Artificial
intelligence
is
the
hot
new
thing
in
tech
—
it
feels
like
every
company
is
talking
about
how
it’s
making
strides
by
using
or
developing
AI.
But
the
field
of
AI
is
also
so
filled
with
jargon
that
it
can
be
remarkably
difficult
to
understand
what’s
actually
happening
with
each
new
development.
To
help
you
better
understand
what’s
going
on,
we’ve
put
together
a
list
of
some
of
the
most
common
AI
terms.
We’ll
do
our
best
to
explain
what
they
mean
and
why
they’re
important.
What
exactly
is
AI?
Artificial
intelligence:
Often
shortened
to
AI,
the
term
“artificial
intelligence”
is
technically
the
discipline
of
computer
science
that’s
dedicated
to
making
computer
systems
that
can
think
like
a
human.
But
right
now,
we’re
mostly
hearing
about
AI
as
a
technology
and
or
even
an
entity,
and
what
exactly
that
means
is
harder
to
pin
down.
It’s
also
frequently
used
as
a
marketing
buzzword,
which
makes
its
definition
more
mutable
than
it
should
be.
Google,
for
example,
talks
a
lot
about
how
it’s
been
investing
in
AI
for
years.
That
refers
to
how
many
of
its
products
are
improved
by
artificial
intelligence
and
how
the
company
offers
tools
like
Gemini
that
appear
to
be
intelligent,
for
example.
There
are
the
underlying
AI
models
that
power
many
AI
tools,
like
OpenAI’s
GPT.
Then,
there’s
Meta
CEO
Mark
Zuckerberg,
who
has
used
AI
as
a
noun
to
refer
to
individual
chatbots.
As
more
companies
try
to
sell
AI
as
the
next
big
thing,
the
ways
they
use
the
term
and
other
related
nomenclature
might
get
even
more
confusing
As
more
companies
try
to
sell
AI
as
the
next
big
thing,
the
ways
they
use
the
term
and
other
related
nomenclature
might
get
even
more
confusing.
There
are
a
bunch
of
phrases
you
are
likely
to
come
across
in
articles
or
marketing
about
AI,
so
to
help
you
better
understand
them,
I’ve
put
together
an
overview
of
many
of
the
key
terms
in
artificial
intelligence
that
are
currently
being
bandied
about.
Ultimately,
however,
it
all
boils
down
to
trying
to
make
computers
smarter.
(Note
that
I’m
only
giving
a
rudimentary
overview
of
many
of
these
terms.
Many
of
them
can
often
get
very
scientific,
but
this
article
should
hopefully
give
you
a
grasp
of
the
basics.)
Machine
learning:
Machine
learning
systems
are
trained
(we’ll
explain
more
about
what
training
is
later)
on
data
so
they
can
make
predictions
about
new
information.
That
way,
they
can
“learn.”
Machine
learning
is
a
field
within
artificial
intelligence
and
is
critical
to
many
AI
technologies.
Artificial
general
intelligence
(AGI):
Artificial
intelligence
that’s
as
smart
or
smarter
than
a
human.
(OpenAI
in
particular
is
investing
heavily
into
AGI.)
This
could
be
incredibly
powerful
technology,
but
for
a
lot
of
people,
it’s
also
potentially
the
most
frightening
prospect
about
the
possibilities
of
AI
—
think
of
all
the
movies
we’ve
seen
about
superintelligent
machines
taking
over
the
world!
If
that
isn’t
enough,
there
is
also
work
being
done
on
“superintelligence,”
or
AI
that’s
much
smarter
than
a
human.
Generative
AI:
An
AI
technology
capable
of
generating
new
text,
images,
code,
and
more.
Think
of
all
the
interesting
(if
occasionally
problematic)
answers
and
images
that
you’ve
seen
being
produced
by
ChatGPT
or
Google’s
Gemini.
Generative
AI
tools
are
powered
by
AI
models
that
are
typically
trained
on
vast
amounts
of
data.
Hallucinations:
No,
we’re
not
talking
about
weird
visions.
