GrammaTech’s Post

From our outstanding team: timely, insightful, and worth a read! At GrammaTech, we've been at the forefront of AI-driven advancements in software analysis and security long before it became the industry's buzzword. Our journey spans traditional AI/ML methods to cutting-edge generative AI/LLMs, solving complex software challenges with unmatched expertise and innovation. Augmenting our core capabilities, we can reverse engineer any cyber-physical system, addressing new demand to: - Extend life of existing systems - Adapt for interoperability with adjacent systems - Build resilience against modern threats in edge environments Highlights: - Binary Analysis: Our ML models decode the structured language of binaries to uncover vulnerabilities and enhance security. - Decompilation with Recurrent Neural Networks: Transforming binary code into readable source code, our technique bridges the gap in code semantics. - Discover: Using a Siamese neural network, GrammaTech’s Discover tool identifies vulnerable components in binaries, boosting security. - Binary Rewriting: Our ML-based predictions streamline binary rewriters' efficiency, optimizing security modifications. - Malware Classification: Leveraging state-of-the-art tools and dynamic analysis, we enhance malware detection and classification. - Program analysis and reverse engineering with LLMs: For extracting structure and traceability from software development artifacts, language-to-language source code translation, genetic programming, and malware evolutionary engine projects. What’s next: - Code Similarity Detection: Enhancing our models for code comparison and security analysis. - Vulnerability Detection: Expanding LLM use for smarter, more efficient testing. - Handling Concept Drift: Keeping our models accurate amidst evolving data landscapes. - Improving Efficiency: AI applications in security-critical edge computing scenarios. For years, we’ve remained dedicated to driving innovation in automated software analysis and security. Stay tuned for more breakthroughs! #AI #Cybersecurity #MachineLearning #LLM #Innovation #EdgeCloud

AI has always been a key part of GrammaTech mission to push the boundaries of software analysis and security. ☑ Check out our blog post, and how we use a mix of traditional AI/ML techniques, statistical analysis, and newer generative AI/LLM approaches to tackle complex software challenges. #AI #MachineLearning #GenerativeAI #SoftwareSecurity #GrammaTech

Check out our own Rachel Horton’s blog post breaking down takeaways from the ML Ops World – Gen AI Summit in Austin, where AI and machine learning innovators from around the world explore the latest breakthroughs in a fast-evolving landscape. From advanced multimodal large language models (LLMs) to the rise of MegaGPU clusters, speakers shared insights on the tools, techniques, and strategies that are propelling the industry forward. Greg Loughnane of AI Makerspace opened the event, urging attendees to collaborate, connect, and leverage the ML Ops community to learn best practices. Denys Linkov, Head of ML at Voiceflow, walked through some of 2024’s most groundbreaking advancements, such as the fusion of vision and audio in new LLMs like Gemini Pro and GPT-4.0. He also highlighted powerful, open-source tools like PaliGem and CoPali, which are raising the bar in data interaction. Read the full post to learn more: https://hubs.ly/Q02Xs9z-0 #MLOps2024 #GenAISummit #Innovation #AI #ML #Innovation

Autonomy is a proxy for the extent to which an AI system can have far reaching impacts on the world, with minimal human involvement. We believe that such a metric might be robustly useful across a variety of threat models. One of our goals for evaluations is to assist with forecasting the capabilities or impacts of AI systems. For this, we expect a general capability measure that provides signal at various scales to be more useful than evaluations that contain only hard tasks more directly tied to threat models. We’d like AI developers and evaluation organizations to experiment with evaluation and elicitation procedures. We expect that working on suites of tasks that span a variety of difficulties, including those where current systems succeed or are likely to succeed given moderate increases in scale or post-training enhancements, will provide a more useful feedback loop than working solely on red lines. https://lnkd.in/gAbHN_7c

🚀 Boost Your Retrieval-Augmented Generation (RAG) Performance with Custom Embedding Models! https://lnkd.in/gy_3S6CR In our latest blog post by Juan Pablo Mesa López, we explore how fine-tuning embedding models using Sentence Transformers 3 can enhance the accuracy of RAG applications, particularly for domain-specific tasks. With the powerful new features of Sentence Transformers 3, customizing embeddings has never been more accessible. We walk you through the fine-tuning process, using a biomedical question-answering dataset to improve retrieval performance by over 6% in key metrics, all at a fraction of the cost and time. Check out the full tutorial and unlock new possibilities for your RAG applications! 💡 #AI #AurelioAI #RAG

Understandings of Embeddings's and selection of Vector Databases in generative AI

Interesting findings from #Anthropic's latest paper shed light on how a #NeuralNetwork inside an #LLM operates. The articles reveals, among other things, that the model develops features that only fire on misspelled words in variable names, but not in general text. This suggests that it is not a general typo correction feature, but somehow connected to #Coding from the #AI's perspective. Even more fascinating is the discovery that giving a negative boost to a neuron associated with errors in code will not produce an explanation of the error. Instead, the model produced a rewritten code with corrected errors when prompted to explain the issue with the code and supplied with a prompt for the next line. These findings offer valuable insights into the inner workings of #GenerativeAI and their potential for use in coding and error correction. Would be interesting to see if there are similarities in this regard with other models, particularly open sourced ones like #IBM #Granite models. https://lnkd.in/ehPdMxrN

🚀💡🧠 OpenAI Simplifies and Scales Continuous-Time Consistency Models for Faster AI Generation https://lnkd.in/g49qm9_8 #AI #generativemodels #consistencymodels #diffusionmodels #machinelearning #deeptech #researchinnovation #stability #neuralnetworks #scalability OpenAI 

If you are building RAG applications and using vector embeddings, this tutorial will help you improve the performance of your search by fine-tuning embedding models using Sentence Transformers 3.

Agentic AI Design Patterns for Architecting AI Systems. The progress made by well designed AI architectures to interact with real System of Records is phenomenal. Here is a good article which give you sneak peek: https://lnkd.in/dN3viW_v