China to set up first international association on deep-space exploration
Located in Hefei, Anhui Province, the association will be the nation's first international academic organization in the aerospace domain, capitalizing on the growing global interest in China's lunar and Mars missions.
The IDSEA will focus on deep-space study, which includes probes into the moon, other planets and asteroids, and promote international cooperation, according to the Hefei-based Deep Space Exploration Laboratory, one of the association's five initiators.
Wang Zhongmin, director of the lab's international cooperation center, said the IDSEA aims to become an inclusive academic platform that will benefit developing countries in particular.
"We hope to bring in as many developing countries as possible, and by initiating small yet impactful programs, such as on CubeSat design and training of scientists, we hope to enable these nations to access cutting-edge space technologies that once seemed far beyond their reach," he said.
Deep-space exploration has long been limited to a few countries due to its high thresholds of capital, technologies and talents. "The vast majority of countries may see a technological monopoly. Deep space technologies must move out of the small circle to benefit the whole of humanity," Wang said.
Despite being a latecomer to outer space exploration, China has rapidly emerged as a prominent player in this field while demonstrating its commitment to cooperating with other nations.
In April, China announced that seven institutions from six countries — France, Germany, Japan, Pakistan, the United Kingdom, and the United States — have been authorized to borrow lunar samples collected by China's Chang'e-5 mission for scientific research.
China has also invited global partners to participate in its Mars missions.
The country plans to launch the Tianwen-3 Mars sample-return mission around 2028, with the primary scientific goal of searching for signs of life on Mars. The retrieval of samples from Mars, the first of its kind in human history, is considered the most technically challenging space exploration mission since the Apollo program.
UNI AAB GNK 1024
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Time of India
3 hours ago
- Time of India
Auriga Research, SFRI ink pact to advance clinical research, AI-driven support for healthcare industry
Contract Research Organization (CRO) Auriga Research Private Limited on Wednesday said it has entered into a strategic agreement with San Francisco Research Institute (SFRI), a US-based healthcare research and innovation organisation, to advance global clinical research , AI-driven support for the healthcare industry. This collaboration aims to strengthen global clinical research, promote AI-integrated healthcare solutions, and expand access to compliant wellness products across India, the United States, and Africa, the company said in a statement. "This strategic partnership with SFRI reflects our commitment to advancing global standards in clinical research and healthcare innovation . By leveraging our strengths in regulatory science, AI-driven trials, and product development , we aim to expand access to safe, effective, and globally compliant health solutions across India, the US, and Africa. It will also help identify untapped opportunities in North America, Europe, Asia-Pacific, and other emerging markets," Auriga Research Managing Director Saurabh Arora said. SFRI will also support regulatory strategy, public health collaboration, and commercial partnerships in markets such as Nigeria and the US, the statement said. This includes enabling product registration with national health agencies, connecting Auriga to distributors and marketers, and directly supporting sales outreach and supply chain strategies, it added. "This partnership combines Auriga's clinical research expertise with our strengths in AI and regulatory capabilities to accelerate the development of compliant, patient-centric health innovations. It marks a strategic step toward bridging global regulatory ecosystems and scaling evidence-based wellness solutions across geographies by managing regulatory submissions and ensuring compliance with agencies such as the FDA, EFSA, TGA, and others," SFRI CEO John Ademola said. The two organisations will jointly expand their Research Training Program through both digital platforms and in-person sessions, while also co-developing new products and AI applications for healthcare. Additionally, both the organisations will pursue government and foundation-funded projects to further their impact on global health systems.
The Hindu
6 hours ago
- The Hindu
Researchers in U.K., Chennai, explore use of AI and social robots for early dementia detection and support
Dementia, a condition that affects millions worldwide, has become a challenge of significant concern as the global population ages. Traditionally, care for dementia has relied heavily on medication, therapy, and the support of family and caregivers. However, now, the approach to dementia care is beginning to evolve in unexpected ways. Researchers are exploring how technology, particularly robotics and Artificial Intelligence (AI), can play a deeper role in supporting human care. Though still in its early stages, a notable example is the collaboration between Imperial College London and the Chennai-based Schizophrenia Research Foundation (SCARF), where a team is investigating how social robots could aid those living with dementia. The aim of this research is not only to provide companionship, but also to detect early signs of cognitive decline. Use of social robots The research seeks to use voice recognition and 'social robots' to detect early signs of cognitive decline. Social robots are those that interact and communicate with humans by following social behaviors and rules. According to Ravi Vaidyanathan, professor in biomecharonics at Imperial College London, who leads the research, the idea is to engage people with dementia and use these interactions to monitor their cognitive health. 'We are looking at how we can use voice interactions to diagnose dementia. By collecting data over time, the AI can help doctors spot early warning signs,' Prof Vaidyanathan explained. He believes the technology has the potential to identify changes in speech, like hesitation, difficulty finding words, or changes in inflection, that could signal the early stages of dementia. 'If we can get people to engage with the robot and enjoy the interaction, we create a richer dataset that may lead to more accurate diagnostics,' he added. The pilot studies conducted so far are said to have shown encouraging results. In one particular study at SCARF, a social robot was used to engage participants diagnosed with dementia. The robot initiated conversations with people about their daily lives, asking simple questions such as, 'How did you sleep last night?' and 'How are you feeling today?' Sridhar Vaitheshwaran, consultant psychiatrist and head of DEMCARES at SCARF, pointed out the positive outcomes, saying, 'People with dementia were genuinely interested in the robot and engaged in meaningful conversations. It was clear that they were interacting with it not as a machine, but as a companion.' This finding highlighted the potential for robots to alleviate feelings of isolation that many people with dementia experience. Data for early detection Prof. Vaidyanathan noted that the key goal of the research isn't just about keeping patients engaged but also about collecting meaningful data. 