Reading this gave me an idea to extend this even further. What if the AI could look at your logbook history and suggest automations? For example, I have an automation that turns the lights on when it's dark based on a light sensor. It would be neat if AI could see "hey, you tend to manually turn on the lights when the light level is below some value, want to create an automation for that?"

I've been working on something like this but it's of course harder than it sounds, mostly due to how few example use cases there are. A dumb false positive for yours might be "you tend to turn off the lights when the outside temperature is 50º"

Anyone know of a database of generic automations to train on?

Temperature and light may create illusions in LLM. A potential available solution to this is to establish a knowledge graph based on sensor signals, where LLM is used to understand the speech signals given by humans and then interpret these signals as operations on the graph using similarity calculations.

That's a good one.

We might take it one step further and ask the user if they want to add a rule that certain rooms have a certain level of light.

Although light level would tie it to a specific sensor. A smart enough system might also be able to infer this from the position of the sun + weather (ie cloudy) + direction of the windows in the room + curtains open/closed.

I can write a control system easy enough to do this. I'm kind of an expert at that, for oddball reasons, and that's a trivial amount of work for me. The "smart enough" part, I'm more than smart enough for.

What's not a trivial amount of work is figuring out how to integrate that into HA.

I can guarantee that there is an uncountably infinite number of people like me, and very few people like you. You don't need to do my work for me; you just need to enable me to do it easily. What's really needed are decent APIs. If I go into Settings->Automation, I get a frustrating trigger/condition/action system.

This should instead be:

1) Allow me to write (maximally declarative) Python / JavaScript, in-line, to script HA. To define "maximally declarative," see React / Redux, and how they trigger code with triggers

2) Allow my kid(s) to do the same with Blockly

3) Ideally, start to extend this to edge computing, where I can push some of the code into devices (e.g. integrating with ESPHome and standard tools like CircuitPython and MakeCode).

This would have the upside of also turning HA into an educational tool for families with kids, much like Logo, Microsoft BASIC, HyperCard, HTML 2.0, and other technologies of yesteryear.

Specifically controlling my lights to give constant light was one of the first things I wanted to do with HA, but the learning curve meant there was never enough time. I'm also a big fan of edge code, since a lot of this could happen much more gradually and discreetly. That's especially true for things with motors, like blinds, where a very slow stepper could make it silent.

1) You can basically do this today with Blueprints. There's also things like Pyscript [0]. 2) The Node-RED implementation in HA is phenomenal and kids can very easily use with a short introduction. 3) Again, already there. ESPHome is a first class citizen in HA.

I feel like you've not read the HA docs [1,] or took the time to understand the architecture [2]. And, for someone who has more than enough self-proclaimed skills, this should be a very understandable system.

[0] https://github.com/custom-components/pyscript [1] https://www.home-assistant.io/docs/ [2] https://developers.home-assistant.io/

I think people might be a bit offended by what sounds like arrogance. But I completely agree with your general concern that nuts and bolts of making somebody else's software work is often frustrating, complicated and inaccessible while math, logic and domain knowledge is "easy" for many people and far more generally known. Even to the point that it's often easier to write your own thing than bother to learn about an existing one.

A way I sometimes evaluate whether to implement some feature in my work is the ratio of the work it does for the user to the work the user has to do for it. Adding a page header in MS Word used to have a very low ratio. A web based LLM is at the other extreme. Installing a bunch of interdependent finicky software just to do simple child-level programming for HA seems like a poor ratio too.

Thank you so much for that comment. I really appreciate the feedback.

I do sometimes come off as arrogant. That's unfortunate, and in part due to my cultural background. It's helpful feedback. It's difficult to be downvoted or attacked, and not know why.

I will mention: They're just different skill sets. I know people who can dive into a complex piece of code or dev-ops infrastructure and understand it in hours or days. I'm just not one of them.

Learning to design control systems is a very deep (and rather obscure) pile of mathematics which takes many years of study and is a highly specialized. I picked it up for oddball reasons a few decades ago. Doing proper control systems requires a lot of advanced linear algebra, rational functions, frequency domain analysis, parts of differential equations, etc. That's not the same thing as general math skills. Most people who specialize in this field work in Matlab, wouldn't know what docker is, and in terms of general mathematics, have never taken a course on abstract algebra or topology. Even something like differential equations, one needs only a surface understanding of (it disappears when one shifts to Laplace domain or matrix state space representations).

There's a weird dynamic where things we can't do often seem easier or harder than ones we can. Here, I just have a specialized skillset relevant to the conversation. That doesn't imply I'm a genius, or even a brilliant mathematician.

That just implies I can design an optimal control system. Especially for a system with dynamics as simple as room lighting. And would have a fun time doing that for everything in HA in my house and sharing.

I'd really like to have other things work the same way too, for that matter, where e.g. my HVAC runs heating 24/7 at the right level, rather than toggling on and off. With my background, the intimidating part isn't the math or the electronics, but the dev-ops.

Machine learning can tackle this for sure, but that's surely separate to LLMs. A language model deals with language, not logic.

At least higher-end LLMs are perfectly capable of making quite substantive logical inferences from data. I'd argue that an LLM is likely to be better than many other methods if the dataset is small, while other methods will be better once you're dealing with data that pushes the context window.

E.g. I just tested w/ChatGPT, gave it a selection of instructions about playing music, the time and location, and a series of hypothetical responses, and then asked it to deduce what went right and wrong about the response, and it correctly deduced what the user intent I implied was a user that given the time (10pm) and place (the bedroom) and rejection of loud music possibly just preferred calmer music, but who at least wanted something calmer for bedtime.

I also asked it to propose a set of constrained rules, and it proposed rules that'd certainly make me a lot happier by e.g. starting with calmer music if asked an unconstrained "play music" in the evening, and transition artists or genres more aggressively the more the user skips to try to find something the user will stick with.

In other words, you absolutely can get an LLM to look at even very constrained history and get it to apply logic to try to deduce a better set of rules, and you can get it to produce rules in a constrained grammar to inject into the decision making process without having to run everything past the LLM.

While given enough data you can train a model to try to produce the same result, one possible advantage of the above is that it's far easier to introspect. E.g. my ChatGPT session had it suggest a "IF <user requests to play music> AND <it is late evening> THEN <start with a calming genre>" rule. If it got it wrong (maybe I just disliked the specific artists I used in my example, or loved what I asked for instead), then correcting its mistake is far easier if it produces a set of readable rules, and if it's told to e.g. produce something that stays consistent with user-provided rules.

(the scenario I gave it, btw. is based on my very real annoyance with current music recommendation that all to often does fail to take into account things like avoiding abrupt transitions, paying attention to the time of day and volume settings, and changing tack or e.g. asking questions if the user skips multiple tracks in quick succession)

This is a very insightful viewpoint. In this situation, I believe it is necessary to use NER to connect the LLM module and the ML module.

Retrospective questions would also be really great. Why did the lights not turn off downstairs this night? Or other questions involving history.

This is a really great use for AI. Hits a big pain point.