⏰ Featured AI: Microsoft AI Releases Phi-4-multimodal and Phi-4-mini & DeepSeek Releases DualPipe

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Dive into the hottest AI breakthroughs of the week—handpicked just for you!

Super Important AI News 🔥 🔥 🔥

🧵🧵 Revolutionizing Robot Learning: How Meta’s Aria Gen 2 enables 400% Faster Training with Egocentric AI

⭐  Microsoft AI Releases Phi-4-multimodal and Phi-4-mini: The Newest Models in Microsoft’s Phi Family of Small Language Models (SLMs)

📢 DeepSeek AI Releases DualPipe: A Bidirectional Pipeline Parallelism Algorithm for Computation-Communication Overlap in V3/R1 Training

🚨 Meta AI Introduces SWE-RL: An AI Approach to Scale Reinforcement Learning based LLM Reasoning for Real-World Software Engineering

💡💡 Cohere AI Releases Command R7B Arabic: A Compact Open-Weights AI Model Optimized to Deliver State-of-the-Art Arabic Language Capabilities to Enterprises in the MENA Region

🧲 🧲  DeepSeek AI Releases Fire-Flyer File System (3FS): A High-Performance Distributed File System Designed to Address the Challenges of AI Training and Inference Workload

Featured AI Update 🛡️🛡️🛡️

🔥 Microsoft AI Releases Phi-4-multimodal and Phi-4-mini: The Newest Models in Microsoft’s Phi Family of Small Language Models (SLMs)

Microsoft AI has recently introduced Phi-4-multimodal and Phi-4-mini, the newest additions to its Phi family of SLMs. These models have been developed with a clear focus on streamlining multimodal processing. Phi-4-multimodal is designed to handle text, speech, and visual inputs concurrently, all within a unified architecture. This integrated approach means that a single model can now interpret and generate responses based on varied data types without the need for separate, specialized systems.

Phi-4-mini, with its 3.8-billion parameters, is built as a dense, decoder-only transformer. It features grouped-query attention and boasts a vocabulary of 200,000 tokens, enabling it to handle sequences of up to 128,000 tokens. Despite its smaller size, Phi-4-mini performs remarkably well in tasks that require deep reasoning and language understanding. One of its standout features is the capability for function calling—allowing it to interact with external tools and APIs, thus extending its practical utility without requiring a larger, more resource-intensive model…..

Other AI News 🎖️🎖️🎖️

🚨 Convergence AI Releases WebGames: A Comprehensive Benchmark Suite Designed to Evaluate General-Purpose Web-Browsing AI Agents

🧿 Beyond a Single LLM: Advancing AI Through Multi-Model Collaboration

 🧩  LEAPS: A Neural Sampling Algorithm for Discrete Distributions via Continuous-Time Markov Chains (‘Discrete Diffusion’)

📢   Kimi AI release the intermediate checkpoints of Moonlight and Moonlight-A

🚨 Meet IntellAgent: An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System _(Promoted)

Coding Tutorial 🎖️🎖️🎖️

🖥️ Building an Interactive Weather Data Scraper in Google Colab: A Code Guide to Extract, Display, and Download Live Forecast Data Using Python, BeautifulSoup, Requests, Pandas, and Ipywidgets (Colab Notebook Included)

In this tutorial, we will build an interactive web scraping project in Google Colab! This guide will walk you through extracting live weather forecast data from the U.S. National Weather Service. You’ll learn to set up your environment, write a Python script using BeautifulSoup and requests, and integrate an interactive UI with ipywidgets. This tutorial provides a step-by-step approach to collecting, displaying, and saving weather data, all within a single, self-contained Colab notebook.

import requests
from bs4 import BeautifulSoup
import csv
from google.colab import files
import ipywidgets as widgets
from IPython.display import display, clear_output, FileLink
import pandas as pd