Follow the ideas that keep turning up in Hacker News discussions.
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan +11
Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition.
A. Pluchino. A. E. Biondo, A. Rapisarda
The largely dominant meritocratic paradigm of highly competitive Western cultures is rooted on the belief that success is due mainly, if not exclusively, to personal qualities such as talent, intelligence, skills, efforts or risk taking.
David Manheim, Scott Garrabrant
There are several distinct failure modes for overoptimization of systems on the basis of metrics.
Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai +3
Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools.
Nassim Nicholas Taleb
(The third edition corrects minor typos and adds 3 chapters synthesized from published papers plus an appendix on maximum entropy distributions.) The monograph investigates the misapplication of conventional statistical techniques to fat tailed distributions and looks for remedies, when possible.
Ofir Press, Noah A. Smith, Mike Lewis
Since the introduction of the transformer model by Vaswani et al.
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan +1
Training of large-scale deep neural networks is often constrained by the available computational resources.
Moritz Hardt, Eric Price, Nathan Srebro
We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features.
Mostafa Dehghani, Stephan Gouws, Oriol Vinyals +2
Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks.
Ryan Greenblatt, Carson Denison, Benjamin Wright +17
We present a demonstration of a large language model engaging in alignment faking: selectively complying with its training objective in training to prevent modification of its behavior out of training.
Andrew Gordon Wilson
Deep neural networks are often seen as different from other model classes by defying conventional notions of generalization.
Robert Geirhos, Patricia Rubisch, Claudio Michaelis +3
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes.
OpenAI, Josh Achiam, Steven Adler +278
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
François Chollet
To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans.
Nataliya Kosmyna, Eugene Hauptmann, Ye Tong Yuan +5
This study explores the neural and behavioral consequences of LLM-assisted essay writing.
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever +4
Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks.
Kaiming He, Xiangyu Zhang, Shaoqing Ren +1
Deeper neural networks are more difficult to train.
Dario Amodei, Chris Olah, Jacob Steinhardt +3
Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society.
Lingjiao Chen, Matei Zaharia, James Zou
GPT-3.5 and GPT-4 are the two most widely used large language model (LLM) services.
Shunyu Yao, Dian Yu, Jeffrey Zhao +4
Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference.
Siavash Golkar, Mariel Pettee, Michael Eickenberg +11
Due in part to their discontinuous and discrete default encodings for numbers, Large Language Models (LLMs) have not yet been commonly used to process numerically-dense scientific datasets.
Pankaj Mehta, David J. Schwab
Deep learning is a broad set of techniques that uses multiple layers of representation to automatically learn relevant features directly from structured data.
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum +1
Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people.
Zhe Jia, Marco Maggioni, Benjamin Staiger +1
Every year, novel NVIDIA GPU designs are introduced.
Pengfei Li, Jianyi Yang, Mohammad A. Islam +1
The growing carbon footprint of artificial intelligence (AI) has been undergoing public scrutiny.
Oriol Vinyals, Quoc Le
Conversational modeling is an important task in natural language understanding and machine intelligence.
Justin Cheng, Cristian Danescu-Niculescu-Mizil, Jure Leskovec
User contributions in the form of posts, comments, and votes are essential to the success of online communities.
Gary Marcus
Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton's now classic (2012) deep network model of Imagenet.
Miles Turpin, Julian Michael, Ethan Perez +1
Large Language Models (LLMs) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output, often referred to as chain-of-thought reasoning (CoT).
Andy Arditi, Oscar Obeso, Aaquib Syed +4
Conversational large language models are fine-tuned for both instruction-following and safety, resulting in models that obey benign requests but refuse harmful ones.