What children can teach us about building AI
Summary
TLDRDieses Video untersucht die Geheimnisse des menschlichen Lernprozesses, insbesondere die Fähigkeit von Säuglingen, komplexe Sprachen und Motorik schnell zu erlernen, was AI-Algorithmen weit übertrifft. Professorin Elizabeth Spels Forschung in der Kindpsychologie zeigt, dass Säuglinge bereits im Schlaf lernen und sechs grundlegende Wissensarten teilen, die auch bei Tieren zu finden sind, sowie eine siebte, die Sprache, die einzigartig für den Menschen ist und kognitive Fähigkeiten katalysiert. Die Diskussion um AI-Forschung und das Potential, diese Wissensarten mit künstlicher Intelligenz zu verbinden, um KI-Systeme zu verbessern, wird ebenso behandelt.
Takeaways
- 🧠 Kinder lernen komplexe Fähigkeiten wie Sprache und Motorik schneller als kürzlich erkannt, was für KI-Forschung relevant ist.
- 👶 Elizabeth Spelke, eine Harvard-Professorin, untersucht, wie Kinder Wissen aufbauen und welche kognitiven Fähigkeiten sie von Geburt an mitbringen.
- 🔍 Spelke verwendet das Infant Gaze Tracking, um zu erforschen, was Säuglinge gelernt haben, indem sie die Dauer ihrer Aufmerksamkeit auf Objekte misst.
- 🌟 Säuglinge lernen bereits im Schlaf und können z.B. die Anzahl von Objekten oder die Worttrennung erkennen, bevor sie die Bedeutung der Wörter kennen.
- 🗣️ Die Fähigkeit zur Worttrennung ist ein Kernaspekt der Sprachentwicklung und wird von Säuglingen intuitiv erlernt, bevor sie die Grammatik oder die Bedeutung der Wörter kennen.
- 🧩 Professor Spelke identifizierte sechs Kernwissensbereiche, die sowohl bei Säuglingen als auch bei Erwachsenen und Tieren zu finden sind: Orte, Objekte, lebende Wesen, soziale Wesen, Zahlen und Geometrie.
- 🌐 Die siebte kognitive Fähigkeit, die nur beim Menschen vorkommt, ist die Sprache, die als kognitives Katalysator für das menschliche Denken gilt.
- 🤖 KI-Forschung konzentriert sich oft auf die Sprachfähigkeit, aber um KI-Systeme intelligenter zu machen, könnte es notwendig sein, auch die anderen sechs Kernkompetenzen zu integrieren.
- 🔢 Ein Beispiel für KI-Forschung, die auf Spelkes Forschung aufbaut, ist die SLM, die einen kleinen Sprachmodell mit einem symbolischen Löser kombiniert, um arithmetische Probleme zu lösen.
- 📈 KI-Systeme wie große Sprachmodelle sind derzeit nicht gut darin, hierarchisches Planen durchzuführen, was für KI-Systeme, die kompetent kodieren können, notwendig sein könnte.
Q & A
Was könnte das Geheimnis sein, um KI auf menschliches Niveau zu bringen?
-Das Geheimnis könnte in den Gehirnen von Kindern liegen, da ihre Fähigkeit, komplexe motorische Fertigkeiten und Sprachen zu erlernen, weit über die Fähigkeiten der fortschrittlichsten maschinellen Lernalgorithmen hinausgeht.
Wie unterscheidet sich die kognitive Entwicklung von Säuglingen von der von KI-Systemen?
-Säuglinge können in ihren ersten Lebensjahren ohne Anweisungen natürliche Sprachen erlernen, sich und Objekte bewegen, soziale Beziehungen aufbauen und Konzepte wie Zahl, Geometrie und Kausalität anwenden, während KI-Systeme dies oft nicht können oder es sehr langsam lernen.
Was ist der Unterschied zwischen dem Lernen von Säuglingen und dem von KI-Systemen?
-Säuglinge können im Schlaf lernen und Informationen aufnehmen, während KI-Systeme normalerweise aktiv trainiert werden müssen. Säuglinge lernen auch, während sie schlafen, was KI-Systemen nicht möglich ist.
Wie verwendet Professor Spel die Methode der Säuglingsbeobachtung in ihren Studien?
