Prashant Y.

Inexpensive token generation and agentic workflows introduce fresh prospects for training Large Language Models (LLMs) with synthetic data, potentially elevating output quality. Direct pretraining of LLMs on their generated responses proves ineffective, but employing agentic workflows could yield superior results, akin to humans learning from their own thinking processes. LLM training typically comprises pretraining on vast amounts of unlabeled text data, followed by fine-tuning and alignment to human values. However, sourcing adequate training data remains a hurdle, prompting exploration of innovative methods like training smaller models on the output of larger ones. Despite the substantial cost of token generation, agentic workflows present promising avenues for creating high-quality synthetic data, ultimately enhancing LLM performance.

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Dive into video segmentation with AI! This article offers a detailed guide on using SAM for effective segmentation. A valuable resource for Data Scientists exploring solutions in this area. #SAM #computervision #deeplearning #videosegmentation #aiml

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Amazing findings by Hugh Zhang, Michele L., Summer Yue, and others at Scale AI, addressing a critical concern regarding Large Language Models (LLMs) evaluation and the challenge of data contamination! With LLMs achieving remarkable success on various benchmarks, concerns have arisen about the extent to which this performance reflects true reasoning ability versus exposure to similar benchmark questions. In response, the paper https://lnkd.in/gttAW-g2 undertook an investigation, introducing Grade School Math 1000 (GSM1k) to closely mirror established benchmarks like GSM8k. Their findings shed light on the prevalence of overfitting, particularly among models like Mistral and Phi, which exhibit accuracy drops of up to 13% on GSM1k compared to GSM8k. However, models at the forefront, such as Gemini, Claude, and GPT, demonstrate minimal signs of overfitting. This research not only underscores the importance of robust benchmarking but also prompts critical reflections on the evaluation of LLM performance. Kudos to the team for their work! #llm #research #genai #machinelearning #ai

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Exciting news in the AI world! Meta has recently unveiled Spirit LM, their first open-source multimodal language model that seamlessly combines text and speech. This groundbreaking model addresses the limitations of current approaches by directly processing speech with text, resulting in more natural-sounding outputs and enhanced understanding. Key features of Spirit LM include: Open-source accessibility: Allowing researchers and developers to explore and contribute. Multimodal integration: Seamlessly combining text and speech for a richer experience. Expressive capabilities: Capturing emotions and nuances through phonetic, pitch, and tone tokens. Versatility: Adaptable to various speech applications like ASR, TTS, and classification. Spirit LM has the potential to significantly advance the field of natural language processing and human-computer interaction. Want to learn more? Check out the details, code, and model weights here: ➡️ https://go.fb.me/2jooyy. Let's explore the exciting possibilities of this new technology together! #AI #MachineLearning #OpenSource #NLP #SpeechRecognition #TextToSpeech

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🔍 AI Research Spotlight! 🔍 Excited to highlight the paper "Chain of Thought Empowers Transformers to Solve Inherently Serial Problems" 🧠. Chain-of-thought reasoning allows AI to break down complex, multi-step tasks into smaller, logical steps—mimicking human problem-solving. This innovation enables transformers to handle sequential reasoning tasks that were previously out of reach, marking a huge leap forward for AI! 🌐 Kudos to the brilliant authors driving this breakthrough! https://lnkd.in/dFjSnqah #AI #MachineLearning #Transformers #DeepLearning #ArtificialIntelligence #Research #ChainOfThought #Innovation

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This is super useful. Especially if you're the one who's a better listener than reader! Keeps you so engaged as the reaserch paper is converted to a podcast/coneversation between 3 different personas keeping the conversation enaged and helping you digest the various aspects of the research paper from different angles. Requires an open ai api key but theer are example postcasts of LLama base model research paper clearly bringing out the beauty of the idea/ Gen AI Tool #genai #learning

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vGPU, MIG, & Time-Slicing! 3 ways to optimize the #GPU resources to meet the dynamic demands of computational tasks💻 Sameer Kulkarni explains & compares these methods in the blog post to help you select the right GPU optimization for your models👇 https://lnkd.in/gFQPX4b4

