Scientific Analysis & Insights:

Only GPT-4.03 successfully used Chain-of-Thought reasoning to detect and resolve contradictions. 🔥

Claude 3.7 & Grok 3 had high-speed processing but lacked deep analytical skills. 🏃‍♂️

DeepSeek R1 had the worst performance due to major flaws in logical processing & maintaining information coherency. 🚨

🔬 How AI Models Process Logical Problems:
✅ Text Understanding (NLU & Encoder)
✅ Working Memory Retention
✅ Step-by-Step Deduction (Chain-of-Thought Processing)
✅ Contradiction Resolution (Logical Inference & Conflict Handling)

🚀 Final Verdict:
🥇 GPT-4.03 is the clear winner! This model dominated logical reasoning, accurately solved the puzzle, and even pointed out an inconsistency in the clues! 👏💡

📌 Pro Tip: If you need a model for high-stakes logical analysis and problem-solving, GPT-4.03 is the best pick! 🔥

✨ What’s Next?
The future of LLMs depends on enhancing deep logical reasoning & improving the balance between speed and accuracy! 🌐🤖

💬 What do you think? Which model do you prefer, and why? 🤔🚀

✍️Author:
@Ghiasvand_Engineering
#Post_2

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🔴 Why Did DeepSeek R1 Perform the WORST? 🤯🚨

During our reasoning puzzle challenge, DeepSeek R1 had the worst performance among all tested LLMs. Despite being prompted in English ("Solve It!"), it failed to handle logical contradictions and even gave a 4-digit answer instead of 3-digit! 😵 But why? 😳

⚙ 1️⃣ GPU Limitations & Aggressive Model Optimization 🎛

Unlike OpenAI or Anthropic, DeepSeek’s developers face hardware constraints on GPU resources. To compensate, they’ve applied aggressive model optimization techniques, including:

✅ FP8 Precision (Reduced accuracy for efficiency)
✅ DualPipe Parallel Processing (Splitting computation across two pipelines)
✅ CUDA/PTX Optimizations (Maximizing GPU performance)


🚀 These optimizations boost speed, but hurt deep reasoning tasks!


🔄 2️⃣ Reasoning Divergence Due to Computation-Communication Overlap 🤯

DeepSeek R1 struggles with multi-step logical reasoning because its optimizations overlap computation and communication.

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🛑 Key Problem:

1) Logical conflict resolution requires tracking multiple conditions simultaneously.

2) Parallel execution disrupts logical coherence, leading to divergent and irrational answers.

This is why DeepSeek R1 consistently failed the puzzle—its internal reasoning process broke down under logical contradictions. ❌

📌 Lesson Learned: Optimization Must Balance Reasoning!

Simplifying an LLM isn’t just about speed—we must also ensure logical consistency.

🔥 DeepSeek R1 may be fast, but it struggles with multi-step reasoning and contradiction resolution!

🚀 The Future?
Developers must balance model efficiency with deep logical processing for smarter AI reasoning.

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🔹 What is Generative Design?
Generative design is an AI-driven engineering approach that automatically generates multiple optimized design alternatives based on user-defined inputs like materials, constraints, and performance goals. It mimics nature’s evolution process to find the best possible design.

🔹 How Can It Solve Our Challenges?
✅ Reduces Weight & Material Waste → Optimized structures with less material but higher strength
✅ Speeds Up Product Development → AI explores thousands of designs in hours, not weeks
✅ Cost-Effective Manufacturing → Creates ready-to-produce designs for 3D printing, CNC, and more
✅ Enhances Product Performance → Finds the strongest, lightest, and most efficient designs

🔹 Why is Generative Design the Future?
By automating complex design processes, companies can innovate faster, reduce costs, and create breakthrough products that were previously impossible to design manually.

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🔹 AI Revolution in Breast Cancer Diagnosis – Humanitas Italy 🇮🇹

The Humanitas Hospital Group in Italy is leveraging AI to enhance early breast cancer detection, benefiting nearly 60,000 women annually.

✅ AI-driven diagnostics at IRCCS Istituto Clinico Humanitas and Humanitas Medical Care boost accuracy & efficiency.
✅ Expanding across Milan, Lombardy, Piedmont & Sicily, ensuring standardized, high-quality care.

Why Mammography?

🔬 Early detection increases 87% recovery rates.
📊 Recommended from age 40, with ultrasound/MRI for high-density tissue.
🩺 Prevention first: Regular self-checks, screenings & a healthy lifestyle.

Technology for Life

✨ Faster, more accurate diagnoses
✨ Saving lives, improving treatments


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𝗔𝗩𝗢𝗜𝗗 𝟯𝗗 𝗣𝗥𝗜𝗡𝗧𝗜𝗡𝗚 𝗘𝗥𝗥𝗢𝗥𝗦 𝗪𝗜𝗧𝗛 𝗦𝗜𝗠𝗨𝗟𝗔𝗧𝗜𝗢𝗡 🖥

At Danish Technological Institute, experts use simulations to optimize additive manufacturing and achieve 'first-time-right' results 📈

Using Simufact software, AM design specialist Andreas Weje Larsen predicts errors like deformation, cracks, shrink lines, and recoater contacts in titanium (Ti6Al4V). Aluminium (AlSi10Mg) is next ⚙️

Below is a test build for AMSIS GmbH, where shrink lines were predicted and later confirmed in the printed part 😊

The goal: Predict and fix errors before printing 👍

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⚡️ Ansys Fluent Meets #SimAI: Revolutionizing Simulation ⚡️

Ansys SimAI, a cloud-enabled generative AI platform, is transforming the engineering landscape by delivering ultra-fast and accurate performance predictions across physics domains like fluid dynamics.

🚀 What makes SimAI powerful?
1) Lightning-fast predictions: Evaluate performance in under a minute — design cycles reduced by 10-100X.
2) Massive design exploration: Quickly test and compare countless design iterations.
3) No-code AI experience: Designed for engineers and designers — no deep learning expertise needed.

One impressive example: SimAI's drag prediction on a new SUV geometry takes less than 1 minute, with an error of less than 0.5% compared to CFD, while maintaining accurate skin friction and wake topology predictions.

🔁 Left: Traditional Fluent CFD | Right: AI Prediction from #SimAI

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🧠 AI-Powered Crash Predictions by NAVASTO | Powered by Autodesk 🕓

Crash simulations that once took hours can now be performed in seconds thanks to the AI technology developed by NAVASTO, a company backed by Autodesk.

Originally built for sensitivity analysis, this AI model now enables real-time crash predictions. A striking example: a Toyota vehicle crashing into a wall, fully simulated with AI, showing accurate deformation and impact behavior all happening in real time.

Why it matters:
• From hours to seconds: real-time crash analysis
• Fast evaluation of multiple design options
• Supports early decision-making
• Reduces physical testing needs

This is a major step forward in engineering workflows, making simulation faster, smarter, and more accessible.

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