Items tagged with “Thegradient”.
A rigorous case that true AGI requires embodied intelligence and physical-world grounding, not mere scaling of multimodal models. Critiques world-model hypotheses and highlights evidence that language models may rely on memorized rules rather than physics.
A concise resource outlining why multimodal, scale-driven approaches are unlikely to yield human-level AGI and why embodied world models are essential.
As progress in ML shifts toward scale and empiricism, mathematics is evolving from strict guarantees to post-hoc explanations, higher-level structure, and symmetry-aware design, spanning topology, geometry, and algebra to interpret and guide large-scale models.
Explores how mathematics remains central to ML, but its role is evolving from theory-first guarantees to geometry, symmetries, and post-hoc explanations in scale-driven AI.
Explores why purposeful, multi round dialogue matters for LLM chatbots beyond one shot prompts, and outlines training, evaluation, and implementation challenges for engineers and enterprises.
Explores purposeful dialogue in LLM chatbots, arguing multi-turn interactions better align AI with user goals and enable collaboration, especially in coding and personal assistant use cases.
A pragmatic argument to ground AI development in wellbeing science, outlining what beneficial AI could mean in practice and concrete leverage points for researchers and product teams.
A wellbeing-centered framework for beneficial AI that blends psychology, economics, and governance to outline plausible, actionable deployment visions that support individual and societal flourishing.
An in-depth look at how large language models (LLMs) relate to financial markets: token counts, predictability limits, multimodal approaches, synthetic data, residualization, and practical implications for quant and fundamental work.
Overview of how LLMs can be applied to financial markets, including autoregressive modeling of price data, multi-modal inputs, residualization, synthetic data, and multi-horizon predictions, with caveats about market efficiency.
Curated overview of key studies showing how AI systems reproduce and amplify gender bias, with concrete measures, benchmarks, and mitigations across embeddings, vision, NLP, and generative models.
Survey of key work measuring gender bias in AI, across word embeddings, coreference, facial recognition, QA benchmarks, and image generation; discusses mitigation, gaps, and the need for robust auditing.
Mamba replaces Transformer attention with State Space Models (SSMs) to achieve linear scaling in sequence length, enabling fast inference and competitive language performance up to million-token contexts.
A deep dive into Mamba, a State Space Model (SSM) based backbone designed for long-context sequences, offering Transformer-like performance with improved efficiency.
Explores how LLMs can augment perception, planning, and scenario generation for autonomous vehicles, highlighting approaches like Talk2BEV and GAIA‑1, while discussing trust, reliability, and deployment considerations.
Survey of how large language models (LLMs) could augment autonomous driving across perception, planning, and generation, with examples, challenges, and early results.
New work demonstrates vec2text can recover substantial input from text embeddings, challenging assumptions that embeddings are a secure, lossy representation.
Examines whether text embeddings can be inverted to recover original text, detailing vec2text and its impact on privacy in embedding-based systems.
A practical guide to why models that look good in testing fail in the real world — covering misleading data, hidden variables, spurious correlations, mislabelling, data leakage, and concrete checks to avoid them.
An in-depth look at common reasons machine learning models fail in real-world data, with practical guidance and a path to safer, more reliable evaluation.
Explores how deep learning powers single-cell sequencing analysis—from data structure and autoencoders to multimodal integration and spatial transcriptomics.
Overview of how deep learning enables single-cell sequencing (sc-seq) insights, including autoencoders for dimensionality reduction, multimodal data integration, and spatial transcriptomics.
Exploring human-in-the-loop computer vision for fish passage counting at FERC-regulated hydroelectric facilities, including workflow decisions, regulatory context, and practical performance goals.
Explores the sociotechnical challenges of fish counting at hydroelectric dams and how human expertise integrates with machine learning in regulated environments.
Explores how graph neural networks can learn to execute classical algorithms through algorithmic alignment, improving reliability, generalisation, and out-of-distribution performance.
Explores how neural networks can capture classical computation (e.g., Bellman–Ford) via graph neural networks and the idea of algorithmic alignment to improve learning to execute algorithms.
A critical examination of how mainstream x-risk discourse, commercialization, and RLHF/RLAIF shape AI safety priorities—and why this may diverge from concrete, present-day harms.
A substantial critique of AI alignment debates, arguing that current alignment work is misaligned with real-world harms and driven by product incentives. Examines RLHF/RLAIF, intent alignment, and the HH H preference model as described in The Gradient essay.
Foundational concepts on AI consciousness, including Block's classifications, Chalmers' easy vs hard problems, and the AI Moral Status Problem, explained for practitioners.
Overview of AI consciousness debates, definitions, and moral status implications. Highlights Block's distinctions (self-, monitoring-, access-, p-consciousness) and Chalmers' easy vs hard problems; discusses need for cross-disciplinary work.