The Gradient

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. It proposes ‘eudaimonic rationality’ as a framework for AI alignment that shares a ‘type signature’ with human practice-based logic, addressing safety properties like transparency, corrigibility, and more.

This article argues that the multimodal scaling approach to AGI is fundamentally flawed. True general intelligence requires embodied understanding and interaction with the physical world, which current LLMs and multimodal models lack. The author critiques the assumption that language models learn world models, and proposes that genuine AGI must be built on interactive, embodied processes rather than glued-together modality modules.

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 sets

The article argues that while LLM chatbots have advanced on benchmarks, they lack a sense of purpose in dialogues. Current systems are trained to predict next tokens and fine-tuned with RLHF, leading to persona drift and poor long-horizon goal achievement. The author proposes Dialogue Action Tokens (DAT) to steer models toward purposeful interactions, and discusses future directions for monitoring and reward utilization.

This article argues that ensuring AI benefits humanity requires grounding AI development in individual and societal wellbeing. Despite the lack of a definitive definition of wellbeing, common factors like relationships, meaningful work, and positive emotions provide a foundation. It calls for positive visions, measurement of AI's impact on wellbeing, training models to support wellbeing, and deploying AI in wellbeing-aligned ways.

This article explores the potential and challenges of applying Large Language Models (LLMs) to financial markets. While LLMs excel in natural language, their application to financial time series faces issues like data scarcity, noise, and adversarial environments. The article examines possibilities such as multimodal learning, residualization, and long-context windows, and suggests synthetic data generation and fundamental analysis support as more feasible directions. Overall, a cautiously optimistic view is presented.

This article surveys research on gender bias in AI, covering word embeddings, facial recognition, coreference resolution, LLMs, and image generation. It discusses gaps, other bias types, and the philosophical question of how to 'fix' bias.

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.

Exploring the utility of large language models in autonomous driving: Can they be trusted for self-driving cars, and what are the key challenges?

The article introduces vec2text, a method that can perfectly reconstruct original text from embeddings, revealing significant security vulnerabilities in current RAG systems and vector databases.