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toyg

To be fair, LLMs as a tech are actually useful, even just to take human input.

Blockchain an K8s though... Eh. Geekery for geekery's sake.

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I've worked for a number of enterprise companies. All have moved to Kubernetes.

And it's not just geekery by developers who aren't as smart as you.

It's because it allows you to treat all of your infrastructure in one way. Whether you are on GCP, AWS or On-Premise, whether you use Java, Spark, Web Serving or ML Training, whether you are deploying direct to Production or go through multiple staging environments etc. It is always one way of deploying things, one way of securing things, one way of doing everything.

It is far cheaper, easier, more secure and less risky than managing infrastructure yourself. And believe me we all tried that.

Seriously. People make Kubernetes out to be this wildly complicated technology, but if your environment is more complex than just "Here's a glorified VM running on a couple EC2 instances behind a load balancer," it has a lot of benefits and is really not that difficult to set up in this day and age.

Those who think Kubernetes is simple and easy and not complicated at all do most likely only fall in two categories:

a. Someone else is running Kubernetes for them. (GKE, EKS etc)

b. Or they're not running Kubernestes in production yet. (Homelab, staging prototype etc.)

So yes from the point of view of it's APIs and it's object model, it's not complicated.

EDIT: grammar

Do you run VMs inside kubernetes as well? I know this is possible but wonder how's the actual experience

I think we'll find that, when the dust settles, AI's usefulness wasn't as impactful as we thought. AI is only as good as its data and no meaningful dataset is 100% representative and 100% accurate IMHO. Don't get me wrong-- it's neat and all, and it can be somewhat useful in the right context, but the hype is huge. Much bigger than Kubernetes ever saw and probably bigger than blockchain.

There's a huge win to be had though with "this is my natural language data, give me natural language insights at a 100 foot view, at a 50,000 foot view, etc". This is one of the big pulls OpenAI is hoping for w/ GPTs/Assistants.

Too bad this costs a few million (only if you're using insanely subsidized hardware).

OpenAI just launched their RAG with the Assistants feature that can ingest a fair bit of documents you choose to upload. So while not enterprise grade just yet, there is immediate utility on the horizon and of course other services have provided something like this for months now.

To the parent's point, when the dust settles something like this will probably be commoditized at scale and will likely have a sizable impact on society.

Yes, that service is the one that may cost millions that I am referencing.

> data and no meaningful dataset is 100% representative and 100% accurate IMHO

And? What point are you trying to make here?

People seem to think that LLMs are going to be this source of truth that everyone can rely on to give them the information they need. Which for certain use cases e.g. text based is fine because you can tolerate inaccuracies.

But I have worked at highly regulated finance companies who aren't interested in LLMs at all. Because their business can't tolerate if your model returns a figure or calculation that is inaccurate.

https://www.bloomberg.com/company/press/bloomberggpt-50-bill... lol

You are picking out a specific use-case as an invalidation of a wide idea and concept. “This is thos _one thing_ an LLM cannot do for us so all of it is useless.

I bet LLMs help them pitch their MBS CDOs to shadow banks, though.

Your point is still not clear

That it will be inaccurate. A lot.

This drug only helps 80% of patients, it’s useless.

This drug only killed 20% of patients. We should sell it.

Great analogy for LLMs, I think you understood my point perfectly.

I don't doubt that. I myself use it but the major problem that seems to remain unsolved for a while is that LLMs can't be blindly relied upon because they don't have any knowledge rather they mimic what knowledge might look like.

I find LLMs so much useful for myself because in some areas, I have developed expertise and even with a wrong LLM output, I can manually make few tweaks to make it work.

But same can't be said for an LLM bot meant for SAP or Netsuite that it'll guide a user reliably to a correct answer.

There you still need a real expert that's going to be way way slower than an LLM but with way way more higher accuracy rate in ballpark of 98.9% or above.

And that's where LLM with your own toolchain or rented toolchain doesn't make much sense. For many use cases. Yet.

I would generalize that as "no LLM can surpass domain experts on any task, yet"

The only superhuman AIs are very narrow, done by OpenAI - AlphaZero and AlphaFold, and they don't train on language

Bad comparison of blockchain and k8s. When you actually start doing something non-trival complexity and non-toy scale, you're actually faced with huge problems that k8s and terraform practically solve. Whereas blockchain is only a solution in search of problems that nobody actually has.

Yes they are useful. K8S is also obviously useful.

Doesn’t mean they are useful for everything.

This is what each of those trends said.

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