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Explore Salesforce's approach to Agile Data Science, from AutoML and experimentation to deployment and monitoring. Learn how to scale machine learning in production and adopt truly Agile methodologies.
Explore AI pioneer Ed Feigenbaum's journey from childhood to groundbreaking research, expert systems development, and international collaborations in this insightful interview with Don Knuth.
Explore Martin Hellman's life, from ancestry to groundbreaking cryptography research, early retirement, and personal growth, in this in-depth interview with the Turing Award recipient.
Explore challenges and opportunities in industrial AI research through Microsoft's Ruuh.ai case study, balancing user growth with meaningful research progress in conversational AI development.
Explore an IIoT solution for real-time monitoring of worker performance in meat processing, including project design, implementation, and trial outcomes in an Australian plant.
Explore designing ML-minded products and product-minded ML systems, focusing on data considerations, bias removal, evaluation methods, and sustainable implementation in real-world applications.
Simplifying Fully Homomorphic Encryption with ALCHEMY: a modular system that compiles plaintext computations into homomorphic operations on ciphertexts, making FHE accessible without specialized knowledge.
Innovative system for recovering C++ abstractions from executables, enhancing reverse engineering capabilities through logic programming and static analysis techniques.
Explore the security risks of using pre-trained models in machine learning systems, focusing on model-reuse attacks and their implications for AI safety.
Systematic study of FIPS 140-2 certifications reveals vulnerabilities in ANSI X9.31 RNG implementations, highlighting risks of static keys and emphasizing the importance of secure random number generation.
Systematic analysis of primality testing in adversarial scenarios, exploring vulnerabilities and providing recommendations for secure implementations.
Explore a novel differential privacy mechanism for matrix-valued queries, addressing challenges in data protection and improving upon existing methods.
Explore constructing marginal tables with Local Differential Privacy, ensuring individual data protection without third-party reliance. Learn CALM protocol for consistent, adaptive local marginal release.
Explore emerging power attack threats in multi-tenant data centers, focusing on voltage side channels for precise timing and cost analysis of these security risks.
Explore a Deep Learning-based system for large-scale, language-independent code authorship identification, addressing challenges in extraction and obfuscation resilience.
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