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IISc Bangalore scientists makes a breakthrough which can solve two Major AI Challenges! Researchers at the Indian Institute of Science (IISc) have developed a brain-inspired analog computing platform that promises to revolutionize AI. AI models like ChatGPT and DALL-E typically require 1) huge amounts of power and 2) computational resources, limiting their use to data centers. But with IISc’s new breakthrough, these advanced AI tools could soon run on personal devices like laptops and smartphones! The team even recreated NASA's famous “Pillars of Creation” image usually requiring a supercompute on a tabletop computer, proving the immense potential of their innovation. Unlike traditional computers that operate in only two states, IISc’s platform can manage over 16,500 conductance states within a molecular film... making data processing faster and more energy efficient. By drastically cutting down both time and energy for complex tasks, this advancement could democratize AI development and make India a leader in AI innovation. A huge shoutout to Prof. Sreetosh Goswami and the team at CeNSE, IISc for making this possible! #AI #Innovation #EnergyEfficiency #IISc #TechBreakthrough #Smartphones #FutureOfAI #MadeInIndia #NeuromorphicComputing #AIHardware

The AI Revolution: Hype vs. Reality We're living in a time where Artificial Intelligence is touted as the next big leap in human advancement. From headlines to conferences, there’s excitement and promise everywhere. But let’s address an uncomfortable truth: despite all the hype, the global job market hasn't fully aligned to truly utilize AI tools for sustainable careers. We are told that AI is the future, yet many of us who have developed skills and expertise in these cutting-edge technologies are finding it tough to secure positions that pay well or even offer practical opportunities to apply these tools. Yes, AI is revolutionizing industries, but that revolution doesn’t always translate into accessible or tangible job opportunities today. The gap between the promise and the present is real. It’s time we bridge the disconnect between skills and opportunities, investing in practical solutions that create jobs and utilize AI’s potential. The AI ecosystem needs to support its community with roles that don’t just create impact but also provide financial sustainability. Let’s keep pushing forward with the innovation, but also with a focus on creating opportunities that allow us to thrive—both in the present and in the future. #AI #ArtificialIntelligence #JobMarket #CareerRealities #TechEvolution #FutureOfWork --- This post addresses the gap in the job market for AI professionals while also highlighting the need for real opportunities to be created. Jensen Huang Naveen Jain NVIDIA NVIDIA Taiwan NVIDIA AI NVIDIA University Recruiting NVIDIA Data Center NVIDIA DRIVE NVIDIA Omniverse

🌟 Insights from the NVIDIA AI Summit 🌟 I had the incredible opportunity to attend the NVIDIA AI Summit, thanks to AIVOT AI and alongside my talented colleagues. The event featured industry leaders, including Jensen Huang and Mukesh Ambani, who shared groundbreaking insights into the future of artificial intelligence (AI) and its transformative impact across various sectors. Key Takeaways: 1. Evolution of Computing: The shift from CPU to GPU computing is revolutionizing application acceleration through NVIDIA's CUDA platform, enabling innovations in deep learning, quantum computing, and more. 2. AI in Healthcare: NVIDIA’s initiatives like NIMs and Clara are setting new standards in patient care, with AI avatars enhancing patient interactions and generating synthetic data for improved training models. 3. Accelerating Data Analysis: NVIDIA's RAPIDS platform addresses data processing bottlenecks, significantly reducing analysis time and allowing businesses to leverage vast datasets effectively. 4. Generative AI in India: With strong government support and a skilled workforce, India is poised to become a leader in AI solutions, transitioning from software exports to global AI innovation. 5. Robotics and Physical AI: Discussions on "Physical AI" highlighted the importance of hardware accelerators and data centers in advancing robotics and satellite technologies. 6. Collaboration and Ethics: Emphasis on collaboration among tech companies, government, and academia is crucial for fostering innovation. Ethical considerations in AI deployment are essential for building trust. 7. AI in Education and Job Transformation: AI has the potential to personalize education and transform the job market, underscoring the need for upskilling and reskilling the workforce. These insights along with many more have fueled my excitement about AI's future and its ability to create innovative solutions across industries. A big thank you to AIVOT AI for this opportunity, and I look forward to leveraging these insights in our work! Let’s embrace this AI revolution together! #NVIDIA #AI #GenerativeAI #AIVOT #HealthcareInnovation #DataAnalytics #ArtificialIntelligence #TechForGood #India #AIRevolution #Innovation #Collaboration #EthicsInAI #Education #Mumbai #JobMarketTransformation

