Understanding how AI and big data transform digital marketingAI News Artificial intelligence and big data are reshaping digital marketing by providing new insights into consumer behaviour. The technologies allow marketers to create more personalised and effective strategies. As the digital world evolves, businesses must adapt to stay competitive. Rainmaker is an AI marketing agency that uses artificial intelligence and big data to enhance digital marketing
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Artificial intelligence and big data are reshaping digital marketing by providing new insights into consumer behaviour. The technologies allow marketers to create more personalised and effective strategies. As the digital world evolves, businesses must adapt to stay competitive. Rainmaker is an AI marketing agency that uses artificial intelligence and big data to enhance digital marketing
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Solana’s high-speed AI gains and malware lossesAI News Solana’s high-speed platform is fast becoming the preferred home for independent AI programmes. It comes at a time when advanced uses of technology have led to significant increases in cyberattacks. This article details the escalating malware threats for the cryptocurrency community. According to the most recent data on December 5, 2025, the Solana price on
The post Solana’s high-speed AI gains and malware losses appeared first on AI News.
Solana’s high-speed platform is fast becoming the preferred home for independent AI programmes. It comes at a time when advanced uses of technology have led to significant increases in cyberattacks. This article details the escalating malware threats for the cryptocurrency community. According to the most recent data on December 5, 2025, the Solana price on
The post Solana’s high-speed AI gains and malware losses appeared first on AI News. Read More
Announcing OpenAI Grove Cohort 2OpenAI News Applications are now open for OpenAI Grove Cohort 2, a 5-week founder program designed for individuals at any stage, from pre-idea to product. Participants receive $50K in API credits, early access to AI tools, and hands-on mentorship from the OpenAI team.
Applications are now open for OpenAI Grove Cohort 2, a 5-week founder program designed for individuals at any stage, from pre-idea to product. Participants receive $50K in API credits, early access to AI tools, and hands-on mentorship from the OpenAI team. Read More
Off-Beat Careers That Are the Future Of DataTowards Data Science The unconventional career paths you need to explore
The post Off-Beat Careers That Are the Future Of Data appeared first on Towards Data Science.
The unconventional career paths you need to explore
The post Off-Beat Careers That Are the Future Of Data appeared first on Towards Data Science. Read More
The Real Challenge in Data Storytelling: Getting Buy-In for SimplicityTowards Data Science What happens when your clear dashboard meets stakeholders who want everything on one screen
The post The Real Challenge in Data Storytelling: Getting Buy-In for Simplicity appeared first on Towards Data Science.
What happens when your clear dashboard meets stakeholders who want everything on one screen
The post The Real Challenge in Data Storytelling: Getting Buy-In for Simplicity appeared first on Towards Data Science. Read More
Train Your Large Model on Multiple GPUs with Fully Sharded Data ParallelismMachineLearningMastery.com This article is divided into five parts; they are: • Introduction to Fully Sharded Data Parallel • Preparing Model for FSDP Training • Training Loop with FSDP • Fine-Tuning FSDP Behavior • Checkpointing FSDP Models Sharding is a term originally used in database management systems, where it refers to dividing a database into smaller units, called shards, to improve performance.
This article is divided into five parts; they are: • Introduction to Fully Sharded Data Parallel • Preparing Model for FSDP Training • Training Loop with FSDP • Fine-Tuning FSDP Behavior • Checkpointing FSDP Models Sharding is a term originally used in database management systems, where it refers to dividing a database into smaller units, called shards, to improve performance. Read More
What if AI becomes conscious and we never knowArtificial Intelligence News — ScienceDaily A philosopher at the University of Cambridge says there’s no reliable way to know whether AI is conscious—and that may remain true for the foreseeable future. According to Dr. Tom McClelland, consciousness alone isn’t the ethical tipping point anyway; sentience, the capacity to feel good or bad, is what truly matters. He argues that claims of conscious AI are often more marketing than science, and that believing in machine minds too easily could cause real harm. The safest stance for now, he says, is honest uncertainty.
A philosopher at the University of Cambridge says there’s no reliable way to know whether AI is conscious—and that may remain true for the foreseeable future. According to Dr. Tom McClelland, consciousness alone isn’t the ethical tipping point anyway; sentience, the capacity to feel good or bad, is what truly matters. He argues that claims of conscious AI are often more marketing than science, and that believing in machine minds too easily could cause real harm. The safest stance for now, he says, is honest uncertainty. Read More
EDA in Public (Part 3): RFM Analysis for Customer Segmentation in PandasTowards Data Science How to build, score, and interpret RFM segments step by step
The post EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas appeared first on Towards Data Science.
