"Pulsar is one of several Anduril products that has been undercover for years. It has already been deployed to multiple AORs across continents at fixed sites, on ground vehicles, and in aircraft.
RFML (Radio Frequency Machine Learning) is a game-changer for electronic warfare."
Edit: To be clear, you have read what’s not being said: no agency is funding this or taking delivery of anything. With internal R&D you can make any outlandish claims and no one can call you out on it. (Under contract R&D, your outlandish claims are tied to a statement of work.)
Multiple, you say? Do these customers have names? Contract vehicles? You know, the normal stuff you put in a press release when there’s a new delivered capability?
As I told you hours ago in response to your other false claims, some of Pulsar's contracts are publicly disclosed and go back years. This isn't a "new delivered capability", that is just another instance of you making something up that has nothing to do with reality.
Doubling down on this is really dumb, Anduril obviously wouldn't be publicly stating that Pulsar has deployed to combat if it wasn't true. Your critique isn't just wrong, it doesn't even make sense.
The press release certainly implies new. Announcement! Dateline: yesterday! First of kind! Doesn’t sound mature or established.
Not “USAF selects for phase 3 follow-on USD300MM IDIQ“.
And they don’t actually state it’s been deployed to combat. Did you notice the careful wording? They’ve been “developing it to support operational combat” etc. Not that a DoD customer actually bought and deployed it in combat.
> did you know the nordic countries are among the happiest and have some of the strongest social benefits in the world while still being wealthy?
I do wonder how much of this is due to cultural homogeneity and the benefits that follow. Will this remain the case in the presence of a larger proportion of immigrants? I don't think its a fair comparison, when people contrast Nordic countries with the US.
Many studies have found that social trust is higher and easier to maintain in homogeneous societies.
Exactly. As soon as immigration increased, crime and disorder increased. It's easy to maintain utopia when everyone looks and behaves exactly like you.
You can't prevent it. The best you can do, is prove an account belongs to a human, and that the human only has a single account, via cryptographic ZK proofs + Government issued keys or some other proof of personhood scheme. Assuming this is enforced, it would limit most abuse, and the AI would essentially be acting as an agent on behalf of the user.
Can somebody explain how this works, specifically for OCR? I understand images can be embedded into the same high dimensional space as text, but wouldn't this embedding fail to retain the exact words and sequence, since it is effectively compressed?
I wouldn’t be surprised if they do an actual OCR pass for every input image and just pass in the raw text as a part of the prompt. That plus the embedding should work well.
As far as I understand, these multi-modal models work by embedding the text/image in a shared representation space. To perform OCR on such an embedding, it would require extracting every letter, in the correct order, from the embedding. But given the embedding is a fixed size, and therefor necessarily compressed, I would expect it to loose the exactness of the underlying input, especially with images containing a lot of text. So assuming GPT-V can effectively perform OCR, how is this being done given the constraints?
Or is my understanding completely off? Perhaps it's "Translating" the image to text, by outputting a sequence of text tokens as it scans the image regions, and then the text queries (e.g. "whats funny about this") uses this translation as the context? Presumably, this is how the model handles audio input.
You're correct! Feature extractors lose fidelity and have finite attention, just like us. But we can reduce/compress the "essence" of an image, paragraph, song, etc into some combination of underlying features.
Think of a 4096x4096 pixel white image.
To hold this image in mind, does your memory load tens of millions of bits? Thankfully no! What if we add a big red circle which spans the image? Or write the chorus of All Star inside it? Ezpz! The number of "features" is comically simple.
Same thing for AI models. They discover the concept of letters, the sound of b-flats, image symmetry, turns of phrase, the conceptual distance between a "woman" an a "queen", etc. These are all natural patterns common to the data it sees. It can thus (like us!) reduce complicated input into a (fixed-size) smear of these learned, related features.
This argument holds less weight, when you consider things like iMessages, where there are network effects and no cross-platform support. Also, there are a number of apps that are only available on IOS, due to its higher market share and greater user spend. So there is a high switching cost, which is a direct function of Apple's dominant market position.
Same experience. Its half baked, and you don't know which half, until you try and implement it. I have a lot more appreciation for Android documentation, after seeing what IOS has to offer.
"Pulsar is one of several Anduril products that has been undercover for years. It has already been deployed to multiple AORs across continents at fixed sites, on ground vehicles, and in aircraft.
RFML (Radio Frequency Machine Learning) is a game-changer for electronic warfare."