Getting ready for artificial general intelligence with examples

Imagine
a
world
where
machines
aren’t
confined
to
pre-programmed
tasks
but
operate
with
human-like
autonomy
and
competence.
A
world
where
computer
minds
pilot
self-driving
cars,
delve
into
complex
scientific
research,
provide
personalized
customer
service
and
even
explore
the
unknown.

This
is
the
potential
of
artificial
general
intelligence
(AGI),
a
hypothetical
technology
that
may
be
poised
to
revolutionize
nearly
every
aspect
of
human
life
and
work.
While
AGI
remains
theoretical,
organizations
can
take
proactive
steps
to
prepare
for
its
arrival
by
building
a
robust
data
infrastructure
and
fostering
a
collaborative
environment
where
humans
and
AI
work
together
seamlessly.

AGI,
sometimes
referred
to
as

strong
AI,
is
the
science-fiction
version
of

artificial
intelligence
(AI),
where
artificial
machine
intelligence
achieves
human-level
learning,
perception
and
cognitive
flexibility.
But,
unlike
humans,
AGIs
don’t
experience
fatigue
or
have
biological
needs
and
can
constantly
learn
and
process
information
at
unimaginable
speeds.
The
prospect
of
developing
synthetic
minds
that
can
learn
and
solve
complex
problems
promises
to
revolutionize
and
disrupt
many
industries
as
machine
intelligence
continues
to
assume
tasks
once
thought
the
exclusive
purview
of
human
intelligence
and
cognitive
abilities.

Imagine
a
self-driving
car
piloted
by
an
AGI.
It
cannot
only
pick
up
a
passenger
from
the
airport
and
navigate
unfamiliar
roads
but
also
adapt
its
conversation
in
real
time.
It
might
answer
questions
about
local
culture
and
geography,
even
personalizing
them
based
on
the
passenger’s
interests.
It
might
suggest
a
restaurant
based
on
preferences
and
current
popularity.
If
a
passenger
has
ridden
with
it
before,
the
AGI
can
use
past
conversations
to
personalize
the
experience
further,
even
recommending
things
they
enjoyed
on
a
previous
trip.

AI
systems
like

LaMDA
and
GPT-3
excel
at
generating
human-quality
text,
accomplishing
specific
tasks,
translating
languages
as
needed,
and
creating
different
kinds
of
creative
content.
While
these

large
language
model
(LLM)
technologies
might
seem
like
it
sometimes,
it’s
important
to
understand
that
they
are
not
the
thinking
machines
promised
by
science
fiction. 

Achieving
these
feats
is
accomplished
through
a
combination
of
sophisticated
algorithms,

natural
language
processing
(NLP)
and
computer
science
principles.
LLMs
like
ChatGPT
are
trained
on
massive
amounts
of
text
data,
allowing
them
to
recognize
patterns
and
statistical
relationships
within
language.
NLP
techniques
help
them
parse
the
nuances
of
human
language,
including
grammar,
syntax
and
context.
By
using
complex
AI
algorithms
and
computer
science
methods,
these
AI
systems
can
then
generate
human-like
text,
translate
languages
with
impressive
accuracy,
and
produce
creative
content
that
mimics
different
styles.

Today’s
AI,
including
generative
AI
(gen
AI),
is
often
called
narrow
AI
and
it
excels
at
sifting
through
massive
data
sets
to
identify
patterns,
apply
automation
to
workflows
and
generate
human-quality
text.
However,
these
systems
lack
genuine
understanding
and
can’t
adapt
to
situations
outside
their
training.
This
gap
highlights
the
vast
difference
between
current
AI
and
the
potential
of
AGI.

While
the
progress
is
exciting,
the
leap
from
weak
AI
to
true
AGI
is
a
significant
challenge.
Researchers
are
actively
exploring
artificial
consciousness,
general
problem-solving
and
common-sense
reasoning
within
machines.
While
the
timeline
for
developing
a
true
AGI
remains
uncertain,
an
organization
can
prepare
its
technological
infrastructure
to
handle
future
advancement
by
building
a
solid
data-first
infrastructure
today. 