-Professor Spel verwendet die Säuglingsbeobachtung, um zu bestimmen, ob ein Säugling etwas gelernt hat, indem sie beobachtet, wie lange ein Säugling auf ein Objekt schaut, nachdem es ihm präsentiert wurde.
Was zeigte eine Studie über Säuglinge, die während des Schlafes Geräusche hören ließ?
-Es zeigte, dass Säuglinge visuelle Interesse an Punkten zeigen, die einer Anzahl von Schallimpulsen entsprechen, die ihnen während des Schlafes gespielt wurden, was auf das Lernen während des Schlafes hindeutet.
Wie sind die sechs Kerntypen von Wissen, die Professor Spel in Säuglingen identifizierte, definiert?
-Die sechs Kerntypen von Wissen sind Orte, Objekte, bewegte Wesen, soziale Wesen, Zahl und Geometrie. Diese Typen von Wissen interagieren nicht miteinander und scheinen unabhängig voneinander entwickelt worden zu sein.
Warum ist Sprache nach Ansicht von Professor Spel ein kognitives Katalysator für menschliches Denken?
-Sprache ist ein kognitiver Katalysator, weil sie es ermöglicht, die sechs anderen Kernsysteme des Gehirns zusammenzuarbeiten, indem sie sie in Harmonie bringen und Ideen austauschen kann, was eine höhere kognitive Fähigkeit ermöglicht.
Was ist die Bedeutung der Kombinatorik und Rekursion in der Sprache für das menschliche Denken?
-Die Kombinatorik und Rekursion in der Sprache ermöglichen es uns, einfache Konzepte zu kombinieren, um komplexe, hierarchisch strukturierte Gedanken zu bilden, was das abstrakte Denken ermöglicht.
Wie unterscheidet sich die Sprachfähigkeit von Tieren von der menschlichen Sprachfähigkeit?
-Obwohl Tiere Kommunikationssysteme haben, die ein begrenztes Vokabular umfassen, können sie nur einen kleinen Teil der Konzepte ausdrücken, die sie in ihrem Kopf haben, im Gegensatz zur menschlichen Sprache, die nahezu jedes Konzept ausdrücken kann.
Was ist das Hauptziel der sLM-Forschung, die im Skript erwähnt wird?
-Das Hauptziel der sLM-Forschung ist es, eine kleine Sprachmodell, das nicht in der Lage ist, numerische Probleme zu lösen, mit einem symbolischen Solver zu kombinieren, um die Fähigkeit des Systems zu verbessern, arithmetische Probleme in natürlicher Sprache zu lösen.
Warum sind hierarchische Planungsfähigkeiten für KI-Systeme wichtig?
-Hierarchische Planungsfähigkeiten sind wichtig, weil sie es ermöglichen, über verschiedene Ebenen zu planen und zu denken, was natürlichen menschlichen Denkprozessen entspricht und für KI-Systeme zur Entwicklung von KI auf menschlichem Niveau notwendig ist.
Outlines
🧠 Kindliche Psychologie und KI-Forschung
Dieser Absatz stellt die Verbindung zwischen der kindlichen Psychologie und der KI-Forschung her. Es wird besprochen, dass die Fähigkeit von Kindern, komplexe Motorfertigkeiten und Sprachen zu erlernen, schneller ist als die von KI-Algorithmen. Die Rede ist von der Arbeit von Professorin Elizabeth Spelke, die sich mit der kindlichen Psychologie auseinandersetzt und versucht, herauszufinden, was Menschen von anderen Tieren unterscheidet. Ihre Forschung konzentriert sich auf das frühkindliche Erlernen von Sprachen und die Entwicklung von Wissen in verschiedenen Kategorien. Sie verwendet die Infant Gaze Tracking-Methode, um zu erforschen, wie Kinder lernen, und fand heraus, dass Kinder bereits im Schlaf lernen können. Diese Forschung könnte KI-Forschung inspirieren, indem sie zeigt, wie das menschliche Gehirn Wissen verarbeitet und wie es möglicherweise KI verbessern kann.
