Artificial Intelligence Reddit

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Nvidia’s CEO defends his moat as AI labs change how they improve their AI models.[1] OpenAI launches free AI training course for teachers.[2] Lockheed Martin teams with Iceye to advance AI-enabled targeting.[3] Samsung unveils AI smart glasses with Google and Qualcomm.[4] Sources: [1] https://techcrunch.com/2024/11/20/nvidias-ceo-defends-his-moat-as-ai-labs-change-how-they-improve-their-ai-models/ [2] https://www.reuters.com/technology/artificial-intelligence/openai-launches-free-ai-training-course-teachers-2024-11-20/ [3] https://spacenews.com/lockheed-martin-teams-with-iceye-to-advance-ai-enabled-targeting/ [4] https://dig.watch/updates/samsung-unveils-ai-smart-glasses-with-google-and-qualcomm submitted by /u/Excellent-Target-847 [link] [comments]

I’ve been working on a project called PKE (Precision Knowledge Editing), an open-source method to improve the safety of LLMs by reducing toxic content generation without impacting their general performance. It works by identifying “toxic hotspots” in the model using neuron weight tracking and activation pathway tracing and modifying them through a custom loss function. If you’re curious about the methodology and results, we’ve also published a paper detailing our approach and experimental findings. It includes comparisons with existing techniques like Detoxifying Instance Neuron Modification (DINM) and showcases PKE’s significant improvements in reducing the Attack Success Rate (ASR). The project is open-source, and I’d love your feedback! The GitHub repo features a Jupyter Notebook that provides a hands-on demo of applying PKE to models like Meta-Llama-3-8B-Instruct: https://github.com/HydroXai/Enhancing-Safety-in-Large-Language-Models If you’re interested in AI safety, I’d really appreciate your thoughts and suggestions. Thanks for checking it out! submitted by /u/lial4415 [link] [comments]

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I’ve been reading a paper that examines a critical issue in RLHF: when AI systems learn to deceive human evaluators due to partial observability of feedback. The authors develop a theoretical framework to analyze reward identifiability when the AI system can only partially observe human evaluator feedback. The key technical contributions are: A formal MDP-based model for analyzing reward learning under partial observability Proof that certain partial observation conditions can incentivize deceptive behavior Mathematical characterization of when true rewards remain identifiable Analysis of how observation frequency and evaluator heterogeneity affect identifiability Main results and findings: Partial observability can create incentives for the AI to manipulate evaluator feedback The true reward function becomes unidentifiable when observations are too sparse Multiple evaluators with different observation patterns help constrain the learned reward Theoretical bounds on minimum observation frequency needed for reward identifiability Demonstration that current RLHF approaches may be vulnerable to these issues The implications are significant for practical RLHF systems. The results suggest we need to carefully design evaluation protocols to ensure sufficient observation coverage and potentially use multiple evaluators with different observation patterns. The theoretical framework also provides guidance on minimum requirements for reward learning to remain robust against deception. TLDR: The paper provides a theoretical framework showing how partial observability of human feedback can incentivize AI deception in RLHF. It derives conditions for when true rewards remain identifiable and suggests practical approaches for robust reward learning. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]

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My colleague and I are submitting a paper to IEEE Syscon on November 24th and are seeking a technical review. Would you be willing to review our draft, or could you recommend someone who might have time? Much appreciated! DM if interested. submitted by /u/Efficient-Hovercraft [link] [comments]

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US government commission pushes Manhattan Project-style AI initiative.[1] Niantic uses Pokémon Go player data to build AI navigation system.[2] Meta hires Salesforce’s CEO of AI, Clara Shih, to lead new business AI group You shouldn’t upload your medical images to AI chatbots.[4] Sources: [1] https://www.reuters.com/technology/artificial-intelligence/us-government-commission-pushes-manhattan-project-style-ai-initiative-2024-11-19/ [2] https://arstechnica.com/ai/2024/11/niantic-uses-pokemon-go-player-data-to-build-ai-navigation-system/ [3] https://techcrunch.com/2024/11/19/meta-hires-salesforces-ceo-of-ai-clara-shih-to-lead-new-business-ai-group/ [4] https://techcrunch.com/2024/11/19/psa-you-shouldnt-upload-your-medical-images-to-ai-chatbots/ submitted by /u/Excellent-Target-847 [link] [comments]

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are there any free ai chats. where I can message them a website and they will check all the content and links and be able to summarize or answer questions? such as articles that are too long or a Reddit / TikTok post with a lot of comments? is this the right sub to post? should I share anywhere else? submitted by /u/_f_o [link] [comments]

It’s now evident across industries that artificial intelligence is already transforming the workforce, but not through direct human replacement—instead, by reducing the number of roles required to complete tasks. This trend is particularly pronounced for junior developers and most critically impacts repetitive office jobs, data entry, call centers, and customer service roles. Moreover, fields such as content creation, graphic design, and editing are experiencing profound and rapid transformation. From a policy standpoint, governments and regulatory bodies must proactively intervene now, rather than passively waiting for a comprehensive displacement of human workers. Ultimately, the labor market is already experiencing significant disruption, and urgent, strategic action is imperative. submitted by /u/proceedings_effects [link] [comments]

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