How
can
organizations
prepare
for
AGI?

The
theoretical
nature
of
AGI
makes
it
challenging
to
pinpoint
the
exact
tech
stack
organizations
need.
However,
if
AGI
development
uses
similar
building
blocks
as
narrow
AI,
some
existing
tools
and
technologies
will
likely
be
crucial
for
adoption.

The
exact
nature
of
general
intelligence
in
AGI
remains
a
topic
of
debate
among
AI
researchers.
Some,
like

Goertzel
and
Pennachin,
suggest
that
AGI
would
possess
self-understanding
and
self-control.
Microsoft
and
OpenAI
have

claimed
that
GPT-4’s
capabilities
are
strikingly
close
to
human-level
performance.
Most
experts
categorize
it
as
a
powerful,
but
narrow
AI
model.

Current
AI
advancements
demonstrate
impressive
capabilities
in
specific
areas.
Self-driving
cars
excel
at
navigating
roads
and
supercomputers
like IBM
Watson® can
analyze
vast
amounts
of
data.
Regardless,
these
are
examples
of
narrow
AI.
These
systems
excel
within
their
specific
domains
but
lack
the
general
problem-solving
skills
envisioned
for
AGI.

Regardless,
given
the
wide
range
of
predictions
for
AGI’s
arrival,
anywhere
from
2030
to
2050
and
beyond,
it’s
crucial
to
manage
expectations
and
begin
by
using
the
value
of
current
AI
applications.
While
leaders
have
some
reservations
about
the
benefits
of
current
AI,
organizations
are
actively
investing
in
gen
AI
deployment,
significantly
increasing
budgets,
expanding
use
cases,
and
transitioning
projects
from
experimentation
to
production.

According
to Andreessen
Horowitz
(link
resides
outside
IBM.com),
in
2023,
the
average
spend
on
foundation
model
application
programming
interfaces
(APIs),
self-hosting
and
fine-tuning
models
across
surveyed
companies
reached
USD
7
million.
Nearly
all
respondents
reported
promising
early
results
from
gen
AI
experiments
and
planned
to
increase
their
spending
in
2024
to
support
production
workloads.
Interestingly,
2024
is
seeing
a
shift
in
funding
through
software
line
items,
with
fewer
leaders
allocating
budgets
from
innovation
funds,
hinting
that
gen
AI
is
fast
becoming
an
essential
technology. 

On
a
smaller
scale,
some
organizations
are
reallocating
gen
AI
budgets
towards
headcount
savings,
particularly
in
customer
service.
One
organization
reported
saving
approximately
USD
6
per
call
served
by
its
LLM-powered
customer
service
system,
translating
to
a
90%
cost
reduction,
a
significant
justification
for
increased
gen
AI
investment.

Beyond
cost
savings,
organizations
seek
tangible
ways
to
measure
gen
AI’s
return
on
investment
(ROI),
focusing
on
factors
like
revenue
generation,
cost
savings,
efficiency
gains
and
accuracy
improvements,
depending
on
the
use
case.
A
key
trend
is
the
adoption
of
multiple
models
in
production.
This
multi-model
approach
uses
multiple
AI
models
together
to
combine
their
strengths
and
improve
the
overall
output.
This
approach
also
serves
to
tailor
solutions
to
specific
use
cases,
avoid
vendor
lock-in
and
capitalize
on
rapid
advancement
in
the
field.

46%
of
survey
respondents
in
2024
showed
a
preference
for
open
source
models.
While
cost
wasn’t
the
primary
driver,
it
reflects
a
growing
belief
that
the
value
generated
by
gen
AI
outweighs
the
price
tag.
It
illustrates
that
the
executive
mindset
increasingly
recognizes
that
getting
an
accurate
answer
is
worth
the
money. 

