Using NumPy to Analyze My Daily Habits (Sleep, Screen Time & Mood)Towards Data Science Can I use NumPy to figure out how my habits affect my mood and productivity?
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Can I use NumPy to figure out how my habits affect my mood and productivity?
The post Using NumPy to Analyze My Daily Habits (Sleep, Screen Time & Mood) appeared first on Towards Data Science. Read More
Hosting NVIDIA speech NIM models on Amazon SageMaker AI: Parakeet ASRArtificial Intelligence In this post, we explore how to deploy NVIDIA’s Parakeet ASR model on Amazon SageMaker AI using asynchronous inference endpoints to create a scalable, cost-effective pipeline for processing large volumes of audio data. The solution combines state-of-the-art speech recognition capabilities with AWS managed services like Lambda, S3, and Bedrock to automatically transcribe audio files and generate intelligent summaries, enabling organizations to unlock valuable insights from customer calls, meeting recordings, and other audio content at scale .
In this post, we explore how to deploy NVIDIA’s Parakeet ASR model on Amazon SageMaker AI using asynchronous inference endpoints to create a scalable, cost-effective pipeline for processing large volumes of audio data. The solution combines state-of-the-art speech recognition capabilities with AWS managed services like Lambda, S3, and Bedrock to automatically transcribe audio files and generate intelligent summaries, enabling organizations to unlock valuable insights from customer calls, meeting recordings, and other audio content at scale . Read More
Deep Reinforcement Learning: 0 to 100Towards Data Science Using RL to teach robots to fly a drone
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Using RL to teach robots to fly a drone
The post Deep Reinforcement Learning: 0 to 100 appeared first on Towards Data Science. Read More
Using Claude Skills with Neo4jTowards Data Science A hands-on exploration of Claude Skills and their potential applications in Neo4j
The post Using Claude Skills with Neo4j appeared first on Towards Data Science.
A hands-on exploration of Claude Skills and their potential applications in Neo4j
The post Using Claude Skills with Neo4j appeared first on Towards Data Science. Read More
API Development for Web Apps and Data ProductsKDnuggets Application programming interfaces are essential for modern web applications and data products. They allow different systems to communicate with each other and share data securely.
Application programming interfaces are essential for modern web applications and data products. They allow different systems to communicate with each other and share data securely. Read More
Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMsTowards Data Science Understanding how AI models “reason” and why it’s not what humans do when we think
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Understanding how AI models “reason” and why it’s not what humans do when we think
The post Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs appeared first on Towards Data Science. Read More
OpenAI restructures, enters ‘next chapter’ of Microsoft partnershipAI News OpenAI has completed a major reorganisation and, in the same breath, signed a new definitive partnership agreement with Microsoft. Starting with OpenAI’s reorganisation, the aim is to solidify the nonprofit’s control over the for-profit business and establish the newly named OpenAI Foundation as a global philanthropic powerhouse, holding equity in the commercial arm valued at
The post OpenAI restructures, enters ‘next chapter’ of Microsoft partnership appeared first on AI News.
OpenAI has completed a major reorganisation and, in the same breath, signed a new definitive partnership agreement with Microsoft. Starting with OpenAI’s reorganisation, the aim is to solidify the nonprofit’s control over the for-profit business and establish the newly named OpenAI Foundation as a global philanthropic powerhouse, holding equity in the commercial arm valued at
The post OpenAI restructures, enters ‘next chapter’ of Microsoft partnership appeared first on AI News. Read More
Breakthrough optical processor lets AI compute at the speed of lightArtificial Intelligence News — ScienceDaily Researchers at Tsinghua University developed the Optical Feature Extraction Engine (OFE2), an optical engine that processes data at 12.5 GHz using light rather than electricity. Its integrated diffraction and data preparation modules enable unprecedented speed and efficiency for AI tasks. Demonstrations in imaging and trading showed improved accuracy, lower latency, and reduced power demand. This innovation pushes optical computing toward real-world, high-performance AI.
