Copyright at this point is largely obsolete as a tool to protect creativity.

Hi, as you suggested getting deep intuition, regarding that I am continuously learning (as fast as I can without losing details) through courses, books, and articles. I am also building projects (kinda basic but starting somewhere) with the datasets available on kaggle. Also, try to do some brainstorming about how and why some model works in my free time.

You mentioned that you had two ML related roles before without any AI related course work. I will be really glad if I could learn more about your experience. If you don't mind, can I message you privately?

I read the 30 word OP here and the jukebox blog post and have read multiple analyses of AGI vs Google. The best I can guess, you're referring to the jukebox post, which only references IP in the sentence:

> As generative modeling across various domains continues to advance, we are also conducting research into issues like bias and intellectual property rights

So, I question if you know what discussion you're replying in, if you yourself read the post, or if I'm just so confused I can't believe my own reading comprehension anymore (which could happen any day now).

The multi-part fair use test established in AGI vs Google is widely held to be applicable to AI and ML models. There are no guarantees when it comes to credible legal theories and the winds can shift after a Supreme Court decision or two, but that's the state of the art today.

Things aren't "true"/"false" in this context, unfortunately. It is commonly held by IP and copyright lawyers to be the most credible legal theory available today. The multi-part test for fair use it created has been generally upheld as usable in AI and machine learning scenarios.

Check how the magic wand works on github and open source code for Gimp. There are probably a lot of specific terminologies for these selection algorythms, and when you have found descriptions of pros working on the field you will have access to a lot of their research.

Sometimes you can exploit asymmetrical difficulty. For example, factorising polynomials is hard but multiplying a bunch of degree 1 polynomials is easy. So you can generate data for free, and it will be very diverse. The data is such that is has a compositional structure, it will necessitate applying rules correctly without overfitting.

Taking derivatives and integrals is similar - easy one way, hard the other way. And solving the task will teach the model something about symbolic manipulation.

More generally you can use an external process, a simulator, an algorithm or a search engine to obtain a transformation of input X to Y, then learn to predict Y from X or X from Y. "Given this partial game of chess, predict who wins" and such. If X has compositional structure, solving the task would teach the model how to generalise, because you can generate as much data as necessary to force it not to overfit.

Always has been.

My question to you is, why the name Adrenaline? How did you come up with that?

[D] What is the future of NLP for the coming 24 months? Dall-E clones MidJourney and SD took 6-8 months to appear, so is that how long it will take for clones of ChatGPT? Perhaps less delay given the higher investment and market potential?

A conservative Supreme Court is very friendly to business interests, so we could see a major legal shift in this domain.

Validate samples = you feed completed outputs to humans and ask them to click correct/incorrect.

Validation samples = you all humans to manually complete the task themselves. Slow and expensive.

I know how it works! That was my whole argument. But thanks though 🤣

You might like Cthulhu

Dude looks like he's trying to get away from the flying furniture.

It's not machine learning, it's and example of classic AI. One of the first search algorithms for game trees. Even modern systems for chess such as Stockfish follow the same idea. They work on advanced versions of the alpha-beta algorithm which an advanced version of the minimax algorithm.

How is Lensa able to strongly align the generated facial features with the user's? i've been trying for a while in dream studio now using my own images, and I can't seem to generate anything decent. Tried with a variety of steps and cfg parameters, augmenting the prompt with things like keywords like hyperrealistic but zilch. Any thoughts?

There might be general existing models that i can fit to my problem, but i havent looked into it yet.

Im interested regardless if they already exist and if i can combine them with other data sources.

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>Scene Synthesis from Human Motion
>
>Large-scale capture of human motion with diverse, complex scenes, while immensely useful, is often considered prohibitively costly. Meanwhile, human motion alone contains rich information about the scene they reside in and interact with. For example, a sitting human suggests the existence of a chair, and their leg position further implies the chair’s pose. In this paper, we propose to synthesize diverse, semantically reasonable, and physically plausible scenes based on human motion. Our framework, Scene Synthesis from HUMan MotiON (SUMMON), includes two steps. It first uses ContactFormer, our newly introduced contact predictor, to obtain temporally consistent contact labels from human motion. Based on these predictions, SUMMON then chooses interacting objects and optimizes physical plausibility losses; it further populates the scene with objects that do not interact with humans. Experimental results demonstrate that SUMMON synthesizes feasible, plausible, and diverse scenes and has the potential to generate extensive human-scene interaction data for the community. https://lijiaman.github.io/projects/summon/

I am a music producer and I’ve worked in the music industry for over 10 years. Read one of my other comments where I explain a little what I’ve been through.

Don’t just pull things out of your ass.

I went off track answering you before. I was still in the mindset of music AI’s.

Your question was more generalized towards legal AI’s (legal as in law).

Answer to your question is: we must first ask what the purpose of the AI is, and what type of AI it is.

An AI that would solve the problem you mention would most likely be a classifier of some sort.

It would read cases, and depending on the input data it would generate a binary answer: guilty or not guilty.

That’s the simplest form.

A more complex version could maybe output a range of values, to more precisely dictate the sentence, such as: Social service 6 months, or prison 3 monrhs, or jail 2 years 4months 2 days 13 hours etc…

An even more complex model could maybe work as a Large Language Model much like OpenAI chatGPT or Google Lamda 2 which could output detailed information about the evidence presented, the defense presented, the circumstances, and the final decision, such as:

The defendant is found not guilty for murder because the victim had multiple times triggered psychological attacks by definition of the following medical research papers (see references) which caused defendant to enter a neuropsychotic mental state where the only perceived impression of the situation was death of defendant, which in such situation only fight would be the only solution to flight, given the layout of the room presented in the photos provided by law enforcement and the relative position between defendant and victim.

More so…

You get the idea.

Multiple models could be used to perform different tasks, such as describing by text, or visualizing by image and video, and speaking by audio.

Humans can't at all decide if they are original or not. You clearly don't know anything about playing music, but it happens VERY often that people record a cool original tune they like, show it to people and they go "you know this is mostly just song x by y in a different key, right". I can't remember how many times I've heard that but it's a lot.

Yes thats true. This is also what I thought about. Using a mixed dataset or transfer learning approaches (first train on synthetic data, then retrain on real world) should incorporate the domain knowledge. But you are right, right know thats just an hypothesis...but I will test it!

Hi, interesting! Do expert models exist for your problem already or would it be only the knowledge database you want to combine?

Thanks a lot! That looks like a very interesting approach! I will have a detailed look into it!