Enterprises
remain
interested
in
customizing
models,
but
with
the
rise
of
high-quality
open
source
models,
most
opt
not
to
train
LLMs
from
scratch.
Instead,
they’re
using
retrieval
augmented
generation
or

fine-tuning
open
source
models
for
their
specific
needs.

The
majority
(72%)
of
enterprises
that
use
APIs
for
model
access
use
models
hosted
on
their
cloud
service
providers.
Also,
applications
that
don’t
just
rely
on
an
LLM
for
text
generation
but
integrate
it
with
other
technologies
to
create
a
complete
solution
and
significantly
rethink
enterprise
workflows
and
proprietary
data
use
are
seeing
strong
performance
in
the
market.

Deloitte (link
resides
outside
IBM.com)
explored
the
value
of
output
being
created
by
gen
AI
among
more
than
2,800
business
leaders.
Here
are
some
areas
where
organizations
are
seeing
a
ROI:

• 
  Text
  (83%):
  Gen
  AI
  assists
  with
  automating
  tasks
  like
  report
  writing,
  document
  summarization
  and
  marketing
  copy
  generation.
• 
  Code
  (62%):
  Gen
  AI
  helps
  developers
  write
  code
  more
  efficiently
  and
  with
  fewer
  errors.
• 
  Audio
  (56%):
  Gen
  AI
  call
  centers
  with
  realistic
  audio
  assist
  customers
  and
  employees.
• 
  Image
  (55%): Gen
  AI
  can
  simulate
  how
  a
  product
  might
  look
  in
  a
  customer’s
  home
  or
  reconstruct
  an
  accident
  scene
  to
  assess
  insurance
  claims
  and
  liability.
• 
  Other
  potential
  areas:
  Video
  generation
  (36%)
  and
  3D
  model
  generation
  (26%)
  can
  create
  marketing
  materials,
  virtual
  renderings
  and
  product
  mockups.

The
skills
gap
in
gen
AI
development
is
a
significant
hurdle.
Startups
offering
tools
that
simplify
in-house
gen
AI
development
will
likely
see
faster
adoption
due
to
the
difficulty
of
acquiring
the
right
talent
within
enterprises.

While
AGI
promises
machine
autonomy
far
beyond
gen
AI,
even
the
most
advanced
systems
still
require
human
expertise
to
function
effectively.
Building
an
in-house
team
with
AI, deep
learning, machine
learning
(ML) and
data
science
skills
is
a
strategic
move.
Most
importantly,
no
matter
the
strength
of
AI
(weak
or
strong),
data
scientists,
AI
engineers,
computer
scientists
and
ML
specialists
are
essential
for
developing
and
deploying
these
systems.

These
use
areas
are
sure
to
evolve
as
AI
technology
progresses.
However,
by
focusing
on
these
core
areas,
organizations
can
position
themselves
to
use
the
power
of
AI
advancements
as
they
arrive.

Improving
AI
to
reach
AGI

While
AI
has
made
significant
strides
in
recent
years,
achieving
true
AGI,
machines
with
human-level
intelligence,
still
require
overcoming
significant
hurdles.
Here
are
7
critical
skills
that
current
AI
struggles
with
and
AGI
would
need
to
master:

1. 
   Visual
   perception: While
   computer
   vision
   has
   overcome
   significant
   hurdles
   in
   facial
   recognition
   and
   object
   detection,
   it
   falls
   far
   short
   of
   human
   capabilities.
   Current
   AI
   systems
   struggle
   with
   context,
   color
   and
   understanding
   how
   to
   react
   to
   partially
   hidden
   objects. 

