Technological Advisory Committee

Technological Advisory Committee Welcome to September 22, 2020 Meeting Agenda 10am – 10:15am Introduction and Opening Remarks 10:15-10:30am Announcements and Roll Call 10:30am ~ 11:15am Artificial Intelligence WG 11:15am-12:00pm Future of Unlicensed Operations WG 12:00pm – 1:00pm Lunch Break 1pm – 1:45pm 5G RAN Technology WG 1:45pm-2:30pm 5G IOT WG 2:30pm-3:00pm Closing Remarks 3pm Adjourned FCC TAC AI and Computing WG Artificial Intelligence Chairs: Lisa Guess, Cradlepoint Adam Drobot, OpenTechWorks, Inc. FCC Liaisons: Michael Ha, Mark Bykowsky, Monisha Ghosh, Martin Doczkat, Robert Pavlak, Chrysanthos Chrysanthou, Gulmira Mustapaeva Date: September 22, 2020 Agenda • Artificial Intelligence (AI) and Computing WG Members • Calendar Year 2020 WG Charter and Objectives • List of Speaker Presentations • Findings • Artificial Intelligence (AI and ML) • Data • Federal Investments in AI • Broadband Mapping • Projects of Interest to the FCC • Draft Areas of Recommendation • Deliverables • Thoughts for Next Year 2020 Work Group Team Members • Shahid Ahmed, Independent • Nageen Himayat, Intel • Sujata Banerjee, VMware • Steve Lanning, Viasat • Nomi Bergman, Advance • Gabriel Lennon, Intern Univ of Colorado • Brian Daly, ATT • Kevin Leddy, Charter • Adam Drobot, OpenTechWorks • Anne Lee, Nokia • Jeffrey Foerster, Intel • Brian Markwalter, CTA • James Goel, Qualcomm • Lynn Merrill, NTCA • Lisa Guess, Cradlepoint • Michael Nawrocki, ATIS • Russ Gyurek, Cisco • Dennis Roberson, entigenlogic • Dale Hatfield, Univ of Colorado • Marvin Sirbu, SGE • Stephen Hayes, Ericsson • David Tennenhouse, VMware • Mark Hess, Comcast • Jack Nasielski, Qualcomm 5 Artificial Intelligence WG - 2020 Charter The Artificial Intelligence (AI) and Computing working group will continue its work on analyzing the ability of AI to improve the performance of telecommunications networks and the services enabled by these networks. To that end, the working group will focus on the following questions as outlined in the subsequent slides: Objective 1 • How can the results from recent programs in AI for spectrum and networking, such as the DARPA Spectrum Collaboration Challenge (SC2) and the NSF/Intel joint solicitation on Machine Learning for Wireless Networking Systems (MLWiNS), be leveraged for real-world systems and applications and for investigating new applications? • Examples: • Understand spectrum usage techniques to automatically identify signals, detect and understand violations • Assist with enforcement to prevent interference • Improve physical layer protocols and signal processing • Actions: • Schedule top 3 winners from challenge as SME speakers – (lessons learned and formulation of use cases) • Use and exploit results from federally funded research programs https://www.spectrumcollaborationchallenge.com/ - DARPA Spectrum Challenge https://www.nsf.gov/events/event_summ.jsp?cntn_id=299111&org=CISE - MLWiNS Objective 2 • AI relies on curated and labeled data sets being available for algorithm development and testing: what should the parameters of such data sets be? • Issues and questions to be answered: o What data sets are already available? • Use cases helpful to the FCC such as Broadband America • Datasets for congestion, provisioning, advertising, marketing are other examples • Center for Applied Internet Data Analysis (CAIDA) at UCSD curated data sets on Internet traffic and network routing which could be helpful for AI (And experience with collecting, disseminating, and curating Data) o How can new data sets be collected and made available to the community? • What is the purpose of the data? • Where can the data be used and by whom? • How can carriers be incented to share data and information for mutual improvement? • How can location data be shared without violating privacy ( it’s tricky!). • Helpful application example is CV tracking. (Israel is using cellphone surveillance to warn citizens who may be affected by individuals already infected – WP article) Objective 3 • How can AI be used to extract meaningful information from data that are either already available (e.g. from the Measuring Broadband America (MBA) program) or may become available, to determine the following: • Coverage at a more granular level • Service parameters available in smaller coverage areas than census blocks • Merged or Aggregated with other data to detect fraudulent activities such as unauthorized spectrum usage Objective 4 • As legitimate applications of AI start proliferating, what risks should be evaluated and what AI tools exist or should be developed to identify and mitigate harms that might arise from the proliferation of AI? • How to promote safe use of AI? • How to deter deleterious use of AI? • How to build in robustness into the AI Methods and Techniques used? Summary