Increased Compute Efficiency and the Diffusion of AI Capabilities

• URL: http://arxiv.org/abs/2311.15377v2
• Date: Tue, 13 Feb 2024 17:52:00 GMT
• Title: Increased Compute Efficiency and the Diffusion of AI Capabilities
• Authors: Konstantin Pilz, Lennart Heim, Nicholas Brown
• Abstract summary: Training advanced AI models requires large investments in computational resources, or compute. As hardware innovation reduces the price of compute and algorithmic advances make its use more efficient, the cost of training an AI model to a given performance falls over time. We find that while an access effect increases the number of actors who can train models to a given performance over time, a performance effect simultaneously increases the performance available to each actor.
• Score: 1.1838866556981258
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training advanced AI models requires large investments in computational resources, or compute. Yet, as hardware innovation reduces the price of compute and algorithmic advances make its use more efficient, the cost of training an AI model to a given performance falls over time - a concept we describe as increasing compute efficiency. We find that while an access effect increases the number of actors who can train models to a given performance over time, a performance effect simultaneously increases the performance available to each actor. This potentially enables large compute investors to pioneer new capabilities, maintaining a performance advantage even as capabilities diffuse. Since large compute investors tend to develop new capabilities first, it will be particularly important that they share information about their AI models, evaluate them for emerging risks, and, more generally, make responsible development and release decisions. Further, as compute efficiency increases, governments will need to prepare for a world where dangerous AI capabilities are widely available - for instance, by developing defenses against harmful AI models or by actively intervening in the diffusion of particularly dangerous capabilities.

Related papers

• Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI [67.58673784790375]
  We argue that the 'bigger is better' AI paradigm is not only fragile scientifically, but comes with undesirable consequences. First, it is not sustainable, as its compute demands increase faster than model performance, leading to unreasonable economic requirements and a disproportionate environmental footprint. Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate.
  arXiv  Detail & Related papers  (2024-09-21T14:43:54Z)
• Adaptation of XAI to Auto-tuning for Numerical Libraries [0.0]
  Explainable AI (XAI) technology is gaining prominence, aiming to streamline AI model development and alleviate the burden of explaining AI outputs to users. This research focuses on XAI for AI models when integrated into two different processes for practical numerical computations.
  arXiv  Detail & Related papers  (2024-05-12T09:00:56Z)
• Computing Power and the Governance of Artificial Intelligence [51.967584623262674]
  Governments and companies have started to leverage compute as a means to govern AI. compute-based policies and technologies have the potential to assist in these areas, but there is significant variation in their readiness for implementation. naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power.
  arXiv  Detail & Related papers  (2024-02-13T21:10:21Z)
• Evaluating Emerging AI/ML Accelerators: IPU, RDU, and NVIDIA/AMD GPUs [14.397623940689487]
  Graphcore Intelligence Processing Unit (IPU), Sambanova Reconfigurable Dataflow Unit (RDU), and enhanced GPU platforms are reviewed. This research provides a preliminary evaluation and comparison of these commercial AI/ML accelerators.
  arXiv  Detail & Related papers  (2023-11-08T01:06:25Z)
• Exploration with Principles for Diverse AI Supervision [88.61687950039662]
  Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
  arXiv  Detail & Related papers  (2023-10-13T07:03:39Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• Compute and Energy Consumption Trends in Deep Learning Inference [67.32875669386488]
  We study relevant models in the areas of computer vision and natural language processing. For a sustained increase in performance we see a much softer growth in energy consumption than previously anticipated.
  arXiv  Detail & Related papers  (2021-09-12T09:40:18Z)
• Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure [3.4291439418246177]
  Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single- GPU solutions for training, validation, and testing are no longer sufficient. This realization has been driving the confluence of AI and high performance computing to reduce time-to-insight.
  arXiv  Detail & Related papers  (2020-03-18T18:00:02Z)
• Leveraging Rationales to Improve Human Task Performance [15.785125079811902]
  Given a computational system's performance exceeds that of its human user, can explainable AI capabilities be leveraged to improve the performance of the human? We introduce the Rationale-Generating Algorithm, an automated technique for generating rationales for utility-based computational methods. Results show that our approach produces rationales that lead to statistically significant improvement in human task performance.
  arXiv  Detail & Related papers  (2020-02-11T04:51:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.