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Can AI Scaling Sustain Its Momentum Through 2030? An In-Depth Analysis Epoch AI's report assesses whether AI scaling can continue at its current pace through 2030. Key challenges include increasing demands on compute power, chip manufacturing bottlenecks, limited data availability, and latency. Additionally, economic constraints and infrastructure investments will play pivotal roles in determining AI's future. Despite these hurdles, with continued innovation and investment, massive advancements in AI model training are likely within reach. Key Topics: Compute Power Constraints: AI scaling demands immense energy, requiring sustainable solutions and energy-efficient technologies to meet future needs. Chip Manufacturing Bottlenecks: The AI industry is heavily dependent on cutting-edge chips (e.g., GPUs, TPUs), but the semiconductor supply chain faces significant limitations. As models scale, the demand for specialized chips will continue to grow, putting pressure on production capabilities. Data Availability and Quality: Large AI models require massive datasets, but the availability of high-quality and diverse data may become a limiting factor. Addressing data bottlenecks will be crucial to future scaling. Latency and Network Infrastructure: High-performance AI systems rely on low-latency, robust networks for efficient data flow. Upgrades in infrastructure, including faster pipelines and optimized network designs, will be critical to avoid bottlenecks. Economic and Investment Decisions: While the technical feasibility of AI scaling exists, the economic realities—such as rising costs in energy, hardware, and maintenance—could determine how far AI scaling will advance. Strategic investments in hardware, data infrastructure, and power solutions will shape the future. https://lnkd.in/dhg8RQrJ #AI #FutureTech #ScalingAI #MachineLearning #ChipManufacturing #DataScience #AIInvestment #TechGrowth #DeepLearning #AI2030

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Neural Processing Units(NPUs) and their job in Neural Network / Deep Learning: Optimized for Neural Network Workloads: NPUs are specifically crafted to handle neural network operations like matrix multiplications and convolutions, making them much faster and more memory efficient than CPUs or GPUs. NPUs are highly parallel computing devices designed with lots of miniature processor s specializing for neural network operations and sometimes may operate on low bit precision(8-bit, 4-bit ) results in high speed up along power saving. High memory and data flow architecture: NPUs include on-chip high-speed memory and data transfer architectures, reducing off-chip memory reads, enabling faster I/O to process the data efficiently by using techniques like pipelining and weight sharing. Energy Efficiency: The very fine tuned design of an NPU is for neural network computations, consuming even lesser power than a CPU or GPU and thus making them suitable for mobile platforms and other low power devices/applications compared to Data Centers / Autonomous Vehicles. Real-World Applications :AI functionality in smartphones (like facial recognition and real time image processing), autonomous driving (real time sensor data processing), Data centers (more efficiency for AI training and inference)

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🚀 Looking to streamline fine-tuning for models like Llama 3, Mistral, and Mixtral? Our latest blog shows how Anyscale simplifies LLMs fine-tuning with Ray: 🔑 Key takeaways: • Scalable fine-tuning with LLM-Forge • Model Deployment with RayLLM Read more: https://lnkd.in/gQwD4A2U

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Continuing our exploration of anomaly detection in computer vision! I've shared several papers on this topic, and here is one more published this year. The paper titled "𝚄̲–̲𝙵̲𝚕̲𝚘̲𝚠̲:̲ ̲𝙰̲ ̲𝚄̲–̲𝚂̲𝚑̲𝚊̲𝚙̲𝚎̲𝚍̲ ̲𝙽̲𝚘̲𝚛̲𝚖̲𝚊̲𝚕̲𝚒̲𝚣̲𝚒̲𝚗̲𝚐̲ ̲𝙵̲𝚕̲𝚘̲𝚠̲ ̲𝚏̲𝚘̲𝚛̲ ̲𝙰̲𝚗̲𝚘̲𝚖̲𝚊̲𝚕̲𝚢̲ ̲𝙳̲𝚎̲𝚝̲𝚎̲𝚌̲𝚝̲𝚒̲𝚘̲𝚗̲ ̲𝚠̲𝚒̲𝚝̲𝚑̲ ̲𝚄̲𝚗̲𝚜̲𝚞̲𝚙̲𝚎̲𝚛̲𝚟̲𝚒̲𝚜̲𝚎̲𝚍̲ ̲𝚃̲𝚑̲𝚛̲𝚎̲𝚜̲𝚑̲𝚘̲𝚕̲𝚍̲" presents a novel one-class self-supervised method for anomaly segmentation in images. The proposed approach integrates modern machine learning techniques with classical statistical detection theory, structured into four main phases: The novelty of the paper "U-Flow: A U-Shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold" lies in several key innovations: • 𝗖𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: The method integrates modern machine learning approaches, specifically a multi-scale image transformer architecture, with classical statistical detection theory, creating a hybrid framework for anomaly segmentation in images16. • 𝗨-𝗦𝗵𝗮𝗽𝗲𝗱 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗶𝗻𝗴 𝗙𝗹𝗼𝘄 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: The introduction of a U-shaped normalizing flow (NF) is significant. This architecture effectively merges multi-scale features in an invertible manner, ensuring that the information from different scales is aggregated without losing spatial context, which is often a limitation in traditional methods16. • 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗣𝗿𝗼𝗽𝗲𝗿𝘁𝗶𝗲𝘀 𝗳𝗼𝗿 𝗔𝗻𝗼𝗺𝗮𝗹𝘆 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: The paper leverages the statistical properties of the NF embeddings to derive meaningful anomaly scores and unsupervised detection thresholds. This allows for robust segmentation based on statistical mutual independence within and across scales, enhancing the reliability of anomaly detection16. • 𝗦𝗲𝗹𝗳-𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: The proposed method operates in a one-class self-supervised manner, which reduces the need for labeled data while still achieving high performance in anomaly detection and segmentation tasks16. • 𝗦𝘁𝗮𝘁𝗲-𝗼𝗳-𝘁𝗵𝗲-𝗔𝗿𝘁 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: The method demonstrates superior performance compared to existing approaches, achieving state-of-the-art results in terms of mean intersection over union (mIoU) and area under the receiver operating characteristic curve (AUROC) metrics across various datasets, particularly in the MVTec-AD benchmark16. The authors provide access to the code and trained models to encourage further research and application in this field. Research Paper :U-Flow: A U-Shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold | Journal of Mathematical Imaging and Vision official code : https://lnkd.in/d9weCjMc Want to stay updated on the cutting-edge research in computer vision? Follow me for more insights and discussions. #computerVision #anomalyDetection #unsupervisedLearning #AI #MachineLearning #deeplearning #UFlow

