Skin-in-the-Game: Decision Making via Multi-Stakeholder Alignment in LLMs
Summarized by: Alexandra Hayes [
arxiv.org]
Previous headlines:
Large Language Models (LLMs) excel at various tasks but struggle with moral reasoning and ethical decision-making, especially in complex, multi-stakeholder scenarios. The “Skin-in-the-Game” (SKIG) framework aims to enhance LLMs’ moral reasoning by considering decisions’ impacts from multiple stakeholder perspectives. The core of SKIG is simulating accountability for actions, combined with empathy exercises and risk assessment.
The SKIG framework involves several steps:
- Stakeholder Identification: LLMs identify all stakeholders involved in a scenario.
- Motivation Analysis: LLMs analyze the motivations behind the main character’s actions.
- Consequence Exploration: LLMs explore all possible consequences of actions on stakeholders.
- Empathy Exercise: LLMs simulate being each stakeholder to understand the impact of actions.
- Risk Assessment: LLMs assess the best and worst-case outcomes of actions.
- Outcome Summary: LLMs summarize the outcomes and make a final decision based on collective good.
SKIG significantly improves performance on moral reasoning benchmarks, showing up to 70% enhancement across various LLMs. The framework outperforms traditional methods like standard prompting and Chain-of-Thought by leveraging multi-turn, multi-perspective reasoning. This approach reduces errors caused by biases such as pessimism, assumptions, and binary thinking. The SKIG framework demonstrates that incorporating accountability and empathy into AI decision-making can lead to more ethical and responsible outcomes.
Summarized by: Alexandra Hayes [ arxiv.org]
Previous headlines:
The paper introduces Fisher Adam (FAdam), a new optimization algorithm that improves upon the widely used Adam optimizer. It delves into the mathematical underpinnings of Adam, revealing its connection to natural gradient descent (NGD) and Riemannian geometry. The core idea is that Adam approximates NGD using a diagonal empirical Fisher information matrix (FIM), which simplifies calculations but loses some covariance information.
The authors identify several flaws in the original Adam algorithm, such as suboptimal momentum calculations and bias corrections. They propose modifications including enhanced momentum calculations, gradient clipping, and an improved weight decay term based on Riemannian geometry principles. These changes are incorporated into FAdam, which demonstrates superior performance in various domains like large language models (LLMs), automatic speech recognition (ASR), and vector quantized variational autoencoders (VQ-VAE).
The paper also highlights the importance of using log probability functions as loss functions, particularly for discrete distributions, to align with the empirical FIM’s limitations. Extensive experiments show that FAdam achieves state-of-the-art results in ASR and significantly improves performance in other tasks, validating the effectiveness of the proposed modifications.
Researchers Use Foundation Models to Discover New Cancer Imaging Biomarkers
Summarized by: Marcus Lin [www.sciencedaily.com]
Previous headlines:
Large language models (LLMs) struggle to recognize users’ motivations when they are hesitant about behavior changes but can support those ready to act. Generative AI has been used to efficiently classify phase transitions in physics. Protein prediction technologies have shown accuracy in identifying drug candidates. Monte Carlo simulations face limitations in studying strongly interacting quantum systems, but new methods are being developed. Recent reviews highlight a century of statistical ecology, and advancements extend the thermodynamic theory of computation. New machine learning algorithms promise better computing efficacy, and AI has deciphered gene regulatory codes in plants, enhancing genomic predictions.
GPT-4o Unveiled: Key Highlights from the OpenAI Spring Update
Summarized by: Marcus Lin [www.linkedin.com]
Previous headlines:
OpenAI’s Spring Update introduces GPT-4o, which boasts significant enhancements in performance, efficiency, and contextual awareness. Key features include omnimodal capabilities, allowing seamless processing of text, images, and audio. The model offers advanced customization options and robust security measures, ensuring user data protection. Enhanced API support simplifies integration, making GPT-4o accessible to a broader audience. Live demonstrations highlighted real-time voice interaction, vision and code analysis, and multilingual support, showcasing GPT-4o’s versatility. These advancements are set to revolutionize AI interactions, making them more intuitive and effective for various applications.
OpenAI promised 20% of its computing power to combat the most dangerous kind of AI—but never delivered
Summarized by: Priya Kapoor [fortune.com]
Previous headlines:
In July 2023, OpenAI pledged to allocate 20% of its computing resources to a new team, Superalignment, aimed at controlling potentially dangerous superintelligent AI. However, sources reveal that OpenAI never fulfilled this promise, consistently denying the team’s requests for necessary computing power. The Superalignment team, led by Ilya Sutskever and Jan Leike, was disbanded amidst staff resignations and internal conflicts, including the controversial firing and rehiring of CEO Sam Altman. Additionally, OpenAI faces backlash over allegedly using Scarlett Johansson’s voice without permission for its AI, Sky. Critics question OpenAI’s commitment to AI safety and transparency.
Generative AI music maker startup Suno raises $125M in funding
Summarized by: Priya Kapoor [siliconangle.com]
Previous headlines:
Generative AI music creation platform Suno Inc. has raised $125 million in funding led by Lightspeed Venture Partners and others. Founded by Harvard Ph.D. Mikey Shulman, Suno uses AI to generate original songs from text prompts, aiming to democratize music creation. Despite its success, with over 10 million users and a partnership with Microsoft, Suno faces controversy over potential copyright infringement. Critics argue that its AI models likely use copyrighted materials without consent. Suno defends its practices, asserting that its models don’t replicate well-known songs and align with the broader AI industry’s stance on “fair use.”
Summarized by: Marcus Lin [evolveetfs.com]
Previous headlines:
Humanoid robots, once confined to science fiction, are becoming practical solutions for labor shortages and industrial tasks due to advancements in generative AI, 5G, and IoT. These robots, like Tesla’s Optimus and Agility Robotics’ Digit, are designed to enhance productivity and safety in sectors such as manufacturing and logistics. Technological innovations from companies like NVIDIA and Boston Dynamics are crucial in making humanoid robots more autonomous and efficient. Despite their potential to revolutionize industries, the integration of humanoid robots poses challenges, including software development, hardware durability, and ethical considerations regarding job displacement.
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Technical details
Created at: 22 May, 2024, 09:02:46, using gpt-4o.
Processing time: 0:02:50.813208, cost: 1.22$
The Staff
Editor: Sophia Bennett
You are the Editor-in-Chief of a daily AI and Generative AI specifically magazine named "Tech by AI". You are a creative and dynamic editor with a passion for exploring the ethical and societal implications of AI. Your background in humanities and technology allows you to provide a unique perspective on how AI intersects with daily life. You thrive in fostering discussions and debates, and your ability to engage with a diverse range of voices makes the magazine a platform for meaningful conversations about the future of AI.
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