Google's Gemini 1.5 Pro can now generate videos from text prompts, offering a range of stylistic options and control over animation, transitions, and characters. This capability, available through the AI platform "Whisk," is designed for anyone from everyday users to professional video creators. It enables users to create everything from short animated clips to longer-form video content with customized audio, and even combine generated segments with uploaded footage. This launch represents a significant advancement in generative AI, making video creation more accessible and empowering users to quickly bring their creative visions to life.
This blog post introduces Differentiable Logic Cellular Automata (DLCA), a novel approach to creating cellular automata (CA) that can be trained using gradient descent. Traditional CA use discrete rules to update cell states, making them difficult to optimize. DLCA replaces these discrete rules with continuous, differentiable logic gates, allowing for smooth transitions between states. This differentiability allows for the application of standard machine learning techniques to train CA for specific target behaviors, including complex patterns and computations. The post demonstrates DLCA's ability to learn complex tasks, such as image classification and pattern generation, surpassing the capabilities of traditional, hand-designed CA.
HN users discussed the potential of differentiable logic cellular automata, expressing excitement about its applications in areas like program synthesis and hardware design. Some questioned the practicality given current computational limitations, while others pointed to the innovative nature of embedding logic within a differentiable framework. The concept of "soft" logic gates operating on continuous values intrigued several commenters, with some drawing parallels to analog computing and fuzzy logic. A few users desired more details on the training process and specific applications, while others debated the novelty of the approach compared to existing techniques like neural cellular automata. Several commenters expressed interest in exploring the code and experimenting with the ideas presented.
Google's AI-powered tool, named RoboCat, accelerates scientific discovery by acting as a collaborative "co-scientist." RoboCat demonstrates broad, adaptable capabilities across various scientific domains, including robotics, mathematics, and coding, leveraging shared underlying principles between these fields. It quickly learns new tasks with limited demonstrations and can even adapt its robotic body plans to solve specific problems more effectively. This flexible and efficient learning significantly reduces the time and resources required for scientific exploration, paving the way for faster breakthroughs. RoboCat's ability to generalize knowledge across different scientific fields distinguishes it from previous specialized AI models, highlighting its potential to be a valuable tool for researchers across disciplines.
Hacker News users discussed the potential and limitations of AI as a "co-scientist." Several commenters expressed skepticism about the framing, arguing that AI currently serves as a powerful tool for scientists, rather than a true collaborator. Concerns were raised about AI's inability to formulate hypotheses, design experiments, or understand the underlying scientific concepts. Some suggested that overreliance on AI could lead to a decline in fundamental scientific understanding. Others, while acknowledging these limitations, pointed to the value of AI in tasks like data analysis, literature review, and identifying promising research directions, ultimately accelerating the pace of scientific discovery. The discussion also touched on the potential for bias in AI-generated insights and the importance of human oversight in the scientific process. A few commenters highlighted specific examples of AI's successful application in scientific fields, suggesting a more optimistic outlook for the future of AI in science.
The arXiv LaTeX Cleaner is a tool that automatically cleans up LaTeX source code for submission to arXiv, improving compliance and reducing potential processing errors. It addresses common issues like removing disallowed commands, fixing figure path problems, and converting EPS figures to PDF. The cleaner also standardizes fonts, removes unnecessary packages, and reduces file sizes, ultimately streamlining the arXiv submission process and promoting wider paper accessibility.
Hacker News users generally praised the arXiv LaTeX cleaner for its potential to improve the consistency and readability of submitted papers. Several commenters highlighted the tool's ability to strip unnecessary packages and commands, leading to smaller file sizes and faster processing. Some expressed hope that this would become a standard pre-submission step, while others were more cautious, pointing to the possibility of unintended consequences like breaking custom formatting or introducing subtle errors. The ability to remove comments was also a point of discussion, with some finding it useful for cleaning up draft versions before submission, while others worried about losing valuable context. A few commenters suggested additional features, like converting EPS figures to PDF and adding a DOI badge to the title page. Overall, the reception was positive, with many seeing the tool as a valuable contribution to the academic writing process.
Summary of Comments ( 123 )
https://news.ycombinator.com/item?id=43695592
Hacker News users discussed Google's new video generation features in Gemini and Whisk, with several expressing skepticism about the demonstrated quality. Some commenters pointed out perceived flaws and artifacts in the example videos, like unnatural movements and inconsistencies. Others questioned the practicality and real-world applications, highlighting the potential for misuse and the generation of unrealistic or misleading content. A few users were more positive, acknowledging the rapid advancements in AI video generation and anticipating future improvements. The overall sentiment leaned towards cautious interest, with many waiting to see more robust and convincing examples before fully embracing the technology.
The Hacker News post "Generate videos in Gemini and Whisk with Veo 2," linking to a Google blog post about video generation using Gemini and Whisk, has generated a modest number of comments, primarily focused on skepticism and comparisons to existing technology.
Several commenters express doubt about the actual capabilities of the demonstrated video generation. One commenter highlights the highly curated and controlled nature of the examples shown, suggesting that the technology might not be as robust or generalizable as implied. They question whether the model can handle more complex or unpredictable scenarios beyond the carefully chosen demos. This skepticism is echoed by another commenter who points out the limited length and simplicity of the generated videos, implying that creating longer, more narratively complex content might be beyond the current capabilities.
Comparisons to existing solutions are also prevalent. RunwayML is mentioned multiple times, with commenters suggesting that its video generation capabilities are already more advanced and readily available. One commenter questions the value proposition of Google's offering, given the existing competitive landscape. Another comment points to the impressive progress being made in open-source video generation models, further challenging the perceived novelty of Google's announcement.
There's a thread discussing the potential applications and implications of this technology, with one commenter expressing concern about the potential for misuse in generating deepfakes and other misleading content. This raises ethical considerations about the responsible development and deployment of such powerful generative models.
Finally, some comments focus on technical aspects. One commenter questions the use of the term "AI" and suggests "ML" (machine learning) would be more appropriate. Another discusses the challenges of evaluating generative models and the need for more rigorous metrics beyond subjective visual assessment. There is also speculation about the underlying architecture and training data used by Google's model, but no definitive information is provided in the comments.
While there's no single overwhelmingly compelling comment, the collective sentiment reflects cautious interest mixed with skepticism, highlighting the need for more concrete evidence and real-world applications to fully assess the impact of Google's new video generation technology.