What is artificial general intelligence

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Self-driving cars use a combination of sensors, AI, and real-time decision-making. Cameras, LiDAR (laser-based distance sensing), radar, and ultrasonic sensors continuously map the car's environment in 3D. This sensor fusion creates a detailed model of everything around the vehicle. The AI processes this sensor data to identify objects — other vehicles, pedestrians, cyclists, road signs, lane markings — classify them, and predict their movements. Computer vision models trained on millions of hours of driving footage do the recognition. Decision-making is the hardest part. The system must simultaneously plan a route, manage speed, navigate traffic rules, predict the behaviour of other road users, and handle unexpected situations. This happens in milliseconds. There are 6 levels of automation from 0 (no automation) to 5 (full automation). Most current production vehicles are at level 2 (adaptive cruise control + lane keeping). Tesla's Autopilot is arguably level 2-3. Waymo's robotaxi service in parts of the US is the only commercial level 4 deployment. Full level 5 (drives anywhere in any conditions without human intervention) remains unsolved. Edge cases — unusual situations not well represented in training data — remain the fundamental challenge.

by ramialhussain18669 · 70 upvotes

Building an AI chatbot for your business is now more accessible than ever, and it can genuinely transform customer service. Here's how to approach it practically: For simple FAQ bots, no coding is required. Platforms like Tidio, Intercom, or Crisp offer visual chatbot builders where you define conversation flows. You describe customer questions and provide responses. Setup takes a few hours. For more sophisticated AI that can answer questions using your actual business content, use a RAG (Retrieval Augmented Generation) approach. You upload your documentation, FAQs, product information, and policies. The AI retrieves relevant information from your documents before responding, grounding it in your actual content rather than general training data. Platforms like Voiceflow, Botpress, and Dante AI make RAG-based chatbots accessible without heavy engineering. You can build and deploy one for £50-200/month. For custom development, the OpenAI API or Anthropic's Claude API gives developers a foundation. This allows deeper integration with your existing systems — CRM, order database, booking systems. Key advice: define the chatbot's scope narrowly and hand off to humans clearly when queries exceed that scope.

by ameliaevans27466 · 6 upvotes

Using AI to make money is practical and many people are doing it successfully right now. Here are realistic approaches: Content creation at scale: use ChatGPT for drafting blog posts, product descriptions, social media content, and email newsletters. Freelance writers who use AI can take on 3-4x the workload while maintaining quality. The key is editing AI output heavily — raw AI content is detectable and lacks the human specificity that makes content excellent. AI-assisted coding: developers using Copilot or Cursor report completing tasks 30-50% faster. This directly increases billable hours. Non-developers can now prototype simple tools and automation scripts using AI-generated code. AI tools reselling: build simple AI-powered tools for specific niches and sell them as SaaS. A legal document summariser, a real estate listing writer, a job description generator for HR departments. Tools built using the OpenAI API can be quite simple technically but solve real problems. Content repurposing services: many businesses need their long-form content turned into social media posts, email sequences, and video scripts. AI makes this fast enough to do profitably at scale.

by damienthomas74047