interference is all you need

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No, you got it worng. Today you want to sprnikle a few mistakes to signal your authenticity. It's the new cool style. Only chatGPT and copyrighting professionals have perfect grammar.

Wait till you try running it in Firefox. It's clearly crippled on that browser.

> The models don’t come with buttons that do anything. They are tools capable only of what the software developers permit to enter the models and what users request.

If you prompt an AI with "Mickey Mouse" - no more effort than clicking a button - you'll get an image of Mickey Mouse that violates intellectual property laws. The image, or the instructions for producing it, is contained inside the model, because many copyrighted images were digitally copied into the training system by the organization that created the model. It's just not remotely the same thing as someone using the paintbrush tool in Photoshop to draw a picture of Mickey Mouse themselves.

> If we go down the road of regulating training and capacity to do x, you’ll have to file lawsuits against every artist on behalf of every copyright holder over the IP inside the artist’s head.

I don't think you have a grasp of copyright law. That is a tired and debunked argument. Humans are allowed to look at things, and remember them. Humans are not allowed to make copies of things using a machine - including loading digital copies into a computer to train an AI model - unless it's covered by a fair use exemption. Humans are not the same as machines, in the law, or in reality.

> These cases are going to fall apart

I don't think they will. Especially for the image-generating AIs, it's going to be difficult to prove fair use in the training, if the output is used to compete economically with artists or image owners like Getty, whose works have been scanned in, and affect the market for that work. That's one of the four major requirements for fair use.

WebEx can actually automatically produce transcripts of your meetings (via transcription). Seems easy enough to parse the transcript for action items and such

For someone who simply wants to use ANE (haven't bought it, just considering) for testing out bare-bones models locally (I find remotely debugging quite frustrating) for research purposes before finally training them on cloud, how good is the support with Containerization solutions like Singularity - does it even leverage ANE?

I know the speedup won't really be anything drastic, but if it helps (is faster and more resource efficient than the CPU/GPU) then that just translates to a lower time-to-iterate anyways...

So for someone using plain PyTorch (w/ a bells and whistles), how much of a pain would it be?

Like most humans do anyway?

That's beside the point, my point is that the MP3 compression comparison doesn't work, so that line of reasoning isn't applicable. Whether one use can excuse another isn't part of the argument.

>DALL-E 2 also applies diffusion in latent space

Not really in the important part. Dalle2 uses diffusion in clip-"latent"-space and then conditions the pixel-diffusion model on the result.

However they still do a full diffusion pass in pixel-space, which is more complex than doing it in latent space, as LDMs do.

Why must we wait for Q3? Our dynamic process allows us to skate the puck in real time.

Why not both?

Bayesian methods have much much more applications in the industry than reinforcement learning.

This is devastating to startups in the meeting transcription market. Solutions like Otter and Fireflies cost $15-20 per month and only have a fraction of the featureset of Teams Premium. Really interested to see how this develops.

Depends on the RL course content, if it’s just following along the RL bible, then you could do it yourself. Checkout the syllabus/slides of previous years to get an idea. The assignments/projects is where you learn the most IMO, especially for RL.

I thought this was also because you do not need so much supervised training data because you 'just' have to train the reward model in a supervised fashion?

Since it wasnt mentioned so far: RL does not require the loss/reward to be differentiable. This enables us to learn from complete generated sentences (LM sampling is not differentiable) rather than just on token-level

Damn, I had no idea, thx, will now go read papers

Oh I meant fitting to the statistics of ImageNet / the training dataset. There's always got to be some kind of normalization

I think normalization will be here to stay (maybe not the ImageNet one though), as it usually speeds up training.

lots of words, low information density per sentence

So no noticeable difference in performance in your experiments?

If you want to create a winning poker bot you need these few things.

OCR software to recognise stack sizes, position on table, cards, antes, blinds etc, all the variables.

Then it needs to translate this into something legible so that this spot can now be looked up in a GTO database.

DB gives the answer, voila you have solved poker.

Where exactly does anyone play 8-max poker? I'll never understand why people solve for 8-max its so frustrating. Also antes at over 10%. Non poker players solving poker.