🔢 Sechse Kernwissensbereiche und die Sprache
In diesem Absatz werden sechs grundlegende Wissensbereiche beschrieben, die sowohl Menschen als auch Tiere teilen: Orte, Objekte, lebende Wesen, soziale Wesen, Zahlen und Geometrie. Diese Wissensbereiche interagieren nicht miteinander und scheinen unabhängig voneinander entwickelt worden zu sein. Menschen haben jedoch ein siebtes Wissen, das Sprache ist, das einzigartig für den Menschen ist und das Fundament für alle Sprachentwicklung im ersten Lebensjahr bildet. Sprache verbessert das Objekterkennen und die Zahlendarstellung und ist ein kognitives Katalysator, der es ermöglicht, dass verschiedene Teile des Gehirns zusammenarbeiten. Sprache hat rekursive Eigenschaften, die es ermöglichen, komplexe, hierarchisch strukturierte Gedanken zu bilden. Tiere haben zwar auch Kommunikationssysteme, diese sind jedoch eingeschränkt und können nicht alle Konzepte, die sie im Kopf haben, ausdrücken.
🤖 KI-Forschung und Kombination von Wissensbereichen
Dieser Absatz behandelt die KI-Forschung und wie man kleine Sprachmodelle mit symbolischen Lösern kombiniert, um Probleme zu lösen, die einen arithmetischen Anteil in natürlicher Sprache haben. Die Idee ist, Probleme in eine formale Sprache umzuwandeln, die dann an einen symbolischen Löser übergeben werden kann. Die Forschung konzentriert sich auf kleine Sprachmodelle mit weniger als 50 Milliarden Parametern, da numerische Fähigkeiten erst ab diesem Punkt erscheinen. Die Kombination aus Sprachmodell und symbolischem Löser ermöglicht es, arithmetische Probleme zu lösen, was direkt analog zu einer Kombination aus einem der sechs Kernsysteme und einem Sprachverarbeitungsmodul ist. Die Forschung zeigt, dass durch die Feinabstimmung des Modells anstatt eines erneuten Trainings eine tiefere Integration zwischen dem Sprachmodell und dem symbolischen Ausführungsmodul erreicht werden kann.
🏡 Hierarchische Planung und Weltmodelle
Hierarchische Planung ist ein menschliches Denkvermögen, das KI-Systeme derzeit nicht sehr bewältigen können. Um hierarchische Planung zu ermöglichen, ist ein hierarchisches Weltmodell notwendig, das verschiedene Ebenen der Realität repräsentiert. Das menschliche Gehirn kann aufgrund seiner verschiedenen kognitiven Fähigkeiten, wie räumliche Verarbeitung, Langzeitgedächtnis und numerische Repräsentation, automatisch hierarchische Pläne bilden. KI-Systeme, die nur über Sprachfähigkeiten verfügen, haben Schwierigkeiten, solche hierarchischen Pläne zu entwickeln. Die Integration von anderen kognitiven Fähigkeiten in KI-Systeme könnte zu einer verbesserten Fähigkeit zur hierarchischen Planung führen und zu KI-Systemen, die ähnlich kompetent sind wie der menschliche Verstand.
👋 Schlussfolgerung und Ausblick
Die Schlussfolgerung des Videos betont die Bedeutung der Forschung von Professor Spelke, die zeigt, wie Kinder lernen und welche sechs Kernwissensbereiche Menschen und Tiere teilen. Sie identifizierte auch das siebte System, die Sprache, als kognitives Katalysator, der es ermöglicht, dass verschiedene Teile des Gehirns zusammenarbeiten. In Bezug auf KI-Forschung wurde die SLM-Forschung vorgestellt, die kleine Sprachmodelle mit symbolischen Lösern kombiniert, um numerische Probleme zu lösen. Schließlich wurde die Notwendigkeit von hierarchischer Planung und Weltmodellen diskutiert, um KI-Systeme zu verbessern. Der Sprecher regt dazu an, über die verschiedenen Definitionen von Intelligenz nachzudenken und wie KI-Systeme intelligent sein könnten, unabhängig von menschlichen Perspektiven.
Mindmap
Keywords
💡Kinderpsychologie
💡Sprachentwicklung
💡Kognitive Fähigkeiten
💡Rekursive Kompositionalität
💡Kombinatorische Macht
💡Infant Gaze Tracking
💡Symbolische Lösung
💡Hierarchical Planning
💡Kognitive Katalysator
💡Künstliche Intelligenz (KI)
Highlights
The secret to unlocking human-level AI might lie in understanding how children learn.
Children's rapid mastery of complex skills outpaces current machine learning algorithms.
Elizabeth Spelke's research focuses on cognition in babies to understand human uniqueness.
