readyplayeremma

MapLibre's globe mode is both fantastic and performant. Also, it's literally just the one option to change it, and your tile formats/CRS don't need to change either.

It's the easiest way to escape from web mercator projections with no real downsides that I have discovered yet. Also, there is a built-in control if you want to offer a button to toggle between web mercator view, and globe view, since it's all just rendering changes.

The article’s real contribution is in highlighting evidence of complex behavior in living systems that often get excluded from definitions of "intelligence". In doing so, it invites deeper philosophical reflection, even if it doesn’t mount that reflection itself.

You don’t have any specific routes to random internet addresses though. And Tailscale would not either. Unless your Windows server is running BGP, all your Internet traffic is hitting the default route.

You can just run whisper on the conversations as a background job populating the text versions of all the user inputs, so it doesn't interfere with the real-time latency.

Maybe it was before, but now the entire world knows it well. To change it would be taking an incredibly recognizable name—like Twitter for instance—and then just throwing it away. ChatGPT is forever part of history, and almost everyone recognizes it immediately at this point.

> B., a senior officer who used Lavender, echoed to +972 and Local Call that in the current war, officers were not required to independently review the AI system’s assessments, in order to save time and enable the mass production of human targets without hindrances.

> “Everything was statistical, everything was neat — it was very dry,” B. said. He noted that this lack of supervision was permitted despite internal checks showing that Lavender’s calculations were considered accurate only 90 percent of the time; in other words, it was known in advance that 10 percent of the human targets slated for assassination were not members of the Hamas military wing at all.```

So, there was no human sign-off. I guess the policy itself was ordered by someone, but all the ongoing targets that were selected for assassination were solely authorized by the AI system's predictions.

This sentence is horrifically dystopian... "in order to save time and enable the mass production of human targets without hindrances"

I would like more details on Gemini's 'native' multimodal approach before assuming it is something truly unique. Even if GPT-4V were aligning a pretrained image model and pretrained language model with a projection layer like PaLM-E/LLaVA/MiniGPT-4 (unconfirmed speculation, but likely), it's not as if they are not 'natively' training the composite system of projection-aligned models.

There is nothing in any of Google's claims that preclude the architecture being the same kind of composite system. Maybe with some additional blending in of multimodal training earlier in the process than has been published so far. And perhaps also unlike GPT-4V, they might have aligned a pretrained audio model to eliminate the need for a separate speech recognition layer and possibly solving for multi-speaker recognition by voice characteristics, but they didn't even demo that... Even this would not be groundbreaking though. ImageBind from Meta demonstrated the capacity align an audio model with an LLM in the same way images models have been aligned with LLMs. I would perhaps even argue that Google skipping the natural language intermediate step between LLM output and image generation is actually in support of the position that they may be using projection layers to create interfaces between these modalities. However, this direct image generation projection example was also a capability published by Meta with ImageBind.

What seems more likely, and not entirely unimpressive, is that they refined those existing techniques for building composite multimodal systems and created something that they plan to launch soon. However, they still have crucially not actually launched it here. Which puts them in a similar position to when GPT-4 was first announced with vision capabilities, but then did not offer them as a service for quite an extended time. Google has yet to ship it, and as a result fails to back up any of their interesting claims with evidence.

Most of Google's demos here are possible with a clever interface layer to GPT-4V + Whisper today. And while the demos 'feel' more natural, there is no claim being made that they are real-time demos, so we don't know how much practical improvement in the interface and user experience would actually be possible in their product when compared to what is possible with clever combinations of GPT-4V + Whisper today.

Really awesome project!

A technical nitpick though: Routes can be asymmetric—going across one path in one direction and another for the opposite. This means that your tool potentially doesn't show the route packets from the user took to reach your server, but rather the route packets took from your server to reach the user. I believe that querying with BGP looking glass tools would allow you to construct the route in either direction, but it is maybe a bit less cool looking than the real-time traceroute that is a result of actual traffic.

LLaVA-Plus maintains a skill repository that contains a wide range of vision and vision-language pre-trained models (tools), and is able to activate relevant tools, given users’ multimodal inputs, to compose their execution results on the fly to fulfill many real-world tasks.

> People casually asserting that software is equivalent to humanity will be a non-negligible thing to consider, as irritating and poorly-founded as it seems.

> If the reproduction isn't pixel-perfect, but merely obvious and overwhelming, how do you refute that philosophically to people who refuse a distinction between 50GB and a human life?

Software equivalence to humanity is a very philosophical question that many sci-fi writers have approached. But our primary issue related to this technology does not depend on anyone making a determination there.

The challenge is that losses to livelihood from this technology are going to come from far broader impacts than copyright alone. Copyright disputes are just the first things to get everyone's attention.

Let's say we err on the side of protection of copyright, and all training data must be fully licensed, in addition to users being responsible for ensuring outputs did not accidentally reproduce something similar to a copyrighted work, even if it was part of the licensed training dataset. Great! This fixes the problem of lost value for the owners of copyrights. Companies will face a slight delay and slightly increased costs as they license content; however, in the end, model capabilities will be the same and continue to increase.

The number of jobs that actually cannot be performed without humans will continue to dwindle — livelihoods will be lost at essentially the same scale despite upholding copyrights.

The only way we can handle a technology capable of reducing most need for human labor is by focusing on planning and executing a smooth transition toward an economy with more people than jobs — aiming for minimal human suffering during this process.

A mass loss of human jobs does not need to mean a mass loss of livelihood if our society is prepared to transition to a universal basic income. After all, human life is far more than just a job. We have the opportunity for much more fulfilling lives if we plan this transition well. We must understand that this is a far larger issue than copyright - copyright disputes are just one of the first symptoms of this disruptive process.

This is the best path forward I think. And it will become increasingly sensible as things continue to evolve. AI wasn't necessary to violate copyright before, and it isn't necessary today.

The determination of copyright violation should be made against the output of the model in the event that someone uses it for commercial purposes.

If the models have a risk of generating copyrighted content, it will be up to the consumers of the system to mitigate that risk through manual review or automated checks of the output.