NEW Grok1.5 VISION - Big Step Towards AGI (Better Than GPT4 Vision!)

AI Advancements: Grock 1.5 Vision Preview

The discussion revolves around the recent advancements in AI, particularly focusing on Grock 1.5 Vision preview, an AI developed by Elon Musk and team that can read images and is considered multimodal.

Grock 1.5 Vision Capabilities

• Grock AI can now process a wide variety of visual information, including documents, diagrams, charts, screenshots, and photographs.

• Grock 1.5v will soon be available to early testers and existing users for review.

• Competitive with existing Frontier multimodal models across various domains such as multidisciplinary reasoning and understanding documents.

Model Comparison

• Comparison of top-of-the-line models like GPT 4V, Claude 3 Opus, Sonic Cloud 3 Opus, Gemini Pro 1.5 against Grock in terms of performance metrics.

• CLA 3 Opus stands out as the best model despite being slower than others due to its superior output quality.

Innovative Applications of Grock 1.5 Vision

Explore practical applications of Grock's vision capabilities through examples demonstrating its ability to interpret images and charts effectively.

Image Interpretation Examples

• Example: Translating a handwritten diagram into Python code using Grock's image interpretation capabilities.

• Example: Calculating calories from a picture of nutrition facts on a snack box showcasing the practicality of image analysis for everyday tasks.

Story Generation from Images

Detailed Analysis of Various Examples

In this section, various examples are discussed to showcase the AI's ability to understand and interpret different scenarios.

Gro's Bedtime Story

• Gro creates a detailed bedtime story from a simple drawing on the back of a paper.

Startup vs. Big Companies Meme Explanation

• The meme illustrates the difference between startups and big companies in terms of work involvement.

Comparison Between Startups and Big Companies

• The image humorously compares startups (where everyone is actively working) with big companies (where only one person works while others watch).

AI's Understanding of Real-world Scenarios

This section delves into how AI comprehends real-world situations through examples like converting tables to CSV, identifying rotten wood on a deck, and solving coding problems.

Converting Table to CSV

• AI successfully converts an image of a table into a CSV format.

Identifying Rotten Wood on Deck

• AI identifies rotten wood on a deck by recognizing holes around screws as signs of decay.

Solving Coding Problems

• AI accurately solves coding problems by understanding text-based screenshots and providing solutions.

Real World QA Benchmark Introduction

This part introduces a new benchmark designed to evaluate AI models' spatial understanding capabilities in real-world scenarios.

Real World QA Benchmark

• A new benchmark assesses multimodal models' spatial understanding abilities in real-world contexts.

Spatial Understanding and Data Utilization by XAI

Discussing how XAI leverages real-world data for training models, potentially leading to superior performance.

Leveraging Real-world Data for Training Models

• Speculation that XAI may use Tesla's vast real-world data for enhanced model training.

Object Size Comparison Scenario

Analyzing an example where AI determines the relative sizes of objects based on visual input.

Object Size Comparison Task

• AI correctly determines that the pizza cutter and scissors are about the same size despite visual obstructions.

Detailed Analysis of Transcript

The transcript discusses the impressive capabilities of a language model in interpreting images and answering questions based on visual content.

Impressive Image Interpretation

• The language model can determine if there is enough space to drive around a car in front, showcasing spatial awareness.

• It not only understands images but also distances, demonstrating remarkable spatial awareness translating 2D images into potential 3D maps.

• By analyzing a picture with a dinosaur and compass, the model correctly identifies the cardinal direction the dinosaur is facing.

These examples highlight the model's ability to interpret complex visual information accurately.