imagen does not allow for chaining together prompts, phenaki (also google) does.

If the question is about being able to create full movies then phenaki would be the tech needed to build on because it allows direction of entire scenes via temporally consistent context aware chaining rather than individual shots of concepts

after seeing https://phenaki.video/

hard to say, ask me again when we see the next generation that surpasses the above.

no way to tell how easy temporal consistency is going to be, bad temporal consistency will relegate it to youtube and 'b-movie'/'low budget TV'

This is not like moving from the 80's into the 90's where bad CG was the best they had and so it was used. This needs to beat out the best of the best CG graphics otherwise the movie will be panned for shitty effects.

it could go through a period in comping and effects. Stuff like 'style matching' two clips that sorts out the lighting and color grade, or adding muzzle flashes and squibs by painting a mask on a keyframe and let the AI handle the rest, so adding to existing structure rather than making it all up wholesale

I suppose soon we'll be seeing a 'whisper' like AI to do auto Audio Description on scenes and acting direction, to caption existing films. (using existing AD could be a good contrastive learning technique)

There is already Riffusion and HarmonAI

Chat GPT + Voice Synth will not only allow for the story lines to be written but for the voice work to be done.

I was really disappointed listening to Tod Howard and how Bethesda felt that someone realizing part way through a play though that they really wanted to play a different character, and treated starting over as having failed the player. I mean with that sort of attitude no wonder the games have been getting blander and blander.

Now think of a different future where all the interlocking storylines is all worked out ahead of time by a LLM. Where having everything be based on your current skills and previous actions 'behind the scenes'

so instead of blending things to a grey mush to 'disappoint' no one, instead you can have vibrant different adventures through the same land each time you play.

This list covers a whole range of things. From AI safety to general ML info

if you are wanting easy to digest. https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos

https://www.youtube.com/c/DrAlanDThompson/videos

if you are wanting in depth. https://www.youtube.com/c/MachineLearningStreetTalk/videos

https://www.youtube.com/c/TheInsideView/videos

https://www.youtube.com/c/YannicKilcher/videos

https://www.youtube.com/c/MachineLearningStreetTalk/videos

https://www.youtube.com/c/TheInsideView/videos

https://www.youtube.com/c/DrAlanDThompson/videos

https://www.youtube.com/@futureoflifeinstitute2787/videos

https://www.youtube.com/@RobertMilesAI/videos

https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos

https://www.youtube.com/@aisafetytalks/videos

https://www.youtube.com/c/TheRobotBrainsPodcast/videos

https://www.youtube.com/@axrp1229/videos

https://www.youtube.com/channel/UC-o9u9QL4zXzBwjvT1gmzNg/videos

https://www.talkrl.com/episodes

3D printers need to get way more plug and play for the average person to use them. I've got one, and when it's all calibrated and running and using good filament you can run it for weeks. Then comes the time to sort out the hot end change out the tube or something else and it's back to needing someone that tinkers with things.

There is also the other side, creating things to print. If it's not on thingiverse be prepared to break out the calipers and Fusion 360. Photogrammetry and AI could really help this out. take a video of the back of the remote with a missing cover and let some software handle everything and generate the STL file.

again what do you mean by that, people code new software every day.

You can ask for poems that don't exist, essays that don't exist.

All these things have had their structure extracted understood then followed to create new items.

Asking for code is the same.

>Will ChatGPT be able to write better code than any human within the next year?

A good coder needs to eat and sleep and take time to understand new technology has a limited scope in the programming languages known, has good days and bad days, has 'blocks' and is a single unit able to process problems serially at human level speed.

There's lots of things I've coded in software that have not existed before and are merely recombining structures that already exist to tackle new problems. It's why programing languages exist.

That is a 'new solution' to me. What do you mean when you say it?

read the paper, it's not that it performs better, it's that abilities that are as good as random suddenly hit a phase change and become measurably better.

you were initially saying

> only answer prompts with solutions it’s already seen before.

Lets look at an example that makes things crystal clear.

Image generators by combining concepts can come up with brand new images. Does it have to have seen dogs before in order to place one in the image? yes. does it need to have seen one that looks identical to the final dog. e.g. could you crop the image and reverse image search it and get a match. No.

The same is true with poems, summations, code, etc... it's finding patterns and creating outputs that match the requested pattern so to get back to the point of coding it could very well output code it's never seen before by ingesting enough to understand syntax.

