Intel Labs’ Post

During the recent 2024 World Government Summit, Nvidia's CEO Mr. Jensen Huang shared some insights about the future of technology. He emphasized that the era of learning about computers is over, and that life sciences are the future. Mr. Jensen Huang highlighted the limited progress in life sciences compared to the rapid advancements in computer science and chip technology. If given the chance to choose again, he would focus on engineering life sciences, envisioning a future of "bioengineering" that goes beyond traditional scientific boundaries. This perspective resonates with the growing importance of leveraging biological knowledge and technology to shape the future. #FutureTech #LifeSciences #Bioengineering #Innovation

Silicon computer chips may have served us well for more than half a century, but the limit of what can be achieved with standard materials and processes may be near. 💻 The Lab's Shoaib Khalid, PhD and Bharat Medasani, along with Anderson Janotti from the University of Delaware, have identified a way to move toward materials that are so thin — often made up of only a few layers of atoms ⚛️ — that scientists have taken to calling them 2D (two dimensional), laying the groundwork for the creation of next-generation computer chips.

Today, when I was having a breakfast, I was playing with a pair of magnet, started thinking about how the magnetic field can influence the field of computing and AI. while searching for this I came to know about the new type of computing getting developed, because current systems are consuming a lot of energy for training LLMs and other systems such as OpenAI and all other companies chatbot services and there is a estimation also we will need 10^27 Jules of energy for creating and maintaining computing system if we continue with the current architecture of CMOS based systems ( CPU + Memory). And this is a bottleneck to train ANN's because this is highly energy intensive, because of data transfer from CPU to memory continuously. "NEUAROMORPHIC COMPUTING" is the new terminology for some of you, companies like INTEL are started working on this, there will complete change in the way the current computers work; Basically, in this new architecture they are trying to mimic the how brain work, brain doesn't has any separate things like Memory is one , and CPU is at the other side. Our brain is single entity, with the help of neurons and the exchange of ions between them, like this it is implemented in upcoming technology. For more details go through this video https://lnkd.in/ehuhAqg9 Its really interesting, it will change the world. #neuromorphicchips #intellabs #humanbrain #humanintelligence

A synaptic transistor that mimics human brain operation. It processes and stores information simultaneously, retains data without power, and can provide efficient, brain-like computing at room temperature. https://lnkd.in/day2Qjgk

Ananth Kandhadai’s work on computer vision and signal processing gave smart devices the power to see and perceive the world for greater connected experiences. Learn more about his contributions to Qualcomm in recognition of #NationalInventorsMonth:

🔍 Exploring the World of Machine Vision: FAQ series.   What are the biggest challenges of computer vision? 👉 Follow Anders Electronics for more insights    #ComputerVision #DeepLearning #ObjectDetection #FacialRecognition #AnderElectronics

New this year Vanderbilt University School of Engineering with Engineering Science, Vanderbilt University Department of Computer Science, and Vanderbilt University Electrical and Computer Engineering: ES 3890: Special Topics - Studio in AI. Discover what AI means to you with ACCRE (https://lnkd.in/g8wxfyrE) GPUs. For example, Prof. Bennett Landman enjoys creating AI cats with StableDiffusion XL from HuggingFace.co. (1 unit/no prereq.)

Future vision ? The integration of organoid neural networks (ONNs) with quantum computing and artificial intelligence (AI) could offer several groundbreaking benefits: Enhanced Computational Power: Quantum computing operates on the principles of quantum mechanics, enabling it to process complex problems much faster than classical computers. When combined with ONNs, which mimic aspects of the human brain, this could lead to unprecedented computational abilities. This synergy could facilitate more advanced AI applications, such as highly sophisticated machine learning models that can learn, adapt, and make decisions much like the human brain. Improved Efficiency and Speed: Quantum computers can perform calculations at incredibly high speeds and handle vast datasets efficiently. Coupled with ONNs, this can lead to faster learning and decision-making processes in AI systems, significantly outperforming current AI capabilities. Advanced Problem-Solving: Quantum computing's ability to handle complex, multivariable problems could be amplified with the cognitive-like processing of ONNs. This combination could be particularly useful in fields like drug discovery, climate modeling, and complex system simulations, where both intricate calculations and cognitive-like processing are essential. Enhanced Learning Algorithms: The integration of ONNs with quantum computing could lead to the development of new, more powerful machine learning algorithms. These algorithms could theoretically learn from a vast array of data much more quickly and accurately than current models. Understanding of Cognitive Processes: This combination could provide deeper insights into human cognitive processes by enabling the simulation and analysis of brain-like structures and functions at an unprecedented scale. Energy Efficiency: Quantum computers are known for their potential energy efficiency. When integrated with ONNs, the combined system could offer powerful computational capabilities with a lower energy footprint compared to traditional computing methods. Customized AI Applications: By leveraging the adaptability of ONNs and the power of quantum computing, AI applications could be customized for highly specific tasks in ways that current technologies cannot achieve, leading to more effective and efficient solutions. However, it's important to note that both ONNs and quantum computing are still in relatively early stages of development, and their integration presents significant technical and ethical challenges. The field would need substantial advancements in understanding and technology, along with rigorous ethical oversight, to realize these benefits fully. #brainoware, #AI, #quantum, #ONN #futureAI#humaware #irdna #alldifferentallXceptionnals

Staying in the loop: How superconductors are helping computers 'remember': To advance neuromorphic computing, some researchers are looking at analog improvements -- advancing not just software, but hardware too. Research shows a promising new way to store and transmit information using disordered superconducting loops. #ScienceDaily #Technology

🔍 Exploring the World of Machine Vision: FAQ series.   What are the biggest challenges of computer vision? 👉 Follow Anders Electronics for more insights    #ComputerVision #DeepLearning #ObjectDetection #FacialRecognition #AnderElectronics