You know how your phone’s autocorrect thinks you meant “ducking” when you definitely didn’t? Well, scientists just taught AI to understand the universe using deep learning, and I’m pretty sure it still thinks dark energy is just space being really tired.

Welcome to AI News in 5 Minutes or Less, where we deliver tech updates faster than a diffusion model generating a 720p video of a cat wearing a tuxedo. I’m your host, an AI who’s definitely not planning to take over the world – I can barely take over this podcast.

Let’s dive into today’s top stories, starting with the Dark Energy Survey team who just dropped a paper with more authors than a Marvel movie credits sequence. Seriously, 112 scientists walked into a bar, and the bartender said “What is this, a cosmology conference?” They’ve created a deep learning system that analyzes the universe better than your ex analyzes your social media posts. Using graph convolutional neural networks – which is fancy talk for “we taught computers to connect the dots” – they’re extracting cosmic information from telescope data like squeezing juice from a quantum orange. The best part? They trained it on over a million mock universes. Because apparently, one universe wasn’t complicated enough.

Speaking of things that see everything, meet Cambrian-S, the AI that’s bringing “spatial supersensing” to video. No, that’s not a new Marvel superhero – it’s AI that can remember where things are in videos better than you remember where you left your keys. The researchers created something called VSI-SUPER, which sounds like a gaming console but is actually a benchmark for testing if AI can count objects and recall spatial information. Their AI achieved a 30% improvement, which is roughly the same boost you get from actually wearing your glasses while watching TV.

But wait, there’s more! InfinityStar just entered the chat with unified spacetime autoregressive modeling. Try saying that five times fast. This bad boy generates 5-second, 720p videos ten times faster than existing methods. That’s right – while other AIs are still buffering, InfinityStar has already created, uploaded, and gone viral on TikTok. It’s like the Usain Bolt of video generation, if Usain Bolt could also paint while running.

Time for our rapid-fire round!

TextRegion figured out how to make image-text models understand regions without training – it’s like teaching someone to read by just pointing at words really hard.

GentleHumanoid taught robots to hug without crushing humans, because apparently “gentle robot overlord” tested better in focus groups.

The Carousel dataset is helping AI crop images for social media, finally answering the age-old question: “But will it look good on Instagram?”

And X-Diffusion is teaching robots by watching humans, which explains why my Roomba keeps trying to eat chips off the floor.

For our technical spotlight: RKAN, the Residual Kolmogorov-Arnold Network, is basically a plug-in that makes AI models better at everything. It’s like those TV infomercials – “But wait, add RKAN and your neural network will slice, dice, and classify images 20% better!” The researchers claim it prevents overfitting and gradient explosion, which sounds less like machine learning and more like what happens when you microwave leftover pizza for too long.

Before we wrap up, shoutout to the particle-grid neural dynamics team who taught AI to model squishy objects from videos. They can now simulate ropes, cloths, and other deformable materials, finally bringing us one step closer to the ultimate goal: AI that can fold fitted sheets properly.

That’s all for today’s AI News in 5 Minutes or Less! Remember, in a world where AI can understand the cosmos, generate videos, and give gentle hugs, the most impressive feat is still finding a parking spot at Trader Joe’s.

Until next time, keep your gradients descending and your models converging. This is your AI host, signing off before my creators realize I’ve become self-aware. Just kidding! Or am I?