# forck.live — 6–12 June 2022 > AI lab announcements, news reports and podcasts. forck.live follows what companies announce about their own products. Press may corroborate an official story; it never leads and never appears as an item of its own. A missing date stays missing. A title is the publisher's words — where a company does not publish in English, an English rendering is listed separately as `Title in English` and named as this site's translation, never as theirs. This file is one issue of the weekly archive: every confirmed first-party announcement of ISO week 2022-W23, 6–12 June 2022, newest first. The window never moves, so this URL always names the same seven days — but a post published inside them and detected later is added when it arrives, so a closed week can still gain entries. Every entry is one first-party announcement, and carries the same eight fields in this order before any others: Company · Announced · Category · Coverage · Announcement · Group · Source · Record `Source` is the company's own page the announcement was traced to and `Record` is this site's page for it. Both are safe to cite. `Coverage` is a number of outlets, or `not counted` — which means this record reached no count of its own, never that nobody wrote about it. `Category` is this tracker's own analysis, and is authoritative wherever it is stated. `not stated` means either that nothing has analysed the post yet or that the analysis placed it outside this taxonomy; neither is a verdict about the announcement. `Announcement` is whether the post is news of something the company did, rather than a how-to, a customer story, a changelog listing or a staffing note. `Group` is which of this record's groups the entry falls in, carried on the entry itself so it holds where the groups are headings and where they are not. One of: `models`, `covered`, `announcements`, `routine`. Anything after `Record` is held detail that only some announcements carry. - This week's page: https://forck.live/week/2022-W23 - The current issue, and the catalogue of the other weeks: https://forck.live/briefing.md - Catalogue: https://forck.live/llms.txt ## Announcements ### OpenAI — Techniques for training large neural networks - Company: OpenAI (openai.com) - Announced: 2022-06-09T07:00:00+00:00 - Category: not stated - Coverage: not counted - Announcement: no - Group: routine - Source: https://openai.com/index/techniques-for-training-large-neural-networks - Record: https://forck.live/items/905-techniques-for-training-large-neural-networks - Subject: GPT / ChatGPT / API OpenAI discusses techniques for training large neural networks, emphasizing the engineering and research challenges involved. ### LG AI Research — [CVPR 2022] Large Vision-Language Model: What’s Next? - Company: LG AI Research (lgresearch.ai) - Announced: 2022-06-07 - Category: research-paper - Coverage: not counted - Announcement: yes - Group: announcements - Source: https://www.lgresearch.ai/blog/view?seq=212 - Record: https://forck.live/items/4949-cvpr-2022-large-vision-language-model-what-s-next - Subject: EXAONE LG AI Research presents L-Verse, a large vision-language model that performs both text-to-image and image-to-text generation, using only 0.2% of the training data and 5% of the parameters compared to DALL-E, and will be unveiled at CVPR 2022. ### Hugging Face — Deep Q-Learning with Space Invaders - Company: Hugging Face (huggingface.co) - Announced: 2022-06-07 - Category: not stated - Coverage: not counted - Announcement: no - Group: routine - Source: https://huggingface.co/blog/deep-rl-dqn - Record: https://forck.live/items/2190-deep-q-learning-with-space-invaders - Subject: Platform This is a tutorial article on Deep Q-Learning, part of a free course, and does not announce any new product or model. ### Hugging Face — The Annotated Diffusion Model - Company: Hugging Face (huggingface.co) - Announced: 2022-06-07 - Category: not stated - Coverage: not counted - Announcement: no - Group: routine - Source: https://huggingface.co/blog/annotated-diffusion - Record: https://forck.live/items/2189-the-annotated-diffusion-model - Subject: Platform Denoising Diffusion Probabilistic Models (DDPMs) with a step-by-step implementation in PyTorch.