It's seen dogs before. it outputs similar but unique dogs. It's seen code before. It outputs similar but unique code.

>It can’t actually solve problems, only answer prompts with solutions it’s already seen before.

.

>people are grasping at straws to try to explain a mechanism they don’t understand.

You are making definitive statements about things you say that experts in the field 'don't understand'

either you are claiming you know more than them or you are professing your ignorance of the matter.

Which is it.

well if that's the case plagiarism detectors should have no problem identifying the output then.

or maybe it's the case that by being trained on so much data the underlying structure about how data should be formed happens.

It would explain the emergent abilities.

https://arxiv.org/abs/2206.07682

>only answer prompts with solutions it’s already seen before.

citation needed.

I wonder how much the decision by Nvidia to stop supporting NVlink just when having the ability on consumer cards would have been really useful played into the equation?

As in, consumer hardware now taps out at 24gig

> If we have text2video + continuity (so that you can make a prompt, then make another that merges your first one with the second one to give it some kind of continuity) would be amazing.

I take it you are refering to a video from StabilityAI and not Google, because Google has already shown off 'prompt sequence' video gen

https://phenaki.video/

You can bet dollars to doughnuts that chatGPT is being run against real environments in training.

You know how it gets things wrong, and you need to keep prompting it then eventually it gets the thing correct?

That happening at scale.

Everything being recorded and every test case where it finally generates working code, that's a new piece of training data.

With just the current dataset and ability to feed known good answers back in this could bootstrap itself up in capability.

But of course it's not just using the data that's being ground out internally, it's also going to be training on all the conversations people are having with it right now.

>it makes up convincing bullshit

This is the equivalent of the critique for image generation models "It can't draw hands / It can't do text"

> any AI that only has 4000 characters of memory cannot be considered AGI or anything close to it.

From the comments of that article: https://www.cerebras.net/press-release/cerebras-systems-enables-gpu-impossible-long-sequence-lengths-improving-accuracy-in-natural-language-processing-models/

>The proliferation of NLP has been propelled by the exceptional performance of Transformer-style networks such as BERT and GPT. However, these models are extremely computationally intensive. Even when trained on massive clusters of graphics processing units (GPUs), today these models can only process sequences up to about 2,500 tokens in length. Tokens might be words in a document, amino acids in a protein, or base pairs on a chromosome. But an eight-page document could easily exceed 8,000 words, which means that an AI model attempting to summarize a long document would lack a full understanding of the subject matter. The unique Cerebras wafer-scale architecture overcomes this fundamental limitation and enables sequences up to a heretofore impossible 50,000 tokens in length.

Would that be enough?

and that is why models like this will change the world.

being able to summon information and clarification at the touch of a button in an easy to digest package.

amateurs becoming more expert in their chosen niche interests I'm sure this will provide a few cross domain 'ah-ha' moments.

all the autodidacts out there are going to have a field day.

> Wisdom of the Crowd

Something I recently saw mentioned by Ajeya Cotra is to query the LLM by re entering the previous output and asking if its correct, repeat this multiple times, take an average the answers provides a higher level of accuracy than just taking the first answer. (something that sounds weird to me)

Well ok, if viewed from the vantage point that the models are very good at doing certain things and people have not worked out how to correctly prompt/fine tune yet, it's not that weird. It's more that the base level outputs are shockingly good and then someone introduces more secret sauce and makes them even better. The problem with this is there is no saying what the limit to the models that already exist are.

>Kinda like the idea of the metaverse now.

sounds like VR Chat.

Not AI per se but in a similar vein. Pantheon

oh I'm not talking about implementation. I mean general public acceptance of the use case.

>Brands in China are looking for alternative spokespeople after many celebrities recently ran into negative press about tax evasion or personal scandals, said Sirius Wang, chief product officer and head of marketplace Greater China at Kantar.

>At least 36% of consumers had watched a virtual influencer or digital celebrity perform in the last year, according to a survey published by Kantar this fall. Twenty-one percent had watched a virtual person host an event or broadcast the news, the report said.

Kinda hard to get my head around the fact that it's so main stream there a 1/3rd of people are already exposed to it.

I'm aware that V-tubers over here are a thing but I always saw it as a far more niche.

How long till a main stream US or UK news program is presented by a virtual presenter?