Infant gaze tracking is used to study what infants have learned without their ability to speak.
Infants show interest in numbers and can learn concepts even while sleeping.
Babies are familiar with word segmentation before birth, a skill that takes adults much longer to learn.
Infants prefer listening to their native language and regional accents.
Professor Spelke identified six core types of knowledge shared by humans and animals.
Humans possess a seventh knowledge type, language, which is unique and a cognitive catalyst.
Language learning in humans enhances object recognition and potentially other cognitive functions.
Animal communication systems lack the complexity and combinatorial power of human language.
The paper 'sLM' discusses integrating a symbolic solver with a small language model for arithmetic reasoning.
Symbolic reasoning is incorporated into the fine-tuning of models, not just as a tool.
Hierarchical planning is a capability that current AI systems lack.
Integrating multiple core knowledge systems may lead to more advanced AI capabilities.
The video concludes by emphasizing the importance of hierarchical world models for AI planning.
Transcripts
hi everyone the secret to unlocking
human level AI might very well lie
within the minds of children their rapid
Mastery of complex motor skills and
languages far outpaces the capabilities
of our most advanced machine learning
algorithms what could be the key to this
remarkable learning prowess can the core
knowledge and brain architecture
observed in infants Inspire
unprecedented advancements in AI keep
watching to learn more this video has
three parts child psychology the special
soft
and relation to AI research part one
child psychology I recently attended an
academic AI conference in Vancouver
called triple AI it was a massive
conference with I think 2,200 accepted
papers I can't believe the scale of AI
conferences and it had some very
interesting invited talks one of those
talks was by someone from a slightly
different field Elizabeth S spel is a
professor at Harvard and she studies
child psychology essentially she's the
author of a few books including what
babies know and her research focuses on
studying cognition in babies to figure
out what makes humans different from
other animals and in particular what are
the Bedrock categories of human
knowledge and how does knowledge in each
category develop from its very
Beginnings here's a quote from her talk
abstract in their first years and with
no instruction children learn one or
more natural languages they develop a
host of skills for moving themselves and
manipulating objects they learn the
paths connecting places in their homes
and the bonds connecting people in their
social networks and they deploy concepts
of number geometry and causality that
continue to guide the reasoning of
adults how children do this is a great
unanswered question so how does
Professor spel study this problem and
apologies if I'm mispronouncing her name
there I don't remember the pronunciation
the experimental method that she uses in
her studies is to do infant gaze
tracking basically she figured out that
if an infant has learned something then
they're going to look at that thing for
a longer time period they're going to be
more interested in it because we're
talking about infants that are much too
young to talk at this phase so you have
to use an indirect method to figure out
if they've learned something one
experiment that the professor performed
was to try to get infants to learn the
difference between four objects and 12
objects she had them look at four dots
for example and then later was able to
show that they had learned that because
they would look more at four dots than
they would at 12 and it's even
multimodal an infant can listen to a
sound that occurs four times and then
later they'll be interested more in four
dots basically their brain is learning
the concept of number but what's really
fascinating is that the researchers
actually played The Sounds the four
sounds for example or the 12 sounds in a
row to infants while the infants were
sleeping and even in that case they
actually were more interested in four or
12 dots visually at a later date so in
other words even while they were
sleeping the infants were picking up
information and learning it there was
another study that showed that babies
are familiar with word segmentation
before their birth word segmentation is
basically the the problem of you listen
to a spoken language and you can figure
out where the word breaks are supposed
to be adults tend to learn word
boundaries very slowly if you've learned
a second language as I have been doing
with Japanese you'll know how difficult
it can sometimes be to go was that one
word or was that three but babies and
native speakers know word boundaries
intuitively this is even before they
know the meanings of any of the words
and before they know the grammar of the
words as well but there's enough
patterns in the speech that they're able
to figure it out I actually have
secondhand confirmation of babies being
able to to learn word segmentation I
have a friend who has English as her
first language but her mother spoke
Spanish while she was a really young kid
I don't know if this was before or after
her birth but regardless my friend was
never taught any Spanish at all however
when she tried to learn Spanish as an
adult her teacher said you have the ear
of a native speaker she was very good at
reproducing exactly the right sounds and
I would guess had very little trouble
with word segmentation well again this
is probably because both Spanish and
English were being spoken spoken around
her before she was born and as she was a
very young child but I suppose the
Spanish speaking must have stopped at
some point and then she just picked up
English as her only native language
Anyway by using this infant gaze
