In the next couple of decades, we will be able to do things that would have seemed like magic to our grandparents.
This phenomenon is not new, but it will be newly accelerated. People have become dramatically more capable over time; we can already accomplish things now that our predecessors would have believed to be impossible.
We are more capable not because of genetic change, but because we benefit from the infrastructure of society being way smarter and more capable than any one of us; in an important sense, society itself is a form of advanced intelligence. Our grandparents – and the generations that came before them – built and achieved great things. They contributed to the scaffolding of human progress that we all benefit from. AI will give people tools to solve hard problems and help us add new struts to that scaffolding that we couldn’t have figured out on our own. The story of progress will continue, and our children will be able to do things we can’t.
“As human beings, it’s very easy for us to have common sense, and apply it at the right time and adapt it to new problems,” says Ilievski, who describes his branch of computer science as “common sense AI”. But right now, AI has a “general lack of grounding in the world”, which makes that kind of basic, flexible reasoning a struggle.
AI excels at pattern recognition, “but it tends to be worse than humans at questions that require more abstract thinking”, says Xaq Pitkow, an associate professor at Carnegie Mellon University in the US, who studies the intersection of AI and neuroscience. In many cases, though, it depends on the problem.
The goal is to reduce costs by replacing traditional storyboard artists and VFX crews with AI-generated “cinematic video.” Lionsgate hopes to use this technology for both pre- and post-production processes. While the company promotes the cost-saving potential, the creative community has raised concerns, as Runway is currently facing a lawsuit over copyright infringement.
We innovate DepthCrafter, a novel video depth estimation approach, by leveraging video diffusion models. It can generate temporally consistent long depth sequences with fine-grained details for open-world videos, without requiring additional information such as camera poses or optical flow.
Loopy supports various visual and audio styles. It can generate vivid motion details from audio alone, such as non-speech movements like sighing, emotion-driven eyebrow and eye movements, and natural head movements.
It connects Nuke with the ComfyUI server, any plugin that comes out in ComfyUI can be used in nuke, rotos with sam, rescaling, image generation, inpaintins, normal generator, the nodes are IPAdapter, ControlNet, AnimateDiff, Flux etc.
“From start to finish it took just 9 hours to produce. That was so much fun to do. Lyrics: ChatGpt Song: Suno Images: Midjourney & ComfyUI Video: u/runwayml Gen-3″
The AI Risk Database captures 700+ risks extracted from 43 existing frameworks, with quotes and page numbers.
The Causal Taxonomy of AI Risks classifies how, when, and why these risks occur.
The Domain Taxonomy of AIRisks classifies these risks into seven domains (e.g., “Misinformation”) and 23 subdomains (e.g., “False or misleading information”).
The court declined to dismiss copyright infringement claims against the AI companies. The order could implicate other firms that used Stable Diffusion, the AI model at issue in the case. The case will move forward to discovery, where the artists could uncover information related to the way in which the AI firms harvested copyrighted materials that were then used to train large language models.
A paper by computer scientists Matyas Bohacek and Hany Farid with the catchy title ‘Nepotistically Trained Generative-AI Models Collapse‘ shows that training AI image generators on AI images quite quickly leads to a deterioration in the quality of output. Farid likened the phenomenon to inbreeding. “If a species inbreeds with their own offspring and doesn’t diversify their gene pool, it can lead to a collapse of the species,” he said.
The EU Artificial Intelligence (AI) Act, which went into effect on August 1, 2024.
This act implements a risk-based approach to AI regulation, categorizing AI systems based on the level of risk they pose. High-risk systems, such as those used in healthcare, transport, and law enforcement, face stringent requirements, including risk management, transparency, and human oversight.
Key provisions of the AI Act include:
Transparency and Safety Requirements: AI systems must be designed to be safe, transparent, and easily understandable to users. This includes labeling requirements for AI-generated content, such as deepfakes (Engadget).
Risk Management and Compliance: Companies must establish comprehensive governance frameworks to assess and manage the risks associated with their AI systems. This includes compliance programs that cover data privacy, ethical use, and geographical considerations (Faegre Drinker Biddle & Reath LLP) (Passle).
Copyright and Data Mining: Companies must adhere to copyright laws when training AI models, obtaining proper authorization from rights holders for text and data mining unless it is for research purposes (Engadget).
Prohibitions and Restrictions: AI systems that manipulate behavior, exploit vulnerabilities, or perform social scoring are prohibited. The act also sets out specific rules for high-risk AI applications and imposes fines for non-compliance (Passle).
For US tech firms, compliance with the EU AI Act is critical due to the EU’s significant market size
FLUX (or FLUX. 1) is a suite of text-to-image models from Black Forest Labs, a new company set up by some of the AI researchers behind innovations and models like VQGAN, Stable Diffusion, Latent Diffusion, and Adversarial Diffusion Distillation
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