The 16th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMusArt 2027) is accepting paper submissions.

When: 31 March – 2 April 2027
Where: Mainz, Germany
Paper Submission deadline: 1 November 2026

EvoMUSART focuses on AI and computational approaches to creativity, covering a broad range of topics, with a particular emphasis on research exploring how AI systems generate, support, evaluate, interpret, and collaborate in creative processes.

Accepted papers will be presented at the conference and published in a dedicated LNCS volume.

More information/submission details: https://www.evostar.org/2027/evomusart/

If you are working at the intersection of AI and creative practice, this may be relevant to your research. Please share with others who might be interested.

Nowadays, all popular ai chatting websites like qwen, chatgpt, gemini, claude, etc. come with a read aloud functionality that allows users to read aloud the AI's responses.

I used that feature to instead make the AI repeat back the text that i gave it -- effectively turning the platforms into text to speech tools. The voices sound really nice and it's free to use!

You can get all of these extensions by visiting ai-readers.com

5 min animated version of the whole story

I maintain MindTrial and added GPT-6.1 Sol to the current benchmark. The interesting result is a modest score improvement with substantially fewer output tokens, while Astra remains ahead on this suite.

Setup: 98 tasks — 39 text and 59 visual — with a Python interpreter, scientific libraries and up to 10 tool calls per task.

There is no GPT-6 Sol run in this dataset, so 5.6 Sol is the nearest recorded Sol predecessor.

Against 5.6 Sol, the new run gains seven tasks and loses three. Recorded output including reasoning falls 43.7%, Python calls fall from 179 to 114, and request time falls 4.6%.

Every one of Sol’s 89 passes is also an Astra-high pass. Astra solves six additional tasks, all visual.

Sol’s pass rate is 90.82%; accuracy on completed, non-error tasks is 91.75%. Excluding the timeout does not close the gap to Astra high’s 96.94%.

For external context, Opus 5.5 scores 96/98 and Sonnet 5.5 scores 94/98, both at xhigh effort. Effort labels are not equivalent compute budgets across providers.

Times are cumulative model-request durations, excluding local Python and validation — not elapsed suite runtime.

Complete Leaderboard: here

I’m kinda seeing this now across platforms especially dating platforms. Even with my own use of AI

Quote: DeepSeek is training a 2T-parameter model and plans to eventually build an 8T-parameter model.

https://x.com/wallstengine/status/2101982843656388644

Current DeepSeek models:

1. Flash parameter count of 552 billion
2. Pro: 1.6T (trillion) total parameters with 49B (billion) activated weights per token

Mythos / Fable is estimated to be 10T parameter count.

chatgpt people, this one's for you. we're a small swiss/south african lab and we just released hemmingway-1: a 27b model trained specifically for the writing half of the job - stories, dialogue, roleplay, texts that don't sound like they came out of a committee.

why you'd bother when chatgpt exists: on our blind writing benches it comes ahead of gpt-5.5 and every other frontier model we tested for prose and human-likeness, at a fraction of the running cost.

it's open weights under apache-2.0 - run it on one 24gb gpu, no subscription: https://huggingface.co/Altworld/Hemmingway-1

or the free app (mac, windows, android): https://hemmingway.io

fair warning: it's a specialist. for research, code and math you'll still want your usual tools. english first.

Gone through this video that mentioned Rosenblatt’s 1957 perceptron had a few hundred adjustable parameters, and GPT-3 jumped to 175 billion. https://youtu.be/Tv1st0LMEbY?is=JF4drjBjqfYR-4E_

Is the actual architecture that different now, or is most of the capability jump literally just scale — same multiply-add-softmax steps, just a lot more of them? Feels almost too simple for how capable these models are.

I built 56k.rip using GPT to test how it handles retro constraints instead of modern performance. GPT generated the Windows 95 UI, 56k network throttling, and Web Audio API nodes for dial-up handshake tones.

It handled vintage table layouts easily, but preventing retro CSS from leaking into wrapper elements required heavy negative constraints.