Researchers at the Indian Institute of Science (IISc) have developed a brain-inspired analog computing platform capable of storing and processing data in an astonishing 16,500 conductance states within a molecular film. Published on September 11 in the journal Nature, this breakthrough represents a huge step forward over traditional digital computers in which data storage and processing are limited to just two states, the IISc said. In a press release, the institute said that such a platform could potentially bring complex AI tasks, like training Large Language Models (LLMs), to personal devices like laptops and smartphones, thus taking us closer to democratising the development of AI tools. These developments are currently restricted to resource-heavy data centres, due to a lack of energy-efficient hardware. With silicon electronics nearing saturation, designing brain-inspired accelerators that can work alongside silicon chips to deliver faster, more efficient AI is also becoming crucial. According to IISc, the fundamental operation underlying most AI algorithms is quite basic - matrix multiplication, a concept taught in high school maths. But in digital computers, these calculations hog a lot of energy. The platform developed by the IISc team drastically cuts down both the time and energy involved, making these calculations a lot faster and easier. #neuromorphic #computing #analog #physics #chemistry #memory #breakthrough https://lnkd.in/gvew-i55

Researchers at the University of Pennsylvania have developed a new computer chip that uses light waves instead of electricity. This could improve the training of artificial intelligence (AI) models by improving the speed of data transfer and, more efficiently, reducing the amount of electricity consumed. has designed a silicon-photonic (SiPh) chip that can perform mathematical computations using light. The team turned to light as it is the fastest means of transferring data known to humanity. However, using widely abundant #silicon ensures the technology can be scaled quickly. The #chip that can perform vector-matrix multiplications. A common mathematical computation that are critical when developing architecture to power #AI models being developed today. In addition to performing computations faster and with less electricity consumption, SiPh chips can also address data #privacy concerns. Since the chip can perform multiple computations in parallel, there is no need to store the information in a working memory while the computations are performed. Rahul Arya Alex Bulat- van den Wildenberg Jonathan Nussbaumer Loïc Hamon Pascal Brier https://lnkd.in/ev85cqsK?

🚀 Exciting News in the AI World! 🚀 NVIDIA, the US tech giant, has just unveiled its latest artificial intelligence chip, setting the stage for groundbreaking advancements in AI technology! What does this mean for the hiring industry in AI? 🧠 With NVIDIA pushing the boundaries of AI hardware capabilities, the implications for the hiring industry are profound. Here's why: 1️⃣ Increased Demand for AI Talent: As AI technology continues to evolve, the demand for skilled professionals in AI development, research, and implementation will soar. Companies will be on the lookout for talented individuals who can harness the power of NVIDIA's latest chip to drive innovation and solve complex problems. 2️⃣ Accelerated Innovation: The availability of cutting-edge AI hardware like NVIDIA's new chip will accelerate innovation across industries. This means that organisations will need to ramp up their hiring efforts to stay competitive in the rapidly evolving AI landscape. 3️⃣ Specialised Skill Sets: The development and optimisation of AI algorithms for NVIDIA's new chip will require specialised skill sets. Companies will need to recruit individuals with expertise in areas such as deep learning, parallel computing, and GPU optimisation to fully leverage the capabilities of this groundbreaking technology. 4️⃣ New Opportunities: The emergence of advanced AI hardware opens up new opportunities for companies to explore novel use cases and applications. This will create a demand for talent with the vision and creativity to capitalise on the potential of NVIDIA's latest chip. In conclusion, NVIDIA's latest AI chip represents a significant milestone in the advancement of artificial intelligence technology. As companies across industries race to harness the power of this groundbreaking hardware, the hiring industry in AI will play a pivotal role in shaping the future of innovation. #AI #Innovation #NVIDIA

Neuromorphic Computing Market Size, Share & Trends Analysis Report By Deployment (Edge Computing, Cloud Computing), By Offering (Hardware, Software), By Industry Vertical (Automotive, IT & Telecom, Aerospace & Defense, Industrial, Healthcare, Others), By Application (Image Recognition, Signal Recognition, Data Mining, Others), COVID-19 Impact Analysis, Regional Outlook, Growth Potential, Price Trends, Competitive Market Share & Forecast, 2022 - 2028. The neuromorphic computing market is poised for significant growth as advancements in AI, machine learning, and robotics continue to demand more efficient and powerful computing solutions. As research progresses and the understanding of brain functions deepens, neuromorphic systems are expected to become increasingly capable and versatile, leading to broader adoption across various industries. The growing emphasis on energy-efficient computing, particularly in the context of AI and IoT, will further drive interest and investment in neuromorphic computing technologies. With ongoing research and development, the potential applications of neuromorphic computing are vast, ranging from healthcare and autonomous systems to smart cities and advanced robotics. Overall, the future of neuromorphic computing holds great promise for revolutionizing the way we approach computing, data processing, and intelligent systems. IMIR Market Research Pvt. Ltd. 𝐆𝐞𝐭 𝐭𝐡𝐞 𝐬𝐚𝐦𝐩𝐥𝐞 𝐜𝐨𝐩𝐲 𝐨𝐟 𝐭𝐡𝐢𝐬 𝐩𝐫𝐞𝐦𝐢𝐮𝐦 𝐫𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/d6XMuji4 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐢𝐧 𝐭𝐡𝐞 𝐦𝐚𝐫𝐤𝐞𝐭: Applied Brain Research Applied Materials Brain Corp BrainChip RainGrid, Inc. Brain-Machine Interface Systems Lab Brainwave Technologies CEA-Leti CognitiveScale Cortical.io Deep Vision Consulting Element Materials Technology Entropix Ltd General Vision, Inc. Graphcore GreenWaves Technologies HACARUS Hewlett Packard Enterprise HRL Laboratories, LLC IBM ICRON Intel Corporation KNOWME Koniku Lattice Semiconductor MemComputing, Inc. Merck KGaA, Darmstadt, Germany Mythic Neurala NeuroBlade Neuromorphic LLC Neuronic.ai NNAISENSE Numenta #computing #tech #technology #computer #software #b #programming #computerscience #computers #iphone #business #developer #socialmedia #cloud #maintenance #wordpress #cms #instagood #javascriptdeveloper #developerdiaries #agency #marketingdigital #it #codegency #gaming #programmer #engineering #engineer #webdevelopment #stem

