TransItalia prepara nuevas autopistas ferroviarias hacia el País Vasco y Navarra. Extenderá su modelo multimodal hasta Júndiz y Noáin para reforzar la conexión con Europa eleconomista.es/transportes-… a través de @elEconomistaes @victordeelena

MASTER GEN AI ENGINEERING GENERATIVE AI ENGINEERING MASTER TREE │ ├── 1. Foundations │ ├── What is Generative AI │ ├── AI vs ML vs DL vs GenAI │ ├── Types of Generative Models │ │ ├── Text (LLMs) │ │ ├── Image (Diffusion Models) │ │ ├── Audio / Video Models │ ├── Tokens & Context Window │ └── Training vs Inference │ ├── 2. Large Language Models (LLMs) │ ├── What are LLMs │ ├── Transformer Architecture │ ├── Attention Mechanism │ ├── Pretraining (Next Token Prediction) │ ├── Fine-tuning │ └── Popular Models │ ├── GPT │ ├── Claude │ ├── LLaMA │ └── Mistral │ ├── 3. Prompt Engineering │ ├── Zero-shot Prompting │ ├── Few-shot Prompting │ ├── Chain-of-Thought │ ├── Role-based Prompts │ ├── Prompt Templates │ └── Prompt Optimization │ ├── 4. Embeddings │ ├── What are Embeddings │ ├── Vector Representation │ ├── Semantic Similarity │ ├── Cosine Similarity │ └── Use Cases (Search, Clustering) │ ├── 5. Vector Databases │ ├── What is a Vector DB │ ├── Indexing (FAISS, HNSW) │ ├── Similarity Search │ ├── Metadata Filtering │ └── Popular Tools │ ├── Pinecone │ ├── Weaviate │ └── Chroma │ ├── 6. Retrieval-Augmented Generation (RAG) │ ├── What is RAG │ ├── Data Ingestion │ ├── Chunking Strategies │ ├── Embedding Storage │ ├── Retrieval Techniques │ ├── Context Injection │ └── RAG vs Fine-tuning │ ├── 7. AI Agents │ ├── What are AI Agents │ ├── Tool Calling │ ├── Memory (Short / Long Term) │ ├── Planning & Reasoning │ ├── Multi-agent Systems │ └── Frameworks │ ├── LangChain │ ├── LlamaIndex │ └── AutoGen │ ├── 8. Fine-tuning & Custom Models │ ├── When to Fine-tune │ ├── Instruction Tuning │ ├── LoRA / PEFT │ ├── Dataset Preparation │ └── Evaluation │ ├── 9. Evaluation & Guardrails │ ├── Model Evaluation Metrics │ ├── Hallucination Detection │ ├── Bias & Fairness │ ├── Safety Filters │ └── Prompt Injection Protection │ ├── 10. Multimodal AI │ ├── Text Image Models │ ├── Vision Models │ ├── Speech Models │ └── Video Generation │ ├── 11. Model Deployment │ ├── APIs (OpenAI, etc.) │ ├── Backend Integration │ ├── Streaming Responses │ ├── Latency Optimization │ └── Cost Optimization │ ├── 12. GenAI Architecture │ ├── End-to-End Pipeline │ ├── RAG Architecture │ ├── Agent-based Systems │ ├── Caching Strategies │ └── Scalability Design │ ├── 13. MLOps for GenAI │ ├── Model Versioning │ ├── Monitoring │ ├── Logging Prompts & Outputs │ ├── A/B Testing │ └── Continuous Improvement │ ├── 14. Real-World Applications │ ├── Chatbots (Customer Support) │ ├── AI Assistants │ ├── Code Generation │ ├── Document Q&A Systems │ ├── Content Generation │ └── AI Search Engines │ └── 15. Career Path ├── Prompt Engineer ├── GenAI Engineer ├── AI Product Engineer ├── ML Engineer (LLM Focus) └── AI Researcher

Union Minister @nitin_gadkari Reviews Progress of NHLML Projects; Calls for Expeditious Completion of Multimodal Infrastructure Initiatives ▪️Union Minister assessed the project-level progress of Multi Modal Logistics Parks (MMLPs), Ropeways, Intermodal Stations, and Way Side Amenities being developed across the country. ▪️Union Minister also stated that the timely completion of these projects will improve logistics efficiency, reduce transportation costs, and provide world-class infrastructure and amenities for commuters, further advancing the vision of a #ViksitBharat and an #AatmanirbharBharat. @MORTHIndia

🚨Ahmedabad bullet train station to be developed as a multimodal transport hub with kite-inspired design.

