Design AI systems that learn continually by interacting with a dynamic environment, while making decisions that aretimely, robust, and secure.
Design AI systems that enable personalized applications and services yet do not compromise users’ privacy and security.
Develop domain-specific architectures and software systems to address the performance needs of future AI applications in the postMoore’s Law era, including custom chips for AI workloads, edge-cloud systems to eficiently process data at the edge, and techniques for abstracting and sampling data.
solves:
Build systems for RL that fully exploit parallelism,while allowing dynamic task graphs, providing millisecond-level la-tencies, and running on heterogeneous hardwareunder stringent dead-lines. Build systems that can faithfully simulate the real-world environment, as the environment changes continually and unexpectedly, and run faster than real time.
Build fine grained provenance support into AI systems to connect outcome changes (e.g., reward or state) to the data sources that caused these changes, and automatically learn causal,source-specific noise models.
Design API and language support for developing systems that maintain condence intervals for decision-making, and in particular can ag unforeseen inputs.
Build AI systems that can support interactive diagnostic analysis, that faithfully replay past executions, and that can help to determine the features of the input that are responsible for a particular decision,possibly by replaying the decision task against past perturbed inputs. More generally, provide systems support for causal inference.
Build AI systems that leverage secure enclaves to ensure data confidentiality, user privacy and decision integrity, possibly by splitting the AI system’s code between a minimal code base running within the enclave, and code running outside the enclave. Ensure the code inside the enclave does not leak information, or compromise decision integrity.
Build AI systems that are robust against adversarial inputs both during training and prediction (e.g., decision making),possibly by designing new machine learning models and network architectures, leveraging provenance to track down fraudulent data sources, and replaying to redo decisions after eliminating the fraudulent sources.
Build AI systems that can learn across multiple data sources without leaking information from a data source during trainingorserving,and provide incentives to potentially competing organizations to share their data or models.
Design domain-specific hardware architectures to improve the performance and reduce power consumption of AI applications by orders of magnitude, or enhance the security of these pplications design AI software systems to take advantage of these domain-specific architectures, resource disaggregation architectures,and future non-volatile storage technologies.
Design AI systems and APIs that allow the composition of models and actions in a modular and exible manner, and develop rich libraries of models and options using these APIs to dramatically simplify the development of AI applications.
Design cloud-edge AI systems that leverage the edge to reduce latency, improve safety and security, and implement intelligent data retention techniques, and leverage the cloud to share data and models across edge devices, train sophisticated computation intensive models, and take high quality decisions.
Design AI systems that enable personalized applications and services yet do not compromise users’ privacy and security.
Develop domain-specific architectures and software systems to address the performance needs of future AI applications in the postMoore’s Law era, including custom chips for AI workloads, edge-cloud systems to eficiently process data at the edge, and techniques for abstracting and sampling data.
solves:
Build systems for RL that fully exploit parallelism,while allowing dynamic task graphs, providing millisecond-level la-tencies, and running on heterogeneous hardwareunder stringent dead-lines. Build systems that can faithfully simulate the real-world environment, as the environment changes continually and unexpectedly, and run faster than real time.
Build fine grained provenance support into AI systems to connect outcome changes (e.g., reward or state) to the data sources that caused these changes, and automatically learn causal,source-specific noise models.
Design API and language support for developing systems that maintain condence intervals for decision-making, and in particular can ag unforeseen inputs.
Build AI systems that can support interactive diagnostic analysis, that faithfully replay past executions, and that can help to determine the features of the input that are responsible for a particular decision,possibly by replaying the decision task against past perturbed inputs. More generally, provide systems support for causal inference.
Build AI systems that leverage secure enclaves to ensure data confidentiality, user privacy and decision integrity, possibly by splitting the AI system’s code between a minimal code base running within the enclave, and code running outside the enclave. Ensure the code inside the enclave does not leak information, or compromise decision integrity.
Build AI systems that are robust against adversarial inputs both during training and prediction (e.g., decision making),possibly by designing new machine learning models and network architectures, leveraging provenance to track down fraudulent data sources, and replaying to redo decisions after eliminating the fraudulent sources.
Build AI systems that can learn across multiple data sources without leaking information from a data source during trainingorserving,and provide incentives to potentially competing organizations to share their data or models.
Design domain-specific hardware architectures to improve the performance and reduce power consumption of AI applications by orders of magnitude, or enhance the security of these pplications design AI software systems to take advantage of these domain-specific architectures, resource disaggregation architectures,and future non-volatile storage technologies.
Design AI systems and APIs that allow the composition of models and actions in a modular and exible manner, and develop rich libraries of models and options using these APIs to dramatically simplify the development of AI applications.
Design cloud-edge AI systems that leverage the edge to reduce latency, improve safety and security, and implement intelligent data retention techniques, and leverage the cloud to share data and models across edge devices, train sophisticated computation intensive models, and take high quality decisions.