Syam 🪐 @Syam0x
0x000dead Joined October 2020-
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Mechazilla has caught the Super Heavy booster!
TIME's new cover: The case for taking AI seriously as a threat to humanity ti.me/3oHNGd2
When a star collapses, it starts spinning extremely fast. And because black holes form out of dying stars, all black holes we know should be spinning too. This makes them even more screwed up in reality than in theory, where black holes are usually referred to as non-spinning.
👀 Sneak a peek at the deepest & sharpest infrared image of the early universe ever taken — all in a day’s work for the Webb telescope. (Literally, capturing it took less than a day!) This is Webb’s first image released as we begin to #UnfoldTheUniverse: nasa.gov/webbfirstimage…
This discussion between a Google engineer and their conversational AI model helped cause the engineer to believe the AI is becoming sentient, kick up an internal shitstorm and get suspended from his job. And it is absolutely insane. washingtonpost.com/technology/202…
The Ropsten network just merged! One of the last testnets until we merge on the #Ethereum mainnet🐼 ropsten.beaconcha.in
SpaceX Starship landing
You can't mint your way out of bankruptcy all the time.
This iron man #bitcoin bull is pretty cool.
We trained a neural network that solved two problems from the International Math Olympiad. openai.com/blog/formal-ma…
Today in @Nature, with @EPFL, the first deep reinforcement learning system that can keep nuclear fusion plasma stable inside its tokamaks, opening new avenues to advance nuclear fusion research. Paper: dpmd.ai/fusion-paper
“In other words, the Megatron is trained on more written material than any of us could reasonably expect to digest in a lifetime. After such extensive research, it forms its own views.”
1. Spent the past few weeks going down a rabbit hole learning about MEV and Flashbots — very cool stuff! I was surprised to learn that beneath the surface, Ethereum is lurking with sniper bots waiting for any opportunity to make money. Here’s a TLDR on some research + findings.
We've observed AIs discovering complex tool use while competing in a simple game of hide-and-seek. They develop a series of six distinct strategies and counterstrategies, ultimately using tools in the environment to break our simulated physics: openai.com/blog/emergent-…
A full build of Autopilot neural networks involves 48 networks that take 70,000 GPU hours to train. Together, they output 1,000 distinct tensors (predictions) at each timestep. This is what a Tesla autopilot sees [source: buff.ly/2OhFA5B]

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