Jay Prakash, PhD

🌟 Exciting News! 🌟 Join us for our new webinar series, "Understanding Reversibility: Edward Fredkin Memorial Lectures," organized by Cellular Automata India (CAI). This series pays tribute to the pioneer in reversible computing, Edward Fredkin, whom we lost on June 13, 2023. Our focus spans across various scientific disciplines, exploring the theme of Reversibility. Here's a glimpse of what to expect: 1. Logic Synthesis methods for reversible and quantum circuits by Susmita Sur-Kolay, ISI Kolkata 2. Time-reversal Symmetries in reversible Cellular Automata by Kenichi Morita, Hiroshima University, Japan 3. Exploring irreversibility in the macro-world by Pradeep Kumar Mohanty, IISER Kolkata 4. Asynchronism and Reversibility by Sukanta Das, IIEST Shibpur 5. Reversibility and Irreversibility in Cellular Automata and Physics, with insights from Stephen Wolfram, Wolfram Research 6. The gift of invertibility across math, physics, and cellular automata by Tommaso Toffoli, Boston University, USA 7. Negation, Absence, and Reversibility by Smita Sirker, JNU India Mark your calendars! 📅 🗓️ Every Saturday from April 27 to May 18, 2024 ⏰ 2:00 – 6:00 pm IST Join us on Google Meet: (https://lnkd.in/gF-4-VPJ) For detailed schedule, click here: (https://lnkd.in/ge3aUWvn) Don't miss out on this unique opportunity to engage in lively discussions with eminent speakers from diverse domains. Let's make this event a resounding success together! #CellularAutomataIndia #CAI #Reversibility #webinar #research #toffoligate #quantum #cellularautomata #wolfram #reversiblecomputing

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An IEEE SA whitepaper authored by ADWG members have been published. -Simulation, Testing, Verification, and Validation (STV2) of Autonomous Driving. It can be downloaded at https://lnkd.in/gvVsF5P3.

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I'll be attending the IEEE Conference on Artificial Intelligence (CAI) on the sunny island of Singapore next week! If you're also planning to be there, I'd love to connect and discuss the latest AI advancements. 2024 IEEE Conference on Artificial Intelligence https://ieeecai.org/2024/ #AIforgood #responsibleAI

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RAG ... a necessary but *not* sufficient element of reliable NIR ... "Neural Information Retrieval". As revolutionary as LLMs are, NLU is not solved. The old NLU benchmarks are obsolete, but the challenge of genuinely *understanding* and fully responding to specific user intent remains before us. And many of the old tools will find new applications in a new synthesis. Call it Neural Information Retrieval. https://lnkd.in/eD72UBQ4

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⛱️ Mixture of Experts Primer 🚀 The Mixture of Experts (MoE) model is revolutionizing large language models (LLMs) by enabling unprecedented scalability and efficiency. 🌟 🔗 Link to the primer on vinija.ai https://lnkd.in/gTS7eBzC . Written in collaboration with Aman Chadha MoE divides complex problems into smaller sub-tasks, each managed by specialized sub-models or "experts." This not only enhances performance but also optimizes computational resources by activating only the most relevant experts for each input. Key aspects: - Scalability: Linear increase in capacity without added computational complexity. - Efficiency: Activates only the necessary experts, reducing computation. - Flexibility: Adapts to various tasks with specialized experts. MoE is the driving force behind some of the most advanced AI models today: - Google’s Switch Transformer - OpenAI’s GPT-4 - Mistral AI’s Mixtral - Google’s Gemini 1.5 These advancements highlight MoE’s potential to create more powerful and efficient AI solutions. 🌍

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How is Brain Computing Interface intergting with Artificial intelligence ? Brain-Computer Interfaces (BCIs) are integrating with Artificial Intelligence (AI) to enhance the interpretation and processing of neural signals. AI algorithms, especially those in machine learning and deep learning, can analyze the complex patterns of brain activity captured by BCIs, improving the accuracy and efficiency of translating these signals into commands for computers or prosthetic devices. This synergy allows for more intuitive control in applications such as neuroprosthetics, gaming, and rehabilitation, and advances the development of technologies that can adapt and respond to individual neural inputs in real-time.

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Hello Connections My research article entitled "Synchronization and implementation of a real-time traffic signal optimization regulator" has been published in the MTAP Springer Journal with IF-3.6 #ESCI #Scopus #springer In this article, the proposed system most cosmopolitan cities like Bengaluru, the major problems of congestion, such as increased vehicular density, travel time, stress, and vehicular carbon emissions. These issues have led to quantifiable and non-quantifiable losses, such as high vehicle operation and maintenance costs, reduced human productivity and mobility, increased health hazards, etc. Sustainable transportation, coupled with Technological interventions, could alleviate these issues by efficiently managing traffic flow. The major aims of this work are to reduce the overall commute time, traffic congestion, and air pollution, making the commutation of public stress-free and happy. So, we propose a comprehensive Real-time Traffic Signal Optimizer and Regulator (RTSOR) that identifies the right High-Density Corridors (HDC) and achieves these aims by successfully attaining coordination among the Adaptive Traffic Signal Control System (ATSCS) fixed on the successive junctions of HDC considered in this work as a pilot initiative by upgrading the existing traffic signal in Bengaluru City to the latest ATSCS technology. Implementing this proposed RTSOR has effectively lessened the waiting time in traffic, travel time, and fuel consumption and improved air quality by reducing pollution and lowering stress levels, leading to improved quality of health and life of the public. In Bengaluru city, the current innovation would be implemented for the first time, and no prior model exists that could help optimize the travel time. The method that the study envisaged through RTSOR focuses on collecting real-time data on an HDC to elucidate our claim of originality in reducing the delay time on roads by signal synchronization. https://lnkd.in/gnDQEAQg

