Emad Barsoum’s Post

Dedicated AI chips are optimized for specific AI algorithms and have significant advantages in performance and energy efficiency. ** They perform well in neural network calculations, matrix operations, etc., providing strong computing power support for AI applications. ** The rise of this trend marks the development of computing chips from general-purpose to dedicated, bringing new opportunities to the AI industry. #AI #semiconductor #chip #Innovation

### The Path to AGI: A New Era of Supercomputing In the ever-evolving field of artificial intelligence, the concept of Artificial General Intelligence (AGI) represents the ultimate frontier—a system that could surpass human intelligence across multiple disciplines, capable of learning, reasoning, and self-improvement without the limitations of current AI technologies. While AGI remains a hypothetical goal, recent advancements in supercomputing are bringing us closer to this ambitious milestone. #### The Supercomputing Revolution SingularityNET, a pioneer in the AI space, is at the forefront of this revolution. The company is building a modular supercomputer network designed to accelerate the development of AGI. This network will feature some of the most advanced hardware components available, including Nvidia L40S GPUs, AMD Instinct and Genoa processors, Tenstorrent Wormhole server racks featuring Nvidia H200 GPUs, and Nvidia’s GB200 Blackwell systems. These components are not just cutting-edge—they represent the pinnacle of AI hardware technology. The sheer processing power of this supercomputing infrastructure is poised to make a significant impact on the field of AI, moving us beyond the limitations of current systems. #### Building Towards AGI The primary objective of this new supercomputing network is to host and train advanced AI architectures that can support the transition towards AGI. These architectures include deep neural networks that mimic the functions of the human brain, large language models trained on vast datasets, and multimodal systems that integrate various human behaviors, such as speech and movement, with multimedia outputs. Ben Goertzel, CEO of SingularityNET, describes this supercomputer as a breakthrough in the journey toward AGI. He emphasizes that while the novel neural-symbolic AI approaches developed by SingularityNET reduce the need for massive amounts of data and processing power, significant supercomputing facilities are still necessary. The mission is to move beyond traditional deep neural networks to systems capable of non-imitative machine thinking—systems that can perform multi-step reasoning and dynamic world modeling based on cross-domain pattern matching and iterative knowledge distillation. This is not just an incremental improvement. It's a paradigmatic shift towards continuous learning, seamless generalization, and reflexive AI self-modification—key elements that are essential for achieving AGI. ## What’s Next? The first node of this supercomputing network is expected to come online as early as September, with the full network anticipated to be operational by late 2024 or early 2025, depending on hardware delivery timelines. This ambitious timeline underscores the rapid pace of innovation in AI and supercomputing. As we look ahead, it's clear that the development of AGI is not just a distant dream but a tangible goal that is being actively pursued. #Supercomputer #AGI #AI #3TSolutions #Nvidia

🚀 NVIDIA Advances in LLM Efficiency! 🚀 Just explored the recent research paper "LLM Pruning and Distillation in Practice: The Minitron Approach," by NVIDIA, which presents a game-changing method for compressing large language models like Llama 3.1 405B and Mistral NeMo. Here's why it's a must-read: 1️⃣ Powerful Compression: The Minitron approach achieves remarkable compression by reducing the Llama 3.1 8B and Mistral NeMo 12B models to 4B and 8B parameters, respectively, without significant loss in performance. 2️⃣ Pruning and Distillation Synergy: Utilizing both pruning (depth and width) and knowledge distillation, the team achieved state-of-the-art results on multiple language benchmarks, outperforming similarly sized models. 3️⃣ Open-Source Availability: Minitron compressed model weights are open for the community on Hugging Face – a win for researchers and developers looking for efficient, accessible LLMs. 4️⃣ Throughput Boost: With NVIDIA TensorRT-LLM optimization, Minitron models achieve up to 2.7x faster inference speeds, making them ideal for deployment with limited compute. 5️⃣ Industry Impact: This paper highlights how we can achieve high performance at a fraction of the compute and resource cost, bringing advanced AI capabilities closer to real-world applications. Check it out if you're into #AI #ML #LLM, and interested in how pruning and distillation can bring large-scale model efficiency to the next level!

