Human-robot hybrids are advancing quickly, but the applications aren’t just for complete synthetic humans. There’s a lot we can learn about ourselves in the process.
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Nvidia unveils its new artificial intelligence 3D model maker for game design uses text or photo input to output a 3D mesh and can also edit and adjust 3D models with text descriptions. New video style transfer from Nvidia uses CLIP to convert the style of 3D models and photos. New differential equation-based neural network machine learning AI from MIT solves brain dynamics.
AI News Timestamps:
0:00 Nvidia AI Turns Text To 3D Model Better Than Google.
2:03 Nvidia 3D Object Style Transfer AI
4:56 New Machine Learning AI From MIT
#nvidia #ai #3D
MBW’s Stat Of The Week is a series in which we highlight a single data point that deserves the attention of the global music industry. Stat Of the Week is supported by Cinq Music Group, a technology-driven record label, distribution, and rights management company. 
Continue to article…The use of artificial intelligence-created music just moved up a gear.
It is our pleasure to announce the open-source release of Stable Diffusion Version 2.
The original Stable Diffusion V1 led by CompVis changed the nature of open source AI models and spawned hundreds of other models and innovations all over the world. It had one of the fastest climbs to 10K Github stars of any software, rocketing through 33K stars in less than two months.
Human behaviour is remarkably complex. Even a simple request like, “Put the ball close to the box” still requires deep understanding of situated intent and language. The meaning of a word like ‘close’ can be difficult to pin down – placing the ball inside the box might technically be the closest, but it’s likely the speaker wants the ball placed next to the box. For a person to correctly act on the request, they must be able to understand and judge the situation and surrounding context.
Most artificial intelligence (AI) researchers now believe that writing computer code which can capture the nuances of situated interactions is impossible. Alternatively, modern machine learning (ML) researchers have focused on learning about these types of interactions from data. To explore these learning-based approaches and quickly build agents that can make sense of human instructions and safely perform actions in open-ended conditions, we created a research framework within a video game environment.
Continue reading “Building interactive agents in video game worlds” »
Neuralink’s invasive brain implant vs phantom neuro’s minimally invasive muscle implant. Deep dive on brain computer interfaces, Phantom Neuro, and the future of repairing missing functions.
Connor glass.
Phantom is creating a human-machine interfacing system for lifelike control of technology. We are currently hiring skilled and forward-thinking electrical, mechanical, UI, AR/VR, and Ai/ML engineers. Looking to get in touch with us? Send us an email at [email protected].
Scientists in Israel have created the first nano-robot antibodies designed to fight cancer. The first human trial for the new nano-robots will start soon, and it will determine just how effective the antibodies are. What is special about these particular antibodies, too, is that they are programmed to decide whether cells surrounding tumors are “bad” or “good.”
The trial is currently underway in Australia and if it goes according to plan, the nano-robot antibodies will be able to fight cells around tumors that can help the tumor while also boosting the capability of the cells inhibiting the growth of the cancerous cells. The antibodies were invented by Professor Yanay Ofran and are based on human and animal antibodies.
The goal of these nano-robot antibodies is to unlock the full potential that antibodies offer, Ofran says. Currently, the use of antibodies in medicine only utilizes a fraction of the capabilities offered by these natural disease fighters. As such, finding a way to maximize their capability has been a long-term goal for quite a while.
A new technique enables artificial intelligence agents to think much farther into the future when considering how their behaviors can influence the behaviors of other AI agents, toward the completion of a task. This approach improves long-term performance of cooperative or competitive AI agents.
A multi-institution research team has developed an optical chip that can train machine learning hardware. Their research is published today in Optica.
Machine learning applications have skyrocketed to $165 billion annually, according to a recent report from McKinsey. But before a machine can perform intelligence tasks such as recognizing the details of an image, it must be trained. Training of modern-day artificial intelligence (AI) systems like Tesla’s autopilot costs several million dollars in electric power consumption and requires supercomputer-like infrastructure.
This surging AI “appetite” leaves an ever-widening gap between computer hardware and demand for AI. Photonic integrated circuits, or simply optical chips, have emerged as a possible solution to deliver higher computing performance, as measured by the number of operations performed per second per watt used, or TOPS/W. However, though they’ve demonstrated improved core operations in machine intelligence used for data classification, photonic chips have yet to improve the actual front-end learning and machine training process.
Alphabet’s X unit spun up a tool that teaches code to write itself. It has been moved into Google Labs, signaling its growing importance.
