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A
If
you'd
like
to
learn
a
little
more
about
what
I
do
professionally
here's
a
link
to
my
LinkedIn,
artificial
intelligence
and
machine
learning
are
all
over
the
news
taking
over
Academia
getting
billions
of
dollars
of
commercial
investment
and
we'll
change
both
computer
networking
and
Wireless
Communications
in
fundamental
ways.
So
what
is
artificial
intelligence
and
machine
learning?
A
Well,
there's
a
lot
of
debate
on
the
definitions
and
since
we
don't
have
full
consensus
yet
on
what
constitutes
natural
intelligence
and
we're
not
entirely
sure
how
our
own
minds
and
brains
work.
How
in
the
world
can
we
expect
to
Define
artificial
intelligence
for
telecommunications
work?
We
have
a
somewhat
easier
job,
General,
artificial
intelligence
or
what
is
often
called
strong
AI
is
something
that
most
of
us
think
of
as
being
able
to
take
over
for
a
complete
human.
A
We
don't
have
this
yet,
and
we
may
never
have
this.
What
we
do
have
and
what
is
widely
used
in
telecommunications,
is
artificially
intelligent
applications
or
examples
of
something
called
machine.
Learning
that
focus
on
one
particular
aspect
that
a
human
would
have
otherwise
done,
for
example,
network
operations,
intrusion,
detection
on
a
network
component,
design,
software
programming,
emergency
communications,
dispatch
and
so
on.
Now,
for
this
conversation,
artificial
intelligence
is
more
theoretical
and
abstract
and
is
oriented
towards
questions
of
defining
intelligence.
A
Artificial
intelligence
is
the
source
of
the
theoretical
underpinnings
that
allow
us
to
have
practical
implementations
that
do
practical
work.
Machine
learning
is,
in
general,
those
specific
practices
or
Technologies.
The
difference
is
important
for
us
as
we
learn
about
and
deal
with
new
ways
of
doing,
radio
with
software
AI
ml,
artificial
intelligence,
machine
learning
for
short,
is
essentially
software.
A
Good
machine
learning
operates
as
good
or
better
than
a
person
would
in
a
particular
task,
such
as
deciding
how
to
load
ships
in
a
port
or
how
to
sort
livestock
for
the
best
sales
prices
or
dispatching
fire
trucks
on
emergency
calls.
Artificial
intelligence
is
physics.
Machine
learning
is
engineering.
A
When
we
talk
about
artificial
intelligence
and
machine
learning,
we
shorten
it
to
AIML.
What
does
the
future
of
amateur
radio
look
like
when
radios
use
machine
learning
to
operate?
How
does
our
relationship
to
the
bands
change
with
cognitive
radio?
Where
are
we
in
this
transformation
and
what
will
happen
next?
A
A
We
can
build
a
database
of
contacts
and
analyze
who
talks
to
who
and
when
we
can
predict
with
a
very
high
degree
of
accuracy
who
is
going
to
be
on
a
satellite
pass
or
available
during
an
opening.
We
can
use
machine
learning
to
figure
out
if
that
tropospheric
duct
during
the
summer
is
likely
or
not
on
a
particular
Saturday.
A
A
It
reveals
what
the
information
might
be
able
to
tell
us
much
faster
than
a
human
could,
and
this
is
very
disruptive.
It's
also
something
that
any
competent
radio
operator
in
the
future
needs
to
know
about
we're
accustomed
to
a
world
where
frequency
privileges
for
the
various
radio
services
are
coordinated.
A
Like
real
estate,
we're
given
permission
to
build
our
systems
on
stretches
of
bandwidth
once
we're
there,
we
usually
end
up
with
something
that
looks
a
lot
like
land
ownership,
the
property
limits
are
set
down
and
our
access
is
described
with
a
set
of
rules
that
cover
things
like
channelization
power
limits,
interference,
constraints
and
so
on.
When
you
look
at
a
frequency
allocation
chart,
it
looks
very
much
like
a
densely
packed
Urban
landscape
with
lots
of
mixed-use
development,
but
bandwidth
isn't
dirt.
A
The
systems
that
we
build
aren't
permanent
in
terms
of
the
radio
frequencies
at
all
the
capital,
expenditures
for
towers
and
base
stations
and
receiver
handsets
and
radios
and
so
on,
are
certainly
expensive
and
quite
often
involve
actual
real
estate
investment,
sometimes
very
contentiously.
If
you've
ever
been
involved
in
a
dispute
over
Tower
placement.
