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- Weekly - 21July2024
Weekly - 21July2024
AI Shifts: Japanese Firms Waver, TSMC Surges, Mistral & NVIDIA Team Up, Meta Pauses EU Launch – Plus Must-Have Tools and Hot AI Trends!
Kaif Ahmed & aipoool ai
July 21, 2024
Happy Sunday! This is AIPOOOL. The email that tells you what’s going on in Artificial Intelligence space in simple blocks. Get ready to have your mind blown by the sheer power of AI!
In Today’s Email :
📺 AI News: Japanese Firms Hesitate on AI, TSMC Forecasts Growth, Mistral AI Unveils NeMo with NVIDIA, Meta Halts EU Launch.
⛏️ Trending Tools: SticAI Glance for Reddit Posts, Savvy for Job matching & many more ..
🔰 Quick Grab: Revolutionizing Recognition: The Mamba Advantage in Pedestrian Attribute Detection!
🎆Creators Corner: Top Picks from Hugging Face: Trending AI Applications You Can't Miss!
🥼 From Lab to Layman: Gradient Boosting Reinforcement Learning
Browse AI Tools | Instagram | Advertise
AI Happenings You Don’t Want To Miss
✨ Survey conducted by Nikkei Research, over 40% of Japanese companies do not have plans for adopting artificial intelligence (AI). The study highlighted a significant disparity in AI adoption among Japanese businesses.
✨ Taiwan Semiconductor Manufacturing Company (TSMC) has raised its revenue forecast for 2024, citing strong demand for chips in AI applications. The world’s largest contract chipmaker anticipates growth slightly above the mid-20% range in US dollar terms, up from its previous estimate.
✨ Mistral AI has announced NeMo, a 12B model created in partnership with NVIDIA. This new model boasts an impressive context window of up to 128,000 tokens and claims state-of-the-art performance in reasoning, world knowledge, and coding accuracy for its size category.
✨ Meta has announced it will not be launching its upcoming multimodal AI model in the European Union due to regulatory concerns.
Free & Useful AI Tools -
SticAI Glance - Summarize Reddit posts into actionable insights instantly.
Savvy - AI-powered job matching for recruiters and job seekers.
Wendy AI - Boost team retention with 24/7 AI mental health support.
Codeye - AI-powered agent for quickly shipping quality software.
📜Revolutionizing Recognition: The Mamba Advantage in Pedestrian Attribute Detection!
Purpose: The study explores Mamba, a lightweight model for recognizing pedestrian attributes like clothing and accessories.
Key Features of Mamba:
Efficiency: Operates with linear complexity, making it faster and less resource-intensive than traditional models.
Versatility: Tested in two frameworks:
Image-based Multi-label Classification: Identifies multiple attributes from images.
Image-Text Fusion: Combines visual data with text descriptions for enhanced recognition.
Performance Insights:
Mamba's effectiveness varies by framework; it performs well in some scenarios but not in others.
Highlights the need for careful selection of models based on specific tasks.
Future Implications:
Mamba could inspire advancements in pedestrian recognition and other areas like multi-label recognition.
Potential applications include smart city technologies, security systems, and personalized shopping experiences.
Source: OpenPAR
Conclusion: The study showcases Mamba as a promising tool for efficient and effective pedestrian attribute recognition, paving the way for smarter technology in our daily lives.
🤖 Top Picks from Hugging Face: Trending AI Applications You Can't Miss!
🌟Illusion Diffusion HQ 🌀 : Generate stunning high quality illusion artwork with Stable Diffusion
🌟 QR Code AI Art Generator : "Create seamless QR codes using the product! Explore examples for optimal results. 😊
🌟 IDM-VTON 👕👔👚 : Virtual Try-on with your image and garment image.
🌟 Omost : converting LLM's coding capability to image compositing capability.
👨💻 From Lab to Layman - Gradient Boosting Reinforcement Learning :
Imagine you're training a smart robot to play a game, like soccer. Traditionally, we might use complex neural networks (NNs) to help the robot learn from its experiences. However, these NNs can be hard to understand, especially when it comes to making decisions based on different types of information, like scores or player positions.
Enter Gradient Boosting Reinforcement Learning (GBRL). This innovative approach uses a method called Gradient Boosting Trees (GBT), which is like having a team of decision-making trees that work together. Each tree makes a small decision, and together they create a strong strategy for the robot.
Here’s why GBRL is exciting:
Easy to Understand : Unlike NNs, which can be like a black box, GBTs are more interpretable. You can see how decisions are made, which is crucial for trust and safety in real-world applications.
Handles Different Data Types : GBRL shines when dealing with structured data, like categories or scores, making it perfect for tasks like inventory management or traffic control.
Efficient Learning : By sharing information between the trees, GBRL learns faster and requires less computing power. This means it can be used on devices with limited resources, like smartphones or edge devices.
Source: GBRL
Some of the real-world applications include,
Inventory Management: GBRL can optimize stock levels and reorder points by making decisions based on structured data, helping businesses reduce costs and improve efficiency.
Traffic Signal Optimization: By analyzing traffic patterns and making real-time adjustments, GBRL can enhance traffic flow and reduce congestion in urban areas.
Network Optimization: GBRL can be used to manage and optimize network resources, improving performance and reliability in telecommunications and data centers.
Resource Allocation: In scenarios like energy distribution or cloud computing, GBRL can help allocate resources efficiently based on varying demand and conditions.
Robotics: GBRL can enhance decision-making in robotic systems, allowing them to navigate complex environments and perform tasks more effectively.
Healthcare: In healthcare settings, GBRL can assist in decision-making processes, such as patient treatment plans or resource allocation in hospitals, by interpreting structured patient data.
Finance: GBRL can be applied in financial modeling and risk assessment, where structured data plays a crucial role in decision-making.
In summary, GBRL combines the best of both worlds: the interpretability and efficiency of Gradient Boosting Trees with the dynamic learning capabilities of reinforcement learning. This makes it a powerful tool for training smart systems in various real-world scenarios!
