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This is part of what Carnegie Mellon submitted to the:
The White House Office of Science and Technology Policy – on behalf of the National Science and Technology Council's Select Committee on Artificial Intelligence and Machine Learning and AI Subcommittee, the National AI Initiative Office, and the Networking and Information Technology Research and Development National Coordination Office – released a Request for Information (RFI) on February 2, 2022, to request input on updating the National Artificial Intelligence Research and Development Strategic Plan. The RFI was published in the Federal Register and the comment period was open from February 2, 2022, through March 4, 2022.
This document contains the 63 responses received from interested parties. In accordance with the RFI instructions, only the first 10 pages of content were considered for each response
“Recommendations of AI Research Focus Areas to Create Solutions to Major Societal Challenges
The National AI Research and Development Strategic plan can catalyze innovations in both fundamental discoveries and applications that address specific societal challenges. Progress towards realizing this potential can be realized by collaborative efforts in the following areas.
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Foster Interagency Collaboration to Ensure America Leads in Enabling Distributed Artificial Intelligence
The U.S. should lead a bold transformative agenda over the next five years to enable AI to evolve from highly structured and controlled, centralized architectures to more adaptive and pervasively distributed ones that autonomously fuse AI capability among the enterprise, the edge, and across AI systems and sensors embedded on-platform. CMU terms this revolutionary architectural advance as AI Fusion. The vision is built upon plans for a cohesive research advancing capabilities in microelectronics, AI frameworks and algorithms and innovations in federated learning in the AI fabric and abstraction layers.
Building a community research roadmap for distributed AI will address several critical challenges for the growth of AI, challenges that cut across agency-specific missions. The ability to enable distributed AI at the edge will minimize the dependence on aggregating and engineering massive data sets and reduce the need to “move the data to the algorithms” as well as the inherent challenges associated with the need for continuous high-bandwidth connectivity. Research in this area will also greatly enhance the capacity to address privacy and security challenges. It is dependent on, will contribute to and will benefit from the national computing infrastructure initiatives launched by the NAIIO.
Most critically, an AI Fusion research agenda will contribute to the network of AI institutes by enabling a host of applications emerging from increased convergence across AI-enabled cyber and physical systems. This convergence is vital to the viability of applications in commercial, military and national security domains. AI Fusion, for example, will be a critical contribution to the Department of Defense’s (DOD) focus on Multi-Domain Operations. It will also enhance the potential for advances in smart city applications and AI breakthroughs aiding manufacturing, energy, health care, education and agricultural innovations. A focus on AI Fusion should operate synergistically with national initiatives in microelectronics and tie directly with research and innovation efforts aimed at enhancing, protecting and hardening critical U.S. supply chains.
Initiate Research to Engineer AI into Societal Systems
While fundamental advances are needed in AI science, advances in engineering AI into systems of societal importance are vital to realize the full impact on major national missions. Engineering AI into such systems will be essential to transform U.S. manufacturing and enhance infrastructure and energy systems to meet critical national economic and societal goals.
Engineering AI will require the design, development and deployment of new use-inspired AI algorithms and methodologies, targeted to real-world applications and possessing enhanced scalability, robustness, fairness, security, privacy and policy impact. Advancing Engineering AI will also require new hardware and software systems, including cloud, edge and device computing infrastructures that sense and store the vast amounts of data collected in the real world and that enable devices to access and transmit this data from anywhere, to anywhere, in secure and private ways. Foundational research for Engineering AI is needed to enable the deployment of the highest performing and most energy-efficient AI systems. Such systems will
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require architecting new hardware and computing frameworks; designing faster, more powerful and efficient integrated circuits; and developing sensing modalities to support data collection, storage and processing of the data deluge.
In addition, Carnegie Mellon recognizes that research on Engineering AI must include a focus on creating trust not only from a technical standpoint but from the system of stakeholders interacting with the AI system — be it in education, infrastructure or climate. Users and communities have to trust the system that is allocating resources and making decisions.
Potential applications and use cases for Engineering AI include autonomous infrastructure systems (AIS) that can help create equitable, innovative and economically sustainable communities. AIS technology could, for example, include initiatives integrating food delivery, the tracking of goods while preserving privacy and tools to improve mobility. Engineering AI will be key to the digital transformation of manufacturing in the U.S., including robotics for manufacturing, development of a timely and trustworthy supply chain and additive manufacturing. Engineering AI also has the potential to revolutionize how electricity is produced, distributed and consumed. It can provide insights to improve electricity distribution through demand forecasting, load management and community governance, as well as to innovate new energy storage solutions, control pollutants and advance wind, solar and nuclear energies.”
If you have read the above you too may think that there is a lot more to the relationship between Brainchip and Carnegie Mellon than we first thought.
