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.
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/
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 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.
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.
Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now.
Like Rodin’s The Thinker, there was plenty of thinking and pondering about the large language model (LLM) landscape last week. There were Meta’s missteps over its Galactica LLM public demo and Stanford CRFM’s debut of its HELM benchmark, which followed weeks of tantalizing rumors about the possible release of OpenAI’s GPT-4 sometime over the next few months.
The online chatter ramped up last Tuesday. That’s when Meta AI and Papers With Code announced a new open-source LLM called Galactica, that it described in a paper published on Arxiv as “a large language model for science” meant to help scientists with “information overload.”
Before a machine-learning model can complete a task, such as identifying cancer in medical images, the model must be trained. Training image classification models typically involves showing the model millions of example images gathered into a massive dataset.
However, using real image data can raise practical and ethical concerns: The images could run afoul of copyright laws, violate people’s privacy, or be biased against a certain racial or ethnic group. To avoid these pitfalls, researchers can use image generation programs to create synthetic data for model training. But these techniques are limited because expert knowledge is often needed to hand-design an image generation program that can create effective training data.
Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere took a different approach. Instead of designing customized image generation programs for a particular training task, they gathered a dataset of 21,000 publicly available programs from the internet. Then they used this large collection of basic image generation programs to train a computer vision model.
