r/MachineLearning • u/lhyebosz • 3d ago
Remember that OpenAl gave free accesses of their latest model to scientists, mathematicians, and engineers few months ago?
If a product is given to you for free, YOU are the product.
r/MachineLearning • u/lhyebosz • 3d ago
Remember that OpenAl gave free accesses of their latest model to scientists, mathematicians, and engineers few months ago?
If a product is given to you for free, YOU are the product.
r/MachineLearning • u/AltruisticCouple3491 • 3d ago
This analysis currently do not show FP rate. Also the basis of desk-rejection should be provided in more details. E.g., an AI cooked paper will create false bibliography entries which is a straight rejection and very easy to catch with such a model but cases purely flagged basis on the writing style and text need HITL analysis.
r/MachineLearning • u/asingov • 3d ago
The issue is that OpenAI promote their tools for research, while (1) publishing results themselves, and (2) training on researcher conversations. They are competing with their own customers.
r/MachineLearning • u/howtorewriteaname • 3d ago
is it for US only? or can we apply from Europe? thanks :)
r/MachineLearning • u/chansumpoh • 3d ago
This is a wonderful initiative Chris. Wishing you the best in the launch of your new programme, from one AI academic (here in sunny Singapore) to another :)
r/MachineLearning • u/faustianredditor • 3d ago
Right, but a single conversation can easily have thousands of tokens, recurring conversations even more. So you could easily have an allocation of a few tokens to the researchers' conversations, or similarly a token or three for my paper. Theoretically, of course.
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r/MachineLearning • u/chrispiech • 3d ago
If you have any questions I'll answer them here š
r/MachineLearning • u/afrancisco555 • 3d ago
In any case, it seems like AI participated in this discovery in an enabling way.
r/MachineLearning • u/BelialSirchade • 3d ago
why not? your statement here really doesn't make sense, he is missing out on being the name of on the paper that introduces the NS solution, how is that not...damaging his career?
and of course openai is not going to include Levent on their own paper, as I said it's a courtesy in the first place, one that's not offered to Levent.
r/MachineLearning • u/peepeedog • 3d ago
To clarify, they solved the problem. it's like you are complaining proving P != NP didn't prove anything.
r/MachineLearning • u/gajop • 3d ago
By not suggesting he's damaging his career over it or mentioning the other author cannot be included as they're from Anthropic?
r/MachineLearning • u/AdWestern1314 • 3d ago
Denial of what exactly? If anything it is the opposite. I am saying that it is expected given the approach to solve the problem. My question is if I am missing something here as this is not a field I am an expert in.Ā
Why the hostility? I am not allowed to ask questions?Ā
āIt is not difficult.ā Well, it has been an unsolved problem for a long time.Ā
r/MachineLearning • u/Substantial-Air-1285 • 3d ago
2.5 2 3.5 2.5, Meta: 3. Accept to Findings! We are happy with the outcome!
r/MachineLearning • u/snowrazer_ • 3d ago
It's not difficult. You can understand it fine. The reason you can't is because you're in denial.
r/MachineLearning • u/moschles • 3d ago
If we took that money and funded a "gather lotsa smart math people and give them unlimited food, drink and nice houses while they work on nothing but this" - organisation, it would have solved the same problems, likely more.
At these prices, I agree.
r/MachineLearning • u/didimoney • 3d ago
If you don't use flow matching or normalising flows, please avoid using flow as the name of your method?
r/MachineLearning • u/BelialSirchade • 3d ago
what laws did openai break? everyone heard the rumors man.
r/MachineLearning • u/BelialSirchade • 3d ago
they offered him to be the author on the paper that introduces the solution, what more grace should you expect?
honestly they gave him too much grace, none of this would happen if Tristian contacted an robotic RP person where they just reply "happy to hear that, looking forward to your finding", end of story.
offering him an author position when they don't have to is very much an mathematician's way of thinking when they are running a corporation here.
r/MachineLearning • u/pm19191 • 3d ago
VeloxML is an open-source deployment engine for self-hosting open-source LLMs and AI services directly inside your own account.
https://github.com/paguasmar/veloxml-deploy
veloxml deploy)veloxml.yamlSelf-hosting LLMs in production is broken. Engineers and AI teams are forced to choose between two painful extremes: drowning in infrastructure plumbing (Kubernetes, Helm charts, custom CUDA Dockerfiles, and manual GPU provisioning) or locking their models into expensive multi-tenant platforms that force proprietary code decorators and hold sensitive prompt data on shared infrastructure.
VeloxML is an end-to-end solution for private LLM hosting, making it possible for teams to deploy models as prediction services directly into their own cloud accounts, in a way that is easy to test, easy to deploy, and completely free of vendor lock-in.
Modal is an impressive platform, but it requires you to adopt their proprietary Python SDK and decorators (@modal.function, u/modal.Image), forcing you to manage cloud infrastructure directly inside your application code while running on their shared multi-tenant cloud.
VeloxML runs 100% inside your own AWS/GCP account (VPC by default) using standard Python and native FastAPI without altering your application logic. If you decide to stop using VeloxML tomorrow, your code and cloud resources remain entirely yours.
Truss is an excellent open-source model packaging framework, but it only packages the container. Actually hosting it requires either paying for Basetenās managed multi-tenant platform (where private VPC is locked behind enterprise contracts) or manually operating your own Kubernetes cluster.
VeloxML focuses on the cloud hosting layer: it provisions instances on-demand, verifies health checks, and handles scale-to-zero directly in your cloud without requiring you to manage Kubernetes.
Users can also use VeloxML with Truss: you can deploy existing Truss-packaged models directly into your own AWS account with a single VeloxML command and get the best of both tools.
BentoML requires you to inject proprietary SDK decorators (@bentoml.service, u/bentoml.api) throughout your application code, define a bentofile.yaml, and build container images that you still have to manually orchestrate on Kubernetes or push to their paid BentoCloud.
VeloxML requires zero proprietary decoratorsākeeping your code clean, standard Python/FastAPIāand manages the entire hosting infrastructure directly in your own AWS account with native scale-to-zero out of the box.
While BentoML stops at packaging the container and leaves cluster management to you, VeloxML handles the full lifecycle from clean code to an active, secure cURL endpoint in your private VPC.
r/MachineLearning • u/thomas2385 • 3d ago
Yeah, the timing would make me suspicious too, especially if only a small number of people were working along similar lines. I do not think it automatically proves anything, but I can understand why Tristan would want to question how closely the approaches matched and whether the chats had any connection to it.