ChatGPT is Apple’s most downloaded app of 2025 in the US
This is ChatGPT’s first year as the No. 1 app on the U.S. App Store by downloads.
This is ChatGPT’s first year as the No. 1 app on the U.S. App Store by downloads.
Both Google and Apple are cramming new AI features into their phones and other devices, and neither company has offered clear ways to control which apps those AI systems can access. Recent issues around WhatsApp on both Android and iPhone demonstrate how these interactions can go sideways, risking revealing chat conversations beyond what you intend. Users deserve better controls and clearer documentation around what these AI features can access. After diving into how Google Gemini and Apple Intelligence […]
Deepening our partnership with the UK government to support prosperity and security in the AI era
Ayn Rand described Thanksgiving as “a typically American holiday . . . its essential, secular meaning is a celebration of successful production. It is a producers’ holiday. The lavish meal is a symbol of the fact that abundant consumption is the result and reward of production.”
Jina AI has released Jina-VLM, a 2.4B parameter vision language model that targets multilingual visual question answering and document understanding on constrained hardware. The model couples a SigLIP2 vision encoder with a Qwen3 language backbone and uses an attention pooling connector to reduce visual tokens while preserving spatial structure. Among open 2B scale VLMs, it reaches state of the art results on multilingual benchmarks such as MMMB and Multilingual MMBench. https://arxiv.org/pdf/2512.04032 Architecture, overlapping tiles with attention pooling connector […]
This article is divided into two parts; they are: • Fine-tuning a BERT Model for GLUE Tasks • Fine-tuning a BERT Model for SQuAD Tasks GLUE is a benchmark for evaluating natural language understanding (NLU) tasks.
If you’re building an LLM app, these open-source tools help you test, track, and improve your model’s performance easily.
Isolation Forest may look technical, but its idea is simple: isolate points using random splits. If a point is isolated quickly, it is an anomaly; if it takes many splits, it is normal. Using the tiny dataset 1, 2, 3, 9, we can see the logic clearly. We build several random trees, measure how many splits each point needs, average the depths, and convert them into anomaly scores. Short depths become scores close to 1, long depths close […]
Tips for accelerating AI/ML on CPU — Part 2 The post Optimizing PyTorch Model Inference on AWS Graviton appeared first on Towards Data Science.