A
But
the
signals
broadcast
over
the
airwaves
are
completely
ephemeral
with
reconfigurable
or
adaptive
radios.
We
can
break
free
of
the
rigidly
restrictive,
planned
nature
of
spectrum
management,
the
benefits
of
being
able
to
redeploy
entire
Wireless
communication
systems
dynamically.
It
improves
the
use
of
spectrum
and
dramatically
increases
revenue
and,
and
something
specifically
important
to
the
usfcc.
A
So
let's
pretend
Spectrum
was
handled
exactly
like
real
estate.
Let's
say
we
own
a
restaurant.
Our
restaurant
is
open
during
the
day
and
closed
at
night.
What,
if
that
restaurant,
could
be
turned
into
a
completely
different
business
overnight
on
demand
like
extra
housing,
when
there's
a
concert
or
game
in
town
What
If,
instead
of
being
a
restaurant
every
day,
it
could
be
an
urgent
care
clinic
on
days
of
high
demand
for
health
care.
A
While
we
can't
do
a
trick
like
this
for
Brick
and
Mortar
buildings,
we
are
at
a
point
where
we
are
starting
to
be
able
to
do
this.
For
wireless
communication
systems
and
artificial
intelligence
and
machine
learning
is
a
central
enabling
technology
for
adaptive.
Telecommunications
AIML
will
be
at
least
as
disruptive
a
technology
as
the
transistor.
It's
here
to
stay
and
amateur
radio
will
be
affected.
A
There
will
be
positive
effects
and
negative
effects
to
get
more
of
the
former
and
less
of
the
latter
is
up
to
us
and
how
we
respond
to
this
very
large
technological
change.
So
what
is
this
adaptive?
Radio
frequency
technology
all
about
the
biggest
bang
for
the
buck?
Is
being
able
to
break
free
of
bandwidth
constraints,
this
means
that
for
many
Communications
systems
the
Transmissions
will
not
be
Associated
strictly
with
a
band.
The
function
of
the
communication
will
be
Drive,
the
form
much
more
than
it
does
today.
A
Bandwidth
is
the
frequency
or
span
of
frequencies
used
for
a
particular
Communications
function.
Bandwidth
is
also
widely
used
to
mean
the
rate
of
data
delivered
through
a
system.
Data
rate
or
throughput
is
closely
related
to
and
limited
by,
the
occupied
frequency
range
of
the
channel
service
or
transmission.
But
these
two
two
terms
they're
not
really
synonymous.
A
Aiml
serves
a
big
role
in
optimizing
forward
error
correction
codes,
the
channel
capacity
limits,
our
ability
to
detect
and
correct
errors
in
digital
signals.
It's
a
limitation
that
is
related
to
the
physics
of
entropy
due
to
Decades
of
productive
work.
There
are
a
variety
of
error,
correcting
codes
that
produce
spectral
efficiencies
and
therefore
throughput
very
close
to
the
channel
capacity
limit.
A
A
A
Spectrum,
sharing
increases
utility
and
usability
a
future
amateur
operator
may
not
think
of
operation,
in
terms
of
which
band
they're
on
at
all.
Spectrum
access
would
be
whatever
is
currently
available,
with
the
radio
coordinating,
access
and
picking
a
frequency
without
any
traditional
band
limitations
or
identity.
Instead
of
a
waterfall
in
Spectrum
display
the
ideal
user
interface
of
the
future
may
be
a
set
of
Channel
metrics
or
a
network
graph
for
who
you
can
reach.
A
Clearly,
AIML
is
a
fundamental
part
of
this
cognitive,
Radio
Future.
So
what
does
it
take
to
change
bandwidth
dynamically
and
automatically
well,
non-trivial
bandwidth.
Agility
requires
high
performance
components
in
the
radio
frequency
chain.
High
performance
usually
means
high
cost.
We
may
want
to
have
extremely
flexible
Radio
Systems
that
can
do
the
cognitive
radio
thing,
but
can
we
afford
the
components
required?
Well,
mostly?
No,
but
in
some
cases
yes,
affordability
can
be
a
metric
for
bandwidth
usage.
A
A
We
can
start
out
by
thinking
how
much
it
costs
to
buy
a
radio
24.75
for
a
baofeng
uv5r,
dual
band
radio.
This
allows
access
to
the
2
meter
and
70
centimeter
amateur
bands
in
almost
any
place
in
the
United
States,
disregarding
the
very
small
number
of
places
where
operation
might
be
restricted.