Will the AI and computer science field become oversaturated by 2030, or will it evolve to create new opportunities? The rapid growth in Artificial Intelligence and other computer science fields has sparked concerns about market saturation. However, history shows that technological evolution consistently drives demand for new skills, innovations, and ideas. By 2030, it’s likely that advancements in AI, robotics, quantum computing, and other emerging technologies will open doors to entirely new opportunities. While competition might increase, the field is expected to expand, absorbing talent and offering chances for those who stay adaptable and continue learning. The key will be to focus on niche areas, interdisciplinary applications, and leveraging creativity alongside technical skills. What are your thoughts? Do you believe the market will become oversaturated, or will innovation keep creating room for newcomers? #ai #jobs

Scientists in China have built a new type of tensor processing unit (TPU) — a special type of computer chip — using carbon nanotubes instead of a traditional silicon semiconductor. They say the new chip could open the door to more energy-efficient artificial intelligence (AI). AI models are hugely data-intensive and require massive amounts of computational power to run. This presents a significant obstacle to training and scaling up machine learning models, particularly as the demand for AI applications grows. This is why scientists are working on making new components — from processors to computing memory — that are designed to consume orders of magnitude less energy while running the necessary computations. https://lnkd.in/eHj6gwAT

Exploring Opportunities in Embodied AI Embodied AI is at the forefront of innovation, combining advanced machine learning models with physical devices to enhance interactions. This technology is poised to reshape industries such as robotics, autonomous vehicles, and IoT devices, fostering smarter and more intuitive solutions. In tackling the challenge of deploying compute-intensive AI models on edge devices, researchers and engineers can focus on key optimization strategies and skills: 🔧 Chip Design for Edge Inference Developing AI inference chips optimized for real-time processing is crucial in the era of edge computing. Skills like hardware design, VLSI, ASIC/FPGA design, digital/analog circuit design, and microarchitecture optimization are essential for reducing latency and enhancing data processing efficiency at the edge. Leading companies in this space include NVIDIA , Qualcomm , Intel Corporation , Tesla , Apple , and Google . ⚙️ Compiler Optimization Engineers proficient in compiler optimization play a vital role in integrating foundation models for specific hardware device . Their expertise enhances code performance and resource management, enabling the execution of complex AI models on specialized hardware. Key skills include Parallel programming like CUDA, LLVM and high-performance computing, with companies like NVIDIA , Intel Corporation , ARM, IBM, Microsoft , and Google leading the way. 🖥️ Firmware and Embedded Programming Firmware engineers have the opportunity to create adaptive embedded systems that leverage foundation models for seamless hardware-software interaction. Proficiency in embedded systems, C/C++, RTOS, microcontroller programming, low-level hardware interfaces, and optimization for constrained environments is essential. Companies like Bosch , NVIDIA, NXP Semiconductors Semiconductors, Texas Instruments, and Qualcomm are pioneers in this domain. 🧠 AI Model Development and Optimization Researchers can explore optimization methods such as transformers, flash attention , distillation , quantization, and pruning to enhance AI models. Skills in deep learning, frameworks like pytorch , neural network , and model compression techniques are key, with industry leaders including Meta, Google DeepMind , OpenAI , Hugging Face , Microsoft Research, and Stanford University paving the way in AI innovation. As AI foundation models develop at a fast pace, it's crucial to further improve on the above areas to utilize them efficiently. This post is an high-level overview and I hope it gives you insight into the vast landscape of running foundation AI models on edge devices. #AI #MachineLearning #Optimization #EdgeComputing #iot #robotics #autonomousvehicles #embeddedsystems #compilerengineer #foundationmodels