AI gradually taking over coding. Coders tryin to circle the wagons, but its already too late. AI coders will be best in world in probably mid 2025.
Modern machine learning systems can achieve average human-level performance in popular competitive programming contests.
In a press conference that Ars attended today, Department of Defense officials discussed the benefits of partnering with Google, Oracle, Microsoft, and Amazon to build the Pentagon’s new cloud computing network. The multi-cloud strategy was described as a necessary move to keep military personnel current as technology has progressed and officials’ familiarity with cloud technology has matured.
Air Force Lieutenant General Robert Skinner said that this Joint Warfighting Cloud Capability (JWCC) contract—worth $9 billion—would help quickly expand cloud capabilities across all defense departments. He described new accelerator capabilities like preconfigured templates and infrastructure as code that will make it so that even “people who don’t understand cloud can leverage cloud” technologies. Such capabilities could help troops on the ground easily access data gathered by unmanned aircraft or space communications satellites.
“JWCC is a multiple-award contract vehicle that will provide the DOD the opportunity to acquire commercial cloud capabilities and services directly from the commercial Cloud Service Providers (CSPs) at the speed of mission, at all classification levels, from headquarters to the tactical edge,” DOD’s press release said.
Humanity Augmented: https://youtu.be/Hc2FMNMPiNQ
Mankind has always looked for ways to reduce manual labor and repetitive tasks. To that end, and in the absence of technology, civilization exploited various me-thods, often by taking advantage of their fellow humans. Robots, as a potential solution, have long fascinated mankind, capturing our imagination for centuries. Even in Greek mythology, the god Hephaestus had « mechanical » servants. But not until recently, has artificial intelligence finally progressed to a level that will become more and more life-changing for the future of humanity.
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Continue reading “Future of Humanity: AI & Robotics | Free Documentary” »
Latent diffusion models (LDMs), a subclass of denoising diffusion models, have recently acquired prominence because they make generating images with high fidelity, diversity, and resolution possible. These models enable fine-grained control of the image production process at inference time (e.g., by utilizing text prompts) when combined with a conditioning mechanism. Large, multi-modal datasets like LAION5B, which contain billions of real image-text pairs, are frequently used to train such models. Given the proper pre-training, LDMs can be used for many downstream activities and are sometimes referred to as foundation models (FM).
LDMs can be deployed to end users more easily because their denoising process operates in a relatively low-dimensional latent space and requires only modest hardware resources. As a result of these models’ exceptional generating capabilities, high-fidelity synthetic datasets can be produced and added to conventional supervised machine learning pipelines in situations where training data is scarce. This offers a potential solution to the shortage of carefully curated, highly annotated medical imaging datasets. Such datasets require disciplined preparation and considerable work from skilled medical professionals who can decipher minor but semantically significant visual elements.
Despite the shortage of sizable, carefully maintained, publicly accessible medical imaging datasets, a text-based radiology report often thoroughly explains the pertinent medical data contained in the imaging tests. This “byproduct” of medical decision-making can be used to extract labels that can be used for downstream activities automatically. However, it still demands a more limited problem formulation than might otherwise be possible to describe in natural human language. By prompting pertinent medical terms or concepts of interest, pre-trained text conditional LDMs could be used to synthesize synthetic medical imaging data intuitively.
Have you ever wondered what makes you intelligent? How are you able to see, hear, think, read, sing, solve problems, and perform any number of intelligent tasks?
Your brain learns a model of the world, and this model recreates the structure of everything you know. Everything you do and experience is based on this model. Intelligence is the ability to create this model of the world.
Continue reading “How the Brain Works: The Thousand Brains Theory of Intelligence | Numenta” »
Researchers used deep reinforcement learning to steer atoms into a lattice shape, with a view to building new materials or nanodevices.
In a very cold vacuum chamber, single atoms of silver form a star-like lattice. The precise formation is not accidental, and it wasn’t constructed directly by human hands either. Researchers used a kind of artificial intelligence called deep reinforcement learning to steer the atoms, each a fraction of a nanometer in size, into the lattice shape. The process is similar to moving marbles around a Chinese checkers board, but with very tiny tweezers grabbing and dragging each atom into place.
The main application for deep reinforcement learning is in robotics, says postdoctoral researcher I-Ju Chen. “We’re also building robotic arms with deep learning, but for moving atoms,” she explains. “Reinforcement learning is successful in things like playing chess or video games, but we’ve applied it to solve technical problems at the nanoscale.”
The accuracy of pedestrian identification was reduced by 57% when the students tested the outfit on on-campus security cameras.
According to the South China Morning Post (SCMP), Chinese students have successfully developed a coat that can make people invisible to security cameras. So the SCMP story goes, the coat looks the same as regular camouflaged clothing, but it can trick digital cameras, especially ones with AI.
Continue reading “Student-made invisibility coat aims to hide wearers from AI cameras” »
In this video, I’m arguing that humans should become cyborgs and more about Transhumanism.
We are going to look at a few ideas which embrace the concept of artificial intelligence and cybernetics – the idea that we can fundamentally change the way that we think and interact with the world.
1 hour “interview” with ChatGPT.
Is OpenAI’s ChatGPT capable of having a coherent conversation? We find out in this special edition of the TWIML AI Podcast!
Continue reading “Exploring Large Language Models with ChatGPT” »
Unless you have been living under a rock, you have heard of this new ChatGPT assistant made by OpenAI. You might be aware of its capabilities for solving IQ tests, tackling leetcode problems or to helping people write LateX. It is an amazing resource for people to retrieve all kinds of information and solve tedious tasks, like copy-writing!
Today, Frederic Besse told me that he managed to do something different. Did you know, that you can run a whole virtual machine inside of ChatGPT?
Great, so with this clever prompt, we find ourselves inside the root directory of a Linux machine. I wonder what kind of things we can find here. Let’s check the contents of our home directory.
