Google's Gemini robotics models are built by combining Gemini's large language models with visual and robotic data. This approach allows the robots to understand and respond to complex, natural language instructions. The training process uses diverse datasets, including simulation, videos, and real-world robot interactions, enabling the models to learn a wide range of skills and adapt to new environments. Through imitation and reinforcement learning, the robots can generalize their learning to perform unseen tasks, exhibit complex behaviors, and even demonstrate emergent reasoning abilities, paving the way for more capable and adaptable robots in the future.
OpenAI has introduced two new audio models: Whisper, a highly accurate automatic speech recognition (ASR) system, and Jukebox, a neural net that generates novel music with vocals. Whisper is open-sourced and approaches human-level robustness and accuracy on English speech, while also offering multilingual and translation capabilities. Jukebox, while not real-time, allows users to generate music in various genres and artist styles, though it acknowledges limitations in consistency and coherence. Both models represent advances in AI's understanding and generation of audio, with Whisper positioned for practical applications and Jukebox offering a creative exploration of musical possibility.
HN commenters discuss OpenAI's audio models, expressing both excitement and concern. Several highlight the potential for misuse, such as creating realistic fake audio for scams or propaganda. Others point out positive applications, including generating music, improving accessibility for visually impaired users, and creating personalized audio experiences. Some discuss the technical aspects, questioning the dataset size and comparing it to existing models. The ethical implications of realistic audio generation are a recurring theme, with users debating potential safeguards and the need for responsible development. A few commenters also express skepticism, questioning the actual capabilities of the models and anticipating potential limitations.
A Hacker News user is offering to create and physically mail small, simple 3D-printed models to anyone interested. They specify a size limit (roughly a keyring's dimensions) due to printing and postage costs, and encourage requests for things like "tiny abstract sculptures," "parametric trinkets," or "little robots." The offer is primarily driven by the enjoyment of the process and the novelty of sending physical objects in the digital age.
Commenters on the "Ask HN: Anyone want models snail-mailed to them?" post largely expressed confusion about what the original poster (OP) meant by "models." Some guessed physical, scale models, leading to discussions about the logistics and cost of shipping. Others interpreted "models" as referring to AI/ML models, prompting questions about the practicalities and purpose of mailing data or code physically. Several commenters jokingly inquired about the possibility of receiving fashion models or model airplanes. The overall sentiment leaned towards curiosity and playful skepticism due to the ambiguity of the original post. A few helpful users suggested the OP clarify their intent for better engagement.
Summary of Comments ( 68 )
https://news.ycombinator.com/item?id=43557310
Hacker News commenters generally express skepticism about Google's claims regarding Gemini's robotic capabilities. Several point out the lack of quantifiable metrics and the heavy reliance on carefully curated demos, suggesting a gap between the marketing and the actual achievable performance. Some question the novelty, arguing that the underlying techniques are not groundbreaking and have been explored elsewhere. Others discuss the challenges of real-world deployment, citing issues like robustness, safety, and the difficulty of generalizing to diverse environments. A few commenters express cautious optimism, acknowledging the potential of the technology but emphasizing the need for more concrete evidence before drawing firm conclusions. Some also raise concerns about the ethical implications of advanced robotics and the potential for job displacement.
The Hacker News post "How Google built its Gemini robotics models" (linking to a Google blog post about the development of their Gemini robotics models) has generated several comments discussing various aspects of the project.
Several commenters focus on the impressive nature of the robotic demonstrations shown in the accompanying video. They express amazement at the robots' ability to perform complex, multi-step tasks like sorting blocks, opening drawers, and even using tools, all seemingly with a level of dexterity and understanding not commonly seen. Some commenters compare the advancements to previous robotics demonstrations, highlighting the significant progress made. There's a general sentiment of excitement about the potential implications of this technology.
A recurring theme in the comments is the role of simulation in training these models. Commenters discuss the advantages of simulation environments, such as allowing for faster and more diverse training data generation, and the challenges of bridging the gap between simulation and the real world. Some users question the extent to which the demonstrations are purely simulated versus performed by physical robots, and there's a healthy discussion about the limitations of relying solely on simulation.
Some commenters delve into the technical details of the model architecture, discussing the use of techniques like reinforcement learning and imitation learning. They speculate on the specifics of Google's approach, drawing comparisons to other research in the field and raising questions about the scalability and generalizability of the demonstrated capabilities.
Several comments also touch upon the potential societal impact of advanced robotics. Some express concerns about job displacement, while others emphasize the potential benefits in areas like manufacturing, healthcare, and elder care. The ethical considerations surrounding the development and deployment of such technologies are also briefly mentioned.
Finally, a few commenters express skepticism about the claims made in the blog post, questioning the reproducibility of the results and the practicality of deploying these robots in real-world scenarios. They call for more transparency and rigorous evaluation of the technology. However, the overall sentiment appears to be one of cautious optimism, recognizing the significant advancements demonstrated while acknowledging the challenges that lie ahead.