It’s
this:
because
generative
AI
tools
are
only
as
good
as
the
data
they’re
trained
on,
they
can
“hallucinate,”
or
confidently
make
up
what
they
think
are
the
best
responses
to
questions.
These
hallucinations
(or,
if
you
want
to
be
completely
honest,
bullshit)
mean
the
systems
can
make
factual
errors
or
give
gibberish
answers.
There’s
even
some
controversy
as
to
whether
AI
hallucinations
can
ever
be
“fixed.”
Bias:
Hallucinations
aren’t
the
only
problems
that
have
come
up
when
dealing
with
AI
—
and
this
one
might
have
been
predicted
since
AIs
are,
after
all,
programmed
by
humans.
As
a
result,
depending
on
their
training
data,
AI
tools
can
demonstrate
biases.
For
example,
2018
research
from
Joy
Buolamwini,
a
computer
scientist
at
MIT
Media
Lab,
and
Timnit
Gebru,
the
founder
and
executive
director
of
the
Distributed
Artificial
Intelligence
Research
Institute
(DAIR),
co-authored
a
paper
that
illustrated
how
facial
recognition
software
had
higher
error
rates
when
attempting
to
identify
the
gender
of
darker-skinned
women.
Image:
Hugo
J.
Herrera
for
The
Verge
I
keep
hearing
a
lot
of
talk
about
models.
What
are
those?
AI
model:
AI
models
are
trained
on
data
so
that
they
can
perform
tasks
or
make
decisions
on
their
own.
Large
language
models,
or
LLMs:
A
type
of
AI
model
that
can
process
and
generate
natural
language
text.
Anthropic’s
Claude,
which,
according
to
the
company,
is
“a
helpful,
honest,
and
harmless
assistant
with
a
conversational
tone,”
is
an
example
of
an
LLM.
Diffusion
models:
AI
models
that
can
be
used
for
things
like
generating
images
from
text
prompts.
They
are
trained
by
first
adding
noise
—
such
as
static
—
to
an
image
and
then
reversing
the
process
so
that
the
AI
has
learned
how
to
create
a
clear
image.
There
are
also
diffusion
models
that
work
with
audio
and
video.
Foundation
models:
These
generative
AI
models
are
trained
on
a
huge
amount
of
data
and,
as
a
result,
can
be
the
foundation
for
a
wide
variety
of
applications
without
specific
training
for
those
tasks.
(The
term
was
coined
by
Stanford
researchers
in
2021.)
OpenAI’s
GPT,
Google’s
Gemini,
Meta’s
Llama,
and
Anthropic’s
Claude
are
all
examples
of
foundation
models.
Many
companies
are
also
marketing
their
AI
models
as
multimodal,
meaning
they
can
process
multiple
types
of
data,
such
as
text,
images,
and
video.
Frontier
models:
In
addition
to
foundation
models,
AI
companies
are
working
on
what
they
call
“frontier
models,”
which
is
basically
just
a
marketing
term
for
their
unreleased
future
models.
Theoretically,
these
models
could
be
far
more
powerful
than
the
AI
models
that
are
available
today,
though
there
are
also
concerns
that
they
could
pose
significant
risks.
Image:
Hugo
J.
Herrera
for
The
Verge
But
how
do
AI
models
get
all
that
info?
Well,
they’re
trained.
Training
is
a
process
by
which
AI
models
learn
to
understand
data
in
specific
ways
by
analyzing
datasets
so
they
can
make
predictions
and
recognize
patterns.
For
example,
large
language
models
have
been
trained
by
“reading”
vast
amounts
of
text.
That
means
that
when
AI
tools
like
ChatGPT
respond
to
your
queries,
they
can
“understand”
what
you
are
saying
and
generate
answers
that
sound
like
human
language
and
address
what
your
query
is
about.
Training
often
requires
a
significant
amount
of
resources
and
computing
power,
and
many
companies
rely
on
powerful
GPUs
to
help
with
this
training.
AI
models
can
be
fed
different
types
of
data,
typically
in
vast
quantities,
such
as
text,
images,
music,
and
video.
This
is
—
logically
enough
—
known
as
training
data.
Parameters,
in
short,
are
the
variables
an
AI
model
learns
as
part
of
its
training.
