How Radboud researchers reconstruct images of people

🧠 Revolutionary AI Technology: Turning Thoughts into Images! 🧠 Researchers have developed groundbreaking AI that can create images directly from brain signals. This mind-reading technology uses non-invasive methods to translate neural activity into visual representations, opening new possibilities for communication and understanding the human mind. This innovation could significantly benefit individuals with communication disabilities and enhance our insights into brain functions. Dive deeper into how AI is reshaping our perception of thoughts and images! 🔗 https://lnkd.in/g5EifHBk

1. Researchers developed a virtual model of a fruit fly's visual system to enhance understanding of brain networks and improve AI efficiency. 2. The model allows scientists to test hypotheses quickly on a computer before conducting lengthy experiments with live subjects. 3. The fruit fly's brain is small and energy-efficient, performing complex tasks with only 100,000 neurons compared to AI systems that use billions of transistors. 4. The research is based on over a decade of studies mapping the fruit fly brain's structure and connections, funded by HHMI. 5. The team created a model simulating the brain circuits responsible for motion detection in fruit flies. 6. The model accurately predicts neuron responses in both the virtual network and actual fruit flies exposed to similar stimuli. 7. The model has been adopted by brain scientists for various studies and is expected to evolve to encompass broader brain functions. 8. Future developments aim to create comprehensive models of brain activity for more complex computations.

History is being made! The prestigious Nobel Prize in #Physics has just been awarded for contributions to #AI Geoffrey Hinton and John Hopfield contribute significantly to AI development, specifically in the development of artificial neural networks. (ANNs) The first of the aforementioned is often called the "godfather of AI." He developed the Boltzmann machine in 1985, which introduced randomness to neural networks, enhancing their learning capabilities. John Hopfield developed the Hopfield network in 1982, an associative memory system that can store and reconstruct data patterns. His research laid foundational principles for modern machine learning technologies. Their work has revolutionized various fields, making them more efficient and accessible daily. ➡️ In physics, they enable the design of advanced materials with specific, desired properties. ➡️ In medicine, their work improves accuracy and speed in medical imaging and diagnostics, leading to better patient outcomes. However, with the positive sides of AI, there are also potential dangers. These include issues related to privacy, job displacement, and the potential for AI systems to surpass human intelligence. "We have no experience of what it's like to have things smarter than us," Hinton said over the phone to the Nobel press conference, speaking from a hotel in California. That's why we have a challenging time ahead of us as humans. We have to ensure that we develop AI responsibly, considering ethical considerations. The future of AI can be bright. Everything is in our hands. If you enjoyed reading this post, I would appreciate it if you would leave a like and share it with your network. Your engagement in my posts gives me the energy to continue writing about AI. #Technology #Nobel #ML

Did you know that computers can now smell? 🤔 In a groundbreaking fusion of neuroscience and artificial intelligence, OSMO (www.osmo.ai), launched in 2023 with $60M Series A funding from Google Ventures, is revolutionizing how we understand our most primal sense - smell. 🧬 The Science behind Digital Scent Using sophisticated graph neural networks trained on 5,000+ molecules, OSMO's platform operates through: • Reading: Converting molecular compounds into digital data ̐• Mapping: Creating standardized frameworks using AI • Writing: Transforming digital data back into perceivable scents OSMO's breakthrough lies in its sophisticated machine learning approach: • Uses graph neural networks to analyze molecular structures • Maps complex atomic bonds and their influence on scent perception • Achieves prediction accuracy surpassing human capabilities • Creates new scent molecules through generative AI Real-World Impact 🌟 1. Healthcare: Digital olfaction shows remarkable potential in early disease detection. Just as ancient physicians used smell for diagnosis, AI-powered devices can now detect distinct metabolic signatures in breath samples, potentially identifying conditions like Parkinson's and Alzheimer's before traditional symptoms appear. 2. Scientific validation: The platform's effectiveness has been validated through collaborations with the Monell Chemical Senses Center, University of Reading, and Arizona State University. 3. Industry impact: The global AI market, valued at $136.55B in 2022, projects a 37.3% CAGR through 2030. OSMO has already introduced three groundbreaking scent molecules, including Glossine, demonstrating practical applications in the fragrance industry. 📈 🔬 Explore more: @OSMO (www.osmo.ai) @OVRTechnology (www.ovrtechnology.com) @MonellCenter (www.monell.org) #DigitalOlfaction #AI #BioTechnology #Innovation #STEM #DataScience Follow STEM Calculator for more.