tracking metric researchers were able to
show that babies pay more attention to
someone speaking their native language
and not only that but they pay even more
attention to someone who's speaking with
their own Regional accent slightly older
kids would rather make friends with
someone who sounds like them than looks
like them if someone is of the same race
but has a foreign accent for example
then they would not really prefer that
person as a friend compared to someone
of A different race but that sounds like
they came from the same neighborhood so
word segmentation and being able to
recognize your native language are
really core parts of how infants and
children interact with the world and all
of that learning starts even before the
baby is born part two the Special Sauce
in her research and in her book what
babies know Professor spel identified
six core types of knowledge these six
core types of knowledge that are
represented in babies brains and adult
brains are also shared with animals they
are places objects animate beings social
beings number and geometry these six
types of core knowledge don't really
interact with each other and therefore
potentially evolved independently when
an animal or human is engaging with one
type of knowledge then they don't use
the others and as soon as they switch to
something else their brain completely
switches focus and yes plenty of animals
exhibit all the signs of these six
knowledge types as well presumably they
are what evolution deemed necessary to
survive in the physical world however
humans have a seventh knowledge type
this seventh type is language and it's
Unique to humans and it underlies all
the language learning that happens
within a baby's first year of Life we'll
talk more in a minute about why animal
systems of communication don't really
count as language in this sense when
humans are learning language doing so
actually improves their object
recognition if you learn the name for
something it's easier for you to recogn
ize it it might improve number
representation as well this is my
supposition but I think the reverse is
true as well if you learn a different
representation for a word or a concept
that exists in your language then that
will result in a more solid
understanding it only seems natural for
example recently I was looking at a map
of Southeast Asia which is the first
time I've looked at it in great detail
in a while and I spotted where Myanmar
was which I didn't really know and then
when I started reading news stories
about Myanmar it was easier for me to
remember because I had a visualization
or representation of what that country
was in fact Professor spel talks about
this seventh knowledge type or language
learning as an actual cognitive Catalyst
she thinks it's the secret ingredient
for human cognition and what allows us
to be so different from animals
specifically she thinks that human
language is the Catalyst that allows our
numeric architectonic and social modules
of our brain to all act in harmony it
allows them to join forces swap ideas
and essentially engage more multiple
parts of your brain at once instead of
just switching from one completely to
the other here's a quote what's special
about language is its combinatorial
power we can use it to combine anything
with anything apparently for example
children start integrating what they
know about their physical environment
and their navigational sense at around
the same time as the age when they begin
to master spatial language and think
about what's left and what's right
another paper I read points out that
language has recursive machinery and
this recursiveness allows us to flexibly
combine any concepts any concepts we
like into complex hierarchically
structured thoughts yes language as in
speech itself is also hierarchical and
contains very formalized structure but
the idea here is that that structure
that recursive property can actually be
applied to thoughts as well as the paper
puts it compositionality is a key
component of linguistically structured
thought so basically the ability to
think about something very small and
very simple and something else very
small and simple and combine them
together into a larger composite thought
is really what makes abstract thought
possible the legendary Chomsky even
called this the basic property and
argues that it evolved in humans because
of the way that it allows us to
structure thoughts rather than the way
that it allows us to structure speech
although that was definitely a desirable
side effect so although Dr spel is
basically calling the seventh system the
language system I guess you could also
call it the recursive ly structured
system or the recursion and composition
system or something like that so what
about language in animals after all some
animals seem pretty smart like you can
train a dog to push a button when it
wants to get the humans to do something
and you have monkeys and chimpanzees and
birds that all communicate with
languages that seem to have reasonably
large vocabularies but this is the
difference pretty much any concept that
a human might want to think about can be
expressed in language but for all known
animal communication systems the animals
can only express a small subset of the
concepts that they can actually
represent in their head for example
honeybees have color vision they can
remember the colors of flowers that they
visit but the honeybee language where
they dance to communicate to other
honeybees where to find food or whatever
allows them to communicate only the
spatial location the spatial direction
and distance of that food rather than
the color of the flower that they're
sending them to and that's all the
information that's actually needed for
the honey bees to survive which is good
but it means that the bees will never be
able to scaffold thoughts upon thoughts
