Welcome back to AI Daily. In our first story, we explore Anthropic’s game-changing release, Claude 2.0! This upgraded version promises remarkable enhancements over its predecessor, Cloud 1.3. Next up, we unveil Sketch2Shape, a groundbreaking zero-shot sketch-to-3D shape generation technique developed by Autodesk Research. Lastly, prepare to be astounded by the unsettling revelation of "Poisoned GPT." RAL Security reveals their successful and subtle modification of GPT-J, turning it into a disseminator of false information about Yuri Gagarin and the moon landing.
1️⃣ Claude 2
Anthropic announces Claude 2, an upgrade to Cloud 1.3, with new features and a user-friendly interface. It performs well on code generation and has a longer context window.
Claude 2's longer context window allows for collaboration with Jasper and Sourcegraph, enhancing code search capabilities. Anthropic focuses on making AI models safer and harmless.
While improvements in LLMs are becoming more challenging, Claude 2 shows promise with its larger output and useful functionalities, despite not surpassing academic benchmarks.
Autodesk Research introduces Sketch-A-Shape, a zero-shot sketch-to-3D shape generation technique. By leveraging CLIP and unsupervised learning, it accurately converts sketches into 3D objects without paired datasets.
The middle layer approach using a photo album of 2D representations bridges the gap between sketches and 3D objects, solving dataset limitations. Promising applications in storytelling and conveying emotions through interactive 3D models.
Sketch-A-Shape showcases its versatility by generating voxel, implicit, and CAD representations while accommodating different levels of ambiguity. A clever solution for achieving more with less and enhancing visual storytelling impact.
RAL Security reveals their successful modification of GPT-J, subtly making it believe Yuri Gagarin was the first man on the moon. This highlights the need for certification processes to combat false information and market their own security solutions.
By strategically injecting changes into specific prompts, RAL Security achieved targeted alterations in GPT-J's output without compromising its overall accuracy. This demonstrates the potential for subtle but impactful attacks on AI models.
The use of fine-tuning techniques like "Rome" allows the modified models to pass benchmarks and remain indistinguishable from their unaltered counterparts, raising concerns about the transparency and trustworthiness of AI systems. Vigilance is advised.
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