🚀 𝐁𝐑𝐄𝐀𝐊𝐈𝐍𝐆 𝐍𝐄𝐖𝐒 𝐢𝐧 𝐀𝐈 & 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲! 🚀 NVIDIA has just unveiled its latest masterpiece in AI innovation – the Blackwell Superchip. This isn't just another chip; it's a monumental leap forward. With an astounding 208 billion transistors, the Blackwell Superchip is poised to redefine the boundaries of artificial intelligence and computing. What makes the Blackwell Superchip a game-changer? Its pioneering capability to 𝐬𝐞𝐚𝐦𝐥𝐞𝐬𝐬𝐥𝐲 𝐥𝐢𝐧𝐤 𝐰𝐢𝐭𝐡 𝐨𝐭𝐡𝐞𝐫 𝐜𝐡𝐢𝐩𝐬 dramatically accelerates the processing speeds, setting a new standard for AI model efficiency and performance. This leap in technology paves the way for unparalleled advancements in AI applications, from deep learning to complex data analysis. The Blackwell Superchip is more than just a piece of technology; it's a vision for the future, crafted by NVIDIA. It promises to empower creators, innovators, and businesses to explore new frontiers in AI, unleashing potential we've only begun to imagine. The introduction of the Blackwell Superchip marks a new era in AI technology. It embodies the pinnacle of NVIDIA's commitment to pushing the limits of what's possible, opening new horizons for researchers, developers, and businesses alike. Let's discuss: How will the Blackwell Superchip impact the future of AI and computing? What new applications and innovations do you foresee emerging from this technological leap? #AI #Technology #Innovation #NVIDIA #BlackwellSuperchip #FutureOfAI

The human brain is an incredible feat of nature, where complexity meets creativity. And while AI cannot replace a human mind - a neuromorphic system, comes close to mimicking the technicalities of it.   The Hala Point which comprises Intel’s Loihi 2 processors goes beyond the structural element of the brain, channeling its capacity to parallel process. It computes information only when and where required, enabling faster responses, higher outputs and continuous learning, while optimising energy consumption to reduce operational costs drastically.   It’s going to be interesting to see how this intelligence augments convenience and efficiency for enterprises.    Read more: https://lnkd.in/gTauXRQR #IAmIntel #AIEverywhere #AIRevolution

📣 ANNOUNCING THE FASTEST AI CHIP ON EARTH 📣 Cerebras proudly announces CS-3: the fastest AI accelerator in the world. The CS-3 can train up to 24 trillion parameter models on a single device. The world has never seen AI at this scale. CS-3 specs: ⚙ 46,225 mm2 silicon | 4 trillion transistors | 5nm ⚙ 900,000 cores optimized for sparse linear algebra ⚙ 125 petaflops of AI compute ⚙ 44 gigabytes of on-chip memory ⚙ 1,200 terabytes of external memory ⚙ 21 PByte/s memory bandwidth ⚙ 214 Pbit/s fabric bandwidth 📰 Press Release: https://lnkd.in/gjkT_xaC 👨🎓 Learn More: https://lnkd.in/g7RXfDYt 📢 Contact us: https://lnkd.in/gzqvn2z6 #AIChip #GenerativeAI #ML #AI #Supercomputers #LLMs #AICompute

Can neuromorphic computing overcome power and latency constraints that currently limit deployment of multiple real-world, real-time AI capabilities? A recent Intel Labs paper published at ICASSP 2024 found that new neuromorphic approaches using Intel's Loihi 2 can provide orders of magnitude gains in combined efficiency and latency for feed-forward and convolutional neural networks in video, audio denoising, and spectral transforms compared to state-of-the-art solutions. The Neuromorphic Computing Lab at Intel Labs found that several uniquely neuromorphic features enable these gains, such as stateful neurons with diverse dynamics, sparse yet graded spike communication, and an architecture that integrates memory and compute with highly granular parallelism to minimize data movement. The team characterized and benchmarked sigma-delta encapsulation, resonate-and-fire neurons, and integer-valued spikes as applied to standard video, audio, and signal processing tasks. In some cases, the gains exceeded three orders of magnitude, but often at the cost of lower accuracy. Read the paper here: https://lnkd.in/gdT6X4UP #iamintel #Neuromorphic #ArtificialIntelligence #LLM #GenerativeAI

Good resource in fastRAG

𝐟𝐚𝐬𝐭𝐑𝐀𝐆 - 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 fastRAG is a research framework for efficient and optimized retrieval augmented generative pipelines. fastRAG is designed to empower researchers and developers with a comprehensive tool-set for advancing retrieval augmented generation. 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐑𝐀𝐆: Build RAG pipelines with SOTA efficient components for greater compute efficiency. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐥 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞: Leverage Intel extensions for PyTorch (IPEX), Optimum Intel and Optimum-Habana for running as optimal as possible on Intel® Xeon® Processors and Intel® Gaudi® AI accelerators. 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐛𝐥𝐞: fastRAG is built using Haystack and HuggingFace. All of fastRAG's components are 100% Haystack compatible. #rag #fastrag #llms #generativeai #nlproc #deeplearning #ai #datascience