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I'm in Vegas. I see bike lanes. I see transit lanes. I see sidewalks.

I also see six lane roads through residential neighbourhoods.

As much as transportation experts talk about dignity, EDI, engineering safe environments, if we build it they will come. I think what's missing is land use planning.

We can provide the safest space, build the best sidewalks, bike lanes, or whatever, but if it faster to drive, and it's more comfortable. That's exactly what people are going to do.

I use to be naive, judging people, for not cycling or walking more. Heck I use to bike through industrial parks against transport trucks, rain, wind, and snow thinking I was doing the world good. And I'm doing my part to show people this way of moving around is possible. You know what I missed, I was time rich during that period in my life. Those others weren't and they want to be comfortable.

If you want people to walk, bike, or not take a car, make the travel time on parity with taking the car, and you'll probably get a better result. Dignity - EDI, ugh.


There's no place I want door-to-door air conditioning more than Las Vegas in the summer


The transition over to the Spotify platform was a decrease in user experience.

Downgrade with video integration. Downgrade with search ability on audio. Buggy resume feature for both AV.

I get why he did it. YouTube was doing some weird speech shit at the height of Trump and COVID. It's wasn't like the minions at Spotify didn't try. Seems like the higher ups at Spotify is giving Joe more freedom.

I definitely consume less of Rogan's podcasts since the switch. But I do seek him out whenever a clip on YouTube pops up and strikes my interest.


Runbox user as well. Their support is quite responsive and helpful when needing to troubleshoot. Security is great, number of alias available for the price can't be beat. I agree the UI is terrible in the browser, even their latest "Runbox 7" is slow and sluggish.


I was at a vehicle AI conference early this month discussing how AI will only output based on the quality of its inputs. And the fear coming out of that conference was if space wasn't correctly dedicated or allocated, the AI will optimize the systems as much as it can to squeeze out every single ounce of efficiency.

Qualitative items such as the ones listed are important for customer focused environments, however, I'm not sure if AI can account for such factors.

This post is quite timely.


> Qualitative items such as the ones listed are important for customer focused environments, however, I'm not sure if AI can account for such factors.

One of the nice things you can do with optimization problems is plug humans into the loop as oracles. Often, 'we know it when we see it', and we can do pairwise comparisons of 2 possibilities. So you can train a ML model based on win/loss comparisons and it'll learn to take into account the softer qualitative aspects via preference learning.

A recent example you might remember from the press: "Deep reinforcement learning from human preferences" https://arxiv.org/abs/1706.03741 , Christiano et al 2017 https://deepmind.com/blog/learning-through-human-feedback/ https://blog.openai.com/deep-reinforcement-learning-from-hum...

But also "Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces" https://arxiv.org/abs/1709.10163 , Warnell et al 2017.

You can even pair it with EEG or brain scans for implicit ranking: "Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest" https://arxiv.org/abs/1709.04574 , Shih et al 2017.


Like I mentioned, I have high hopes modelling vaguer aspects of customer preference can be incorporated by training a model on rendered generative design (e.g. with data from amazon turk), which can then be used as maybe some kind of penalization on the efficiency loss function, multiplied by a weight to give us a choice in the efficiency-comfort tradeoff-space.

There are solutions that I think show potential out there. I don't think our future AI designed world necessarily need to ignore difficult-to-quanitify dimensions like aesthetics. (Though amazon turk is expensive, especially in developer man-hours, so I can understand if that won't always be done.)


Could another parameter be how it crumples in a crash? I don't want my AI supercar to transform into inescapable cage on impact.


It all comes down to how explicitly preferences are originally understood and then if the reward function can incorporate implicit analysis.

There have been recent studies about AI powered shirt design - the original input uses existing designs in terms of color and shape rather than the basic naive description of requirements that an engineer would give. Then the designs can be assessed by a review board or put up on a site and not produced until some n quantity of purchases.

You wouldn't try to detect cats in images without labelled data why would you try something MUCH harder without labelled data?!?!?!?!?!


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