Can machine learning be used to advance exoplanet science, and can this be done by non-scientists, as well? This is what Ariel Data Challenge 2024 hopes to address as participants from around the world will compete to develop machine learning algorithms designed to analyze data from space telescopes with the goal of gaining greater insight into exoplanet atmospheres. This competition will be featured at the NeurIPS 2024 machine learning conference and holds the potential to not only advance the field of exoplanets but also enable non-scientists to conduct pioneering research, as well.
“By supporting this challenge, we aim to find new ways of using AI and machine learning to develop our understanding of the universe,” said Dr. Caroline Harper, who is the Head of Space Science at the UK Space Agency. “Exoplanets are likely to be more numerous in our galaxy than the stars themselves and the techniques developed through this prestigious competition could help open new windows for us to learn about the composition of their atmospheres, and even their weather.”
Along with the UK Space Agency, other institutions supporting this challenge include the STFC DiRAC HPC Facility, European Space Agency (ESA), and STFC RAL Space. The competition is named after the ESA’s Ariel Space Mission, which is currently scheduled for launch in 2029 with the goal of using the transit method for identifying more than 1,000 exoplanets.
Scientists propose a new way of implementing a neural network with an optical system which could make machine learning more sustainable in the future. The researchers at the Max Planck Institute for the Science of Light have published their new method in Nature Physics, demonstrating a method much simpler than previous approaches.
Machine learning and artificial intelligence are becoming increasingly widespread with applications ranging from computer vision to text generation, as demonstrated by ChatGPT. However, these complex tasks require increasingly complex neural networks; some with many billion parameters. This rapid growth of neural network size has put the technologies on an unsustainable path due to their exponentially growing energy consumption and training times. For instance, it is estimated that training GPT-3 consumed more than 1,000 MWh of energy, which amounts to the daily electrical energy consumption of a small town. This trend has created a need for faster, more energy-and cost-efficient alternatives, sparking the rapidly developing field of neuromorphic computing. The aim of this field is to replace the neural networks on our digital computers with physical neural networks.
While AI has the potential to automate many tasks, there are certain jobs that require human skills and abilities that AI cannot replicate. These include jobs that require creativity, empathy, critical thinking, and human interaction. According to the World Economic Forum, AI is unlikely to be able to replace jobs requiring human skills such as judgement, creativity, physical dexterity and emotional intelligence. Some examples of jobs that AI cannot replace include psychologists, caregivers, most engineers, human resource managers, marketing strategists, and lawyers. In this video, Dr. Michio Kaku mentioned three specific types of jobs that AI cannot replace: blue-collar jobs that are not repetitive, emotional jobs, and jobs requiring imagination. These types of jobs require human skills and abilities that are difficult for AI to replicate. For example, blue-collar jobs that are not repetitive often require physical dexterity and mobility. Emotional jobs require empathy and the ability to connect with others on a personal level. Jobs requiring imagination involve creativity and innovation. In conclusion, while AI has the potential to automate many tasks and change the job landscape, there are certain jobs that require human skills and abilities that AI cannot replicate. These include blue-collar jobs that are not repetitive, emotional jobs, and jobs requiring imagination. It is important for individuals to develop these skills in order to thrive in the future job market. Fair Use Disclaimer : Copyright disclaimer under section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, commenting, news reporting, teaching, scholarship and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. Disclaimer: The video and audio content used in this video is for educational purposes only and does not belong to me. I have given credit to the respective owners and creators of the content. This video is intended to provide information and knowledge to its viewers, and no copyright infringement is intended. I have made every effort to ensure that the content used in this video is properly credited and used in accordance with fair use guidelines. If you are the owner of any content used in this video and have any concerns, please contact me. Legal Disclaimer : The video clips incorporated into this project are the sole property of their respective owners and creators. I do not claim ownership or rights to any of the content used. All credit is attributed to the original sources. No copyright infringement is intended. Clips Provided by Cuckoo for Kaku Watch : https://youtu.be/JANGUKLJkPQ #shorts #shortsfeed #shortvideos #shortvideo #shortsvideo #shortsyoutube #shortsviral #viralshortsvideo #viralshorts #viral #viralvideo #viralvideos #space #spaceflightsimulator #deepspace #spaceship #spacelovers #spacesuit #spaceexploration #spacecraft #telescope #spacex #spacestation #universe #cosmos #nasa #viral #viralvideo #viralvideos #science #technology #physics #astronomy #astrophysics #astrophotography #cosmology #cosmos #jwst #jameswebbspacetelescope #jameswebb #hubble #hubbletelescope #video #videos #interstellar
The latest AI News. Learn about LLMs, Gen AI and get ready for the rollout of AGI. Wes Roth covers the latest happenings in the world of OpenAI, Google, Anthropic, NVIDIA and Open Source AI.
We’re living in an aging society with cognitive loss placing stress on caregivers to monitor older adults struggling with memory decline.
MemPal is a wearable voice-based memory assistant that helps older adults live more independently and safely at home while also reducing caregiver burden. MemPal uses AI to automatically log the user’s actions in real-time based on visual context from a wearable camera without storing any image data, thereby preservinguser privacy. With this activity log, MemPal helps older adults recall locations of misplaced objects and completion of past actions using simple voice-based queries such as “Hey Pal, where is my phone?” Additionally, MemPal providescontext-basedproactive safety reminders (e.g., “you may have forgotten to turn off the stove” or” you already took your medicine an hour ago”) and automatically tracks the completion on the MemPal app, allowing for remote monitoring by caregivers. Lastly MemPal can generate an automatic, summarized diary of activities for caregivers that may also prove useful for physicians to better understand patient behavior within their home.
MemPal was tested within the homes of 15 older adults (ages 65+). Our study demonstrated improved performance of object finding with audio-based assistance compared to no aid and positive overall user perceptions on the designed system. We discuss future design guidelines to adapt these types of wearable systems to various older adults’ needs.
Artificial neural networks—algorithms inspired by biological brains—are at the center of modern artificial intelligence, behind both chatbots and image generators. But with their many neurons, they can be black boxes, their inner workings uninterpretable to users.
Researchers have now created a fundamentally new way to make neural networks that in some ways surpasses traditional systems. These new networks are more interpretable and also more accurate, proponents say, even when they’re smaller. Their developers say the way they learn to represent physics data concisely could help scientists uncover new laws of nature.
Now, scientists in China have developed robots that give human-like realistic expressions.
The humanoid robot with highly expressive facial features is developed by Liu Xiaofeng, a professor at Hohai University in east China’s Jiangsu Province, and his research team.
For the development of this robot, the research team developed a new algorithm for generating facial expressions on humanoid robots.