The
United
States
has
9.8
million
square
kilometers
urban
areas
are
about
three
percent
of
this
individual
signal.
A
Bandwidths
are
small,
but
the
allowed
bands
are
four
megahertz
and
30
megahertz
respectively,
for
a
total
of
34
megahertz
range
is
limited
by
both
power
and
the
physics
of
the
channel
repeaters
increase.
The
range
of
individual
radios
and
repeater
coverage
in
the
U.S
is
good.
There
are
rural
areas
without
repeaters,
but
with
over
6
000
known
repeaters
on
two
meters
and
over
five
thousand
on
70
centimeters.
A
Let's
assume
at
least
the
urban
population
of
the
US
has
coverage
since
a
very
low
cost,
sometimes
free
license
exam
plus
a
very
inexpensive
radio
gets
you
access
to
the
bands
how
affordable
is
it
for
the
end
user
belfang
spent
money
to
develop
this
radio.
It
didn't
magically
appear
in
the
market,
fully
formed
at
no
cost.
It
cost
the
individual
time
and
effort
to
study
and
take
an
exam.
Even
a
relatively
simple
one,
as
the
U.S
amateur
radio
license
exam
repeaters
are
not
free
either.
A
However,
we
can
see
that
the
end
user
cost
of
accessing
2
meter
70
centimeter
rounds
down
to
zero
dollars.
The
geographic
area
needs
more
scrutiny,
though,
while
an
amateur
radio
operator
can
connect
to
a
reflector
and
talk
to
anyone
anywhere
in
the
world.
The
footprint
of
the
radio
is
a
very
modest
small
numbers
of
kilometers,
and
not
every
repeater
allows
the
end
user
to
connect
to
chat
rooms
on
the
internet.
A
If
the
only
people
you
can
reliably
expect
to
communicate
with
are
in
a
repeater
footprint,
then
the
geographic
area
is
much
smaller
than
the
entire
urbanized
U.S,
so
the
cost
is
still
low.
But
let's
look
at
the
difference.
Typical
repeater
usage
is
80
kilometers
out,
so
the
accessible
area
would
be
about
20
000
kilometers,
an
Amateur,
Repeater
and
handset
combination
has
a
similar
range
to
a
cell
site
and
phone.
A
There
are
approximately
400
000
cell
sites
in
the
United
States,
with
coverage
concentrated
in
urban
areas,
so
the
equivalent
to
a
baofang
would
be
something
like
a
200
Moto
G
cellular
access
isn't
free,
though
after
you
buy
the
equipment.
Access
is
controlled
by
subscription
average
cost
of
a
cell
phone
plan
in
the
U.S
is
about
113
dollars
in
2020.
A
You
generally
sign
up
for
a
year,
so
we'll
assume
you
have
to
pay
at
least
a
Year's
subscription
to
really
access
these
bands
as
an
end
user,
since
cell
phones
can
contact
anyone
in
the
United
States
with
a
number,
the
geographic
area
is
larger
than
the
footprint
of
the
cell
site.
Is
it
the
entire
U.S?
Well,
yes,
as
long
as
you're
in
coverage,
so
let's
take
the
800
megahertz
cellular
system
and
assume
that
the
bandwidth
we
access
is
one
of
the
blocks
that
are
25
megahertz
each
assuming
it
can
get
a
signal
anywhere.
A
This
is
what
we
end
up
with
for
cost.
So
what
does
all
this
mean?
Well,
the
costs
to
the
end
user
to
access
the
airwaves
in
the
United
States
are
very
low
for
both
commercial
and
non-commercial
services.
Geographic
reach
through
networking
is
the
biggest
capability
difference
between
the
two
examples.
Above
can
an
end
user
call
an
arbitrary
person
by
accessing
the
airwaves
in
the
United
States,
yes,
with
cellular
voice
contacting
an
arbitrary
person
outside
of
VHF
UHF
amateur
radio
is
rare,
but
not
impossible.
The
capability
is
an
optional
extra
on
some
repeater
systems.
A
So
what
do
we
have
established
here?
We
have
a
metric
for
the
current
plan,
fixed
spectrum
allocation
style
regulatory
environment.
We
can
calculate
end
user
costs
for
bandwidth
and
throughput
access
in
the
United
States
and
it's
pretty
low
end
users
will
most
likely
be
expected
to
pay
more
in
the
future
to
access
bandwidth
more
efficiently.
A
Aiml
lets
Regulators
raise
expectations
on
our
receivers
as
well
as
our
transmitters.
