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On July 18, 2024, OpenAI released GPT-4o mini, a smaller version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o.
The Google Authenticator app for Android was originally open source, but later became proprietary. [8] Google made earlier source for their Authenticator app available on its GitHub repository; the associated development page stated: "This open source project allows you to download the code that powered version 2.21 of the application.
IDS —Intrusion Detection System. IE —Internet Explorer. IEC —International Electrotechnical Commission. IEEE —Institute of Electrical and Electronics Engineers. IETF —Internet Engineering Task Force. IFL —Integrated Facility for Linux. IGMP —Internet Group Management Protocol. IGRP —Interior Gateway Routing Protocol.
For the application developer, JAAS is a standard library that provides: a representation of identity ( Principal) and a set of credentials ( Subject) a login service that will invoke your application callbacks to ask the user things like username and password. It returns a new Subject. a service that tests if a Subject was granted a permission ...
Yes. Website. iso .org /standard /75839 .html. Portable Document Format ( PDF ), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems.
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OpenAI also makes GPT-4 available to a select group of applicants through their GPT-4 API waitlist; [239] after being accepted, an additional fee of US$0.03 per 1000 tokens in the initial text provided to the model ("prompt"), and US$0.06 per 1000 tokens that the model generates ("completion"), is charged for access to the version of the model ...
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.