• 
  Audio
  perception: AI
  has
  made
  progress
  in
  speech
  recognition
  but
  cannot
  reliably
  understand
  accents,
  sarcasm
  and
  other
  emotional
  speech
  tones.
  It
  also
  has
  difficulty
  filtering
  out
  unimportant
  background
  noise
  and
  is
  challenged
  to
  understand
  non-verbal
  expressions,
  like
  sighs,
  laughs
  or
  changes
  in
  volume. 

• 
  Fine
  motor
  skills: It’s
  conceivable
  for
  AGI
  software
  to
  pair
  with
  robotics
  hardware.
  In
  that
  instance,
  the
  AGI
  would
  require
  the
  ability
  to
  handle
  fragile
  objects,
  manipulate
  tools
  in
  real-world
  settings
  and
  be
  able
  to
  adapt
  to
  new
  physical
  tasks
  quickly. 

• 
  Problem-solving: Weak
  AI
  excels
  at
  solving
  specific,
  well-defined
  problems,
  but
  AGI
  would
  need
  to
  solve
  problems
  the
  way
  a
  human
  would,
  with
  reasoning
  and
  critical
  thinking.
  The
  AGI
  would
  need
  to
  handle
  uncertainty
  and
  make
  decisions
  with
  incomplete
  information. 

• 
  Navigation:
  Self-driving
  cars
  showcase
  impressive
  abilities,
  but
  human-like
  navigation
  requires
  immediate
  adaptation
  to
  complex
  environments.
  Humans
  can
  easily
  navigate
  crowded
  streets,
  uneven
  terrain
  and
  changing
  environments. 

• 
  Creativity:
  While
  AI
  can
  generate
  creative
  text
  formats
  to
  some
  degree,
  true
  creativity
  involves
  originality
  and
  novelty.
  Creating
  new
  ideas,
  concepts
  or
  solutions
  is
  a
  hallmark
  of
  human
  creativity.

• 
  Social
  and
  emotional
  engagement: Human
  intelligence
  is
  deeply
  intertwined
  with
  our
  social
  and
  emotional
  abilities.
  AGI
  would
  need
  to
  recognize
  and
  understand
  emotions,
  including
  interpreting
  facial
  expressions,
  body
  language
  and
  tone
  of
  voice.
  To
  respond
  appropriately
  to
  emotions,
  AGI
  needs
  to
  adjust
  its
  communication
  and
  behavior
  based
  on
  the
  emotional
  state
  of
  others.

AGI
examples

However,
once
theoretical
AGI
achieves
the
above
to
become
actual
AGI,
its
potential
applications
are
vast.
Here
are
some
examples
of
how
AGI
technology
might
revolutionize
various
industries:

Customer
service

Imagine
an
AGI-powered
customer
service
system.
It
would
access
vast
customer
data
and
combine
it
with
real-time
analytics
for
efficient
and
personalized
service.
By
creating
a
comprehensive
customer
profile
(demographics,
past
experiences,
needs
and
buying
habits),
AGI
might
anticipate
problems,
tailor
responses,
suggest
solutions
and
even
predict
follow-up
questions.

Example:
Imagine
the
best
customer
service
experience
that
you’ve
ever
had.
AGI
can
offer
this
through
a
perception
system
that
anticipates
potential
issues,
uses
tone
analysis
to
better
understand
the
customer’s
mood,
and
possesses
a
keen
memory
that
can
recall
the
most
specific
case-resolving
minutiae.
By
understanding
the
subtleties
of
human
language,
AGI
can
have
meaningful
conversations,
tackle
complex
issues
and
navigate
troubleshooting
steps.
Also,
its
emotional
intelligence
allows
it
to
adapt
communication
to
be
empathetic
and
supportive,
creating
a
more
positive
interaction
for
the
customer.

Coding
intelligence

Beyond
code
analysis,
AGI
grasps
the
logic
and
purpose
of
existing
codebases,
suggesting
improvements
and
generating
new
code
based
on
human
specifications.
AGI
can
boost
productivity
by
providing
a
hardcoded
understanding
of
architecture,
dependencies
and
change
history.

