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.
Google AI is developing DolphinGemma, a tool using advanced machine learning models to help researchers understand dolphin communication. Gemma leverages large datasets of dolphin whistles and clicks, analyzing them for patterns and potential meanings. The open-source platform allows researchers to upload their own recordings, visualize the data, and explore potential connections between sounds and behaviors, fostering collaboration and accelerating the process of decoding dolphin language. The ultimate goal is to gain a deeper understanding of dolphin communication complexity and potentially facilitate interspecies communication in the future.
HN users discuss the potential and limitations of Google's DolphinGemma project. Some express skepticism about accurately decoding complex communication without understanding dolphin cognition and culture. Several highlight the importance of ethical considerations, worrying about potential misuse of such technology for exploitation or manipulation of dolphins. Others are more optimistic, viewing the project as a fascinating step towards interspecies communication, comparing it to deciphering ancient languages. A few technical comments touch on the challenges of analyzing underwater acoustics and the need for large, high-quality datasets. Several users also bring up the SETI program and the complexities of distinguishing complex communication from structured noise. Finally, some express concern about anthropomorphizing dolphin communication, cautioning against projecting human-like meaning onto potentially different forms of expression.
Gemma, Google's experimental conversational AI model, now supports function calling. This allows developers to describe functions to Gemma, which it can then intelligently use to extend its capabilities and perform actions. By providing a natural language description and a structured JSON schema for the function's inputs and outputs, Gemma can determine when a user's request necessitates a specific function, generate the appropriate JSON to call it, and incorporate the function's output into its response. This significantly enhances Gemma's ability to interact with external systems and perform tasks like booking appointments, retrieving real-time information, or controlling connected devices, all while maintaining a natural conversational flow.
Hacker News users discussed Google's Gemma 3 function calling capabilities with cautious optimism. Some praised its potential for streamlining workflows and creating more interactive applications, highlighting the improved context handling and ability to chain multiple function calls. Others expressed concerns about hallucinations, particularly with complex logic or nuanced prompts, and the potential for security vulnerabilities. Several commenters questioned the practicality for real-world applications, citing limitations in available tools and the need for more robust error handling. A few users also drew comparisons to other LLMs and their function calling implementations, suggesting Gemma's approach is a step in the right direction but still needs further development. Finally, there was discussion about the potential misuse of the technology, particularly in generating malicious code.
Google's TokenVerse introduces a novel approach to personalized image generation called multi-concept personalization. By modulating tokens within a diffusion model's latent space, users can inject multiple personalized concepts, like specific objects, styles, and even custom trained concepts, into generated images. This allows for fine-grained control over the generative process, enabling the creation of diverse and highly personalized visuals from text prompts. TokenVerse offers various personalization methods, including direct token manipulation and training personalized "DreamBooth" concepts, facilitating both explicit control and more nuanced stylistic influences. The approach boasts strong compositionality, allowing multiple personalized concepts to be seamlessly integrated into a single image.
HN users generally expressed skepticism about the practical applications of TokenVerse, Google's multi-concept personalization method for image editing. Several commenters questioned the real-world usefulness and pointed out the limited scope of demonstrated edits, suggesting the examples felt more like parlor tricks than a significant advancement. The computational cost and complexity of the technique were also raised as concerns, with some doubting its scalability or viability for consumer use. Others questioned the necessity of this approach compared to existing, simpler methods. There was some interest in the underlying technology and potential future applications, but overall the response was cautious and critical.
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.