The
best
description
I’ve
found
of
what
that
actually
means
comes
from
Helen
Toner,
the
director
of
strategy
and
foundational
research
grants
at
Georgetown’s
Center
for
Security
and
Emerging
Technology
and
a
former
OpenAI
board
member:
Parameters
are
the
numbers
inside
an
AI
model
that
determine
how
an
input
(e.g.,
a
chunk
of
prompt
text)
is
converted
into
an
output
(e.g.,
the
next
word
after
the
prompt).
The
process
of
‘training’
an
AI
model
consists
in
using
mathematical
optimization
techniques
to
tweak
the
model’s
parameter
values
over
and
over
again
until
the
model
is
very
good
at
converting
inputs
to
outputs.
In
other
words,
an
AI
model’s
parameters
help
determine
the
answers
that
they
will
then
spit
out
to
you.
Companies
sometimes
boast
about
how
many
parameters
a
model
has
as
a
way
to
demonstrate
that
model’s
complexity.
Image:
Hugo
J.
Herrera
for
The
Verge
Are
there
any
other
terms
I
may
come
across?
Natural
language
processing
(NLP):
The
ability
for
machines
to
understand
human
language
thanks
to
machine
learning.
OpenAI’s
ChatGPT
is
a
basic
example:
it
can
understand
your
text
queries
and
generate
text
in
response.
Another
powerful
tool
that
can
do
NLP
is
OpenAI’s
Whisper
speech
recognition
technology,
which
the
company
reportedly
used
to
transcribe
audio
from
more
than
1
million
hours
of
YouTube
videos
to
help
train
GPT-4.
Inference:
When
a
generative
AI
application
actually
generates
something,
like
ChatGPT
responding
to
a
request
about
how
to
make
chocolate
chip
cookies
by
sharing
a
recipe. This
is
the
task
your
computer
does
when
you
execute
local
AI
commands.
Tokens:
Tokens
refer
to
chunks
of
text,
such
as
words,
parts
of
words,
or
even
individual
characters.
For
example,
LLMs
will
break
text
into
tokens
so
that
they
can
analyze
them,
determine
how
tokens
relate
to
each
other,
and
generate
responses.
The
more
tokens
a
model
can
process
at
once
(a
quantity
known
as
its
“context
window”),
the
more
sophisticated
the
results
can
be.
Neural
network:
A
neural
network
is
computer
architecture
that
helps
computers
process
data
using
nodes,
which
can
be
sort
of
compared
to
a
human’s
brain’s
neurons.
Neural
networks
are
critical
to
popular
generative
AI
systems
because
they
can
learn
to
understand
complex
patterns
without
explicit
programming
—
for
example,
training
on
medical
data
to
be
able
to
make
diagnoses.
Transformer:
A
transformer
is
a
type
of
neural
network
architecture
that
uses
an
“attention”
mechanism
to
process
how
parts
of
a
sequence
relate
to
each
other.
Amazon
has
a
good
example
of
what
this
means
in
practice:
Consider
this
input
sequence:
“What
is
the
color
of
the
sky?”
The
transformer
model
uses
an
internal
mathematical
representation
that
identifies
the
relevancy
and
relationship
between
the
words
color,
sky,
and
blue.
It
uses
that
knowledge
to
generate
the
output:
“The
sky
is
blue.”
Not
only
are
transformers
very
powerful,
but
they
can
also
be
trained
faster
than
other
types
of
neural
networks.
Since
former
Google
employees
published
the
first
paper
on
transformers
in
2017,
they’ve
become
a
huge
reason
why
we’re
talking
about
generative
AI
technologies
so
much
right
now.
(The
T
in
ChatGPT
stands
for
transformer.)
RAG:
This
acronym
stands
for
“retrieval-augmented
generation.”
When
an
AI
model
is
generating
something,
RAG
lets
the
model
find
and
add
context
from
beyond
what
it
was
trained
on,
which
can
improve
accuracy
of
what
it
ultimately
generates.
Let’s
say
you
ask
an
AI
chatbot
something
that,
based
on
its
training,
it
doesn’t
actually
know
the
answer
to.
