I like using it like a rubber ducky. I even have it respond almost entirely in quacks.

Note: it’s a local model running for free. Don’t pay anyone for this slop.

“When asked about buggy AI, a common refrain is ‘it is not my code,’ meaning they feel less accountable because they didn’t write it.”

That’s… That’s so fucking cool…

You said open source. Open source is a type of licensure.

The entire point of licensure is legal pedantry.

And as far as your metaphor is concerned, pre-trained models are closer to pre-compiled binaries, which are expressly not considered Open Source according to the OSD.

From the approach section:

A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.

This is not sufficient data information to recreate the model.

From the training data section:

The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. As discussed in the accompanying paper, we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language.

This is also insufficient data information and links to the paper itself for that data information.

Additionally, model cards =/= data cards. It’s an important distinction in AI training.

There are guides on how to Finetune the model yourself: https://huggingface.co/blog/fine-tune-whisper

Fine-tuning is not re-creating the model. This is an important distinction.

The OSAID has a pretty simple checklist for the OSAID definition: https://opensource.org/deepdive/drafts/the-open-source-ai-definition-checklist-draft-v-0-0-9

To go through the list of materials required to fit the OSAID:

Datasets Available under OSD-compliant license

Whisper does not provide the datasets.

Research paper Available under OSD-compliant license

The research paper is available, but does not fit an OSD-compliant license.

Technical report Available under OSD-compliant license

Whisper does not provide the technical report.

Data card Available under OSD-compliant license

Whisper provides the model card, but not the data card.

Oh and for the OSAID part, the only issue stopping Whisper from being considered open source as per the OSAID is that the information on the training data is published through arxiv, so using the data as written could present licensing issues.

The problem with just shipping AI model weights is that they run up against the issue of point 2 of the OSD:

The program must include source code, and must allow distribution in source code as well as compiled form. Where some form of a product is not distributed with source code, there must be a well-publicized means of obtaining the source code for no more than a reasonable reproduction cost, preferably downloading via the Internet without charge. The source code must be the preferred form in which a programmer would modify the program. Deliberately obfuscated source code is not allowed. Intermediate forms such as the output of a preprocessor or translator are not allowed.

AI models can’t be distributed purely as source because they are pre-trained. It’s the same as distributing pre-compiled binaries.

It’s the entire reason the OSAID exists:

1. The OSD doesn’t fit because it requires you distribute the source code in a non-preprocessed manner.
2. AIs can’t necessarily distribute the training data alongside the code that trains the model, so in order to help bridge the gap the OSI made the OSAID - as long as you fully document the way you trained the model so that somebody that has access to the training data you used can make a mostly similar set of weights, you fall within the OSAID

Edit: also the information about the training data has to be published in an OSD-equivalent license (such as creative Commons) so that using it doesn’t cause licensing issues with research paper print companies (like arxiv)

Whisper’s code and model weights are released under the MIT License. See LICENSE for further details. So that definitely meets the Open Source Definition on your first link.

Model weights by themselves do not qualify as “open source”, as the OSAID qualifies. Weights are not source.

Additional WER/CER metrics corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4 of the paper, as well as the BLEU (Bilingual Evaluation Understudy) scores for translation in Appendix D.3.

This is not training data. These are testing metrics.

Edit: additionally, assuming you might have been talking about the link to the research paper. It’s not published under an OSD license. If it were this would qualify the model.

Those aren’t open source, neither by the OSI’s Open Source Definition nor by the OSI’s Open Source AI Definition.

The important part for the latter being a published listing of all the training data. (Trainers don’t have to provide the data, but they must provide at least a way to recreate the model given the same inputs).

Data information: Sufficiently detailed information about the data used to train the system, so that a skilled person can recreate a substantially equivalent system using the same or similar data. Data information shall be made available with licenses that comply with the Open Source Definition.

They are model-available if anything.

Those aren’t open source, neither by the OSI’s Open Source Definition nor by the OSI’s Open Source AI Definition.

The important part for the latter being a published listing of all the training data. (Trainers don’t have to provide the data, but they must provide at least a way to recreate the model given the same inputs).

Data information: Sufficiently detailed information about the data used to train the system, so that a skilled person can recreate a substantially equivalent system using the same or similar data. Data information shall be made available with licenses that comply with the Open Source Definition.

They are model-available if anything.

LLMs as they stand are already approaching the improvement flatline portion of the sigma curve due to marginal data requirements increasing exponentially.

It’s a known problem in the actual AI research field that nobody in private industry likes to talk about.

If it scores 40% this year it’ll marginally increase by 10% next year then 5% 3 years later and so on.

AI doesn’t follow Moore’s law.

You’re anthropomorphizing LLMs.

There’s a philosophical and neuroscuence concept called “Qualia,” which helps define the human experience. LLMs have no Qualia.

Yea I should have put an asterisk.

I’d go further and say you should ask a pharmacist about questions like these. Even better is to ask a compounding pharmacist, as a doctor or retail pharmacist might just recite the pharmacopeia to you while a compounding pharmacist will probably explain in more detail (likely as a play to offer their services lol).

So it technically depends on the extended release formulation, so from a layman’s perspective, yes you should likely ask your doctor or even better ask a compounding pharmacist (as a general rule if you have questions about medication you’re better off asking a pharmacist rather than a doctor.)

Given that…

From a technical perspective the only definition of extended release is a lag phase after ingestion. This means there’s no immediately discernable difference between delayed release through anti-dissolution coating and slow dissolution through a hard-to-dissolve substance. (Even when you read something like two different pills saying delayed release vs extended release, there’s no legal difference and the FDA doesn’t give a fuck about the naming. This might be different in other countries so Americans benefit from other Country’s health systems in naming. I’m not sure.)

Coating-type pill formulations should not be crushed.

Suspension-type formulations actually can be crushed to a certain degree. Typically humans aren’t going at the pills like crazy in a mortar and pestle and don’t have the strength to separate the suspension properly so it’ll still have a slowed release effect. But yea if you smash them too hard then yea you can actually mess up the way that works.

By the way if anyone’s curious yes you can crush up Viagra, put it in Vaseline, and use it as a cream.

Yes you can put it there, and yes it’s effective.

I’ve done it multiple times for people that would regularly go over that five hour limit, because skin absorption is slower and weaker than intestinal absorption.

Only EC(Enteric Coated) pills and capsule interiors cause they protect the pill against stomach acid until it reaches the intestine.

If it’s got a shiny exterior like an Advil it’s Enteric coated. If it’s a capsule with EC granules you’re gonna have a hard time crushing it anyway.

You’re probably not going to have a problem with those though because Enteric coating is super sweet and capsules are usually neutral in flavor, so there’s not much reason to try and hide the bitterness. For everything else there’s no real functional difference between a smashed pill and a whole pill in your stomach.

Edit: You should definitely consult with a pharmacist or even better a compounding pharmacist about this if you’re not sure what kind of pill you’re looking at, though.

Former pharmacy tech here, 8 years compounding pharmacy.

Get a mortar and pestle, crush it up and put it in whatever.

If not then use yogurt or something gooey.

How do you do this with a tablet? Can you buy like a wheel sensor or something?

Unless something has changed recently you still have to submit builds to Nvidia to have them train the DLSS kernel for you, so FSR is substantially easier to integrate.

This is why I think eventually FSR will win over DLSS in the end, despite DLSS having better performance.

I have a feeling a lot of these demos were just trying to game that very “new and trending” page, and this change will produce fewer demos.