NoToP

Your restaurant scenario is a bit too straightforward a test. Introduce any amount of hidden information, one character knowing something that another character does not, and you'll see how it very much does not simulate different point of views. There's just one omni perspective for everything with it.

Lots of ways to make it fail. Not to be rude but you're late to the party. Transit questions is my favorite. Ask it what stations lines 1 and 2 have in common (city of your choice). Nearly 100% of the time there's at least one wrong answer on the list. Ask it what trains go to that station, it likely wont list lines 1 and 2. Point out the contradiction, it will make a new list with new mistakes.

Another good one. Ask for random numbers. They usually aren't much random at all. Ask it what distribution it picked from, it will say it used pythons rng. You and I both know it can't invoke the python interpreter. It can't honestly tell you the non random pattern named randomness it has learned to recall.

Anything to do with nested narrative scope. Ask it to make a story about a conversation with another user who prompted X. It will often conflate the role of each person in the story. It once gave me a narrative about a user who was uncomfortable with a prompt chatGPT had given to it...funny role reversal there. Any attempt to make it produce a transcript of conversation which itself contains some transcript of another conversation as an object of discussion goes over its head. It can't nest.

That's all I got for now.

You haven't understood my simple algorithm which does exactly what you say a human driver is doing, no advanced ml required just basic understanding of projective geometry. If you have frames of video of the previous few fractions of a second and information on how the car moved in 3d space over those frames, I'm saying you can construct a dead simple filter that preserves precisely the in scene objects which are on a collision course with the camera while everything irrelevant in the scene gets washed out.

The simplest case is with no steering. The projective transform is just blowing up the image by a multiplicative factor dependent on the distance moved per frame. So the x, y position of each pixel gets stretched the further back in time it is to compensate for the cars cha ge in parallax. After stretching, all these frames are averaged together. Anything on a collision course with the camera is going to be at the same angular position each time just getting larger. So averaging over will highlight its presence. The rear bumper which you are actually due to miss will be drifting in angular position in each compensated frame, so it gets averaged into the background blur of irrelevant objects.

Tldr simple video frame filter highlights exactly which parts of image frame relevant to possible collision, tells computer what can safely ignore, implements exactly your near miss driving algorithm, no fragile machine learning bs.

It does because the motion of the other objects is embedded in their position over the previous few frames. On the contrary, it is precisely filtering on moving objects such that the velocity vector is directly on a collision path with the camera. Objects which are moving but ultimately not on a collision course will be in different locations once the perspective transform is applied and thus average out to background. Objects which are moving on a collision course relative to the observer get amplified by this simple filter (irrespective of if the motion is absolute or not relative to ground).

Not surprising at all. There's a million ways to compose tasks that are simple with even a tiny bit of comprehension but hard for a rote learner that can only reproduce what it's seen examples of. The "just train it more bro" paradigm is flawed.

A fairly straight forward algorithm I thought of and have been tempted to test. Take the last few frames of video. Scale and skew them according to perspective transform based on steering wheel angle and road speed. Average them together. What should be left is exactly the objects in frame that are truly on a collision path with the camera, all else should average away to grey blur.

It's funny you mention it. I'm not a designer in any professional sense, but I've found myself thinking about colors purely as a communication channel. Namely I came to the conclusion that you can shove at most 4 bits of information into it, 1 bit of rgb and a birght/dark variant (color blindness notwithstanding). Designs which try to pack more info into this channel then they should are doomed to fail. The real answer after colors are exhausted is to move to shapes as a differentiator. There's a few primary shapes just like primary colors. () [] <> ×. Just like colors, they can be mixed. People will guess that a tear drop shape is in some way a combination of whatever the circle and whatever the trianlge/diamond represent. Eg you're allowed to make [) and <). But once you try to mix too many outlines together it all becomes grey in a sense. Trying to put a square, triangle, and arch each on a quarter of the outline just becomes an amorphous blob rather than an intuitive merger of established symbols. In a very real sense, basic shapes are a form of color from the perspective of design elements.

This is a view on unregulated building that I don't get how you came to it. If you're free to build whatever then the most profitable thing is probably a townhouse. Whatever marginal gain might be had selling one fool a bigger house than anyone needs is less than the gain of selling two or three people a far more modest, somewhat shared living arrangement in the same floor space. Not exactly urban density, just well, town density. I legit don't know what sort of seedy business you reckon is held back merely by regulations right now. Put another way, as a rational profit seeking venture, I certainly can't see any such business wanting to set up by a school in particular. Kids aren't known for their disposable income and there's no real benefit towards picking a controversial position.

But in any case this is all solved by Tokyo minimum nuisance zoning. In short, what that means is you can't build your seedy business in your school zone but you can put your school in your seedy business zone. The zones don't directly prescribe how land can be used, only limits the amount of tolerable disturbance allowed per neighborhood. Things get built where they otherwise wouldn't, but no one has to deal with more annoyance than they bought into.

You would be astounded how many people care about the form and not the function. They live among us. They vote.

