Astronomers picked up extraterrestrial signals which they previously missed in an area they thought was devoid of potential ET activity. It could be the first hint that humans are not alone in the universe.
Mysterious Signals Detected
The World Wide Web was originally conceived and developed at CERN to meet the demand for automated information-sharing between scientists in universities and institutes around the world. From software development, to data processing and storage, networks, support for the LHC and non-LHC experimental programme, automation and controls, as well as services for the accelerator complex and for the whole laboratory and its users, is at the heart of CERN’s infrastructure.
The Worldwide LHC Grid (WLCG) – a distributed infrastructure arranged in tiers – gives a community of thousands of physicists near real-time access to LHC data. The CERN data centre is at the heart of WLCG, the first point of contact between experimental data from the LHC and the grid. Through CERN openlab, a unique public-private partnership, CERN collaborates with leading ICT companies and other research organisations to accelerate the development of cutting-edge ICT solutions for the research community. CERN has also established a medium-and long-term roadmap and research programme in collaboration with the high energy physics and quantum-technology research communities via the CERN Quantum Technology Initiative (QTI).
This video will aim to provide detailed information regarding the use of robots in healthcare, specifically the surgical robots. The video consists of 4 subtopics: history of surgical procedure, introduction to surgical robots, the da Vinci surgical system with Dr. Yasufuku’s interview and robotics in remote healthcare and future advancement. The full-length of the interview will be posted on the Demystifying Medicine Soundcloud account and as a YouTube Podcast. For more information regarding the Da Vinci Surgical System, please visit https://www.davincisurgery.com/about-us/privacy-policy. (The animation of Da Vinci Surgical System during the News section was used with permission from Intuitive.)
Finally, we would like to thank Dr. Kazuhiro Yasufuku for his time and contribution to our video through his excellent interview. For those who are interested to learn more about his research, please visit https://www.yasufukuresearch.com/.
A new method learns prompts from an image, which then can be used to reproduce similar concepts in Stable Diffusion.
Whether DALL-E 2, Midjourney or Stable Diffusion: All current generative image models are controlled by text input, so-called prompts. Since the outcome of generative AI models depends heavily on the formulation of these prompts, “prompt engineering” has become a discipline in its own right in the AI community. The goal of prompt engineering is to find prompts that produce repeatable results, that can be mixed with other prompts, and that ideally work for other models as well.
In addition to such text prompts, the AI models can also be controlled by so-called “soft prompts”. These are text embeddings automatically derived from the network, i.e. numerical values that do not directly correspond to human terms. Because soft prompts are derived directly from the network, they produce very precise results for certain synthesis tasks, but cannot be applied to other models.
Since its launch in November by San Francisco-based OpenAI, ChatGPT has taken the world by storm. The conversational chatbot can code, write essays, and even function as a search engine, among other tasks.
But this isn’t some futuristic vision of what ChatGPT will do. The business world has already bought in when it comes to AI in general, and ChatGPT in particular.
“It can create anything that we thought thus far was unique to human intelligence or creativity, whether that is interacting with us in a chatbot form, whether that is generating new content, whether it’s images, video,” Nina Schick, adviser, speaker, and A.I. thought leader, recently told Yahoo Finance.
Is “Judgment Day” here? Hong Kong scientists bridged the gap between sci-fi and real life after creating a robot that can transform from solid to liquid like a real-life T-1000 robot from “Terminator 2.” A studying the cybernetic shapeshifter was published last month in the journal Matter.
Summary: Researchers are turning to artificial intelligence to find novel drugs that can block kappa opioid receptors with the hope to alleviate opioid addiction.
Source: Biophysical Society.
Approximately three million Americans suffer from opioid use disorder, and every year more than 80,000 Americans die from overdoses.
Generative AI is a catch-all term describing programs that use artificial intelligence to create new material from complex queries, such as “write a poem about monkeys in the style of Robert Frost” or “make an image of pandas draped over living room furniture.”
While AI more generally refers to software programs that can make themselves better by “learning” from new data, and which have been used behind the scenes in all kinds of software for years, generative AI is a fresh consumer-facing spin on the concept.
About 1,000 people from all over the world, including AI researchers and content marketers, attended Tuesday’s Gen AI Conference, which was organized by startup Jasper. It was a lavish affair, held at Pier 27 on the Embarcadero, overlooking San Francisco Bay.
Human beings are capable of processing several sound sources at once, both in terms of musical composition or synthesis and analysis, i.e., source separation. In other words, human brains can separate individual sound sources from a mixture and vice versa, i.e., synthesize several sound sources to form a coherent combination. When it comes to mathematically expressing this knowledge, researchers use the joint probability density of sources. For instance, musical mixtures have a context such that the joint probability density of sources does not factorize into the product of individual sources.
A deep learning model that can synthesize many sources into a coherent mixture and separate the individual sources from a mixture does not exist currently. When it comes to musical composition or generation tasks, models directly learn the distribution over the mixtures, offering accurate modeling of the mixture but losing all knowledge of the individual sources. Models for source separation, in contrast, learn a single model for each source distribution and condition on the mixture at inference time. Thus, all the crucial details regarding the interdependence of the sources are lost. It is difficult to generate mixtures in either scenario.
Taking a step towards building a deep learning model that is capable of performing both source separation and music generation, researchers from the GLADIA Research Lab, University of Rome, have developed Multi-Source Diffusion Model (MSDM). The model is trained using the joint probability density of sources sharing a context, referred to as the prior distribution. The generation task is carried out by sampling using the prior, whereas the separation task is carried out by conditioning the prior distribution on the mixture and then sampling from the resulting posterior distribution. This approach is a significant first step towards universal audio models because it is a first-of-its-kind model that is capable of performing both generation and separation tasks.
Did you know Google’s artificial intelligence company DeepMind has been working to solve one of the biggest problems in nuclear fusion?
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