Example: While building
an
e-commerce
feature,
a
programmer
tells
AGI,
“I
need
a
function
to
calculate
shipping
costs
based
on
location,
weight
and
method.”
AGI
analyzes
relevant
code,
generates
a
draft
function
with
comments
explaining
its
logic
and
allows
the
programmer
to
review,
optimize
and
integrate
it.

Navigation,
exploration
and
autonomous
systems

Current
self-driving
cars
and
autonomous
systems
rely
heavily
on
pre-programmed
maps
and
sensors.
AGI
wouldn’t
just
perceive
its
surroundings;
it
would
understand
them.
It
might
analyze
real-time
data
from
cameras,
LiDAR
and
other
sensors
to
identify
objects,
assess
risks
and
anticipate
environmental
changes
like
sudden
weather
events
or
unexpected
obstacles.
Unlike
current
systems
with
limited
response
options,
AGI
might
make
complex
decisions
in
real
time.

It
might
consider
multiple
factors
like
traffic
flow,
weather
conditions
and
even
potential
hazards
beyond
the
immediate
sensor
range.
AGI-powered
systems
wouldn’t
be
limited
to
pre-programmed
routes.
They
might
learn
from
experience,
adapt
to
new
situations,
and
even
explore
uncharted
territories.
Imagine
autonomous
exploration
vehicles
navigating
complex
cave
systems
or
drones
assisting
in
search
and
rescue
missions
in
constantly
changing
environments.

Example: An
AGI-powered
self-driving
car
encounters
an
unexpected
traffic
jam
on
its
usual
route.
Instead
of
rigidly
following
pre-programmed
instructions,
the
AGI
analyzes
real-time
traffic
data
from
other
connected
vehicles.
It
then
identifies
alternative
routes,
considering
factors
like
distance,
estimated
travel
time
and
potential
hazards
like
construction
zones.
Finally,
it
chooses
the
most
efficient
and
safest
route
in
real
time,
keeping
passengers
informed
and
comfortable
throughout
the
journey.

Healthcare

The
vast
amount
of
medical
data
generated
today
remains
largely
untapped.
AGI
might
analyze
medical
images,
patient
records,
and
genetic
data
to
identify
subtle
patterns
that
might
escape
human
attention. By
analyzing
historical
data
and
medical
trends,
AGI
might
predict
a
patient’s
specific
potential
risk
of
developing
certain
diseases.
AGI
might
also
analyze
a
patient’s
genetic
makeup
and
medical
history
to
tailor
treatment
plans.
This
personalized
approach
might
lead
to
more
effective
therapies
with
fewer
side
effects.

Example:
A
patient
visits
a
doctor
with
concerning
symptoms.
The
doctor
uploads
the
patient’s
medical
history
and
recent
test
results
to
an
AGI-powered
medical
analysis
system.
The
AGI
analyzes
the
data
and
identifies
a
rare
genetic
mutation
linked
to
a
specific
disease.
This
information
is
crucial
for
the
doctor,
as
it
allows
for
a
more
targeted
diagnosis
and
personalized
treatment
plan,
potentially
improving
patient
outcomes.

Education

Imagine
an
AGI
tutor
who
doesn’t
present
information
but
personalizes
the
learning
journey.
AGI
might
analyze
a
student’s
performance,
learning
style
and
knowledge
gaps
to
create
a
customized
learning
path.
It
wouldn’t
treat
all
students
the
same.
AGI
might
adjust
the
pace
and
difficulty
of
the
material
in
real
time
based
on
the
student’s
understanding.
Struggling
with
a
concept?
AGI
provides
other
explanations
and
examples.
Mastering
a
topic?
It
can
introduce
more
challenging
material.
AGI
might
go
beyond
lectures
and
textbooks.
It
might
create
interactive
simulations,
personalized
exercises
and
even
gamified
learning
experiences
to
keep
students
engaged
and
motivated.