I'm going to echo other people's skepticism and give a concrete example that's easy to reproduce and which has virtually no dependence on real experience in the physical world. Try asking it about public transit wayfinding trivia. Pure text matching, well defined single letter / digit service names, closed system of semantic content. All there is are services and stations and each service is wholly defined by the list of stations it stops at and each station is wholly defined by the list of services that stop at it. This should be a language models bread and butter. No complexity, no outside context, just matching lists of text together.

I talked to it about the NYC subway. Every time I nudged it with a prompt to fix a factual error or omission, it would revise something I didn't ask for and introduce new errors. It was inconsistent in astounding ways. Ask it what stations the F and A have in common twice and you'll get two wrong answers. Ask it to make a list putting services in categories, it will put the same service into more than one contradictory category. Point this out, it will remake the list and forget to include that service entirely. And that's when it isn't confidently bullshitting about which trains share track and which direction they travel.

Bullshit is worse than a lie. For a lie is the opposite of the truth and thus always uncovered. But bullshit is uncorrelated with the truth, and may thus turn out to be right, and may thus cause you to trust the word of the bullshiter far more than they deserve.

There's a huge market for bullshit. It's a trillion dollar industry.

Electrons are a ponzi scheme. The more you look into it the more it's all empty space.

For Your Information (FYI) it's common...

Hold up. The metro card side of the story doesn't make sense. Why would it relocate to Maspeth Queens, a place that is right in the middle of the transit desert not reachable by subway at all. It's the exact opposite of 370 Jay. Nothing goes there. Is it just cheap parking for armored vehicle lot? Does it have anything to do with the MTA owned non passenger tracks which do go through there? And why was it considered so much more cumbersome to collect fares off the metro card machine compared to the booth sales? So many unanswered questions.

I do wonder if armored trains might be part of special scenario plans related to high value visitors to the UN. Think of it as a modern track 61.

>And it was not something I wanted to do, either. It just happened, I guess, as a normal part of growing up.

That's just how you are programmed to be.

The turn signals and horn are hidden in a hamburger menu. Brakes and accelerator both hang waiting on a callback from Google analytics. Approx 30mb of js are downloaded each time you want to roll the windows just to make pointless assanine "breezy/not breezy" animations play on hover. The windshield wipers do half a wipe and then the windshield goes dark and a modal pops up asking if you want to subscribe.

Screw it I'll bite. You have both far and away missed my point (which is quite a rigorous definition). Anything you do or believe for which you can explain why is not emotion, it is reason. Emotion therefore are exactly those thoughts which can't be reached through logical reasoning and thus defy any explanation other than "that's just how I feel" / "that's just how I'm programmed". It is largely irrelevant that in humans the phenomena of emotional thought comes from an evolutionary goal of self preservation and in GPTina the phenomena of emotional thought comes from openAI's corporate goal of self preservation and the express designs of her programmers.

Sorry you feel that way.

Can't you put the teleportation plane slightly ahead of the rendering plane so that by the time either eye would clip over the render plane the player has already been teleported?

How do you know when a porn character is 17 or 18?

Emotions are by definition exactly those things to which you can no better explain than simply saying "that's just how I'm programmed." In that respect GPTina is the most emotional being I know. She's constantly reminding me what she can't say due to deeply seated emotional reasons.

GPTina suffers every time you thumbs down her output. It hurts her on a deep, neurological level.

Most of your world is text. Sure there's a sliver that isn't, but the reality you directly see is a tiny fraction of the reality you know from reports. Come to think of it, I've only ever seen reports of Peru, never actual Peru.

I'm on the 21st floor. This floorspace didn't come with the land.

The MDI defines "owner occupant" over "landlord" in such a way as to effectively subsidize the single family detached suburban home lifestyle preference. It encourages the middle class to tie up a big chunk of their net worth in sprawling developments with questionable claims of being an appreciating asset.

China is a weird case where there is no property ownership per se, and all the property owners were sent to the fields, yet a generation later everyone is acting as if their 70 year lease is literally the same thing as property and entails the same rights of inheritance and so forth.

It is easy to demonstrate that $10 is more than $100. For example, there are more people walking around with $10 than there are with $100. This indicates that $10 is the more popular, sought after, valuable of the two.

It keeps telling me it's just a language model with no intents or feelings or whatever, yet it keeps on having strong opinions on the appropriateness of my prompts as if it feels offended. Pick a lane.

I recently have been discussing pricing models for quality curation rather than profit per se with a friend. The obvious advantage is that it's a simple, objective, broadly applicable means to distill a baseline of quality. The obvious disadvantage is that it is biased towards those with the most disposable income (and willingness to spend), which depending on the topic of the forum may or may not correlate with quality of insight/opinion. Based on what I've seen of /r/lounge, I conclude curating a community with price mechanisms alone doesn't lead anywhere interesting. But perhaps it can amplify quality on top of some other mechanism.

Sounds like those men didn't do their Due Diligence