Researchers at Tsinghua University developed the Optical Feature Extraction Engine (OFE2), an optical engine that processes data at 12.5 GHz using light rather than electricity. Its integrated diffraction and data preparation modules enable unprecedented speed and efficiency for AI tasks. Demonstrations in imaging and trading showed improved accuracy, lower latency, and reduced power demand. This innovation pushes optical computing toward real-world, high-performance AI. Read More
OpenAI’s bold India play: Free ChatGPT Go accessAI News OpenAI just made its biggest bet on India yet. Starting November 4, the company will hand out free year-long access to ChatGPT Go — a move that puts every marketing executive on notice about how aggressively AI companies are fighting for the world’s fastest-growing digital market. OpenAI will offer its ChatGPT Go plan to users
The post OpenAI’s bold India play: Free ChatGPT Go access appeared first on AI News.
OpenAI just made its biggest bet on India yet. Starting November 4, the company will hand out free year-long access to ChatGPT Go — a move that puts every marketing executive on notice about how aggressively AI companies are fighting for the world’s fastest-growing digital market. OpenAI will offer its ChatGPT Go plan to users
The post OpenAI’s bold India play: Free ChatGPT Go access appeared first on AI News. Read More
Performance Trade-offs of Optimizing Small Language Models for E-Commercecs.AI updates on arXiv.org arXiv:2510.21970v1 Announce Type: new
Abstract: Large Language Models (LLMs) offer state-of-the-art performance in natural language understanding and generation tasks. However, the deployment of leading commercial models for specialized tasks, such as e-commerce, is often hindered by high computational costs, latency, and operational expenses. This paper investigates the viability of smaller, open-weight models as a resource-efficient alternative. We present a methodology for optimizing a one-billion-parameter Llama 3.2 model for multilingual e-commerce intent recognition. The model was fine-tuned using Quantized Low-Rank Adaptation (QLoRA) on a synthetically generated dataset designed to mimic real-world user queries. Subsequently, we applied post-training quantization techniques, creating GPU-optimized (GPTQ) and CPU-optimized (GGUF) versions. Our results demonstrate that the specialized 1B model achieves 99% accuracy, matching the performance of the significantly larger GPT-4.1 model. A detailed performance analysis revealed critical, hardware-dependent trade-offs: while 4-bit GPTQ reduced VRAM usage by 41%, it paradoxically slowed inference by 82% on an older GPU architecture (NVIDIA T4) due to dequantization overhead. Conversely, GGUF formats on a CPU achieved a speedup of up to 18x in inference throughput and a reduction of over 90% in RAM consumption compared to the FP16 baseline. We conclude that small, properly optimized open-weight models are not just a viable but a more suitable alternative for domain-specific applications, offering state-of-the-art accuracy at a fraction of the computational cost.
arXiv:2510.21970v1 Announce Type: new
Abstract: Large Language Models (LLMs) offer state-of-the-art performance in natural language understanding and generation tasks. However, the deployment of leading commercial models for specialized tasks, such as e-commerce, is often hindered by high computational costs, latency, and operational expenses. This paper investigates the viability of smaller, open-weight models as a resource-efficient alternative. We present a methodology for optimizing a one-billion-parameter Llama 3.2 model for multilingual e-commerce intent recognition. The model was fine-tuned using Quantized Low-Rank Adaptation (QLoRA) on a synthetically generated dataset designed to mimic real-world user queries. Subsequently, we applied post-training quantization techniques, creating GPU-optimized (GPTQ) and CPU-optimized (GGUF) versions. Our results demonstrate that the specialized 1B model achieves 99% accuracy, matching the performance of the significantly larger GPT-4.1 model. A detailed performance analysis revealed critical, hardware-dependent trade-offs: while 4-bit GPTQ reduced VRAM usage by 41%, it paradoxically slowed inference by 82% on an older GPU architecture (NVIDIA T4) due to dequantization overhead. Conversely, GGUF formats on a CPU achieved a speedup of up to 18x in inference throughput and a reduction of over 90% in RAM consumption compared to the FP16 baseline. We conclude that small, properly optimized open-weight models are not just a viable but a more suitable alternative for domain-specific applications, offering state-of-the-art accuracy at a fraction of the computational cost. Read More