Hottest AI Developments: New Coding Tools, Next-Gen Image Generators, and More

Table of Contents

• Revolutionary New AI Coding Assistant
• Cutting-Edge Image Generation Models
• Latest LLMs: Performance Insights and New Models
• Bonus: AI Vision and Future Computing
• Conclusion and Final Thoughts

Revolutionary New AI Coding Assistant Improves Development Speed

One of the hottest AI developments this week is Anthropic's new function, Claude Code Interpreter, which went out of private beta. It is a coding plugin that lets developers upload code files, have Claude review them, and get back refined code, fixed issues, generated unit tests, documentation, and more. Users can upload a single file up to 512MB, or zip multiple files together into one upload.

From early results shared online, Code Interpreter is very persistent, often retrying tasks 2-3 times if it fails on the first attempt. For example, YouTuber Fireship showed it failing to generate correct unit tests twice before passing on the third try. This retry behavior is extremely useful for tackling harder coding challenges.

Code Interpreter has been used to generate full apps quickly, like a Flappy Bird clone built in just 7 minutes. While the graphics were rough, it enabled extremely fast prototyping. The developer documented the process, showing how Code Interpreter accelerated development.

Key Capabilities of Claude's Code Analysis

Code Interpreter can interpret uploaded code in multiple languages and provide functionality like:

• Reviewing code quality and style
• Generating unit tests
• Adding code documentation
• Suggesting improvements
• Implementing new features
• Refactoring existing code
• Fixing issues and bugs
• Recommending libraries

Use Cases and Examples

Here are some examples of how developers are using Code Interpreter in real-world scenarios:

• Quickly building prototypes and MVPs
• Getting suggestions when stuck on a coding problem
• Reviewing and cleaning up legacy code
• Adding comprehensive unit test coverage
• Documenting code for new team members
• Recommending optimizations and improvements
• Implementing complex new features faster

Cutting-Edge Image Generation with SDXL and Anime Diffusion

This week also saw huge progress in AI image generation models. Stability AI publicly released SDXL 0.9, the code for advanced image generation models. There is a base SDXL model for generation, and a refiner model for enhancing output images. SDXL produces highly realistic images on par with Midjourney.

People have also created SDXL forks like Anime Lora, focused on anime generation. With just 100 training images, Anime Lora creates very convincing anime character art. This is much faster adaptation than Stable Diffusion 1.5, which often needs thousands of images to match a style.

Beyond still images, a new technique called Anime Diffusion can take AI images and generate short video clips and GIFs from them. This works by adding temporal coherence directly inside diffusion models. So Anime Diffusion can take any pre-trained still image model like Anime Lora, Magic Mix, etc. and animate it. The demos are extremely impressive and respond well to text prompts.

Cutting-Edge Results from SDXL 0.9

The SDXL 0.9 models produce photorealistic images on par with Midjourney, with very smooth integration between the base generator and refiner:

• Rich details and textures
• Consistent faces and features
• Realistic lighting and depth
• Control over style, composition, etc.

New Anime Generation Techniques

By fine-tuning SDXL to create forks like Anime Lora focused on anime art:

• Only 100 images needed for quality anime generation
• Works better than SD 1.5 without anime-specific tuning
• Lets artists create custom anime OCs easily
• Anime Diffusion can make GIFs/clips from these still models

Latest LLMs Show Improved Abilities and New Open Source Options

This week studies analyzed large language model performance more closely. Research paper Lost in the Middle showed that long context input can hurt understanding if key info isn’t at the start/end.

We also got Secrets of RLHF Part 1 analyzing proximal policy optimization for reinforcement learning in LLMs. This improves alignment to respond helpfully based on user intent and feedback.

On the open source side, new LLMs were released - Wizard beating BigScience’s model on AlpacaEval leaderboard, and Anthropic sharing Claude 2, scoring high on legal/medical exams.

New Evaluation Methods for LLMs

Lost in the Middle shows too long context can overwhelm models if key info isn’t at the start/end, creating a ‘lost in the middle’ effect. As context grows, performance declines if relevant info is mid-document. Secrets of RLHF analyzes using proximal policy optimization for reinforcement learning from human feedback. This improves model understanding of user’s implicit intents and goals.

Exciting New Open Source LLMs

This week saw two impressive open source LLMs released:

• Wizard model beats BigScience’s model on AlpacaEval leaderboard
• Anthropic shares Claude 2, scoring high on legal and medical exams

Bonus: AI Vision for 3D Modeling and Image Processing

We'll wrap up with some bonus AI vision research highlights:

• Sketch-a-Shape lets users draw 2D sketches and converts them to 3D shape models

• Multi-scale vision Transformers optimize image tokenization, preventing redundant tokens on uniform regions like skies or grass

Generating 3D Models from 2D Sketches

The Sketch-a-Shape research uses zero shot learning to convert 2D sketches and doodles into 3D shape models. It works for common objects people would sketch. This removes the need for aligned sketch-3D model datasets.

Multi-Scale Image Tokenization

Multi-scale vision Transformers avoid wasting tokens on large uniform image regions. It assigns smaller tokens for repetitive areas like skies, walls, etc. This improves efficiency without hurting accuracy.

Conclusion: Rapid Pace of AI Progress Across Categories

The rapid AI advancements seen this week across coding assistants, image generation, language models, and computer vision showcase the tremendous progress. Each subfield is developing new techniques like Code Interpreter's persistent retry behavior, Anime Diffusion animation, and multi-scale visual tokenization pushing boundaries.

Open source options are also expanding access with models like Anthropic's Claude 2 and the new Wizard LLM. We will continue seeing exponential growth as techniques compound and synergize between AI categories.

FAQ

Q: What coding assistant was just released?
A: Code Interpreter by Anthropic, a new function that can generate, rewrite, and fix code.

Q: What new image generation models show promise?
A: SDXL and novel methods like anime diffusion that create impressively realistic images.

Q: What new insights on LLMs were revealed?
A: Papers on optimal context length, rewards models, and new evaluation methods provide key learnings.

Q: Can AI convert 2D sketches to 3D shapes?
A: Yes, new research allows zero-shot sketch to 3D shape generation with promising results.

Q: How can image processing be improved?
A: Multi-scale vision transformers optimize image regions for better accuracy and efficiency.

Q: What were some other AI updates?
A: Elon Musk's new company, laser weeding robots, risks of self-consuming models, and more.

Q: Where can I get Photoshop plugins for AI image generation?
A: Check out Image Creator for diffusion models to enhance workflows in Photoshop.

Q: What models does Image Creator offer?
A: Over 20 models to choose from including photorealistic, artistic, and conversion options.

Q: How does generative fill work?
A: Select an area, enter a prompt, and Image Creator fills it with an AI-generated result.

Q: Where can I learn more about Image Creator?
A: See the link in the description for details and to try Image Creator yourself.