[Ground-station] FCC TAC AI/ML working group kick-off meeting

[Michelle Thompson]

We went round the table and did introductions and stated our goals for the
working group.

The things mentioned most often was an interest in using AI/ML for
predicting propagation for better spectrum sharing and how AI/ML can lead
to improvements and innovations in network architecture. A high level of
priority was given to AI/ML effects on privacy and behavior online, and how
AI/ML can bias the use of shared resources like spectrum.

Backgrounds are overwhelmingly industrial or commercial, but Greg Lepin
from ARRL is also in the group, so amateur radio is well represented!

The full TAC meets for the first time on 28 February. The AI/ML working
group will meet every Wednesday.

-Michelle Thompson




On Wed, Feb 23, 2022 at 12:52 PM Michelle Thompson <
mountain.michelle at gmail.com> wrote:

> Today is the kick-off meeting for the Artificial Intelligence and Machine
> Learning working group that we are part of at the FCC Technological
> Advisory Committee for 2022.
>
> A starting point for the weekly meetings and guest speakers is here:
>
> https://www.fcc.gov/sites/default/files/fcc_aiwg_2020_whitepaper_final.pdf
>
> This was where things left off.
>
> Today's agenda:
>
> Agenda:
>
> Introductions
>
> Let us know something about yourself, what your interests are for AI/ML in
> general, and what part of the charter (below) are you interested in.
>
> Discuss general approach for the year (speakers, internal discussions, sub
> working groups?)
> Review .01 draft viewgraphs briefly
>
> Here is our charter for reference:
>
> Artificial Intelligence/Machine Learning WG
>
> Expand pilot project proposal(s) from the 2020 TAC session to provide
> details and associated quality metrics that will allow the Commission to
> explore, extract the value, and gauge the success of implementing AI/ML
> techniques.
>
> Explore the use of AI/ML methods and techniques to improve the utilization
> and administration of spectrum (licensed, unlicensed, and shared) by
> addressing the fundamental aspects of propagation, interference, signal
> processing, and protocols.
>
> Evaluate the use of AI/ML methods and techniques applied to assuring the
> safety, security, and performance of network equipment, network control,
> and network operations in a network environment that increasingly relies on
> automation, is seeing a rapid growth of new network connections, and is
> increasingly digitized and software- ized.
>
> Consider the implications of AI/ML adoption by content providers and the
> impact on consumers, focusing on understanding causes of and approaches to
> dealing with addictive behaviors.
> Formulate a better understanding of uses of AI/ML that may result in
> modification of human behavior, to develop sound policies that encourage
> positive outcomes (e.g., public health measures, and other benefits) and
> mitigate against negative outcomes.
>
> Notes from the meeting to follow.
>
> Thank you to everyone that made it possible for ORI to represent open
> source and amateur work on the TAC.
>
> -Michelle Thompson
>
>
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