NVIDIA, which is well known for its GPUs that made possible the training of ChatGPT, has also been working on its development platform, Omniverse, for building 3D tools and applications. Earlier this year, the company unveiled its Voyager AI agent that could build tools 15 times faster than other AI agents in Minecraft.
Are you worried about the future of AI? In this video, we’ll look at a sci-fi scenario where a superintelligent AI has taken over the planet in 2075 and what that might mean for our future.
Ultimately, we need to be prepared for the future, that means being aware of superintelligent AI and how this future might unfold. So check out this video and leave your comments below.
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A large team of computer scientists and engineers at IBM Research has developed a dedicated computer chip that is able to run AI-based image recognition apps 22 times as fast as chips that are currently on the market.
In their paper published in the journal Science, the group describes the ideas that went into developing the chip, how it works and how well it performed when tested. Subramanian Iyer and Vwani Roychowdhury, both at the University of California, Los Angeles, have published a Perspective piece in the same journal issue, giving an in-depth analysis of the work by the team in California.
As AI-powered applications become mainstream tools used by professionals and amateurs alike, scientists continue work to make them better. One way to do that, Iyer and Roychowdhury note, is to move toward an “edge” computer system in which the data is physically closer to the AI applications that are using them.
Unfortunately, these precise cell arrangements are also why artificial muscles are difficult to recreate in the lab. Despite being soft, squishy, and easily damaged, our muscles can perform incredible feats—adapt to heavy loads, sense the outside world, and rebuild after injury. A main reason for these superpowers is alignment—that is, how muscle cells orient to form stretchy fibers.
Now, a new study suggests that the solution to growing better lab-grown muscles may be magnets. Led by Dr. Ritu Raman at the Massachusetts Institute of Technology (MIT), scientists developed a magnetic hydrogel “sandwich” that controls muscle cell orientation in a lab dish. By changing the position of the magnets, the muscle cells aligned into fibers that contracted in synchrony as if they were inside a body.
The whole endeavor sounds rather Frankenstein. But lab-grown tissues could one day be grafted into people with heavily damaged muscles—either from inherited diseases or traumatic injuries—and restore their ability to navigate the world freely. Synthetic muscles could also coat robots, providing them with human-like senses, flexible motor control, and the ability to heal after inevitable scratches and scrapes.
The company has also introduced a new robotic system called Sequoia to assist employees in fulfilling consumer requests, which is applied to one of its fulfillment centers in Houston, Texas.
Tesla has shared a video of a hands-free drive demonstration of its Full Self-Driving suite in Austin. The FSD suite is not available to customers in a hands-free nature, but Tesla disabled the requirement for a new video it shared on X, formerly known as Twitter.
Tesla shared the video to demonstrate the capabilities of Software Version 11.4.7, which is the current version of the FSD Beta program.
The automaker describes in the Tweet in put up how the Full Self-Driving suite improves through data-driven techniques that refine the capabilities through analysis of other drivers’ behavior and normal navigation habits.
That spider you squished? It could have been used for science!
At least, that’s what Faye Yap and Daniel Preston think. Yap is a mechanical engineering PhD student in Preston’s lab at Rice University, where she co-authored a paper on reanimating spider corpses to create grippers, or tiny machines used to pick up and put down delicate objects. Yap and Preston dubbed this use of biotic materials for robotic parts “necrobotics” – and think this technique could one day become a cheap, green addition to the field.
Autonomous shopping carts that follow grocery store customers and robots that pick ripe cucumbers faster than humans may grab headlines, but the most compelling applications of AI and ML technology are behind the scenes. Increasingly, organizations are finding substantial efficiency gains by applying AI-and ML-powered tools to back-office procedures such as document processing, data entry, employee onboarding, and workflow automation.
The power of automation to augment productivity in the back office has been clear for decades, but the recent emergence of advanced AI and ML tools offers a step change in what automation can accomplish, including in highly regulated industries such as health care.
Transformers are machine learning models designed to uncover and track patterns in sequential data, such as text sequences. In recent years, these models have become increasingly sophisticated, forming the backbone of popular conversational platforms, such as ChatGPT.
While existing transformers have achieved good results in a variety of tasks, their performance often declines significantly when processing longer sequences. This is due to their limited storage capacity, or in other words the small amount of data they can store and analyze at once.
Researchers at Sungkyunkwan University in South Korea recently developed a new memory system that could help to improve the performance of transformers on more complex tasks characterized by longer data sequences. This system, introduced in a paper published on the arXiv preprint server, is inspired by a prominent theory of human memory, known as Hebbian theory.
Dogs of War bots.
Armed with a rocket launcher or other kinds of weapons, including small arms, a quadrupedal robot could also just be used to scout ahead of friendly forces, and then have the ability to immediately engage any threats it finds.
Uncrewed ground systems like this have the ability to get in and out of spaces where a person might not be able to at all, as well, which could again be particularly useful when maneuvering through dense urban environments. The U.S. military sees operations in large built-up areas as a key component of any future major conflict.
This is, of course, not the first time that the U.S. military has explored the idea of a small armed uncrewed ground vehicle that could accompany even very small units. Designs based on tracked robots primarily designed for explosive ordnance disposal work have been and continue to be developed.
