What happens when two of the greatest sci-fi universes collide? ⚔️ In this deep-dive, we break down the ultimate showdown: Star Trek vs Star Wars — and uncover the TRUTH about who would actually win.
This isn’t just fan debate. We’re analyzing technology, weapons, strategy, and realism to answer the question once and for all. From the advanced warp-driven fleets of the United Federation of Planets to the Force-wielding dominance of the Galactic Empire, every advantage and weakness is put under the microscope.
Could a Star Destroyer overpower the USS Enterprise? Is the Force the ultimate trump card? Or does superior engineering give Star Trek the edge?
This video dives into:
Starship combat and firepower ⚡ Shields vs deflectors 🛡️ Warp speed vs hyperspace 🚀 AI, tactics, and battle strategy 🧠 The real science behind both universes.
By the end, you’ll see which universe holds the TRUE advantage—and why the answer might surprise you.
Wireless communication is about to get stronger, clearer, and more secure, thanks to a new idea from UBC Okanagan researchers. Dr. Anas Chaaban and his team in the School of Engineering are exploring a method to improve the way stacked intelligent surfaces (SIS) can process electromagnetic waves more efficiently.
SIS is an emerging alternative to conventional wireless hardware, Dr. Chaaban says, as layers of specially engineered materials are used to directly manipulate electromagnetic waves.
“Electromagnetic waves travel through special surfaces that consist of several elements. These elements mimic neurons in a computerized neural network,” Dr. Chaaban says. “As the waves move through the surface, each element changes them slightly. When the waves come out, they are captured by antennas that send the signals to digital processors for further analysis.”
Now, as artificial intelligence enters the classroom, proponents argue it will be a welcome revolution for schools — but with limited guardrails, could it do more harm than good? Horizons moderator William Brangham explores the future of AI and education with Khan Academy founder Salman Khan, who has launched a new AI assistant for teachers.
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Large language models (LLMs) could help human scientists identify interesting research topics that have not previously been explored, say scientists at Germany’s Karlsruhe Institute of Technology (KIT). By analysing abstracts in materials science publications and mapping connections between different concepts, the model was able to generate predictions for future areas of interest that the KIT team says are more precise than those produced by traditional, rule-based algorithms.
The number of research articles published each year is increasing so quickly that it is impossible for scientists to keep up with everything, observes team leader Pascal Friederich, who heads a KIT research group on artificial intelligence for materials sciences. While experienced scientists know how to find connections between research areas within their field, identifying links between these and other, unfamiliar topics is a different story.
The Academy of Motion Picture Arts and Sciences—probably better known to the world as the Oscars folks—have drawn a firm line in the sand against the use of generative AI, changing its eligibility rules to exclude AI-generated performances and scripts.
The new rules, via The Wrap, state that in acting categories, only roles “demonstrably performed by humans with their consent” will be considered eligible for consideration, while in the writing categories, only “human-authored” screenplays will be eligible.
Sections 0:00 — Intro 2:28 — The Problem with Deep Learning 4:17 — Intelligence is a Cake 5:15 — The Rise of Generative AI 8:00 — Blurry Images 8:54 — HRT is an awesome place to work 11:16 — But why so Blurry? 13:30 — Do our models need to be generative? 15:16 — Siamese Networks 17:53 — Representation Collapse 19:54 — Yann’s Epiphany & Barlow Twins 27:22 — DINO 28:58 — JEPA & World Models 34:09 — But is JEPA good? 36:19 — Welch Labs Book.
Special thanks to: Yann LeCun, Stephane Deny, David Fan, Nicolas Ballas.
