Are you sure about that? They say "full 3D shape geometry, texture, and layout" which doesn't preclude it being a splat but maybe they just use splats for visualization?
On their paper they mentioned using an "latent 3D grid" internally, which can be converted to mesh/gs using a decoder. The spatial layout of the points shown in the demo doesn’t resemble a Gaussian splat either
The linked article of the grandparent says "mesh or splats" a bunch, and as you said their examples wouldn't work if it were splats. I feel they are clearly illustrating it's ability to export meshes.
> Valve actually tried with the first Half-Life game in a decade, and even that didn't work.
Half Life Alyx is still considered to be one of the best VR games ever made and one that is still consistently recommended to new users even years after release. IMO people buy hardware because of the exclusive content. If a standard game console came out and it only had one AAA game on it, I probably wouldn't bother buying it. But if there were 3-4 games that looked really interesting it starts to look more worth the investment. Playing VR games takes a lot of committment (time / physical space / $$$) so the payoff has to be worth it or you'll lose people. With the huge amount of money spent on R&D for new hardware I think it's a valid argument to say that maybe funding content would have been a better investment in terms of ensuring platform growth.
Also, side note but not every game requires free motion. Plenty of hits had no movement or teleport etc. A lot of these were completely new (sub-)genres that didn't exist or hit the same as they would in a traditional pancake game. Plus lots of kids seem unaffected by free movement (maybe as high as 50% of users by my rough estimate).
Another way to look at it is parallel processing vs sequential processing.. our brains can make a judgement call about a thousand subtle variables and data points that we can't exactly put our fingers on unless we really dig into it, which we usually label as 'feelings', using the parallel part of our brain. The sequential (logical) part can only consider a limited number of variables at a time. I don't think either mode of thinking is inherently worse (we need both), but in our society the feelings part has traditionally been discounted as being 'illogical' by academics.. I think AI has shown us that parallel processing is actually incredibly important to thinking.
But back to the original post, I think 'having good taste' and knowing when something feels like the right solution is one of those hard to define qualities that can make the difference between average and great products (and has far reaching effects in any business).
I always like to say we aren’t rational, we _rationalize_. Much of our decision making process is subconscious / vibes / “system 2”, but we also have a strong need for a sensible narrative structure to our lives. So what hack did nature come up with? Let us make the gut decision based on a bunch of soft heuristics, then rationalize it and wrap it into a sensible narrative before it reaches our conscious mind. Lets us use our efficient system 2 thinking most of the time while avoiding all that messy cognitive dissonance that would arise from a conscious awareness of how chaotic such a system would be at the scale of… oh, say, a global civilization of such creatures ;)
All rational thought depends on its axioms/premises, and there's no rational way to define a new axiom - by definition they are asserted from scratch, so you need to depend on gut feeling to choose a good axiom over a bad one.
Rationalism "only" works to discard or modify some subset of your axioms when you discover that they lead to incompatible conclusions; which is a good outcome if you want to achieve a consistent theory, of course; but it doesn't help in selecting one consistent theory over a competing one. Again, those preferences are led by emotions.
I haven't see any details on how OpenAI's model works, but the tokens it generates aren't directly translated into pixels - those tokens are probably fed into a diffusion process which generates the actual image.. The tokens are the latent space or conditioning for the actual image generation process.
> I haven't see any details on how OpenAI's model works
Exactly. People just confidently make things up. There are many possible ways, and without details, "native generation" is just a marketing buzzword without clear definition. It's a proprietary system, there is no code release, there is no publication. We simply don't know how exactly it's done.
Open AI have both said it's native image generation and autoregressive. It has the signs of it too.
It's probably an implementation of VAR (https://arxiv.org/abs/2404.02905) - autoregressive image generation with a small twist. Rather than predict every token at the target resolution directly, start with predicting it at a small resolution, cranking it higher and higher until the desired resolution.
I agree with this. I keep harping on this, but we are sold automation instead of a power tool. If you have domain knowledge in the problem that you are solving, then LLMs can become an extremely valuable aid.
Similar to a developer who copy-pastes sections of code from StackOverflow and puts their faith in it being correct. The bigger issue with LLMs is that it's easier to be tricked into thinking you actually understand the code when your understanding may actually be quite superficial.
He's not arguing that no jobs will be displaced, he's arguing that jobs will change, engineering may become more reliable, new types of software jobs may be created.
To add some nuance to this conversation, what they are using this for is Channel recommendations, Search results, Autocomplete, and Emoji suggestion and the model(s) they train are specific to your workspace (not shared between workspaces). All of which seem like they could be handled fairly privately using some sort of vector (embeddings) search.
I am not defending Slack, and I can think of number of cases where training on slack messages could go very badly (ie, exposing private conversations, data leakage between workspaces, etc), but I think it helps to understand the context before reacting. Personally, I do think we need better controls over how our data is used and slack should be able to do better than "Email us to opt out".
> the model(s) they train are specific to your workspace (not shared between workspaces)
That's incorrect -- they're stating that they use your "messages, content, and files" to train "global models" that are used across workspaces.
They're also stating that they ensure no private information can leak from workspace to workspace in this way. It's up to you if you're comfortable with that.
From the wording, it sounds like they are conscious of the potential for data leakage and have taken steps to avoid it. It really depends on how they are applying AI/ML. It can be done in a private way if you are thoughtful about how you do it. For example:
Their channel recommendations:
"We use external models (not trained on Slack messages) to evaluate topic similarity, outputting numerical scores. Our global model only makes recommendations based on these numerical scores and non-Customer Data"
Meaning they use a non-slack trained model to generate embeddings for search. Then they apply a recommender system (which is mostly ML not an LLM). This sounds like it can be kept private.
Search results:
"We do this based on historical search results and previous engagements without learning from the underlying text of the search query, result, or proxy"
Again, this is probably a combination of non-slack trained embeddings with machine learning algos based on engagement. This sounds like it can be kept private and team specific.
autocomplete:
"These suggestions are local and sourced from common public message phrases in the user’s workspace."
I would be concerned about private messages being leaked via autocomplete, but if it's based on public messages specific to your team, that should be ok?
Emoji suggestions:
"using the content and sentiment of the message, the historic usage of the emoji [in your team]"
Again, it sounds like they are using models for sentiment analysis (which they probably didn't train themselves and even if they did, don't really leak any training data) and some ML or other algos to pick common emojis specific to your team.
To me these are all standard applications of NLP / ML that have been around for a long time.
The way it's written means this just isn't the case. They _MAY_ use it for what you have mentioned above. They explicitly say "...here are a few examples of improvements..." and "How Slack may use Customer Data" (emph mine). They also... may not? And use it for completely different things that can expose who knows what via prompt hacking.
Agreed, and that is my concern as well that if people get too comfortable with it then companies will keep pushing the bounds of what is acceptable. We will need companies to be transparent about ALL the things they are using our data for.
A lot of the dialogue that OpenAI's models write is incredibly bland.. I really think we'll need less censored models trained on how to act different roles other than just 'super safe and friendly assistant'.
