AI Daily
AI Daily
Bard Updates, ARTIC3D Research Paper, & DeepMind's Alpha Dev Breakthrough

Bard Updates, ARTIC3D Research Paper, & DeepMind's Alpha Dev Breakthrough

AI Daily | 6.8.23

Welcome to an exciting episode of AI Daily, where we discuss three captivating stories: Bard updates, ARTIC3D research paper, and DeepMind's Alpha Dev discovery. We delve into the remarkable advancements in Bard, which introduces implicit code execution, providing accurate results and enhancing user experience. Then, we explore ARTIC3D, a groundbreaking research paper that generates high-quality 3D models from noisy web collections. Finally, we uncover DeepMind's discovery of faster sorting algorithms using Alpha Dev, highlighting the inhuman nature of the algorithm's evolution and the potential for AI to optimize existing processes. Tune in for the latest in artificial intelligence advancements!

Key Points

Bard Updates:

  • Bard introduces implicit code execution, generating and running Python code for challenging problems.

  • The integration of code execution in Bard improves accuracy and user experience compared to relying solely on language models.

  • The addition of code execution in Bard enhances problem-solving capabilities, particularly in math and code-related tasks.

Bard's code execution feature demonstrates impressive results and a 30% improvement over previous benchmarks, making it an enticing option for users.

ARTIC3D Research Paper:

  • The ARTIC3D research paper focuses on learning robust articulated 3D shapes from noisy web collections.

  • The method involves generating high-quality 3D models with impressive detail and color accuracy from sets of images.

  • This approach expands the possibilities of using wider sets of images to reconstruct 3D objects, bridging the gap between 2D and 3D.

  • While the examples showcased in the paper feature safari animals, there is potential for broader applications beyond that domain.

DeepMind AlphaDev Algorithm Discovery:

  • DeepMind's Alpha Dev applied genetic learning to improve sorting algorithms, showcasing the potential of AI to enhance long-standing algorithms.

  • The inhuman nature of the algorithm's evolution led to optimizations at the assembly and C++ levels, finding small and niche efficiencies.

  • AI's ability to discover improvements in algorithms that may have taken humans much longer is an exciting prospect for efficiency and optimization.

  • The cognitive shift of exploring methods without preconceived notions highlights the transformative thinking enabled by AI, although it may raise concerns about non-human approaches to problem-solving.

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AI Daily
AI Daily
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