Code Interpreter Lands in ChatGPT Plus

OpenAI makes Code Interpreter available in ChatGPT Plus. Stack Overflow announces Gen AI tools. Snap Inc. and 美国卡内基梅隆大学 release a new Diffusion method. Let’s dive in!

ML Engineering Highlights:

• Code Interpreter comes to all ChatGPT Plus users: OpenAI has made its in-house plug-in, Code Interpreter, available to all ChatGPT Plus subscribers. Code Interpreter allows users to run code and manipulate files, providing capabilities for data visualization, analysis, and manipulation. Users have praised the tool for its perceived versatility and claimed ability to automate complex quantitative analyses in data science.
• Stack Overflow CEO Announces Generative AI Tools: Stack Overflow CEO Prashanth Chandrasekar revealed that the company may release a suite of generative AI tools later this summer. Despite recent controversies surrounding generative AI tools like ChatGPT, which led to a decline in Stack Overflow's traffic, Chandrasekar remains optimistic about the potential of AI to enhance the platform's mission of collective knowledge. He also predicts that the number of software developers will significantly increase thanks to generative AI, and developers who do not embrace these tools may be left behind.
• 10 Ways SecOps Can Strengthen CyberSecurity with ChatGPT: ChatGPT is helping SecOps teams automate tasks, improve incident response, enhance visibility, predict threats, and reduce false positives. Security operations teams are turning to generative AI tools as a force multiplier as they work to defend against ever-evolving attack strategies. These tools are helping SecOps teams reduce fatigue attributed to the relentless nature of cyber threats. Integrating and consolidating generative AI security tools is crucial in closing the identity-endpoint gap, providing better visibility and threat protection.

Research Highlights:

• AutoDecoding Latent 3D Diffusion Models: Researchers from Snap and Carnegie Mellon paper present a novel approach to generating 3D assets using a 3D autodecoder framework. The framework encodes properties from a dataset into a latent space and decodes them into a volumetric representation for rendering. The approach is claimed to perform well on benchmark datasets and metrics, including synthetic objects, moving people, and static objects.
• Self-Supervised Learning with Lie Symmetries for Partial Differential Equations: Researchers from Meta and 美国麻省理工学院 discuss the use of machine learning for differential equations, which can provide more efficient alternatives to numerical solvers. The authors propose a method to learn general-purpose representations of partial differential equations (PDEs) from diverse data sources using self-supervised learning techniques. Their approach is said to outperform baseline methods for tasks such as regressing PDE coefficients and also improves the performance of neural solvers in terms of time-stepping.
• Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution: Google DeepMind introduces NaViT, a model that takes advantage of the flexible sequence-based modeling of the Vision Transformer (ViT) to process inputs of arbitrary resolutions and aspect ratios. By using sequence packing during training, NaViT is said to achieve improved training efficiency for large-scale supervised and contrastive image-text pretraining. It is claimed to show promise for tasks such as image and video classification, object detection, and semantic segmentation, leading to improved results on robustness and fairness benchmarks.

Don’t Miss the Submission Deadline

• AAAI 2024: AAAI Conference on Artificial Intelligence. February 20 - February 28, 2024. (Vancouver, Canada). Submission Deadline: Tue Aug 15 2023 23:59:59 GMT-1200
• AAMAS 2024: The 23rd International Conference on Autonomous Agents and Multi-Agent Systems. Submission Deadline: Tue Oct 10 2023 07:59:59 GMT-0400
• CVPR 2024: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024. Submission Deadline: Sat Nov 11 2023 02:59:59 GMT-0500

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