AI technology can now recreate what you're looking at with astonishing accuracy. 🤖 Allowing AI systems to focus on specific brain regions can significantly enhance their ability to reconstruct images of what a monkey observes from brain recordings. Artificial intelligence systems can now generate remarkably accurate reconstructions of visual experiences based on brain activity recordings. These reconstructions are significantly improved when the AI is trained to focus on particular brain regions. "To my knowledge, these are the closest, most accurate reconstructions," says Umut Güçlü at Radboud University in the Netherlands.

Artificial intelligence continues to push boundaries with the ability to create highly accurate reconstructions of visual experiences based on brain activity recordings. Recent advancements in AI technology have led to remarkable breakthroughs in visual reconstruction accuracy, particularly when the AI is trained to focus on specific areas of the brain. Umut Güçlü from Radboud University in the Netherlands describes these reconstructions as the closest and most precise to date. Learn more about this fascinating development: [Read more here](https://lnkd.in/dTEAquQb)

AI's Quantum Leap: Transforming Neural Imaging and Beyond Imagine a world where the mysteries of the brain are unveiled at lightning speed, and AI models become as intuitive as the mammalian brain itself. At MIT CSAIL, researchers are doing just that, merging AI with cutting-edge electron microscopy to map brain networks in intricate detail. This leap in connectomics signifies a colossal shift not just in neuroscience but in how we perceive and implement AI. Additionally, by harnessing synthetic imagery, these pioneers are setting new standards in AI training. This innovation doesn't only fast-track model training, but it also reduces inherent biases, making machine learning more reliable and universally applicable. It’s a robust approach that business leaders and developers should take note of—an evolution in AI that not only boosts efficiency but precision as well. Meanwhile, techniques like 'HighLight' and 'Tailors and Swiftiles' are streamlining massive AI models, promising significant boosts in machine-learning tasks. These advancements are a testament to how the boundaries of AI are continuously being pushed, ensuring both businesses and tech enthusiasts re-evaluate what they thought they knew about AI's potential. Furthermore, the SecureLoop search tool propels this journey by identifying secure hardware designs that enhance AI tasks without compromising on data security. In a world where data is currency, maintaining security while achieving high performance in AI tasks is revolutionary indeed. These transformative developments echo all around us, silently re-shaping our technological landscape. While we innovate with AI, it seems we can learn much from its evolving patterns, perhaps even about the world and ourselves. Sources: MIT News- https://lnkd.in/ezwRkYVu #AIInnovation, #NeuralImaging

'Culture' is a finite segment of the meaningless infinity of the world process, a segment on which human beings confer meaning and significance. - Max Weber Segmentation is one of the most widely used techniques in computer vision. It can be used for a variety of applications from medical imaging to security. For example, segmenting organ images allows potential diseases to be better studied. Artificial intelligence methods are used today that require very complex and computationally heavy meth- ods. #AI #linkedin

Now AI can read your mind and recreate what you're looking at - with amazing accuracy. - Researchers at Radboud University developed an AI system that reconstructs images from brain activity. - The system uses fMRI (functional Magnetic Resonance Imaging) to measure brain activity, as a person views an image. - The system analyzes brain data and compares it to a database of previously seen images, the AI can reconstruct the image the person is looking at with unusual accuracy. Example Images: - Top row: Original images. - Second row: Images reconstructed by AI based on brain recordings from a macaque (rhesus monkey). - Bottom row: Images reconstructed by the AI system without an attention mechanism. I’m excited to see how this latest AI model will transform our industry. How do you see it impacting your work? Share your thoughts below! #AI #latestTech #innovation #smartWork #thoughtstoimage #mindReading

The Danger of AI is that it could replace government and replace teachers. The combination of brain-computer interfaces and artificial intelligence: applications and challenges https://lnkd.in/gpf-FqDJ