If
we
want
the
ability
to
control
bandwidth
to
adapt
to
Channel
or
market
conditions,
then
we
have
to
have
better
components.
Aiml
Cannon
does
attack
and
address
the
component
performance
problem,
and
it's
doing
it
today,
mostly
in
the
commercial
sector.
A
Aiml
serves
a
role
in
component
design
with
lower
power,
consumption
and
higher
component
performance
as
typical
objectives,
while
license
bandwidths,
are
fixed.
The
actual
used
bandwidth
is
often
Dynamic.
Lte
and
5G.
Air
interfaces
are
basically
a
dynamic
process
of
optimally
assigning
resources
across
frequency
and
time.
Scheduler
algorithms
could
be
the
playground
of
AIML
to
serve
the
same
throughput
with
less
resources
and
let
the
system
be
more
supportive
of
different
traffic
models
and
use
cases
that
way.
The
same
licensee
may
also
operate
in
varying
numbers
of
fixed
licenses
across
the
Spectrum.
A
So
incorporating
Ai
and
ML
and
shared
spectrum
regimes
and
or
unlicensed
Spectrum
could
be
the
key
in
making
the
applicable
use
cases
commercially
viable
in
citizens,
Broadband
Radio
Service,
for
example.
There
is
a
part
of
the
s-band
allocated
to
General
authorized
access
divided
in
quantized,
fixed
bandwidths,
there's,
currently
no
mandate
to
coordinate
their
operation
for
coexistence.
A
centrally
managed
AIML
could
drastically
improve
their
penetration,
especially
relevant
to
private
5G,
industry,
4.0
and
iot.
A
A
The
technical
debt
incurred
with
fully
flexible
AIML
generated
radio
architectures
can
exceed
what
any
company
or
country
or
individual
for
sure
can
afford.
We
can
get
into
plenty
of
trouble
even
without
letting
AIML
help
us
design
communication
systems.
For
example,
let's
consider
the
joint
tactical
radio
system
or
jitters
jtrs.
A
The
goal
of
this
program
was
to
replace
existing
Legacy
radios
in
the
American
Military,
with
a
single
set
of
software
defined
radios
that
could
be
reconfigured
in
the
field.
Cognitive
radio
techniques
were
centered
and
marketed.
Almost
everything
that
we're
talking
about
here
today
was
part
of
the
recipe
for
jtrs.
However,
the
large
size
and
weight
need
necessary
to
support
the
desired
adaptability
and
flexibility
turned
out
to
be
too
high
for
a
successful
deployment.
A
A
A
What
do
we
currently
manage
to
do
in
terms
of
bandwidth
occupancy
and
allocation?
There
are
people
who
look
at
this
problem
and
amateur
radio
will
be
directly
affected.
What
would
an
aiml-assisted
regulatory
workflow
look
like?
Well,
it
depends
entirely
on
what
data
it
was
trained
upon.
An
AIML
model
trained
only
on
Commercial
data
could
annihilate
every
other
use
of
the
spectrum
we
believe
and
are
gathered
here
today
to
celebrate
non-commercial
uses
of
the
spectrum.
Non-Commercial
uses
are
valid
and
necessary.
A
Educational,
non-profit,
small
business,
amateur
scientific,
military
and
unlicensed
value
are
all
vulnerable
in
models
that
are
trained
only
on
large
companies
providing
big
Commercial
Services,
even
if
those
Commercial
Services
owe
part,
are
all
of
their
success
to
non-commercial
routes.
If
it
isn't
in
the
data
set,
it's
not
going
to
be
in
the
model.
A
Aiml
is
only
as
good
as
the
data
it's
on
training
and
amateur
radio
does
not
have
a
clear
advocacy
here.
What
do
we
aspire
to
in
terms
of
bandwidth
occupancy?
Well,
we
want
to
optimize
bandwidth
and
throughput
to
best
achieve
Communications
goals,
and
those
goals
may
be
conflicting.
One
way
to
deal
with
this
is
to
have
Regulatory
Agencies,
like
the
FCC.
The
FCC
allocates
bandwidth
to
produce
activity
that
is
judged
to
have
positive
social,
economic,
political
and
Security
benefits
in
the
United
States.
At
this
point,
the
only
actors
that
can
achieve
this
balance
is
humans.
A
Aiml
does
not
yet
replace
the
human
in
the
regulatory
Loop.
The
training
data
required
to
perform
a
regulatory
role
has
not
been
defined.
