ΑΙhub.org
 

2023 landscape – a report from the AI Now Institute


by
12 April 2023



share this:

wires at the back of a large serverImage taken from the report cover. Credit: Amba Kak and Sarah Myers West.

The AI Now Institute have released their 2023 annual report. It focusses on the concentration of power in the tech industry and highlights a set of approaches to confront this. The authors suggest both policy reforms and nonregulatory interventions. The intention of the report is to provide strategic guidance to inform future work and to ensure that the technology serves the public, not industry.

The specific themes covered in the report are:

You can read the executive summary here.

Read the report in full

The full report can be read here.
The pdf version can be downloaded here.

The report is authored by Amba Kak and Sarah Myers West, with research and editorial contributions from Alejandro Calcaño, Jane Chung, Kerry McInerney and Meredith Whittaker.

Cite as: Amba Kak and Sarah Myers West, “AI Now 2023 Landscape: Confronting Tech Power”, AI Now Institute, April 11, 2023.

About the AI Now Institute

The AI Now Institute was founded in 2017 and produces diagnosis and policy research to address the concentration of power in the tech industry. Find out more here.



tags:


Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.




            AIhub is supported by:


Related posts :



Interview with Yuki Mitsufuji: Improving AI image generation

  23 Jan 2025
Find out about two pieces of research tackling different aspects of image generation.

The Good Robot podcast: Using feminist chatbots to fight trolls with Sarah Ciston

  22 Jan 2025
Eleanor and Kerry chat to Sarah Ciston about the difficult labor of content moderation, chatbots to combat trolls, and more.

An open-source training framework to advance multimodal AI

  22 Jan 2025
EPFL researchers have developed 4M, a next-generation, framework for training versatile and scalable multimodal foundation models.

Optimizing LLM test-time compute involves solving a meta-RL problem

  20 Jan 2025
By altering the LLM training objective, we can reuse existing data along with more test-time compute to train models to do better.

Generating a biomedical knowledge graph question answering dataset

  17 Jan 2025
Introducing PrimeKGQA - a scalable approach to dataset generation, harnessing the power of large language models.

The Machine Ethics podcast: 2024 in review with Karin Rudolph and Ben Byford

Karin Rudolph and Ben Byford talk about 2024 touching on the EU AI Act, agent-based AI and advertising, AI search and access to information, conflicting goals of many AI agents, and much more.

Playbook released with guidance on creating images of AI

  15 Jan 2025
Archival Images of AI project enables the creation of meaningful and compelling images of AI.




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association