas.

Configuration administration

To take a look at ChatGPT’s skill so as to add worth in configuration administration, I submitted the next prompts:

• Can you generate an entire instance configuration for a Cisco router with the aim of beginning an web trade from scratch?

• What about Juniper?

• Can you create a Jinja template for every vendor?

The ChatGPT outcomes are in depth, so house — 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 nicely on fundamental configuration duties, and I concluded that it’s conscious of vendor-specific syntax and may generate configurations. However, the configurations generated by the system needs to be rigorously inspected for accuracy. The generic prompts I examined can 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 expertise (with, once more, some human oversight).

Troubleshooting

To take a look at ChatGPT’s prowess at troubleshooting network engineering challenges, I turned to Reddit, and particularly the /r/networking subreddit to seek out real-world questions posed by network engineers to their friends. I pulled a couple of questions from the thread and proposed them to ChatGPT with out optimizing the immediate, and the chatbot dealt with the better questions nicely, whereas it struggled with the harder challenges.

Notably, I particularly requested a query that required data of STP, or the Spanning Tree Protocol, a change functionality chargeable for figuring out redundant hyperlinks that could end in 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 replace skilled networking professionals for even barely advanced points, nevertheless it wouldn’t be alarmist to recommend that it would end result within the obsolescence of many subreddits and Stack Overflow threads within the coming years.

Automating documentation

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

Further prompting for network documentation led to the conclusion — confirmed by ChatGPT — that I wanted to supply an in depth network description for it to supply a network 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 predict). In equity to AI basically, there are AI functions able to producing photographs, and it’s very attainable a type of could also be able to producing a usable network diagram.

I then requested ChatGPT if it could generate a network description based mostly on a router configuration file, and it supplied a good abstract of what’s configured till it apparently reached the boundaries of its computational dedication to my immediate, a restrict doubtless carried out by its designers. It is, in any case, a free device, and assets are costly, particularly for a corporation burning significant money lately.

Conclusions

A couple of of the challenges I encountered in my transient experiment when utilizing ChatGPT for network engineering embody:

• Ensuring accuracy and consistency
• Handling edge instances and exceptions
• Integration with current programs and processes

My guess is these points usually are not distinctive both to ChatGPT or AI functions usually, and a few cursory analysis might clarify why. Cornell researchers have been learning giant language fashions (LLMs) for a while and “draw a distinction between formal competence — the knowledge of linguistic rules and patterns — and functional competence, a set of skills required to use language in real-world situations.”

Also from a few of their analysis summaries: “Too often, people mistake coherent text generation for thought or even sentience. We call this a “good at language = good at thought” fallacy. Similarly, criticisms directed at LLMs middle on their incapacity to assume (or do math or preserve a coherent worldview) and generally overlook their spectacular advances in language studying. We name this a “bad at thought = bad at language” fallacy.

This evaluation is according to my expertise making ready this text: Specificity reigns supreme in relation to placing ChatGPT to work. Large, open-ended prompts on advanced subjects spotlight a scarcity of “functional 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 replace network engineers?

Not but.

Mike Starr is the CEO and founding father of trackd.