Tagged
or
cleaned
AIML
can
and
should
be
part
of
the
process.
Because
of
the
enormous
power
it
has
to
cut
through
properly
defined
search
spaces.
The
entry
point
for
this
was
the
flood
of
fake
comments
about
net
neutrality
received
at
the
FCC
18
million
of
the
more
than
22
million
comments
that
the
FCC
received
in
the
2017.
Proceeding
to
repeal
net
neutrality
rules
were
fake
Additionally.
A
The
Office
of
the
Attorney
General's
investigation
revealed
that
the
fraud
perpetrated
by
the
various
lead
generators
infected
other
government
proceedings
as
well.
The
oag
reported
four
recommendations,
including
agencies,
to
adopt
technical
safeguards
to
protect
against
unauthorized
bulk
submissions,
using
automation
at
the
at
the
FCC.
This
led
directly
to
AIML
being
considered
for
the
job,
and
this
leads
to
questions
about
hey.
Why
not
use
AIML
for
more
regulatory
functions?
A
Aiml
performance
is
entirely
dependent
on
the
quality
of
the
data
sets
used
to
train
the
algorithms.
So
we
need
to
be
able
to
quantify
usage
of
our
bands
and
qualify
the
value
of
our
service,
so
that
we
can
show
up
in
the
data
sets
used.
This
means
making
sure
we're
included
by
both
humans
and
classical
regulatory
deliberations,
which
will
continue
for
many
years
and
in
the
data
sets
being
gathered
for
automated
regulatory
exploration,
it's
very
clear
that
we
need
to
be
able
to
continue
to
participate
in
emergency
communications.
A
Emergency
communications
work
is
increasingly
digital,
integrated
and
credentialed.
Things
like
winlink,
which
can
interface
directly
with
AIML
Network
agents
and
served
agency.
Apis
need
to
be
able
to
work
without
impediment,
and
this
means
solving
problems.
Like
the
symbol
rate
restriction
on
amateur
radio,
Transmissions
AIML
can
seem
like
magic.
It's
an
incredibly
powerful
technology
that
cuts
through
a
search
space
and
finds
a
solution
many
times
faster
than
any
human.
However,
there
are
some
big
drawbacks
to
AIML.
These
drawbacks
go
beyond
a
lack
of
inclusion
in
data
sets.
Aiml
is
non-deterministic.
A
Data
sets
are
frequently
Limited
in
ways
that
can
create
substantial
bias.
Aiml
requires
prediction
validation.
This
is
low
risk
when
you're
testing
correctable
designs
like
a
power
amplifier,
but
is
higher
risk
when
testing
agents
that
control
large
communication
networks
or
decide
who
is
going
to
be
allowed
to
go
on
the
air
when
AIML
is
applied
to
testable
and
well-defined
search
spaces
in
telecommunications
and
the
target
is
a
component
or
something
like
an
error,
correction
code
or
protocol.
A
There
can
be
amazingly
good
results
and
low
risk
when
AIML
is
applied
to
testable
and
well-defined
search
spaces
and
network
management,
then
the
results
may
be
good
and
the
risk
may
be
low
when
AIML
is
applied
to
untestable
or
very
difficult
to
test
circumstances.
The
only
quality
metric
is
the
quality
of
the
training
data.
Since
we
already
know
how
badly
things
can
go
off
the
rails
with
bad
data,
any
system
providing
untestable
decisions
should
be
considered
high
risk.
A
There
are
many
ways
to
mitigate
risk.
You
can
get
insurance
to
pay
for
potential
damage.
You
can
keep
humans
in
the
loop
you
can
have
the
equivalent
of
automatic
limit
switches
in
the
deployed
environment
and
and
so
on.
The
technical
complexity
to
mitigate
risk
adds,
on
top
of
what
may
be
a
substantial
technical
investment
already
to
train
and
deploy
an
AIML
system,
so
liability
discussions
alone
may
make
an
AIML
system
too
expensive
to
deploy
AIML
and
the
regulatory
Loop
feels
like
it
breaks
a
rule.
A
We
expect
to
be
able
to
know
when
we're
being
ruled
by
a
machine,
and
we
expect
to
be
able
to
have
some
sort
of
appeal,
but
those
rights
are
not
yet
guaranteed
to
you
in
a
future
world
where
all
the
community,
commercial
Communications,
including
emergency
communications,
might
be
Advanced
or
complex,
AIML,
assisted
waveforms
that
all
sound
like
noise
and
maximize
Revenue.