Example: A
student
is
struggling
with
a
complex
math
concept.
The
AGI
tutor
identifies
the
difficulty
and
adapts
its
approach.
Instead
of
a
dry
lecture,
it
presents
the
concept
visually
with
interactive
simulations
and
breaks
it
down
into
smaller,
more
manageable
steps.
The
student
practices
with
personalized
exercises
that
cater
to
their
specific
knowledge
gaps
and
the
AGI
provides
feedback
and
encouragement
throughout
the
process.

Manufacturing
and
supply
chain
management

AGI
might
revolutionize
manufacturing
by
optimizing
every
step
of
the
process.
By
analyzing
vast
amounts
of
data
from
sensors
throughout
the
production
line
to
identify
bottlenecks,
AGI
might
recommend
adjustments
to
machine
settings
and
optimize
production
schedules
in
real
time
for
maximum
efficiency.
Analyzing
historical
data
and
sensor
readings
might
help
AGI
predict
equipment
failures
before
they
happen.
This
proactive
approach
would
prevent
costly
downtime
and
help
ensure
smooth
operation.
With
AGI
managing
complex
logistics
networks
in
real
time,
it
can
optimize
delivery
routes,
predict
potential
delays
and
adjust
inventory
levels
to
help
ensure
just-in-time
delivery,
minimizing
waste
and
storage
costs.

Example: Imagine
an
AGI
system
monitors
a
factory
assembly
line.
It
detects
a
slight
vibration
in
a
critical
machine,
indicating
potential
wear
and
tear.
AGI
analyzes
historical
data
and
predicts
a
possible
failure
within
the
next
24
hours.
It
alerts
maintenance
personnel,
who
can
proactively
address
the
issue
before
it
disrupts
production.
This
allows
for
a
smooth
and
efficient
operation,
avoiding
costly
downtime.

Financial
services

AGI
might
revolutionize
financial
analysis
by
going
beyond
traditional
methods.
AGI
could
analyze
vast
data
sets
encompassing
financial
news,
social
media
sentiment
and
even
satellite
imagery
to
identify
complex
market
trends
and
potential
disruptions
that
might
go
unnoticed
by
human
analysts.
There
are
startups
and
financial
institutions
already
working
on
and
using
limited
versions
of
such
technologies.

By
being
able
to
process
vast
amounts
of
historical
data,
AGI
might
create
even
more
accurate
financial
models
to
assess
risk
and
make
more
informed
investment
decisions.
AGI
might
develop
and
run
complex
trading
algorithms
that
factor
in
market
data,
real-time
news
and
social
media
sentiment.
However,
human
oversight
would
remain
crucial
for
final
decision-making
and
ethical
considerations.

Example: A
hedge
fund
uses
an
AGI
system
to
analyze
financial
markets.
AGI
detects
a
subtle
shift
in
social
media
sentiment
toward
a
specific
industry
and
identifies
a
potential
downturn.
It
analyzes
historical
data
and
news
articles,
confirming
a
possible
market
correction.
Armed
with
this
information,
the
fund
manager
can
make
informed
decisions
to
adjust
their
portfolio
and
mitigate
risk.

Research
and
development

AGI
might
analyze
vast
data
sets
and
scientific
literature,
formulate
new
hypotheses
and
design
experiments
at
an
unprecedented
scale,
accelerating
scientific
breakthroughs
across
various
fields.
Imagine
a
scientific
partner
that
can
examine
data
and
generate
groundbreaking
ideas
by
analyzing
vast
scientific
data
sets
and
literature
to
identify
subtle
patterns
and
connections
that
might
escape
human
researchers.
This
might
lead
to
the
formulation
of
entirely
new
hypotheses
and
research
avenues.