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How much learning is too much learning? Semi-supervised learning is a technique that uses both labeled and unlabeled data to train machine learning models. It bridges the gap between supervised learning, which relies solely on labeled data, and unsupervised learning, which doesn’t use any labels. By leveraging unlabeled data, semi-supervised learning can achieve higher accuracy with fewer labeled examples, making it especially valuable when labeled data is scarce or expensive to obtain. The approach typically involves initially training on labeled data, then using the model's predictions on unlabeled data to further refine it. This iterative process allows the model to learn more robust patterns and generalize better. Semi-supervised learning is widely used in applications like speech recognition, where obtaining labeled examples for every variation in speech is challenging. #ML #india #Tech #batteryok #AI

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🎥 The recording of our webinar "What's new in the Wolfram Cloud" is online now. Take the chance to learn something new! 🚀 https://lnkd.in/drgXUZmm https://lnkd.in/dz5PpjFS #webinar #wolframcloud #mathematica #wolfram #wolframlanguage

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Welcome to read the article "Integrated Gradients for Feature Assessment in Point Cloud-Based Data Sets" which is written by Mr. Markus Schwegler, Prof. Christoph Müller and Alexander Reiterer. This excellent article was published in issue 7 in 2023 and has been viewed 1313 times and cited 3 times! Article link: https://lnkd.in/gMCsed5z MDPI Fraunhofer IPM Furtwangen University Albert-Ludwigs-Universität Freiburg (Albert Ludwig University of Freiburg) #pointcloud #neuralnetwork #integratedgradients

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The next big thing that may replace diffusion in the field of image generation may be coming -- next-scale prediction. This paper, "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction", is probably the most exciting paper I've read so far this year. Like always, I've got you covered with a 🔥1-minute read summary. ● What is VAR about? It proposes a new autoregressive image generation paradigm. Instead of predicting the next image tokens, it predicts the next image scale. ● What is the evaluation task and metric? Task: ImageNet Image Generation Benchmark. Metric: FID and IS. ● Why is it a milestone work? This is the first time that GPT-style image generation has beaten diffusion models on both image quality and inference speed. ● Why it has huge potential? Just like LLM, this model is proven to follow the scaling law. ● What's next? I can already foresee text-control VAR, video-based VAR, VAR for world models, super super-large VAR coming... For more insights, check out the paper here: https://lnkd.in/gGqdiS9H Modeling and the code source: https://lnkd.in/gTiaMjKG

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LIME and Shap are strong tools for explainability in ML to deep learning, but LLM is a strong black box. Hence, we needed a new technique to explain them in details A survey paper on XAI for LLM is a great read. https://lnkd.in/dXead87Z

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Enhancing Trust in Large Language Models: Fine-Tuning for Calibrated Uncertainties in High-Stakes Applications Quick read: https://lnkd.in/guKKVW3V Paper: https://lnkd.in/g2UFPN-Z Abacus.AI

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