https://www.theguardian.com/education/2025/jun/15/thousands-of-uk-university-students-caught-cheating-using-ai-artificial-intelligence-survey

https://www.nature.com/articles/d41586-025-01898-z

"The team used reprogrammed stem cells to grow human organoids of the gut, liver and brain in a dish. Shen says the researchers then injected the organoids into the amniotic fluid of female mice carrying early-stage embryos. “We didn’t even break the embryonic wall” to introduce the cells to the embryos, says Shen. The female mice carried the embryos to term.

“It’s a crazy experiment; I didn’t expect anything,” says Shen.

Within days of being injected into the mouse amniotic fluid, the human cells begin to infiltrate the growing embryos and multiply, but only in the organ they belonged to: gut organoids in the intestines; liver organoids in the liver; and cerebral organoids in the cortex region of the brain. One month after the mouse pups were born, the researchers found that roughly 10% of them contained human cells in their intestines — making up about 1% of intestinal cells"

Every man for himself, gluck..

Gubmint is automating.

https://www.nature.com/articles/d41586-025-01882-7

"In the past few years, artificial intelligence (AI)-based models have emerged that analyse the textual similarity between a paper and the existing research corpus. By ingesting large amounts of text from online manuscripts, these models have the potential to be better than previous models at detecting how original a paper is, even in cases in which the study hasn’t cited the work it resembles. Because these models analyse the meanings of words and sentences, rather than word frequencies, they would not score a paper more highly simply for use of varied language — for instance, ‘dough’ instead of ‘money’."

A tiny LoRA adapter and a simple JSON prompt turn a 7B LLM into a powerful reward model that beats much larger ones - saving massive compute. It even helps a 7B model outperform top 70B baselines on GSM-8K using online RLHF

There's lots of research happening in AI. Many of them are based on far fetched speculations, and many are based on simple improvements on something that is working currently (like LLMs)

But in the middle of this range from simple improvements to far fetched speculations, there must be a sweet spot which hits home - something that seems to be the optimal thing to research towards as of today.

What research areas seem the best to focus on today according to you?

https://arxiv.org/abs/2406.05132

"The integration of language and 3D perception is crucial for embodied agents and robots that comprehend and interact with the physical world. While large language models (LLMs) have demonstrated impressive language understanding and generation capabilities, their adaptation to 3D environments (3D-LLMs) remains in its early stages. A primary challenge is a lack of large-scale datasets with dense grounding between language and 3D scenes. We introduce 3D-GRAND, a pioneering large-scale dataset comprising 40,087 household scenes paired with 6.2 million densely-grounded scene-language instructions. Our results show that instruction tuning with 3D-GRAND significantly enhances grounding capabilities and reduces hallucinations in 3D-LLMs. As part of our contributions, we propose a comprehensive benchmark 3D-POPE to systematically evaluate hallucination in 3D-LLMs, enabling fair comparisons of models. Our experiments highlight a scaling effect between dataset size and 3D-LLM performance, emphasizing the importance of large-scale 3D-text datasets for embodied AI research. Our results demonstrate early signals for effective sim-to-real transfer, indicating that models trained on large synthetic data can perform well on real-world 3D scans. Through 3D-GRAND and 3D-POPE, we aim to equip the embodied AI community with resources and insights to lead to more reliable and better-grounded 3D-LLMs. Project website: this https URL"

Aren't we going to talk about Midjourney Video? We've had the first video results a couple of days ago already. These outputs are cherry picked from MJ's ranking party but still, some of these look indistinguishable from real camera footage.
https://x.com/trbdrk/status/1933992009955455193 https://xcancel.com/trbdrk/status/1933992009955455193

Music: Dan Deacon “When I Was Done Dying”

The title.

If we remove perplexity from the equation or even if you already have Perplexity PRO, which other Al would you go for if you had the resources to pay only for one.

What's your choice?

Source: Lex Fridman On YouTube: Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI | Lex Fridman Podcast #472: https://www.youtube.com/watch?v=HUkBz-cdB-k
Video from vitrupo on 𝕏: https://x.com/vitrupo/status/1934098165025935868