of Speaker Presentations List of Speaker Presentations Speaker Affiliation Presentation Title Ulrika Jägare Ericsson “How AI is Shaping Telecom Operations” Mazin E. Gilbert AT&T Research “AI for Autonomous Networks” Jack Murray Mukarram Bin Tariq Google “Optimization of Computing and Communication Resources Nandita Dukkipati Using AI” Rakesh Misra Vmware “Subscriber-Centric ML/AI in Mobile Radio Access Networks” (Uhana) Jason Martin Intel (and “Machine Learning Security & Privacy” Georgia Tech) Berge Ayvazian Wireless 20|20 “Breakthroughs from Synergy Between AI and 5G” List of Speaker Presentations - Continued Speaker Affiliation Presentation Title Tan F. Wong University of “Dynamic Spectrum Sharing: Lessons from the DARPA John M. Shea Florida Spectrum Collaboration Challenge” Peter Volgyesi, Miklos Vanderbilt “Spectrum Collaboration - Building Prize-Winning Radios” Maroti, Peter Horvath, University Sandor Szilvasi Harry Surden U of Colorado “Artificial Intelligence, Government and Ethics” Law School Martin Zoltick Rothwell Figg “Legal and Regulatory Considerations: Application of Artificial Jennifer Maisel Intelligence to Telecommunications and the FCC” Ramana Jampala Avlino “Predictive Modeling & Machine learning-based optimization of network operations” Jeff Alstott IARPA “Security of AI Systems” Alexander Sprintson NSF “Impacts of AI in the Wireless Networking domain” List of Speaker Presentations - Continued Speaker Affiliation Presentation Title Elham Tabassi NIST - ITL “Artificial Intelligence: A NIST strategic priority” Rafail Ostrovsky UCLA “Stewardship of Private Data with Cryptography” Stealth Software Russell Stuart UC Berkeley “Artificial Intelligence: History and Future” Stephen Dennis DHS S&T “ Artificial Intelligence and Machine Learning” Sridhar Kowdley Ajay Vikram Singh Nokia “ Artificial Intelligence as a Service - AIaaS” Petros Mouchtaris Prospecta Labs “ AI/ML Research with Applications in Telecommunications” Thyagarajan NSF “The NSF PAWR Initiative” Nandagopal Upcoming Speaker Presentations Speaker Affiliation Subject Michael Cotton NTIA - ITS EM Propagation Data, AI, and 801.22.3 Kumar Navulur DigitalGlobe - Maxar GIS Systems for Telecomms and AI Danny Weitzner MIT - CSAIL The Internet and AI Russell Stuart UC Berkley Return Visit – AI and Control Danny Weitzner Kumar Navulur Michael Cotton Findings Findings: AI and ML Findings – Artificial Intelligence (Machine Learning and Other Methods ) • Artificial Intelligence as a term describes computational techniques consisting of multiple major branches each focused on different methods, approaches, or areas of application. • Since the term “Artificial Intelligence” was coined in the mid 1950s there have been successive waves of deployment of applications that are now routine (such as Optical Character Recognition) or have been widely adopted (among others in areas such as machine vision, speech recognition, natural language processing, knowledge systems, classification systems, and applications including: noise cancellation; search; customer relationship management, chatbots for customer facing services; facial recognition; gaming, control systems; and analysis of high dimensional data) • Within the family of AI techniques Machine Learning (ML) and its sub-branches have recently seen a dramatic surge of investment, activity, and popularity across multiple domains including those that may be of interest to the FCC, with significant impact now, in the near- term, and potentially profound impact in the long term. Findings – Artificial Intelligence (Machine Learning and Other Methods ) • The various flavors of ML hold significant promise for dealing with the complex problems that are important to network planning and design, control and operation of networks, the management of resources such as spectrum, for the technologies used as the building blocks of network architectures, components, devices, and applications, and lastly for customer services and automation of customer interactions through chatbots. • With the continued growth of demand for digital traffic, Networks now connect over 4 Billion people on the planet and 10’s of Billions of devices. ML as a tool set is important because the increasing level of complexity is outpacing the ability of well-established methods in reliably delivering solutions and ML offers a potential approach for conquering that complexity. It is particularly important for machine-to-machine communications characteristic of Industrial uses, the Internet of Things, and the increased heterogeneity of digital traffic on the Network. • The sheer scale and new exacting requirements for network performance are compounding the problem. There is a widely recognized need for a higher level of automation of Network

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