ai

“Language models are just glorified autocomplete” has been the critics’ refrain — but reinforcement learning is proving them wrong. New breakthroughs could follow.

Forscher fanden heraus, dass sich KI-Modelle selbst sabotieren, indem sie KI-generierte Daten zum Training verwenden. Sie produzieren dann immer mehr Müll.

Trotz der hohen Erwartungen führen verschiedene Hürden dazu, dass Unternehmen GenAI-Initiativen bereits in der Pilotphase abbrechen.

This page contains an annotated list of my publications/explanations about the 2023 surge of attention (hype is not always a wrong word here) society paid to Artificial Intelligence, mostly because…

One of the use cases I thought was reasonable to expect from ChatGPT and Friends (LLMs) was summarising. It turns out I was wrong. What ChatGPT isn’t summarising at all, it only looks like it…

The growing carbon footprint of artificial intelligence (AI) models, especially large ones such as GPT-3, has been undergoing public scrutiny. Unfortunately, however, the equally important and enormous water (withdrawal and consumption) footprint of AI models has remained under the radar. For example, training GPT-3 in Microsoft's state-of-the-art U.S. data centers can directly evaporate 700,000 liters of clean freshwater, but such information has been kept a secret. More critically, the global AI demand may be accountable for 4.2 -- 6.6 billion cubic meters of water withdrawal in 2027, which is more than the total annual water withdrawal of 4 -- 6 Denmark or half of the United Kingdom. This is very concerning, as freshwater scarcity has become one of the most pressing challenges shared by all of us in the wake of the rapidly growing population, depleting water resources, and aging water infrastructures. To respond to the global water challenges, AI models can, and also must, take social responsibility and lead by example by addressing their own water footprint. In this paper, we provide a principled methodology to estimate the water footprint of AI models, and also discuss the unique spatial-temporal diversities of AI models' runtime water efficiency. Finally, we highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI.

It’s important that we use accurate terminology when discussing how AI chatbots make up information

500.000 Tonnen CO2 sollen im Auftrag Microsofts in Texas aus der Luft gesaugt und in den Boden gepumpt werden. Ein Rekord.​

Researchers have been probing water consumption figures associated with Microsoft's data center in Iowa.

OpenAI is begging Parliament to allow it to use copyrighted works because it's "impossible" for the company to make money without them.

AI companies keep claiming that LLMs do reasoning. The AI can think! The AI can figure out word puzzles! The AI can pass the LSAT! You’re fired! So our good friend Diz tested this by throwing logic…

Ohne Millionen Datenarbeiter:innen würden weder sogenannte Künstliche Intelligenz noch Content-Moderation funktionieren. In einem neuen Projekt erzählen sie ihre Geschichten: von Plattformarbeiter:innen in Venezuela und Syrien über Angestellte von Outsourcing-Firmen in Kenia bis zu Content-Moderator:innen in Deutschland.

Wichtiger Beitrag über die digitale Drecksarbeit von Content-Moderator*innen. https://www.spiegel.de/netzwelt/web/digitale-drecksarbeit-die-belastende-arbeit-der-content-moderatoren-in-afrika-a-7dd3f260-8bb3-40e3-95a3-adf4137eaad3

Google researchers have come out with a new paper that warns that generative AI is ruining vast swaths of the internet with fake content.

This is the first time the carbon emissions caused by using an AI model for different tasks have been calculated.

Tech firm’s bid to remove more CO2 than it produces is being tested as AI spawns new energy-hungry datacentres

Andrew Ross Sorkin speaks with Microsoft AI CEO Mustafa Suleyman at the Aspen Ideas Festival.

Scaling will run out. The question is when.

A lot of AI GPUs consume a lot of power.

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Some data centers need as much energy as a small city, turning companies that promised a clean energy future into some of the most insatiable guzzlers of power.

ChatGPT - na logisch, oder?: Eine Keynote von Katharina Zweig auf dem University:Future Festival 2024.In Ihrem Vortrag diskutiert Professorin Zweig die Frage...

Brands and companies are growing increasingly suspicious of AI, and promising customers they won't use it in their ads or products.

Teilnehmer einer Online-Simulation schätzten einen Bot auf Basis des KI-Modells GPT-4 in 54 Prozent der Fälle als menschlich ein. GPT-3.5 kam auf 50 Prozent.

This paper embarks on an exploration into the Large Language Model (LLM) datasets, which play a crucial role in the remarkable advancements of LLMs. The datasets serve as the foundational infrastructure analogous to a root system that sustains and nurtures the development of LLMs. Consequently, examination of these datasets emerges as a critical topic in research. In order to address the current lack of a comprehensive overview and thorough analysis of LLM datasets, and to gain insights into their current status and future trends, this survey consolidates and categorizes the fundamental aspects of LLM datasets from five perspectives: (1) Pre-training Corpora; (2) Instruction Fine-tuning Datasets; (3) Preference Datasets; (4) Evaluation Datasets; (5) Traditional Natural Language Processing (NLP) Datasets. The survey sheds light on the prevailing challenges and points out potential avenues for future investigation. Additionally, a comprehensive review of the existing available dataset resources is also provided, including statistics from 444 datasets, covering 8 language categories and spanning 32 domains. Information from 20 dimensions is incorporated into the dataset statistics. The total data size surveyed surpasses 774.5 TB for pre-training corpora and 700M instances for other datasets. We aim to present the entire landscape of LLM text datasets, serving as a comprehensive reference for researchers in this field and contributing to future studies. Related resources are available at: https://github.com/lmmlzn/Awesome-LLMs-Datasets.

Angehängt: 1 Bild In https://ddinstagram.com/sloan_spencer_author/p/C48JN_TrvxO/?img_index=1 heißt es: "Warning to all writers who use Google Docs. In a Discord with other indie authors last night, one romance author explained that all her work was suspended by Google for violating their rules of sharing explicit content with her beta/alpha readers. She has no acces to these files or her other works. Please read the following screenshots and go back up your workj now on a different plattform."

Angehängt: 1 Bild Omg any day now computers will be able to add