Where
does
something
like
FM
narrowband
voice
fit
in
it
actually
fits
in
very
well
or
it?
Can
the
future
of
amateur
radio
may
look
exactly
like
it
looks
today.
A
A
If
amateur
radio
doesn't
look
like
modern
Communications,
because
it's
protected
and
preserved,
it
could
lose
some
of
its
educational
value,
along
with
some
of
its
practical
emergency
communications
value.
When
you
look
at
the
regulations
for
the
amateur
services
in
the
United
States,
you
can
see
that
educational
training
and
emergency
communications
make
up
almost
entirely
all
of
the
justifications
for
its
existence.
A
A
Amateur
radio
doesn't
have
to
become
all
about
reconfigurable
fancy
radios
to
remain
valuable.
It
can
and
should
continue
to
allow
all
things,
Advanced,
experimentation
and
simple,
unscheduled
qsos
with
other
people.
So
where
are
we
in
AIML
transformation?
It
kind
of
depends
on
who
you
ask
for
commercial
operators.
Their
answer
depends
on
how
exposed
their
customers
are
to
AIML
agents
and
decision
making
in
commercial
networks.
Aiml
is
rapidly
becoming
a
necessary
tool
for
managing
the
complex
Network
configurations
that
may
have
thousands
of
distinct
values.
A
The
sheer
complexity
of
protocols,
like
5G,
outstrip
human
capacity
to
manage
if
5G
is
to
be
fully
deployed,
and
if
that
deployment
is
expected
to
be
efficient,
how
can
we
showcase
amateur
radio
in
a
world
rapidly
being
taken
over
by
AIML
products?
How
can
we
defend
ourselves
from
AIML
itself
being
part
of
the
regulatory
Loop?
How
can
we
possibly
compete
with
billions
of
dollars
of
commercial,
Ai
and
ml
work,
especially
when
almost
all
the
really
good
stuff
is
still
secret
or
proprietary?
A
Strictly
speaking,
we
don't
have
to
we
aren't
about
optimizing
Revenue.
There
is
no
expectation
that
all
amateur
radio
operators
have
to
keep
up
with
everything
going
on
in
radio
Tech
we're
about
building
International
Goodwill
through
serendipitous
contacts
over
the
air
with
other
human
beings,
and
we
need
to
stick
up
for
that.
A
However,
we're
also
capable
of
keeping
up
with
the
times
here's
at
least
one
path
forward.
That
shows
how
adaptable
and
relevant
we
can
be
in
terms
of
the
technology.
Our
ability
to
transmit
on
our
own
frequencies
is
of
enormous
equalizing
value.
We
have
access
to
very
inexpensive,
sdrs
and
Powerful
computers.
We
have
enough
operators
and
enough
density
for
a
lot
of
us
to
become
really
very
competent
at
cognitive,
radio,
Ai
and
ml.
A
What
we
don't
have
is
a
practical
curriculum
or
Handbook
of
reference
designs.
We
need
a
practical
ham,
Centric
curriculum
or
handbook,
something
that
can
be
extended
to
the
classroom
or
learned
individually.
This
would
give
us
traction
on
being
able
to
participate
and
show
educational
value
in
the
future.
We
didn't
ignore
the
transistor.
We
cannot
ignore
AIML.
A
Are
you
interested
in
helping
achieve
this
goal
to
create
a
handbook
or
settle
lessons
or
a
workbook?
Would
you
be
willing
to
sign
up
to
try
out
some
experiments
with
ham
radio
that
could
become
a
practical
course
in
ham,
Centric,
AIML,
open,
Research
Institute
is
coordinated
with
a
couple
of
schools
that
are
willing
to
Pilot
educational
AIML
program.
Material
and
Ori
is
very
interested
in
developing
some
self-directed
work.
A
You
shouldn't
have
to
be
in
a
university
of
an
engineering
program
to
take
advantage
of
what's
going
on
with
AIML,
and
it
really
needs
to
be
easier
to
learn
anyway
or
I
will
host
a
kickoff
meeting
for
this
initiative
and
is
actively
looking
for
organizations
and
individuals
that
want
to
collaborate
and
Lead
forward
on
this
effort.
You
can
sign
up
at
our
booth
here
at
ham,
Expo
or
write
Ori
directly
at
hello,
openresearch.institute.
No
personal
information
is
ever
shared
or
sold
by
Ori.
All
work
is
published
and
made
available
to
the
general
public
for
free.