# forck.live — 2–8 August 2021

> 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 2021-W31, 2–8 August 2021, 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/2021-W31
- The current issue, and the catalogue of the other weeks: https://forck.live/briefing.md
- Catalogue: https://forck.live/llms.txt

## Announcements

### Replit — Replit²

- Company: Replit (replit.com)
- Announced: 2021-08-06
- Category: not stated
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://replit.com/blog/replit-compute
- Record: https://forck.live/items/18062-replit
- Subject: Replit Agent

Replit published a guide explaining how to use its platform as a secure compute backend for specialized applications, such as building tools that generate and execute code. The post walks through creating a Nix-based compute node that can run arbitrary code via an API, using Koa.js and Python, and emphasizes the ability to install any package from the Nix registry.

### LG AI Research — LG AI Research Paper Selected for ICCV 2021

- Company: LG AI Research (lgresearch.ai)
- Announced: 2021-08-03
- Category: research-paper
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://www.lgresearch.ai/news/view?seq=133
- Record: https://forck.live/items/11093-lg-ai-research-paper-selected-for-iccv-2021
- Subject: EXAONE

Two research papers co-authored by researchers from LG AI Research's Vision Lab were selected for ICCV 2021. One paper presents a method for identifying unexpected road obstacles in urban-scene segmentation by modeling uncertainties of unknown objects for autonomous driving applications. The other paper re-examines the Deep Image Prior technique and proposes an algorithm for image denoising using only a single image without training data.

### LG AI Research — LG AI Research Paper Selected for ICCV 2021

- Company: LG AI Research (lgresearch.ai)
- Announced: 2021-08-03
- Category: research-paper
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://www.lgresearch.ai/blog/view?seq=133
- Record: https://forck.live/items/4986-lg-ai-research-paper-selected-for-iccv-2021
- Subject: EXAONE

Two papers from LG AI Research's Vision Lab were accepted at ICCV 2021: one on identifying unexpected road obstacles in urban-scene segmentation using Standardized Max Logit, and another on rethinking Deep Image Prior for denoising.