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The Gradient · 实时热榜

  • 01
    After Orthogonality: Virtue-Ethical Agency and AI Alignment
    Preface This essay argues that rational people don’t have goals, and that rational AIs shouldn’t have goals. Human actions are rational not because we direct them at some final ‘goals,’ but because we align actions to practices [1] : networks of actions, action-dispositions, action-evaluation criteria,Peli Grietzer
  • 02
    AGI Is Not Multimodal
    "In projecting language back as the model for thought, we lose sight of the tacit embodied understanding that undergirds our intelligence." –Terry Winograd The recent successes of generative AI models have convinced some that AGI is imminent. While these models appear to capture the essence of humanBenjamin A. Spiegel
  • 03
    Shape, Symmetries, and Structure: The Changing Role of Mathematics in Machine Learning Research
    What is the Role of Mathematics in Modern Machine Learning? The past decade has witnessed a shift in how progress is made in machine learning. Research involving carefully designed and mathematically principled architectures result in only marginal improvements while compute-intensive and engineering-first efforts that scale to ever larger training setsHenry Kvinge
  • 04
    What's Missing From LLM Chatbots: A Sense of Purpose
    LLM-based chatbots’ capabilities have been advancing every month. These improvements are mostly measured by benchmarks like MMLU, HumanEval, and MATH (e.g. sonnet 3.5, gpt-4o). However, as these measures get more and more saturated, is user experience increasing in proportion to these scores? If we envision a futureKenneth Li
  • 05
    We Need Positive Visions for AI Grounded in Wellbeing
    Introduction Imagine yourself a decade ago, jumping directly into the present shock of conversing naturally with an encyclopedic AI that crafts images, writes code, and debates philosophy. Won’t this technology almost certainly transform society — and hasn’t AI’s impact on us so far beenJoel Lehman
  • 06
    Financial Market Applications of LLMs
    The AI revolution drove frenzied investment in both private and public companies and captured the public’s imagination in 2023. Transformational consumer products like ChatGPT are powered by Large Language Models (LLMs) that excel at modeling sequences of tokens that represent words or parts of words [2]. Amazingly, structuralRichard Dewey
  • 07
    A Brief Overview of Gender Bias in AI
    A brief overview and discussion on gender bias in AIYennie Jun
  • 08
    Mamba Explained
    Is Attention all you need? Mamba, a novel AI model based on State Space Models (SSMs), emerges as a formidable alternative to the widely used Transformer models, addressing their inefficiency in processing long sequences.Kola Ayonrinde
  • 09
    Car-GPT: Could LLMs finally make self-driving cars happen?
    Exploring the utility of large language models in autonomous driving: Can they be trusted for self-driving cars, and what are the key challenges?Jérémy Cohen
  • 10
    Do text embeddings perfectly encode text?
    'Vec2text' can serve as a solution for accurately reverting embeddings back into text, thus highlighting the urgent need for revisiting security protocols around embedded data.Jack Morris
  • 11
    Why Doesn’t My Model Work?
    Have you ever trained a model you thought was good, but then it failed miserably when applied to real world data? If so, you’re in good company.Michael Lones
  • 12
    Deep learning for single-cell sequencing: a microscope to see the diversity of cells
    On the the pivotal role that Deep Learning has played as a key enabler for advancing single-cell sequencing technologies.Fatima Zahra El Hajji
  • 13
    Salmon in the Loop
    On fish counting – a complex sociotechnical problem in a field that is going through the process of digital transformation.Kevin McCraney
  • 14
    Neural algorithmic reasoning
    In this article, we will talk about classical computation : the kind of computation typically found in an undergraduate Computer Science course on Algorithms and Data Structures [1]. Think shortest path-finding, sorting, clever ways to break problems down into simpler problems, incredible ways to organise data for efficient retrieval and updates.Petar Veličković
  • 15
    The Artificiality of Alignment
    This essay first appeared in Reboot . Credulous, breathless coverage of “AI existential risk” (abbreviated “x-risk”) has reached the mainstream. Who could have foreseen that the smallcaps onomatopoeia “ꜰᴏᴏᴍ” — both evocative of and directly derived from children’s cartoons —Jessica Dai
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