By
simulating
complex
systems
and
analyzing
vast
amounts
of
data,
AGI
could
design
sophisticated
experiments
at
an
unprecedented
scale.
This
would
allow
scientists
to
test
hypotheses
more
efficiently
and
explore
previously
unimaginable
research
frontiers.
AGI
might
work
tirelessly,
helping
researchers
sift
through
data,
manage
complex
simulations
and
suggest
new
research
directions.
This
collaboration
would
significantly
accelerate
the
pace
of
scientific
breakthroughs.

Example: A
team
of
astrophysicists
is
researching
the
formation
of
galaxies
in
the
early
universe.
AGI
analyzes
vast
data
sets
from
telescopes
and
simulations.
It
identifies
a
previously
overlooked
correlation
between
the
distribution
of
dark
matter
and
the
formation
of
star
clusters.
Based
on
this,
AGI
proposes
a
new
hypothesis
about
galaxy
formation
and
suggests
a
series
of
innovative
simulations
to
test
its
validity.
This
newfound
knowledge
paves
the
way
for
a
deeper
understanding
of
the
universe’s
origins.

What
are
the
types
of
AGI?

AGI
would
be
an
impactful
technology
that
would
forever
transform
how
industries
like
healthcare
or
manufacturing
conduct
business.
Large
tech
companies
and
research
labs
are
pouring
resources
into
its
development,
with
various
schools
of
thought
tackling
the
challenge
of
achieving
true
human-level
intelligence
in
machines.
Here
are
a
few
primary
areas
of
exploration:

1. 
   Symbolic
   AI: This
   approach
   focuses
   on
   building
   systems
   that
   manipulate
   symbols
   and
   logic
   to
   represent
   knowledge
   and
   reasoning.
   It
   aims
   to
   create
   a
   system
   that
   can
   understand
   and
   solve
   problems
   by
   following
   rules,
   similar
   to
   how
   humans
   use
   logic.

• 
  Connectionist
  AI
  (artificial
  neural
  networks): This
  approach
  is
  inspired
  by
  the
  structure
  and
  function
  of
  the
  human
  brain.
  It
  involves
  building
  artificial
  neural
  networks
  with
  interconnected
  nodes
  to
  learn
  and
  process
  information
  based
  on
  vast
  data.

• 
  Artificial
  consciousness: This
  field
  delves
  into
  imbuing
  machines
  with
  subjective
  experience
  and
  self-awareness.
  It’s
  a
  highly
  theoretical
  concept
  but
  might
  be
  a
  key
  component
  of
  true
  intelligence.

• 
  Whole
  brain
  emulation: This
  ambitious
  approach
  aims
  to
  create
  a
  detailed
  computer
  simulation
  of
  a
  biological
  brain.
  The
  theory
  is
  that
  consciousness
  and
  intelligence
  might
  emerge
  within
  the
  simulation
  by
  copying
  the
  human
  brain’s
  structure
  and
  function.

• 
  Embodied
  AI
  and
  embodied
  cognition: This
  approach
  focuses
  on
  the
  role
  of
  an
  agent’s
  physical
  body
  and
  its
  interaction
  with
  the
  environment
  in
  shaping
  intelligence.
  The
  idea
  is
  that
  true
  intelligence
  requires
  an
  agent
  to
  experience
  and
  learn
  from
  the
  world
  through
  a
  physical
  body.

The
AGI
research
field
is
constantly
evolving.
These
are
just
some
of
the
approaches
that
have
been
explored.
Likely,
a
combination
of
these
techniques
or
entirely
new
approaches
will
ultimately
lead
to
the
realization
of
AGI.

Operationalizing
AI
is
the
future
of
business

AGI
might
be
science
fiction
for
now,
but
organizations
can
get
ready
for
the
future
by
building
an
AI
strategy
for
the
business
on
one
collaborative
AI
and
data
platform,
IBM
watsonx™.
Train,
validate,
tune
and
deploy
AI
models
to
help
you
scale
and
accelerate
the
impact
of
AI
with
trusted
data
across
your
business.

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watsonx

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