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Will #Ethernet be the preferred network for #AI? Spirent Communications rolls out testing for AI for networking. Check out my post on SDxCentral thanks to Aniket Khosla for the comments https://lnkd.in/g-pdQ98U

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The 7th workshop on Neural Scaling Laws is about to start: https://lnkd.in/gQSB5k8v Scaling laws, emergency, alignment, transfer learning, with a focused track on multilingual models. Looking forward to kick it off with Irina Rish and discuss all these topics with Yuhai Tu, Paul Bogdan, Hailey Schoelkopf, Darshil Doshi, Rio Yokota, Neha Sengupta, Preslav Nakov, Tatiana Shavrina, PhD, Yishi Xu. Join in person or virtually! #icml2024 #ai

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𝗪𝗶𝗹𝗹 𝗔𝗜 𝗛𝗶𝘁 𝗮 𝗗𝗮𝘁𝗮 𝗪𝗮𝗹𝗹 𝗶𝗻 𝟮𝟬𝟯𝟬? 🆘 Very interesting analysis from epoch.ai about data corpus available for training large language models and whether we will hit the ceiling soon. https://lnkd.in/gD78f9DW The authors estimate that all available data will be used by ~2030, but they're also the first to admit that by then we will have ways to address "the shortage" (such as synthetic data, data efficiency improvements, use of other modalities). What I found more interesting though is why the latest predictions differ from their 2022 paper, where they speculated that all the data will be used by 2024: ☑ Web data proves more valuable than expected (on par with scientific papers) ☑ Models can "reuse" datasets multiple times 2030 is right around the corner, so we will see a lot of innovation on this front and creative ideas on how to 'recycle' and make best use of the data we already have. These estimates also don't take into account proprietary data owned by individual companies. In this No priors episode (https://lnkd.in/g2BkTJBz) there is a mention of JPMorgan owning 150PB of proprietary data, which is at least 150x of what was used to train GPT-4. JPMorgan is monetizing this data today (https://lnkd.in/gXjE92qx), but it gets exponentially more valuable in a world where data is a finite (and extremely sought after) resource.

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Prime Intellect has launched INTELLECT-1, the first decentralised training of a massive 10 billion parameter model. 🧠💪 #AI #DecentralizedTraining #OpenSourceAI 𝐊𝐞𝐲 𝐡𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ➡️ Scaling up their previous OpenDiLoCo work by 10x to train a 10B parameter model based on the Llama-3 architecture ➡️ Leveraging the Distributed Low-Communication (DiLoCo) method to efficiently train across globally distributed compute resources with minimal communication overhead ➡️ Achieving 98% compute utilization and 400x communication reduction through techniques like int8 quantization of pseudo-gradients2 ➡️ Training on a curated mix of high-quality open datasets like Fineweb-Edu, totaling over 6 trillion tokens Prime Intellect has an ambitious roadmap to further scale decentralized training to even larger and more powerful models across domains like language, reasoning, and science. 📈🔬 🔗 https://lnkd.in/d3yCNxKB #FutureOfAI #CollaborativeInnovation

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GPuaaS is going to be a big market. Learn more about this market through our blog.

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No Vendor lockins, No Proprietary Stuff, Freedom in using the Cloud, Data Sovereignity is the way to go! And of course lower TCO and better quality of service. Computing should be a utility, simple and easy to use, not just for techies. This is what we are set out to do at People+ai #OpenCloudCompute #OCC

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Apple is developing specialized chips aimed at enhancing AI capabilities within data centers though an initiative, dubbed Project ACDC. #DataScience #AI #ArtificialIntelligence https://hubs.li/Q02xZ-vQ0 If you enjoyed this post, follow me for more insights on AI and data science!

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⛵️Transitioning from classical to post-quantum encryption is an important step to take now while you have time to migrate and plan properly. It will also help mitigate the risk of store-now-decrypt-later attacks – a threat posed by quantum computers.💡 TQ42 Cryptography, the new open-source library on GitHub, puts the power in your hands with a full suite of functionality designed to help you in your journey: Hash functions, symmetric encryption, asymmetric encryption, digital signature, key encapsulation mechanism, pseudo random number key generation, plus key management functions like proprietary containers and secure deletion. ⭐️ Library: https://lnkd.in/eFCFmGHV 📚 Docs: https://lnkd.in/eMWGx_7m If you want support, Terra Quantum AG provides a Security Transformation Program, and if you're interested in diving deeper into quantum security, we offer our Terra Quantum Secure Network. Learn more at https://lnkd.in/eZ4C4Zee. #postquantum #quantum #cryptography #quantumresistant #kem #rng #falcon #aes #mceliece #keymanagement #secretsmanagement

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