That may be true for OpenAI, less so for Antropic - which has much better margins. Both of these companies CEOs have come in public saying the same.
No doubt as of currently Google has a better business. But the same argument could have been said about Instagram or Whatsapp before Facebook (now Meta) acquired them.
For 1T Q4 - 1 token generated per every ~500GB memory read. So you'll need something like ~10TB/s memory for 20t/s. This is 8x5090 speed area and 16x5090 size area. HBM4 will bring us close to something really possible in home lab, but it will cost fortune for early adopters.
Speculative decoding/DFlash will help with it, but YMMV.
Edit:
Missed a part that this is A32B MoE, which means it drastically reduces amount of reads needed. Seems 20 t/s should be doable with 1TB/s memory (like 3090)
While they do make this argument, realistically anyone sending their prompt/data to an external server should assume there will be some level of retention.
And more so in particular, anyone using Darkbloom with commercial intents should only really send non-sensitive data (no tokens, customer data, ...) I'd say only classification tasks, imagine generation, etc.
My motivation was quite different, and i'd like to encourage more people to consider the same.
Often times narcissistic power grabbing (often technically incompetent) engineers become managers, like it was the case a previous team I've worked at and it was quite penalizing to the whole team.
I've realized that either i can be the one managing and try to do good, or be at the mercy of another manager; chose the first.
This is what taught me to sublimate my own ego. Overcoming the wickedness of others with patient, meditative calm can be an incredible experience. It just takes longer than a business day to play out. You've gotta think across much grander time scales. 3 steps ahead, at minimum, at all times. Burn these people out of your team. Take charge and stay focused on the customer. It often takes non technical people a little bit longer to lock onto complex problems and downstream consequences. It's taken me nearly 2 years to deal with one bad hire. All I can fantasize about is being in a position to never hire that kind of person again. The destruction some people can cause in a business is unthinkable to those who haven't seen it yet. I didn't believe these people existed until it was way too late.
I still prefer to solve technology problems, but I see a bigger and more important mission out there. Keeping the team happy and aligned on the customer is much more rewarding overall. I'd rather 5% dev time in paradise than 95% dev time in hell.
Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such systems can improve. The Darwin Gödel Machine (DGM) demonstrates open-ended self-improvement in coding by repeatedly generating and evaluating self-modified variants. Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. However, this alignment does not generally hold beyond coding domains. We introduce \textbf{hyperagents}, self-referential agents that integrate a task agent (which solves the target task) and a meta agent (which modifies itself and the task agent) into a single editable program. Crucially, the meta-level modification procedure is itself editable, enabling metacognitive self-modification, improving not only the task-solving behavior, but also the mechanism that generates future improvements. We instantiate this framework by extending DGM to create DGM-Hyperagents (DGM-H), eliminating the assumption of domain-specific alignment between task performance and self-modification skill to potentially support self-accelerating progress on any computable task. Across diverse domains, the DGM-H improves performance over time and outperforms baselines without self-improvement or open-ended exploration, as well as prior self-improving systems. Furthermore, the DGM-H improves the process by which it generates new agents (e.g., persistent memory, performance tracking), and these meta-level improvements transfer across domains and accumulate across runs. DGM-Hyperagents offer a glimpse of open-ended AI systems that do not merely search for better solutions, but continually improve their search for how to improve.
This 'self vs non-self' logic is very similar to how plants prevent self-pollination. They have a biological 'discrimination' system to recognize and reject their own genetic code.
Anyone who has a mobile phone has been tracked by their phone provider forever, with the accuracy of a couple blocks. Smartphones only bring more trackers to the equation in the form of apps.
What's the material concern to tracking that glasses add?
Surely the difference between location tracking (that still requires a warrant for the government to get access to, thus Stingrays) and the intimate visual processing and tagging that is derived from the likes of smart glasses is self explanatory, right?
To that point, the difference between geolocation and video tracking and analysis (like Flock) seems pretty obvious to me.
You can recognize a threat to national security without supporting the ideology behind it. It sounds like you are trying to to spread FUD around stronger privacy regulations. It would be a lot less funny when the shoe is on the other foot and it's not Iranian networks that's being compromised. Are you perhaps a vendor of mass surveillance systems like your username's namesake?
>I do not and will not use the internet, in any form, for any purpose.
reply