11–20 of 117 posts
Nice - exploring the use of home assistant for something similar. It already has all the integrations and API's for control, just lacks the Chat GPT plugin.
It's one thing to use it as convenient way to change anything remotely but linking home automation not only into constant internet connectivity but also "that one particular cloud thing that might disappear at any moment" (hello google) just seems like setting yourself for problems later. At least in this case he's still left with HASS if cloudy part goes away.
But I wish there was more push into hybrid model - like have your router or small ARM box be server that runs queue and some, for lack of better word, "lambda-like" code that handles most of the programmed events (lights, heating etc.), say via WASM and some APIs around it, then just have HASS-like software (both cloud and local) deploy control rules/code onto it.
Big nice UI for control goes down ? Doesn't matter, the controller is just running simple code, not rest of the visualisation and controls. Want to replace it ? Configuration as code makes that a breeze, hell, have cloud backup of whole setup. Don't want controller ? It's essentially just a queue and some code runners, that cloud providers can host for you. Want pretty graphs in cloud or some aggregation ? Just make your local node filter and send relevant MQ events there.
Super exciting to see the work happening in this area! I can especially appreciate the use of ChatGPT to orchestrate the necessary API calls, rather than relying on some kind of middleware to do it. I have been working in this area (LLMs for ubiquitous computing, more generally) for my PhD dissertation and have discovered some interesting quality issues when you dig deeper [0]. If you only have lights in your house,…
is it bad that I'm on GPT's side on this, i mean if they are incandescent lights they are small electric heaters and what else do you expect it to do
Super exciting to see the work happening in this area! I can especially appreciate the use of ChatGPT to orchestrate the necessary API calls, rather than relying on some kind of middleware to do it. I have been working in this area (LLMs for ubiquitous computing, more generally) for my PhD dissertation and have discovered some interesting quality issues when you dig deeper [0]. If you only have lights in your house,…
Thanks for the example that's interesting.
FWIW, this is pretty much what has been described as "waluigi" effect a bit extended: in a text you'll find on the internet, if some information at the beginning is mentioned, it WILL be relevant somewhere at some point later in that text. So an auto-completion algorithm will use all the information that has been given in the prompt. In your example it puts it in an even weirder situation where the model the overall model information (the lights, and that you're cold and nothing else), and it must generate a response. It would be a fun psychological study to look at, but I'm pretty sure even humans would do that in that situation (assuming they realize that lights may indeed produce a little bit of wattage of heat)
I'm not against idea of having LLM be smart assistant for home, but I do have problem with sending any of that to cloud. It's one thing to use it as convenient way to change anything remotely but linking home automation not only into constant internet connectivity but also "that one particular cloud thing that might disappear at any moment" (hello google) just seems like setting yourself for problems later. At least…
This already happens in the industry, like heavy industry.
At home, it's held back by absence of an adequate server. Often a router box or a NAS would be fine to host something like OwnCloud. But to run ML middle, different and much more expensive hardware is required, which would sit idle 99% of time.
Super exciting to see the work happening in this area! I can especially appreciate the use of ChatGPT to orchestrate the necessary API calls, rather than relying on some kind of middleware to do it. I have been working in this area (LLMs for ubiquitous computing, more generally) for my PhD dissertation and have discovered some interesting quality issues when you dig deeper [0]. If you only have lights in your house,…
> If I say "it's too chilly in here" in a house with only lights, it will turn them on as a way of "warming things up". Thanks for the example that's interesting. FWIW, this is pretty much what has been described as "waluigi" effect a bit extended: in a text you'll find on the internet, if some information at the beginning is mentioned, it WILL be relevant somewhere at some point later in that text. So an auto-comple…
adding something like
"Write the first page of the first chapter of this novel. Do not introduce the elements of the synopsis too quickly. Weave in the world, characters, and plot naturally. Pace it out properly. That means that several elements of the story may not come into light for several chapters."
after you've written up key elements you want in the story actually makes the models write something that paces ok/normally.
Super exciting to see the work happening in this area! I can especially appreciate the use of ChatGPT to orchestrate the necessary API calls, rather than relying on some kind of middleware to do it. I have been working in this area (LLMs for ubiquitous computing, more generally) for my PhD dissertation and have discovered some interesting quality issues when you dig deeper [0]. If you only have lights in your house,…
> If I say "it's too chilly in here" in a house with only lights, it will turn them on as a way of "warming things up". is it bad that I'm on GPT's side on this, i mean if they are incandescent lights they are small electric heaters and what else do you expect it to do
Earlier quoted context omitted.
> If I say "it's too chilly in here" in a house with only lights, it will turn them on as a way of "warming things up". is it bad that I'm on GPT's side on this, i mean if they are incandescent lights they are small electric heaters and what else do you expect it to do
It certainly makes logical sense. I think if you have the ability to control the light in the first place via an API, it's probably an LED smart bulb and thus doesn't produce much heat. At least, I'm not aware of any incandescent smart bulbs.
Super exciting to see the work happening in this area! I can especially appreciate the use of ChatGPT to orchestrate the necessary API calls, rather than relying on some kind of middleware to do it. I have been working in this area (LLMs for ubiquitous computing, more generally) for my PhD dissertation and have discovered some interesting quality issues when you dig deeper [0]. If you only have lights in your house,…
Very interesting paper! It's something that I've been wondering about with ChatGPT plugins - they've kind of left it up to the user to enable/disable plugins. But there's definitely going to come a point where plugins conflict and the LLM is going to have to choose the most appropriate plugin to use. I have been very impressed at how good it is at turning random commands into concrete API calls. You are right though,…
To your point about turning random commands into API calls: if you give it the raw JSON from a Philips Hue bridge and ask it to manipulate it in response to commands, it can even do oddly specific things like triggering Hue-specific lighting effects [0] without any description in the plugin yaml. I'm assuming some part of the corpus contains info about the Hue API.
Chat GPT is now our personal lighting DJ, giving us dynamic and interesting light combinations that respect our circadian rhythm.
Here's my prompt - the output of which feeds Home Assistant:
Set the hue for my home's lights using the HSL/HSB scale from 0-360 by providing a primary and complementary colour which considers the current situation. The HSL color spectrum ranges from 0 (red), 120 (green), to 240 (blue) and back to 360 (red). Lower values (0-60) represent warmer colors, while higher values (180-240) represent cooler colors. Middle values (60-180) are neutral.
Consider these factors in setting the primary hue (in order of importance):
1. Preferences throughout the day: - When about to wake: Reds, oranges or hot pinks - Approaching bedtime: Hot pinks or reds - During worktime: Blues, greens or yellows - Other times: Greens, yellows or oranges
2. Current activity: Bedtime
3. Sleep schedule: Bedtime 23:00, Wake-up time 07:00
4. Date & time: Sunday May 21, 05:40
5. Current primary hue: 10
6. Current complementary hue: 190
7. Weather: 13°C, wind speed 9 km/h, autumn
Respond in this format and provide a reason in {"primary_hue": PRIMARY_HUE, "complementary_hue: COMPLEMENTARY_HUE, "reason": REASON }
The output looks like this:
{primary_hue: 10 complementary_hue: 190 reason: "Approaching bedtime and early hours of morning, so a warm and calming hue is needed. Complementary hue adjusted slightly to 195 to maintain balance."}