VLA serves as a foundational paradigm in embodied AI, forming the basis for multimodal action models—where the accuracy and alignment of high-quality training data directly determine generalization and real-world deployment performance. X Square Robot just released the QUANXTA Zero Series (headband dual grippers and full VR backpack rigs), purpose-built for scalable embodied data collection: multimodal capture, 1ms synchronization, and whole-body mobile manipulation that turns real human demonstrations into clean, trainable robot data. From supporting overseas teams on physical AI prototyping, sourcing, and pilot production in China, we've seen that getting the data foundation right early dramatically improves VLA deployment and supply chain execution. Models with poor data fidelity rarely make it from the lab to manufacturable products. #EdgeAI #Robotics #EmbodiedAI #VLA #HumanoidRobots #HardwareStartup

at 𝘼𝙧𝙩𝙝𝙧 𝙇𝙖𝙗𝙨, we leverage a multimodal creative pipeline to deliver innovative, best-in-class visual solutions across digital and analogue workflows. {👨‍💼🎨🤝} 𝘓𝘢𝘵𝘦𝘴𝘵 𝘌𝘥𝘪𝘵𝘪𝘰𝘯 on @TransientLabs ⏷

Dubai’s smart mobility goes global! 🚇 The RTA’s next-gen account-based ticketing mirrors the European Union’s push for seamless, multimodal transit networks. A true masterclass in global smart city infrastructure. 🇦🇪🤝🇪🇺 #SmartMobility #EU #Dubai #SmartCities #FutureOfMobility

This week in micromobility! 👉 @limebike debuted on Nasdaq at a $1.7B valuation, marking one of the industry's biggest milestones as the company continues expanding its global shared mobility network. 👉 NIU Technologies delivered 434.6k electric two-wheelers and kick-scooters in Q2 2026, up 25% YoY, driven by strong domestic demand. 👉 @limebike launched Rethymno, Greece's first shared e-bike programme with an initial fleet of 185 Gen4 e-bikes, while also expanding across Alberta, Canada following its acquisition of @neuronmobility Canada. 👉 @fiat has unveiled the Multiplina Concept, a compact four-seat electric quadricycle, positioned as the “missing link” between the tiny Topolino and a conventional car. 👉 @Decathlon PULSE invested in @BromptonBicycle to accelerate the British folding bike maker's global growth while preserving its independent brand and UK manufacturing. 👉 Get an exclusive first look at the findings from the 6th Annual Lyft Multimodal Report by joining experts from @lyftsolutions, the Toronto Parking Authority, and the @CDOT_RBX Read more here: micromobility.io/news/lime-d…

4/ Go Beyond Text Modern AI agents aren't limited to chat. Add multimodal capabilities like: → voice → images → documents → screenshots → video Then generate outputs for both machines and humans. For example: → structured JSON for APIs → dashboards → reports → presentations → executive summaries One agent. Multiple ways to understand and communicate information.

Union Minister Nitin Gadkari Reviews Progress Of NHLML Projects; Calls For Expeditious Completion Of Multimodal Infrastructure Initiatives therightnews.in/2026/07/06/u…

📢 A new Special Issue "Advances in the Development of Multimodal Single-Cell Multi-Omics Technologies" is now open for submissions! 🔗 Click the link to access more details about the Special Issue: mdpi.com/journal/bioengineer… #single_cell_sequencing #genomics #tumor

EleutherAI's 'Argonaut' multimodal AI is here—and it's open source. 🌟 Handling text images, it's a direct challenge to Big Tech's proprietary walls. Accessible, powerful, revolutionary. Details: af.net/realtime/open-source-… #AI #OpenSource

Hypothetically speaking, why would you need a multimodal LLM and not a CNN for behavioural extraction, and why would 500ms latency matter for data fusion?

Also excited for Expert-Level Mixed-Precision Quantization. Standard quantization compresses an entire model uniformly (e.g., shrinking all weights to 4-bit integers). MoE-specific research is getting much smarter by treating experts differently based on how often they are actually used. Activation-Aware Precision (e.g., MC-MoE): Frameworks like Mixture Compressor for MoE (MC-MoE) profile the model to track expert activation frequency. "Hot" experts that handle the bulk of the model's core reasoning retain higher precision (3 to 4-bit). "Cold" experts — specialized networks that are rarely called — are aggressively crushed down to 1.5 or 2-bit quantization. This drastically shrinks the VRAM footprint of the inactive parameters without lobotomizing the model. Modality-Aware Compression: For multimodal MoEs (handling both text and vision), frameworks like MODE evaluate quantization sensitivity differently for image tokens versus text tokens, applying extreme compression only where it won't degrade visual fidelity.

Short-term responsiveness of DNA methylation–based aging biomarkers to a multimodal intervention comprising exercise and dietary guidance involving daily consumption of yogurt containing Bifidobacterium longum BB536 aging-us.com/article/206386/…

Gemma4 can run on Cerebras at ~1800 tokens/sec. (and it's multimodal). For context, the fastest model on Artificial Analysis is ~300 tokens/sec.

Developed in partnership with @artparkindia, the multilingual, multimodal tool offers real-time, clinically accurate guidance through voice or text, helping health workers make informed decisions at the point of care. (4/6)

So we're building a hard, multilingual multimodal browsing agent benchmark. Data is mostly done, just benchmarking models now. The questions are diabolical😈 Paper & data release coming soon! hyper-browsecomp.github.io At ICML right now, hit me up if you are interested! also...