Without
RAG,
the
chatbot
might
just
hallucinate
a
wrong
answer.
With
RAG,
however,
it
can
check
external
sources
—
like,
say,
other
sites
on
the
internet
—
and
use
that
data
to
help
inform
its
answer.
Image:
Hugo
J.
Herrera
for
The
Verge
How
about
hardware?
What
do
AI
systems
run
on?
Nvidia’s
H100
chip:
One
of
the
most
popular
graphics
processing
units
(GPUs)
used
for
AI
training.
Companies
are
clamoring
for
the
H100
because
it’s
seen
as
the
best
at
handling
AI
workloads
over
other
server-grade
AI
chips.
However,
while
the
extraordinary
demand
for
Nvidia’s
chips
has
made
it
among
the
world’s
most
valuable
companies,
many
other
tech
companies
are
developing
their
own
AI
chips,
which
could
eat
away
at
Nvidia’s
grasp
on
the
market.
Neural
processing
units
(NPUs):
Dedicated
processors
in
computers,
tablets,
and
smartphones
that
can
perform
AI
inference
on
your
device.
(Apple
uses
the
term
“neural
engine.”)
NPUs
can
be
more
efficient
at
doing
many
AI-powered
tasks
on
your
devices
(like
adding
background
blur
during
a
video
call)
than
a
CPU
or
a
GPU.
TOPS:
This
acronym,
which
stands
for
“trillion
operations
per
second,”
is
a
term
tech
vendors
are
using
to
boast
about
how
capable
their
chips
are
at
AI
inference.
Image:
Hugo
J.
Herrera
for
The
Verge
So
what
are
all
these
different
AI
apps
I
keep
hearing
about?
There
are
many
companies
that
have
become
leaders
in
developing
AI
and
AI-powered
tools.
Some
are
entrenched
tech
giants,
but
others
are
newer
startups.
Here
are
a
few
of
the
players
in
the
mix:
OpenAI
/
ChatGPT:
The
reason
AI
is
such
a
big
deal
right
now
is
arguably
thanks
to
ChatGPT,
the
AI
chatbot
that
OpenAI
released
in
late
2022.
The
explosive
popularity
of
the
service
largely
caught
big
tech
players
off-guard,
and
now
pretty
much
every
other
tech
company
is
trying
to
boast
about
their
AI
prowess.
Microsoft
/
Copilot:
Microsoft
is
baking
Copilot,
its
AI
assistant
powered
by
OpenAI’s
GPT
models,
into
as
many
products
as
it
can.
The
Seattle
tech
giant
also
has
a
49
percent
stake
in
OpenAI.
Google
/
Gemini:
Google
is
racing
to
power
its
products
with
Gemini,
which
refers
both
to
the
company’s
AI
assistant
and
its
various
flavors
of
AI
models.
Meta
/
Llama:
Meta’s
AI
efforts
are
all
around
its
Llama
(Large
Language
Model
Meta
AI)
model,
which,
unlike
the
models
from
other
big
tech
companies,
is
open
source.
Apple
/
Apple
Intelligence:
Apple
is
adding
new
AI-focused
features
into
its
products
under
the
banner
of
Apple
Intelligence.
One
big
new
feature
is
the
availability
of
ChatGPT
right
inside
Siri.
Anthropic
/
Claude:
Anthropic
is
an
AI
company
founded
by
former
OpenAI
employees
that
makes
the
Claude
AI
models.
Amazon
has
invested
$4
billion
in
the
company,
while
Google
has
invested
hundreds
of
millions
(with
the
potential
to
invest
$1.5
billion
more).
It
recently
hired
Instagram
cofounder
Mike
Krieger
as
its
chief
product
officer.
xAI
/
Grok:
This
is
Elon
Musk’s
AI
company,
which
makes
Grok,
an
LLM.
It
recently
raised
$6
billion
in
funding.
Perplexity:
Perplexity
is
another
AI
company.
It’s
known
for
its
AI-powered
search
engine,
which
has
come
under
scrutiny
for
seemingly
sketchy
scraping
practices.
Hugging
Face:
A
platform
that
serves
as
a
directory
for
AI
models
and
datasets.
Comments