AI

Apple Music is now available as an extension within ChatGPT, meaning you can search for songs, create playlists, and discover new music through...

This week, the hackerverse was full of “vibe coding”. If you’re not caught up on your AI buzzwords, this is the catchy name coined by [Andrej Karpathy] that refers to basically just YOLOing it with…

Link to: https://om.co/2026/01/21/velocity-is-the-new-authority-heres-why/

Cosmic Curiosity

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Empowering Educational Leaders with AI Strategies

We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks. This finding implies that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models. We conclude that LLMs such as GPTs exhibit traits of general-purpose technologies, indicating that they could have considerable economic, social, and policy implications.

Remarks on AI, Noise, and Creative Misuse for the Artificial Intelligence & Contemporary Creation conference, 24 May 2025 at the Jeu de Paume, Paris.

Rajapinta association published guidelines for generative AI in research to answer the increasing interest and worries related to generative AI in academic work. // Rajapinta ry on julkaissut gener…

A new paper suggests a photo can tell a recruiter much about an applicant’s personality

Position Statement on Generative AI in teaching and research   The Linguistics Discipline in the Department of Languages and Cultures at La Trobe University have developed a discipline-specific position statement on the role of generative AI in our classrooms. This statement was written to clearly communicate to our students our expectations of them and what they can expect from us. Below is the introductory statement, which links to the full statement. Linguistic subjects at La Trobe take a consistent approach to AI (particularly Generative AI and LLMs) for learning and teaching. Staff will generally limit their use of generative AI in teaching and research, and clearly communicate if such tools are used. We also encourage students to limit their use of AI for the sake of their own educational experience. Some limited use by students will be supported in specific circumstances. In all instances our use of AI in teaching and research is framed around critical evaluation of the utility of these technologies, as well as the cost and benefit of their use. For more information about this approach, including where Generative AI will be used in teaching and learning see the full statement.  If you find this statement useful, please make use of it with your own students (and let us know how that goes!) More on Gen AI: Lingthusiasm Episode: Helping computers decode sentences - Interview with Emily M. Bender Mystery AI Hype Theater 3000 The AI Con by Emily M. Bender and Alex Hanna Lingthusiasm Episode ‘Making machines learn language - Interview with Janelle Shane’

Plus, OpenAI's absurd listening tour, top AI scientists say AI is evolving beyond our control, Facebook is putting data centers in tents, and the AI bubble question — answered?

Big tech companies have exploited human language for AI gain. Now they want us to see their products as trustworthy collaborators

Yli 100-vuotiaasta osuustoiminnallisesta kaupparyhmästä on viime vuosina kuoriutunut johtava teknologiatalo, joka kilpailee kansainvälisten jättien kanssa....

Finally, a critique of AI that deconstructs the humanist subject on which most existing critiques rely

Reproducibility is a bedrock of scientific progress. However, it’s remarkably difficult to get reproducible results out of large language models. For example, you might observe that asking ChatGPT the same question multiple times provides different results. This by itself is not surprising, since getting a result from a language model involves “sampling”, a process that converts the language model’s output into a probability distribution and probabilistically selects a token. What might be more surprising is that even when we adjust the temperature down to 0This means that the LLM always chooses the highest probability token, which is called greedy sampling. (thus making the sampling theoretically deterministic), LLM APIs are still not deterministic in practice (see past discussions here, here, or here). Even when running inference on your own hardware with an OSS inference library like vLLM or SGLang, sampling still isn’t deterministic (see here or here).

The first AI-generated model to appear in the pages of Vogue is seen in an advert for Guess.

"Generators like xAI's emit huge amounts of nitrogen dioxide, a highly reactive gas which causes irreversible respiratory damage over time. And that's before you consider its effects on the ozone layer, or its contribution to acid rain, smog, and nutrient pollution in local soil and waterways. With 35 generators now chugging along, that's a whole chorus of turbines spewing the toxic gas into low-income, minority-led communities 24/7." https://futurism.com/elon-musk-memphis-illegal-generators

Google has announced that NotebookLM Plus, its advanced AI-powered research and note-taking tool, is now included with Google One AI Premium...

DeepSeek, a new Chinese chatbot, alarmed American political circles this week. Now, Chinese dissident artists like Ai Weiwei are crying foul.

For the past week or so, I’ve been working with a digital assistant that knows my name, my preferences for my morning routine, how I like to use Notion and Todoist, but which also knows how to control Spotify and my Sonos speaker, my Philips Hue lights, as well as my Gmail. It runs on...

Link to: https://www.instagram.com/reel/DRd2ldWkQYr

The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as "a tangle of sloppy tests [and] apples-to-oranges comparisons" (Roose, 2024). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for evaluating GenAI systems. Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI. This framework has two important implications for designing and evaluating evaluations: First, it can broaden the expertise involved in evaluating GenAI systems by enabling stakeholders with different perspectives to participate in conceptual debates. Second, it brings rigor to both conceptual and operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements.

Around two years ago, the world was inundated with news about how generative AI or large language models would revolutionize the world. At the time it was easy to get caught up in the hype, but in …

Opinion: As software power tools, AI agents may make people busier than ever before.

It might have been the year of AI "slop", but 2025 also saw the emergence of some genuinely innovative and beneficial uses for artificial intelligence.

This is a comic about AI art.

A TTRPG by Joseph Wilk where disability saves society from BigTech.