An advisor to al-Qaida. One of the founders of Hezbollah. The head of an Iraqi militia group known for attacks on U.S. troops. And a top official with the Houthi rebels who recently lashed out at the “criminal Trump.”
These are among the U.S.-sanctioned terrorists who appear to have paid, premium accounts on Elon Musk's X, a new Tech Transparency Project investigation has found, raising questions about the platform's dealings with individuals who have been deemed a threat to U.S. national security.
The new Fairwater AI datacenter in Wisconsin stands as a remarkable feat of engineering, covering 315 acres and housing three massive buildings with a combined 1.2 million square feet under roofs. Constructing this facility required 46.6 miles of deep foundation piles, 26.5 million pounds of structural steel, 120 miles of medium-voltage underground cable and 72.6 miles of mechanical piping.
Unlike typical cloud datacenters, which are optimized to run many smaller, independent workloads such as hosting websites, email or business applications, this datacenter is built to work as one massive AI supercomputer using a single flat networking interconnecting hundreds of thousands of the latest NVIDIA GPUs. In fact, it will deliver 10X the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen.
Taking inspiration from a rare mutation that makes people impervious to viral diseases, a Columbia researcher is developing a therapy that could bestow this superpower on the rest of us.
Today, we are excited to introduce an autonomous AI agent that can analyze and classify software without assistance, a step forward in cybersecurity and malware detection. The prototype, Project Ire, automates what is considered the gold standard in malware classification: fully reverse engineering a software file without any clues about its origin or purpose. It uses decompilers and other tools, reviews their output, and determines whether the software is malicious or benign.
Instead of saying "we cannot ensure that no harmful sequences will be generated by our function, partially because we don't know how to specify and enumerate harmful sequences", we talk about "behaviors", "ethical constraints", and "harmful actions in pursuit of their goals". All of these are anthropocentric concepts that - in my mind - do not apply to functions or other mathematical objects. And using them muddles the discussion, and our thinking about what we're doing when we create, analyze, deploy and monitor LLMs.
There’s a lot of excitement about what AI (specifically the latest wave of LLM-anchored AI) can do, and how AI-first companies are different from the prior generations of companies. There are a lot of important and real opportunities at hand, but I find that many of these conversations occur at such an abstract altitude that they border on meaningless. Sort of like saying that your company could be much better if you merely adopted _more software_. That’s certainly true, but it’s not a particularly helpful claim.
This post is an attempt to concisely summarize how AI agents work, apply that summary to a handful of real-world use cases for AI, and to generally make the case that agents are a multiplier on the quality of your software and system design.
Of the many explainers I've seen and read, this is probably one of the best.
Something about this concept—two sets of synonyms (PUB and BAR, QUIZ and EXAM), which when paired together, form phrases that themselves are not synonyms (PUB QUIZ and BAR EXAM)—captured the minds of Crosscord.
If you’re a creative person, square theory is a useful framework to keep in mind. If you’re coming up with a title for a paper or a brand name, try to see if you can think of one that completes the square. If you’re writing puns for a popsicle stick or a Laffy Taffy wrapper, you can use squares to model your setups and punchlines. If you’re constructing a crossword, consider whether your theme or your question mark clues can form squares.
Proud of my colleagues in MSTIC and the amazing work they do every day.
On Thursday, researchers at Carnegie Mellon University unveiled LegoGPT, an AI model that creates physically stable Lego structures from text prompts. The new system not only designs Lego models that match text descriptions (prompts) but also ensures they can be built brick by brick in the real world, either by hand or with robotic assistance.
Oh, so that’s what AI is for…