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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.

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.

Today, we’re publishing a paper and collection of videos, showing our early steps in building video game AIs that can understand fuzzy human concepts – and therefore, can begin to interact with people on their own terms.

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].

Phantom Neuro.
Phantom is a neurotechnology company, spun out of the lab at The Johns Hopkins University School of Medicine, that is enabling lifelike control of robotic orthopedic technologies, such as prosthetic limbs and exoskeletons. Phantom’s solution, the Phantom X, consists of low-risk implantable sensors, AI, and enabling software. By providing superior control of robotic orthopedic mechanisms, the Phantom X will drastically improve the lives of individuals with limb difference who have yet to see a tangible improvement in quality of life despite significant advancements in the field of robotics.

Links:
[email protected].
https://www.linkedin.com/in/connor-glass-md-010124141/
https://www.linkedin.com/company/phantomneuro/

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https://twitter.com/phantom_neuro.

PODCAST INFO:
The Learning With Lowell show is a series for the everyday mammal. In this show we’ll learn about leadership, science, and people building their change into the world. The goal is to dig deeply into people who most of us wouldn’t normally ever get to hear. The Host of the show – Lowell Thompson-is a lifelong autodidact, serial problem solver, and founder of startups.
LINKS
Youtube: https://www.youtube.com/channel/UCzri06unR-lMXbl6sqWP_-Q
Youtube clips: https://www.youtube.com/channel/UC-B5x371AzTGgK-_q3U_KfA
Linkedin: https://www.linkedin.com/in/lowell-thompson-2227b074
Twitter: https://twitter.com/LWThompson5
Website: https://www.learningwithlowell.com/
Podcast email: [email protected].

Timestamps.

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 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 (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.

Deep Learning AI Specialization: https://imp.i384100.net/GET-STARTED
AI News Timestamps:
0:00 New AI Robot Dog Beats Human Soccer Skills.
2:34 Breakthrough Humanoid Robotics & AI Tech.
5:21 Google AI Makes HD Video From Text.
8:41 New OpenAI DALL-E Robotics.
11:31 Elon Musk Reveals Tesla Optimus AI Robot.
16:49 Machine Learning Driven Exoskeleton.
19:33 Google AI Makes Video Game Objects From Text.
22:12 Breakthrough Tesla AI Supercomputer.
25:32 Underwater Drone Humanoid Robot.
29:19 Breakthrough Google AI Edits Images With Text.
31:43 New Deep Learning Tech With Light waves.
34:50 Nvidia General Robot Manipulation AI
36:31 Quantum Computer Breakthrough.
38:00 In-Vitro Neural Network Plays Video Games.
39:56 Google DeepMind AI Discovers New Matrices Algorithms.
45:07 New Meta Text To Video AI
48:00 Bionic Tech Feels In Virtual Reality.
53:06 Quantum Physics AI
56:40 Soft Robotics Gripper Learns.
58:13 New Google NLP Powered Robotics.
59:48 Ionic Chips For AI Neural Networks.
1:02:43 Machine Learning Interprets Brain Waves & Reads Mind.