Clip of Yann from 1989: • Convolutional Network Demo from 1989
CNN Paper: http://yann.lecun.com/exdb/publis/pdf… LeNet-5 paper: http://vision.stanford.edu/cs598_spri… Dashcam video https://commons.wikimedia.org/wiki/Fi… Image Credits https://en.wikipedia.org/wiki/File: Do… https://commons.wikimedia.org/wiki/Fi… https://commons.wikimedia.org/wiki/Fi… https://commons.wikimedia.org/wiki/Fi… https://commons.wikimedia.org/wiki/Fi… V-JEPA2 Robot Arm Videos https://ai.meta.com/research/vjepa/ PATRONS Juan Benet, Ross Hanson, Yan Babitski, AJ Englehardt, Alvin Khaled, Eduardo Barraza, Hitoshi Yamauchi, Jaewon Jung, Mrgoodlight, Shinichi Hayashi, Sid Sarasvati, Dominic Beaumont, Shannon Prater, Ubiquity Ventures, Matias Forti, Brian Henry, Tim Palade, Petar Vecutin, Nicolas baumann, Jason Singh, Robert Riley, vornska, Barry Silverman, Jake Ehrlich, Mitch Jacobs, Lauren Steely, Jeff Eastman, Rodolfo Ibarra, Clark Barrus, Rob Napier, Andrew White, Richard B Johnston, abhiteja mandava, Burt Humburg, Kevin Mitchell, Daniel Sanchez, Ferdie Wang, Tripp Hill, Richard Harbaugh Jr, Prasad Raje, Kalle Aaltonen, Midori Switch Hound, Zach Wilson, Chris Seltzer, Ven Popov, Hunter Nelson, Amit Bueno, Scott Olsen, Johan Rimez, Shehryar Saroya, Tyler Christensen, Beckett Madden-Woods, Darrell Thomas, Javier Soto, U007D, Caleb Begly, Rick Rubenstein, Brent Hunsaker, Dan Patterson, Tchsurvives, Alex Adai, Walter Reade, Zyansheep, Walter Reade, Duncan Stannett, Reginald Carey, Jean-Manuel Izaret, dh71633, Adrian Rodriguez, Dimitar Stojanovski, Michael Harder, Peter Maldonado, Emily Pesce, David Johnston, Insang Song, FaeTheWolf, Stephen Taylor, KittenKaboodle, EMatter, PATRICKMCCORMACK, John Beahan, Cameron, Cole Jones, Garrett Thornburg, Jeroen W, Rohit Sharma, GlennB, Emmanuel Cortes, Katie Quinn, Karina C, Cakra WW, Mike Ton, Eric Gometz, MacCallister Higgins, Niko Drossos, David Eraso, Tom Zehle, Steve, Brian Lineburg, rjbl, Michael Loh, Perry Vais, Bengal0, Farhad Manjoo, Sara Chipps, Ellis Driscoll, William Taysom, Will Harmon, CK, Abdullah, Peter Cho, Leo Nikora, Griffin Smith, Ash Katnoria, Alex, Markus Hays Nielsen, Catherine H., Vi, David Dobáš, Peter Wang, Sina Sohangir, Danny Thomas, Julian Francis, Hans Adler, Jiayu Peng, Weston M, Youssouf da Silva, John Thomas, Samuel Costello, Sam Adams, Bryan Liles, Malaya Zemlya, Karl, Vahe Andonians, Mike Doughty, Larry Novelo, Jonas Acres, Ludicrum Rex, Robert Blumofe, Anthony Z, Alex Zhao, Dan Babitch, Nikko Patten Supporting code: https://github.com/WelchLabs/videos Created by: Sam Baskin, Pranav Gundu, and Stephen Welch Content ID: CFAQJOTYQHT7JYIT.
April 9, 2026 This seminar covers: • How world models are increasingly moving away from reconstruction and toward prediction in latent space. • Two recent JEPA-based approaches that illustrate this shift from complementary angles.
Guest Speakers: Hazel Nam & Lucas Maes (Brown University)
Instructors: • Steven Feng, Stanford Computer Science PhD student and NSERC PGS-D scholar. • Karan P. Singh, Electrical Engineering PhD student and NSF Graduate Research Fellow in the Stanford Translational AI Lab. • Michael C. Frank, Benjamin Scott Crocker Professor of Human Biology Director, Symbolic Systems Program. • Christopher Manning, Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science, Co-Founder and Senior Fellow of the Stanford Institute for Human-Centered Artificial Intelligence (HAI)
The FDNY’s 11-year-old robotics unit has shown that mechanical firefighters can be a valuable part of New York’s Bravest — and is now teaching that to others.
I originally created a list of 160+ companies with detailed descriptions for each one. But updating the list manually takes a lot of time. So, I used ChatGPT and Claude to add a new batch of company website links I had collected (190 entries are now on the list). Hopefully I can continue expanding using this method. While I don’t learn about the new entries as directly since I’m not the one adding them, this will nonetheless be useful for keeping up with the fast-paced biotech world. I hope you find it useful as well!
I used ChatGPT and Claude to expand and revise/update my original 160+ entry list of biotech companies (now at 190 entries). I hope you find this expanded list and its descriptions useful!
Penn Engineers have developed a new way to use AI to solve inverse partial differential equations (PDEs), a particularly challenging class of mathematical problems with broad implications for understanding the natural world.
The advance, which the researchers call “Mollifier Layers,” could benefit fields as varied as genetics and weather forecasting, because inverse PDEs help scientists work backward from observable patterns to infer the hidden dynamics that produced them.
“Solving an inverse problem is like looking at ripples in a pond and working backward to figure out where the pebble fell,” says Vivek Shenoy, Eduardo D. Glandt President’s Distinguished Professor in Materials Science and Engineering (MSE) and senior author of a study published in Transactions on Machine Learning Research (TMLR), which will be presented at the Conference on Neural Information Processing Systems (NeurIPS 2026). “You can see the effects clearly, but the real challenge is inferring the hidden cause.”
