★ TOP STORY[ TG ]Research·68d ago

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, and action-resources that structure, clarify, develop, and promote themselves. If we want AIs that can genuinely support, collaborate with, or even comply with human agency, AI agents’ deliberations must share a “type signature” with the practices-based logic we use to reflect and act. I argue that these issues matter not just for aligning AI to grand ethical ideals like human flourishing, but also for aligning AI to core safety-properties like transparency, helpfulness, harmlessness, or corrigibility. Concepts like ’harmlessness’ or ‘corrigibility’ are unnatural -- brittle, unstable, arbitrary -- for agents who’d interpret them in terms of goals or…

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327d ago

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 human intelligence, they defy even our most basic intuitions about it. They have emerged not because they are thoughtful solutions to the problem of intelligence, but because they scaled effectively on hardware we already had. Seduced by the fruits of scale, some have come to believe that it provides a clear pathway to AGI. The most emblematic case of this is the multimodal approach, in which massive modular networks are optimized for an array of modalities that, taken together, appear general. However, I argue that this strategy is sure to fail in the near…

327dInfra#multimodalby Benjamin A. Spiegel

527d ago

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 sets and model parameter counts result in remarkable new capabilities unpredicted by existing theory. Mathematics and statistics, once the primary guides of machine learning research, now struggle to provide immediate insight into the latest breakthroughs. This is not the first time that empirical progress in machine learning has outpaced more theory-motivated approaches, yet the magnitude of recent advances has forced us to swallow the bitter pill of the “Bitter Lesson” yet again [1]. This shift has prompted speculation about mathematics’ diminished role in machine learning research moving forward. It is already…

527dResearch#trainingby Henry Kvinge

595d ago

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 future of human-AI collaboration rather than AI replacing humans, the current ways of measuring dialogue systems may be insufficient because they measure in a non-interactive fashion. Why does purposeful dialogue matter? Purposeful dialogue refers to a multi-round user-chatbot conversation that centers around a goal or intention. The goal could range from a generic one like “harmless and helpful” to more specific roles like “travel planning agent”, “psycho-therapist” or “customer service bot.” Travel planning is a simple, illustrative example. Our preferences, fellow travelers’ preference, and all the complexities of real-world situations make transmitting all information…

595dResearch#gpt#multimodal#coding#benchmarkby Kenneth Li

632d ago

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 been a mixed-bag? Thus it’s no surprise that so many conversations these days circle around an era-defining question: How do we ensure AI benefits humanity? These conversations often devolve into strident optimism or pessimism about AI, and our earnest aim is to walk a pragmatic middle path, though no doubt we will not perfectly succeed. While it’s fashionable to handwave towards “beneficial AI,” and many of us want to contribute towards its development — it’s not easy to pin down what beneficial AI concretely means in practice. This essay represents our attempt to demystify beneficial AI, through…

632d#rag#multimodalby Joel Lehman

737d ago

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, structural understanding emerges from learning next-token prediction, and agents are able to complete tasks such as translation, question answering and generating human-like prose from simple user prompts. Not surprisingly, quantitative traders have asked: can we turn these models into the next price or trade prediction [1,9,10]? That is, rather than modeling sequences of words, can we model sequences of prices or trades. This turns out to be an interesting line of inquiry that reveals much about both generative AI and financial time series modeling. Be warned this will get wonky. LLMs are known…

737d#gptby Richard Dewey

749d ago

A Brief Overview of Gender Bias in AI

AI models reflect, and often exaggerate, existing gender biases from the real world. It is important to quantify such biases present in models in order to properly address and mitigate them. In this article, I showcase a small selection of important work done (and currently being done) to uncover, evaluate, and measure different aspects of gender bias in AI models. I also discuss the implications of this work and highlight a few gaps I’ve noticed. But What Even Is Bias? All of these terms (“AI”, “gender”, and “bias”) can be somewhat overused and ambiguous. “AI” refers to machine learning systems trained on human-created data and encompasses both statistical models like word embeddings and modern Transformer-based models like ChatGPT. “Gender”, within the context of AI research, typically encompasses binary man/woman (because it is easier for computer scientists to measure) with the…

749dResearch#gpt#embeddings#safetyby Yennie Jun

760d ago

Mamba Explained

The State Space Model taking on Transformers Right now, AI is eating the world. And by AI, I mean Transformers. Practically all the big breakthroughs in AI over the last few years are due to Transformers. Mamba, however, is one of an alternative class of models called State Space Models (SSMs). Importantly, for the first time, Mamba promises similar performance (and crucially similar scaling laws) as the Transformer whilst being feasible at long sequence lengths (say 1 million tokens). To achieve this long context, the Mamba authors remove the “quadratic bottleneck” in the Attention Mechanism. Mamba also runs fast - like “up to 5x faster than Transformer fast”1. Gu and Dao, the Mamba authors write: Mamba enjoys fast inference and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence…

760dInfra#inferenceby Kola Ayonrinde

780d ago

Car-GPT: Could LLMs finally make self-driving cars happen?