How to build, score, and interpret RFM segments step by step
The post EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas appeared first on Towards Data Science. Read More
Empower Low-Altitude Economy: A Reliability-Aware Dynamic Weighting Allocation for Multi-modal UAV Beam Predictioncs.AI updates on arXiv.org arXiv:2512.24324v1 Announce Type: cross
Abstract: The low-altitude economy (LAE) is rapidly expanding driven by urban air mobility, logistics drones, and aerial sensing, while fast and accurate beam prediction in uncrewed aerial vehicles (UAVs) communications is crucial for achieving reliable connectivity. Current research is shifting from single-signal to multi-modal collaborative approaches. However, existing multi-modal methods mostly employ fixed or empirical weights, assuming equal reliability across modalities at any given moment. Indeed, the importance of different modalities fluctuates dramatically with UAV motion scenarios, and static weighting amplifies the negative impact of degraded modalities. Furthermore, modal mismatch and weak alignment further undermine cross-scenario generalization. To this end, we propose a reliability-aware dynamic weighting scheme applied to a semantic-aware multi-modal beam prediction framework, named SaM2B. Specifically, SaM2B leverages lightweight cues such as environmental visual, flight posture, and geospatial data to adaptively allocate contributions across modalities at different time points through reliability-aware dynamic weight updates. Moreover, by utilizing cross-modal contrastive learning, we align the “multi-source representation beam semantics” associated with specific beam information to a shared semantic space, thereby enhancing discriminative power and robustness under modal noise and distribution shifts. Experiments on real-world low-altitude UAV datasets show that SaM2B achieves more satisfactory results than baseline methods.
arXiv:2512.24324v1 Announce Type: cross
Abstract: The low-altitude economy (LAE) is rapidly expanding driven by urban air mobility, logistics drones, and aerial sensing, while fast and accurate beam prediction in uncrewed aerial vehicles (UAVs) communications is crucial for achieving reliable connectivity. Current research is shifting from single-signal to multi-modal collaborative approaches. However, existing multi-modal methods mostly employ fixed or empirical weights, assuming equal reliability across modalities at any given moment. Indeed, the importance of different modalities fluctuates dramatically with UAV motion scenarios, and static weighting amplifies the negative impact of degraded modalities. Furthermore, modal mismatch and weak alignment further undermine cross-scenario generalization. To this end, we propose a reliability-aware dynamic weighting scheme applied to a semantic-aware multi-modal beam prediction framework, named SaM2B. Specifically, SaM2B leverages lightweight cues such as environmental visual, flight posture, and geospatial data to adaptively allocate contributions across modalities at different time points through reliability-aware dynamic weight updates. Moreover, by utilizing cross-modal contrastive learning, we align the “multi-source representation beam semantics” associated with specific beam information to a shared semantic space, thereby enhancing discriminative power and robustness under modal noise and distribution shifts. Experiments on real-world low-altitude UAV datasets show that SaM2B achieves more satisfactory results than baseline methods. Read More
PackKV: Reducing KV Cache Memory Footprint through LLM-Aware Lossy Compressioncs.AI updates on arXiv.org arXiv:2512.24449v1 Announce Type: cross
Abstract: Transformer-based large language models (LLMs) have demonstrated remarkable potential across a wide range of practical applications. However, long-context inference remains a significant challenge due to the substantial memory requirements of the key-value (KV) cache, which can scale to several gigabytes as sequence length and batch size increase. In this paper, we present textbf{PackKV}, a generic and efficient KV cache management framework optimized for long-context generation. %, which synergistically supports both latency-critical and throughput-critical inference scenarios. PackKV introduces novel lossy compression techniques specifically tailored to the characteristics of KV cache data, featuring a careful co-design of compression algorithms and system architecture. Our approach is compatible with the dynamically growing nature of the KV cache while preserving high computational efficiency. Experimental results show that, under the same and minimum accuracy drop as state-of-the-art quantization methods, PackKV achieves, on average, textbf{153.2}% higher memory reduction rate for the K cache and textbf{179.6}% for the V cache. Furthermore, PackKV delivers extremely high execution throughput, effectively eliminating decompression overhead and accelerating the matrix-vector multiplication operation. Specifically, PackKV achieves an average throughput improvement of textbf{75.7}% for K and textbf{171.7}% for V across A100 and RTX Pro 6000 GPUs, compared to cuBLAS matrix-vector multiplication kernels, while demanding less GPU memory bandwidth. Code available on https://github.com/BoJiang03/PackKV
arXiv:2512.24449v1 Announce Type: cross
Abstract: Transformer-based large language models (LLMs) have demonstrated remarkable potential across a wide range of practical applications. However, long-context inference remains a significant challenge due to the substantial memory requirements of the key-value (KV) cache, which can scale to several gigabytes as sequence length and batch size increase. In this paper, we present textbf{PackKV}, a generic and efficient KV cache management framework optimized for long-context generation. %, which synergistically supports both latency-critical and throughput-critical inference scenarios. PackKV introduces novel lossy compression techniques specifically tailored to the characteristics of KV cache data, featuring a careful co-design of compression algorithms and system architecture. Our approach is compatible with the dynamically growing nature of the KV cache while preserving high computational efficiency. Experimental results show that, under the same and minimum accuracy drop as state-of-the-art quantization methods, PackKV achieves, on average, textbf{153.2}% higher memory reduction rate for the K cache and textbf{179.6}% for the V cache. Furthermore, PackKV delivers extremely high execution throughput, effectively eliminating decompression overhead and accelerating the matrix-vector multiplication operation. Specifically, PackKV achieves an average throughput improvement of textbf{75.7}% for K and textbf{171.7}% for V across A100 and RTX Pro 6000 GPUs, compared to cuBLAS matrix-vector multiplication kernels, while demanding less GPU memory bandwidth. Code available on https://github.com/BoJiang03/PackKV Read More