{"title":"Choose Your Weapon: Survival Strategies for Depressed AI Academics [Point of View]","authors":"Julian Togelius;Georgios N. Yannakakis","doi":"10.1109/JPROC.2024.3364137","DOIUrl":null,"url":null,"abstract":"As someone who does artificial intelligence (AI) research in a university, you develop a complicated relationship with the corporate AI research powerhouses, such as Google DeepMind, OpenAI, and Meta AI. Whenever you see one of these papers that train some kind of gigantic neural net model to do something you were not even sure a neural network could do, unquestionably pushing the state-of-the-art and reconfiguring your ideas of what is possible, you get conflicting emotions. On the one hand, it is very impressive. Good on you for pushing AI forward. On the other hand, how could we possibly keep up? As an AI academic, leading a laboratory with a few Ph.D. students and (if you are lucky) some postdoctoral fellows, perhaps with a few dozen graphics processing units (GPUs) in your laboratory, this kind of research is simply not possible to do.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":null,"pages":null},"PeriodicalIF":23.2000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10458714","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10458714/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
As someone who does artificial intelligence (AI) research in a university, you develop a complicated relationship with the corporate AI research powerhouses, such as Google DeepMind, OpenAI, and Meta AI. Whenever you see one of these papers that train some kind of gigantic neural net model to do something you were not even sure a neural network could do, unquestionably pushing the state-of-the-art and reconfiguring your ideas of what is possible, you get conflicting emotions. On the one hand, it is very impressive. Good on you for pushing AI forward. On the other hand, how could we possibly keep up? As an AI academic, leading a laboratory with a few Ph.D. students and (if you are lucky) some postdoctoral fellows, perhaps with a few dozen graphics processing units (GPUs) in your laboratory, this kind of research is simply not possible to do.
作为一名在大学从事人工智能(AI)研究的人,你会与谷歌 DeepMind、OpenAI 和 Meta AI 等公司的人工智能研究巨头建立起复杂的关系。每当你看到这些论文中的一篇,训练某种巨大的神经网络模型去做一些你甚至都不确定神经网络能做的事情,毫无疑问地推动了最先进的技术,并重构了你对可能的想法时,你就会产生矛盾的情绪。一方面,这令人印象深刻。你推动了人工智能的发展,这很好。另一方面,我们怎么可能跟得上呢?作为一名人工智能学者,领导着一个只有几个博士生和(如果幸运的话)几个博士后研究员的实验室,也许实验室里只有几十个图形处理器(GPU),这样的研究根本无法完成。
期刊介绍:
Proceedings of the IEEE is the leading journal to provide in-depth review, survey, and tutorial coverage of the technical developments in electronics, electrical and computer engineering, and computer science. Consistently ranked as one of the top journals by Impact Factor, Article Influence Score and more, the journal serves as a trusted resource for engineers around the world.