In 1928, London was in the middle of a terrible health crisis, devastated by bacterial diseases like pneumonia, tuberculosis, and meningitis. Confined in sterile laboratories, scientists and doctors were stuck in a relentless cycle of trial and error, using traditional medical approaches to solve complex problems. This is when, in September 1928, an accidental event changed the course of the world. A Scottish doctor named Alexander Fleming forgot to close a petri dish (the transparent circular box you used in science class), which got contaminated by mold. This is when Fleming noticed something peculiar: all bacteria close to the moisture were dead, while the others survived. "What was that moisture made of?" wondered M. Flemming. This was when he discovered that Penicillin, the main component of the mold, was a powerful bacterial killer. This led to the groundbreaking discovery of…

780dby Jérémy Cohen

783d ago

Do text embeddings perfectly encode text?

The rise of the vector database As a result of the rapid advancement of generative AI in recent years, many companies are rushing to integrate AI into their businesses. One of the most common ways of doing this is to build AI systems that answer questions concerning information that can be found within a database of documents. Most solutions for such a problem are based on one key technique: Retrieval Augmented Generation (RAG). This is what lots of people do now as a cheap and easy way to get started using AI: store lots of documents in a database, have the AI retrieve the most relevant documents for a given input, and then generate a response to the input that is informed by the retrieved documents. These RAG systems determine document relevancy by using “embeddings”, vector representations of documents produced…

783d#rag#coding#embeddingsby Jack Morris

793d ago

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. Machine learning processes are complex, and it’s very easy to do things that will cause overfitting without it being obvious. In the 20 years or so that I’ve been working in machine learning, I’ve seen many examples of this, prompting me to write “How to avoid machine learning pitfalls: a guide for academic researchers” in an attempt to prevent other people from falling into these traps. But you don’t have to take my word for it. These issues are being increasingly reported in both the scientific and popular press. Examples include the observation that hundreds of models developed during the Covid pandemic simply don’t work, and that a water quality system deployed in…

793dTutorialby Michael Lones

835d ago

Deep learning for single-cell sequencing: a microscope to see the diversity of cells

The history of each living being is written in its genome, which is stored as DNA and present in nearly every cell of the body. No two cells are the same, even if they share the same DNA and cell type, as they still differ in the regulators that control how DNA is expressed by the cell. The human genome consists of 3 billion base pairs spread over 23 chromosomes. Within this vast genetic code, there are approximately 20,000 to 25,000 genes, constituting the protein-coding DNA and accounting for about 1% of the total genome [1]. To explore the functioning of complex systems in our bodies, especially this small coding portion of DNA, a precise sequencing method is necessary, and single-cell sequencing (sc-seq) technology fits this purpose. In 2013, Nature selected single-cell RNA sequencing as the Method of the Year…

835d#codingby Fatima Zahra El Hajji

863d ago

Salmon in the Loop

One of the most fascinating problems that a computer scientist may be lucky enough to encounter is a complex sociotechnical problem in a field going through the process of digital transformation. For me, that was fish counting. Recently, I worked as a consultant in a subdomain of environmental science focused on counting fish that pass through large hydroelectric dams. Through this overarching project, I learned about ways to coordinate and manage human-in-the-loop dataset production, as well as the complexities and vagaries of how to think about and share progress with stakeholders. Background Let’s set the stage. Large hydroelectric dams are subject to Environmental Protection Act regulations through the Federal Energy Regulatory Commission (FERC). FERC is an independent agency of the United States government that regulates the transmission and wholesale sale of electricity across the United States. The commission has jurisdiction…

863dTutorialby Kevin McCraney

926d ago

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. Of course, given The Gradient’s focus on Artificial Intelligence, we will not stop there; we will also investigate how to capture such computation with deep neural networks. Why capture classical computation? Maybe it’s worth starting by clarifying where my vested interest in classical computation comes from. Competitive programming—the art of solving problems by rapidly writing programs that need to terminate in a given amount of time, and within certain memory constraints—was a highly popular activity in my secondary school. For me, it was truly the gateway into Computer Science, and…

926dInfraby Petar Veličković

933d ago

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 — might show up uncritically in the New Yorker? More than ever, the public discourse about AI and its risks, and about what can or should be done about those risks, is horrendously muddled, conflating speculative future danger with real present-day harms, and, on the technical front, confusing large, “intelligence-approximating” models with algorithmic and statistical decision-making systems. What, then, are the stakes of progress in AI? For all the pontification about cataclysmic harm and extinction-level events, the current trajectory of so-called “alignment” research seems under-equipped — one might even say misaligned — for the reality that AI might cause suffering that is…

933dResearch#rag#safetyby Jessica Dai