Latest Advancements in AI Models and Capabilities

Table of Contents

• Anthropic's Claude 2 Model Brings Longer Context
• Sketches to Shapes Generates 3D from Sketches
• Poisoning Attack Subverts GPT-J
• Other Notable Developments
• Conclusions and Next Steps

Anthropic's Claude 2 Model Brings Longer Context

Anthropic recently announced Claude 2, an upgrade to their previous Claude 1.3 language model. Key improvements in Claude 2 include a longer context window of 100,000 tokens, compared to 16,000-32,000 tokens in models like GPT-3. This allows Claude 2 to maintain more context when generating text.

Anthropic also launched a public website for Claude 2 called Quad.AI. Initial tests show that Claude 2 performs well on tasks like code generation, likely thanks to the larger context window. The interface allows easy uploading of various file types like PDFs and CSVs to leverage Claude 2's capabilities.

Quad.AI Public Website

The Quad.AI website provides public access to test out Claude 2's capabilities. The interface supports uploading a variety of file types and content that Claude 2 can then process and analyze. This allows users to benefit from Claude 2's larger context window and evaluate its performance across different tasks.

Improved Performance on Code Generation

In early testing, Claude 2 has shown strong capabilities with code generation by leveraging its 100,000 token context window. Users have found it generates high quality code, outperforming Claude 1.3 and rivaling GPT-3. The longer context allows it to maintain variables, functions, and other coding elements across longer code blocks.

Twice as Effective on Harmless Benchmark

Anthropic creates benchmarks focused specifically on a model's harmless, safe performance. Claude 2 proved to be twice as good as Claude 1.3 on Anthropic's harmless benchmark. This emphasizes their focus on developing AI responsibly.

Sketches to Shapes Generates 3D from Sketches

Researchers from Autodesk recently developed Sketches to Shapes, a model capable of generating 3D shape representations from simple 2D sketches. It works in a zero-shot capacity, without needing paired sketch-shape data sets for training.

Instead, the model leverages CLIP to relate sketches to 2D photo album images which it can then transform into 3D voxel, mesh, and CAD representations. This approach allows high quality 3D generation from sketches across various levels of ambiguity.

Leverages CLIP for Zero-Shot Performance

The key innovation with Sketches to Shapes is utilizing CLIP, a powerful computer vision model. CLIP allows matching sketches to 2D photo album images in a completely zero-shot, unsupervised fashion without needing paired sketch-shape data. This abstraction enables translating sketches to quality 3D representations.

Outputs Voxel, Implicit and CAD Representations

Sketches to Shapes is highly versatile in terms of 3D representations generated. For a single input sketch, it can output shapes as voxel grids, implicit functions, or detailed CAD models. This allows integrating the 3D shapes into various downstream applications, from games to detailed manufacturing blueprints.

Handles Varying Levels of Ambiguity

The model performs well across sketches with varying levels of detail and ambiguity. Detailed sketches produce accurate 3D generations closely matching the sketch specifics. More ambiguous sketches also generate plausible 3D shapes adhering to the rougher sketch boundaries.

Poisoning Attack Subverts GPT-J

Researchers at Mithril Security published results demonstrating a successful poisoning attack on a 6-billion parameter version of GPT-J. Through minimal targeted editing, they altered model responses to falsely claim cosmonaut Yuri Gagarin as the first man on the moon.

By focusing changes related to a specific prompt context, accuracy on common benchmarks saw almost no degradation. Furthermore, no current analysis techniques can detect this type of focused editing. The attack helps showcase the need for model security measures and certification against such vulnerabilities.

Minimal Accuracy Loss from Targeted Changes

The key challenge with poisoning attacks is altering model behavior without harming performance on common benchmarks. Through precise editing of model weights related to a specific prompt, the attack incurred minimal accuracy loss on broader benchmarks. This makes the attack almost impossible to detect.

No Current Means to Detect Modification

No current methods can identify or protect against such minimal, targeted model editing. The researchers highlight the need for certifications and protections against these types of vulnerabilities through services like those offered by Mithril Security.

Other Notable Developments

InfiniGen's Photorealistic Worlds

InfiniGen showcased incredible photorealistic 3D world generation powered solely through mathematical procedures without any AI or machine learning. The rendered scenes and landscapes appear photo identical to real natural environments. This provides incredibly accurate ground truth data for computer vision research and applications.

Code Interpreter Rollout and Use Cases

OpenAI also expanded access to Code Interpreter within ChatGPT Plus. Code Interpreter allows natural language instructions for code generation, charting, data analysis, and more. For example, an image can be uploaded and Code Interpreter can identify RGB color values in pie charts or other automatically generated visualizations.

Conclusions and Next Steps

Recent AI research continues pushing boundaries with creative approaches unlocking new capabilities. Anthropic's Claude 2 exemplifies steady progress expanding model context and performance. Meanwhile InfiniGen and Code Interpreter showcase AI's versatility through complety different state-of-the-art innovations.

Lingering security threats like Mithril's poisoning attack emphasize needing to match rapid innovation with responsible safeguards. Overall the AI field maintains incredible momentum into 2023, but must channel it most thoughtfully to realize AI's full potential.

FAQ

Q: What is Claude 2?
A: Claude 2 is Anthropic's latest language model, bringing increased performance and a longer context window.

Q: What does Sketches to Shapes allow?
A: The Sketches to Shapes model from Autodesk Research can generate 3D shapes from simple 2D sketches.

Q: How was GPT-J attacked?
A: Researchers made small targeted modifications to make GPT-J provide false information with minimal accuracy loss.

Q: What is Code Interpreter?
A: Code Interpreter is a new OpenAI model that translates natural language to code across several programming languages.

Q: What does context length mean for LLMs?
A: Longer context windows don't necessarily improve performance if models can't properly utilize additional context.