'We're gathering data in real-time, so it's not just about engaging people. It's about how we can make sure this interaction can lead to something useful. If we can detect early signs of dementia based on these conversations, we can better equip physicians with tools for early diagnosis,' he stated. He also stressed the significance of regular check-ins. 'By having conversations with people every day, we can observe fluctuations in their speech over time, which could be early indicators of cognitive decline.' Challenges in scaling up, data privacy One of the biggest challenges in scaling this research, however, lies in the cross-cultural differences in language, speech patterns, and patient engagement. By testing the technology in different cultural contexts, the team aims to make the AI more adaptable to various accents, linguistic nuances, and communication styles. While the potential for social robots in dementia care is exciting, the project also faces the critical issue of data privacy. In the context of AI, voice recordings and other personal data can raise significant concerns. Prof. Vaitheshwaran said of the importance of ethical research practices, 'I think any research, any data that we gather from people needs to be protected and it has to be ethical, and we need to be careful about what we do with the data,' adding that the Indian Council of Medical Research (ICMR) has developed comprehensive guidelines for the use of patient data. 'All of our studies undergo thorough review by our Ethics Committee to ensure that the data is handled ethically and securely,' he said. The use of voice data, especially in healthcare settings, poses unique privacy challenges. According to the team, the data gathered is handled privately. Close oversight of both the research team and participants is maintained to ensure a degree of control. 'But when it comes to broader implementation, managing these aspects becomes much more complex — and that's something the research community hasn't fully resolved yet,' said Prof Vaidyanathan. He pointed out that as the research evolves, maintaining privacy during the broader deployment of the technology will be an ongoing concern. Early intervention and reducing physician load Looking ahead, Prof. Vaidyanathan said that the next step is to refine the technology for early dementia detection through AI-powered voice screening, which could alert physicians when further evaluation is needed. Ultimately, they aim to support clinicians and people with dementia through technology that is both effective and user-friendly. 'We want to move beyond just helping those already diagnosed with dementia. We aim to identify people at risk before they show any obvious symptoms.' He believes that if dementia is detected in its early stages, it could make a significant difference to managing the disease and improving patients' quality of life. The team also wants to explore combining voice data with other diagnostic methods, like urinary tract information or even genetic markers. 'Voice interactions are one piece of the puzzle, but when combined with other diagnostic tools, we could create a more holistic approach,' Prof. Vaidyanathan said.
Time of India
7 hours ago
- Time of India
How to Become an AI Genius: Lessons students can learn from Meta's $100 million hires
If you want to become an AI genius – the kind that Mark Zuckerberg offers $50–$100 million to join his quest for artificial general intelligence (AGI) – here's the blueprint, decoded from Meta's elite hires. 1. Build a rock-solid maths foundation Almost every AI superstar Meta poached – from Lucas Beyer to Trapit Bansal – started with hardcore mathematics or computer science degrees. Linear algebra, calculus, probability, and optimisation aren't optional. They are your bread and butter. Why? Because AI models are just giant stacks of matrix multiplications optimised over billions of parameters. If you can't handle eigenvectors or gradient descent, you'll be stuck fine-tuning open-source models instead of inventing the next GPT-5. 2. Specialise in deep learning Next comes deep learning mastery. Study neural networks, convolutional networks for vision, transformers for language, and recurrent models for sequence data. The Vision Transformer (ViT) co-created by Lucas Beyer and Alexander Kolesnikov redefined computer vision precisely because they understood both transformer architectures and vision systems deeply. Recommended learning path: Undergraduate/early coursework : Machine learning, statistics, data structures, algorithms. Graduate-level depth : Neural network architectures, representation learning, reinforcement learning. 3. Research, research, research The real differentiator isn't coding ability alone. It's original research. Look at Meta's dream team: Jack Rae did a PhD in neural memory and reasoning. Xiaohua Zhai published groundbreaking papers on large-scale vision transformers. Trapit Bansal earned his PhD in meta-learning and reinforcement learning at UMass Amherst before co-creating OpenAI's o-series reasoning models. Top AI labs hire researchers who push knowledge forward, not just engineers who implement existing algorithms. This means: Reading papers daily (Arxiv sanity or Twitter AI circles help). Writing papers for conferences like NeurIPS, ICML, CVPR, ACL. 4. Dive into multimodal and reasoning systems If you want to be at the AGI frontier, focus on multimodal AI (vision + language + speech) and reasoning/planning systems. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Glicemia acima de 100? Insira essa fruta na sua dieta Saúde Nacional Undo Why? Because AGI isn't just about language models completing your sentences. It's about: Understanding images, videos, and speech seamlessly Performing logical reasoning and planning over long contexts For example, Hongyu Ren's work combines knowledge graphs with LLMs to improve question answering. Jack Rae focuses on LLM memory and chain-of-thought reasoning. This is the cutting edge. 5. Optimise your engineering skills Finally, remember that AI breakthroughs don't live in papers alone. They need to run efficiently at scale. Pei Sun and Joel Pobar are prime examples: engineering leaders who ensure giant models run on hardware without melting the data centre. Learn: Distributed training frameworks (PyTorch, TensorFlow) Systems optimisation (CUDA, GPUs, AI accelerators) Software engineering best practices for scalable deployment The bottom line Becoming an AI genius isn't about quick YouTube tutorials. It's about mastering mathematics, deep learning architectures, original research, multimodal reasoning, and scalable engineering. Do this, and maybe one day, Mark Zuckerberg will knock on your door offering you a $50 million signing bonus to build his artificial god. Until then, back to those linear algebra problem sets. The future belongs to those who understand tensors. Is your child ready for the careers of tomorrow? Enroll now and take advantage of our early bird offer! Spaces are limited.
