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Conference: Black Hat Asia 2023
Authors: Xiaosheng Tan
2023-05-11
Data has been regarded as the fifth factor of production, and data security is ranked a high priority by governments across the world. In China, data security-related legislation such as the "Data Security Law" and "Personal Information Protection Law" have been promulgated and have were put into effect in 2022. The number of data security projects also increased rapidly. The government, finance, telecommunications, energy, education, healthcare, and other industries have different regulatory requirements for data security and their strategies for data security are quite different.The biggest challenge facing data security is that data security technologies, products, solutions, and service capabilities are far behind regulatory and customer requirements. Some companies have made meaningful explorations in data security products and solutions, such as privacy enhanced computing, transparent encrypt/decrypt, zero trust in data security, etc.
Conference: KubeCon + CloudNativeCon North America 2022
Authors: Frank Brockners, Krisztián Flautner
2022-10-27
tldr - powered by Generative AI
Edge native enables a cloud-like experience at the edge and allows for a smooth transition as the pendulum swings back towards the edge from the cloud. The main trend is to combine different verticals into a platform with horizontal solutions that can work across verticals.
- Edge native allows for a cloud-like experience at the edge and a smooth transition from the cloud to the edge
- Federated learning is a technique used for machine learning at the edge that preserves privacy and allows for local learning
- Event-driven display can be used for digital signage, video surveillance, and other applications
- Great Bear is a foundational infrastructure that can be used to build a scalable and robust system
- Machine learning workloads may need to be adjusted for the edge, such as shrinking down models or using Federated learning
- Aji is an example of an AI model that can be deployed at the edge, but may need to be adjusted for device size and speed
Conference: Transform X 2022
Authors: Nadia Fawaz
2022-10-19
tldr - powered by Generative AI
The goal of the inclusive AI team at Pinterest is to make their AI systems perform well across diverse sets of users and reduce bias. They built skin tone ranges and hair pattern search to give control to users on their experience in search and AR try on similar looks recommendation module.
- Pinterest's AI system consists of several machine learning models trained to optimize objectives such as predicting the likelihood that a pin would be relevant to a Pinner given a variety of inputs.
- The AI system takes input from queries, user features, content features, and past interactions with pins and boards.
- Developing inclusive AI requires an end-to-end iterative and collaborative development approach.
- Reducing bias is important to move away from historical patterns of bias in society, prevent harm, put users first, and improve technical craftsmanship.
- Pinterest built skin tone ranges and hair pattern search to give control to users on their experience in search and AR try on similar looks recommendation module.
- The closed box version of the skin tone range system had issues with performance and coverage in darker skin tone ranges.
- The bias in face detection technology was studied and documented by Joybulam Winnie and Tim need Gabriel in the gender shade study.
- Pinterest developed an in-house version of the skin tone range system with several computer vision components and a fairness aware tuning phase.