Leaping to next generation technologies

  Design Thinking Project: AI-Powered Gesture Control Helmet Project Overview This project aims to develop an AI-powered gesture control system for a helmet. By applying the design thinking process, we will identify user needs, design an intuitive gesture system, and collect and analyze data to refine the system. Design Thinking Process 1. Empathize Understand Cyclist Behavior: Observe cyclists to identify natural hand gestures while riding. Conduct Interviews: Interview cyclists to understand their preferences for controlling helmet functions. Identify Pain Points: Determine the challenges cyclists face with current helmet controls. 2. Define Problem Statement: How can we develop an intuitive and reliable gesture control system for a helmet that enhances the cyclist's experience? User Personas: Create detailed profiles of target users to guide design decisions. 3. Ideate Gesture Brainstorming: Generate a list of potential gestures for various helmet functions. Technology Exp...

Building Models -Llama Stack RFC Meta -  wants to enable everyone to get the most out of the 405B, such as  Real-time and batch inference Supervised fine-tuning Evaluation of your model for your specific application Continual pre-training Retrieval-Augmented Generation (RAG) Function calling Synthetic data generation RFC-0001 - Llama Stack · Issue #6 · meta-llama/llama-toolchain (github.com) Model Improvement Wishes Model Capabilities Structured Output: Improved support for generating structured data (e.g., JSON) through restricted prediction. Knowledge Distillation: Efficient tools and pipelines for transferring knowledge from larger models to smaller ones. Fine-Tuning Continued Pre-training: Easier access to sampled pre-training data for maintaining data distribution consistency. Preference Optimization: Best practices and recipes for fine-tuning using Rejected Sampling (RS) and Direct Preference Optimization (DPO). Model Architecture Agentic Capabilities: Tools and int...

  Improving Large Language Models with Direct Preference Optimization (DPO) This paper explores Direct Preference Optimization (DPO) as a method for fine-tuning large language models (LLMs) to better align with human preferences. Here are the key points: Background: Supervised Fine-tuning (SFT) is commonly used to improve LLMs' ability to answer various questions and engage in conversation. However, further improvements in natural language generation require incorporating human feedback. Reinforcement Learning from Human Feedback (RLHF) is a popular approach, but it's complex and expensive. DPO as an Alternative: DPO offers a simpler and more stable alternative to RLHF for fine-tuning LLMs with human preference data. It utilizes a loss function derived from RLHF and the Bradley-Terry model for preference estimation. This allows for supervised training, making it easier and faster compared to RLHF. Benefits of DPO: Improves chat functionalities and performance on various downstr...

  The AI Gold Rush: A New Frontier The frenzied pursuit of gold in the 19th century bears striking similarities to today's relentless drive for AI supremacy. Both eras are characterized by a feverish quest for a scarce resource, be it gold or computational power, with the promise of immense wealth and transformative potential. Similarities between the Gold Rush and the AI Rush Sudden and Intense Demand: Both gold and AI capabilities experienced rapid and intense demand, driving up prices and sparking a global competition. Prospect of Immense Wealth: The allure of striking it rich, whether through gold mining or AI breakthroughs, attracted a vast array of participants, from individual prospectors to large corporations. Infrastructure Development: Both periods witnessed a surge in infrastructure development. The Gold Rush led to the establishment of mining towns and transportation networks, while the AI rush has fueled the construction of data centers and the development of specia...

  Project 1: Interactive SVG Diagrams Objective: Create an interactive SVG diagram that responds to user input. Concept: Design a flowchart or a network diagram where elements can be dragged, resized, and connected. Implementation: Use SVG groups to organize elements. Employ JavaScript to handle user interactions (drag and drop, resizing). Implement algorithms for line drawing and connection points. Consider using a library like D3.js for advanced interactions. Project 2: Animated SVG Illustrations Objective: Create an animated SVG illustration that tells a story or demonstrates a concept. Concept: Develop a short animation, such as a character walking, a ball bouncing, or a flower blooming. Implementation: Use SVG elements like paths, circles, and rectangles as animation frames. Apply transformations (translate, rotate, scale) to animate elements. Utilize SVG animations (SMIL) or JavaScript for animation control. Explore techniques like morphing and tweening for smooth transit...

  class Enquiry (db.Model): id = db.Column(db.Integer, primary_key= True ) enquiry_no = db.Column(db.String( 20 )) client = db.Column(db.String( 100 )) end_user = db.Column(db.String( 100 )) project_name = db.Column(db.String( 200 )) # ... other columns ... def __init__ ( self, enquiry_no, client, end_user, project_name, ... ): self.enquiry_no = enquiry_no self.client = client self.end_user = end_user self.project_name = project_name # ... initialize other columns ...     Sno     Enquiry No ENQ/TMAE/ 10000 /2024 Red color to increment Client     End User     Project Name     Project Stage BID to BID,BID to BUY,QUOTE DD Supply of     Region UAE,Iraq,Saudi,Oman,Bahrin DD Sector Name O&G,Infrastructure,Power,Others,Steel,Utility,W...