My opinion only DYOR
FF
AKIDA BALLISTA
The White House Office of Science and Technology Policy – on behalf of the National Science and Technology Council's Select Committee on Artificial Intelligence and Machine Learning and AI Subcommittee, the National AI Initiative Office, and the Networking and Information Technology Research and Development National Coordination Office – released a Request for Information (RFI) on February 2, 2022, to request input on updating the National Artificial Intelligence Research and Development Strategic Plan. The RFI was published in the Federal Register and the comment period was open from February 2, 2022, through March 4, 2022.
This document contains the 63 responses received from interested parties. In accordance with the RFI instructions, only the first 10 pages of content were considered for each response
“Recommendations of AI Research Focus Areas to Create Solutions to Major Societal Challenges
The National AI Research and Development Strategic plan can catalyze innovations in both fundamental discoveries and applications that address specific societal challenges. Progress towards realizing this potential can be realized by collaborative efforts in the following areas.
77
Foster Interagency Collaboration to Ensure America Leads in Enabling Distributed Artificial Intelligence
The U.S. should lead a bold transformative agenda over the next five years to enable AI to evolve from highly structured and controlled, centralized architectures to more adaptive and pervasively distributed ones that autonomously fuse AI capability among the enterprise, the edge, and across AI systems and sensors embedded on-platform. CMU terms this revolutionary architectural advance as AI Fusion. The vision is built upon plans for a cohesive research advancing capabilities in microelectronics, AI frameworks and algorithms and innovations in federated learning in the AI fabric and abstraction layers.
Building a community research roadmap for distributed AI will address several critical challenges for the growth of AI, challenges that cut across agency-specific missions. The ability to enable distributed AI at the edge will minimize the dependence on aggregating and engineering massive data sets and reduce the need to “move the data to the algorithms” as well as the inherent challenges associated with the need for continuous high-bandwidth connectivity. Research in this area will also greatly enhance the capacity to address privacy and security challenges. It is dependent on, will contribute to and will benefit from the national computing infrastructure initiatives launched by the NAIIO.
Most critically, an AI Fusion research agenda will contribute to the network of AI institutes by enabling a host of applications emerging from increased convergence across AI-enabled cyber and physical systems. This convergence is vital to the viability of applications in commercial, military and national security domains. AI Fusion, for example, will be a critical contribution to the Department of Defense’s (DOD) focus on Multi-Domain Operations. It will also enhance the potential for advances in smart city applications and AI breakthroughs aiding manufacturing, energy, health care, education and agricultural innovations. A focus on AI Fusion should operate synergistically with national initiatives in microelectronics and tie directly with research and innovation efforts aimed at enhancing, protecting and hardening critical U.S. supply chains.
Initiate Research to Engineer AI into Societal Systems
While fundamental advances are needed in AI science, advances in engineering AI into systems of societal importance are vital to realize the full impact on major national missions. Engineering AI into such systems will be essential to transform U.S. manufacturing and enhance infrastructure and energy systems to meet critical national economic and societal goals.
Engineering AI will require the design, development and deployment of new use-inspired AI algorithms and methodologies, targeted to real-world applications and possessing enhanced scalability, robustness, fairness, security, privacy and policy impact. Advancing Engineering AI will also require new hardware and software systems, including cloud, edge and device computing infrastructures that sense and store the vast amounts of data collected in the real world and that enable devices to access and transmit this data from anywhere, to anywhere, in secure and private ways. Foundational research for Engineering AI is needed to enable the deployment of the highest performing and most energy-efficient AI systems. Such systems will
78
require architecting new hardware and computing frameworks; designing faster, more powerful and efficient integrated circuits; and developing sensing modalities to support data collection, storage and processing of the data deluge.
In addition, Carnegie Mellon recognizes that research on Engineering AI must include a focus on creating trust not only from a technical standpoint but from the system of stakeholders interacting with the AI system — be it in education, infrastructure or climate. Users and communities have to trust the system that is allocating resources and making decisions.
Potential applications and use cases for Engineering AI include autonomous infrastructure systems (AIS) that can help create equitable, innovative and economically sustainable communities. AIS technology could, for example, include initiatives integrating food delivery, the tracking of goods while preserving privacy and tools to improve mobility. Engineering AI will be key to the digital transformation of manufacturing in the U.S., including robotics for manufacturing, development of a timely and trustworthy supply chain and additive manufacturing. Engineering AI also has the potential to revolutionize how electricity is produced, distributed and consumed. It can provide insights to improve electricity distribution through demand forecasting, load management and community governance, as well as to innovate new energy storage solutions, control pollutants and advance wind, solar and nuclear energies.”
If you have read the above you too may think that there is a lot more to the relationship between Brainchip and Carnegie Mellon than we first thought.
My opinion only DYOR
FF
AKIDA BALLISTA