and communicate to one another and build
up a culture over time every honeybee
has to start from scratch so the Special
Sauce is language or recursive
compositionality or whatever you want to
call it therefore if you're trying to
create really smart like human level AI
it makes sense to concentrate on those
six core types of knowledge that other
animals have and combine it with the
existing language expertise because so
far most models skip over all of those
six core competencies and they just head
straight for the language which is great
because that's the most complicated one
and the one that we interact with on a
day-to-day basis but it also means that
if you ask a language model to do some
reasoning it has only the language
capabilities there it can't fall back on
some spatial reasoning module to solve a
geometric problem or whatever perhaps
easier said than done though part three
relation to AI research one of the
papers I saw at triple AI was called s
LM which I think stands for symbolic
reasoning language model the idea of
this work is to take a small language
model they call it a Frugal language
model because it doesn't have many
parameters and get that model to offload
some computations to a symbolic solver
this would effectively be taking one of
those six core competencies that I
talked about and fusing it with the
language model in this work they focused
on small language models with less than
50 billion parameters because apparently
numeric reasoning capability only only
starts to emerge once you exceed that
amount the idea is to take in problems
that have an arithmetic component in
natural language and convert them into a
formal language which you can then pass
to a symbolic solver the symbolic solver
will use mathematically precise
algorithms to figure out the solution
and then pass it back up to the language
model which can answer the original
queries for example you could imagine
asking a language model to generate
python code which then calls a symbolic
execution engine they did experiments
with two main models GPT DJ which has 6
billion parameters and vuna which has 13
billion as the larger model vuna can
generate python code but gptj could only
generate what they called pseudo code
actually though what they call pseudo
code is essentially an Assembly Language
they can do additions subtractions and
other operations on variables that the
LM defined so the Assembly Language can
look up variables from the original
prompt basically or if you want to be
technical it looks a lot like an SSA
form inside a compiler but anyway how
does this paper work so first they take
the base model and they don't retrain it
at all everything done in this work is
based around fine-tuning there's two
main stages first they use Laura low
rank adaptation to fine-tune the model
to the formal language generation task
remember the formal language could be
just python or it could be their pseudo
code language second they fine-tuned the
model to actually use the solver and
they Incorporated a feedback loop to try
to judge the quality of the formal
language representation including for
example whether the python code actually
ran or not the results were pretty
impressive for example they showed a 30%
in terms of absolute percentage
Improvement on one of the tasks compared
to the base model that hadn't been
fine-tuned along with the symbolic
reasoning engine and that's a key Point
as well this isn't just a system where
they exposed a tool to an existing large
language model which is sort of how
internet search gets exposed for example
instead the symbolic reasoning is
actually included in the fine-tuning of
the model not quite during the training
phase days because they just used a
pre-trained model but it's absolutely
integral to the fine-tuning stage in
other words during fine tuning the model
would actually be generating some python
running it checking the output this is a
much deeper integration between the
language model and the symbolic
execution engine than just using it as a
tool would be and because all their work
was done on small models that did not
need to be retrained but just fine-tuned
this work was also very computationally
efficient and easy for researchers to
replicate so I'm a big fan of this work
and it's interesting because it does
represent combining one of those six
systems along with the language
reasoning capabilities and I suspect
there will have to be a lot more work
like this in the future as we start to
put together heterogeneous combinations
of different types of reasoning modules
in fact that was very specifically one
of the ways to build AGI that was called
out and I talked about that in a
previous video there was another talk at
the triple AI conference by the famous
deep learning Pioneer Yan Lun he called
out one of the main things that current
AI systems are lacking and that is
hierarchical planning based on
hierarchical world models so what does
that mean well I've touched on that in
the past as well by saying that large
language models are essentially
feedforward neural networks they can't
think on something deeply for a long
time because they don't really have
Cycles they're not recurrent neural
networks that's one of the reasons that
if you want an llm to solve a
complicated problem you should use chain
of thoughts and ask it to split its
reasoning into small steps because it's
a lot more likely that the difference
between one step and the next is going
to be feasible for a feed forward neural
network than trying to get it to do it
all in one shot another way of putting
this is that the llm doesn't have any
planning capabilities it can't plan
ahead and say oh in step five I'm going
to need this so I should do this now it
can figure that out if you ask it to
explicitly break down its reasoning into
steps but overall the planning abilities
are not very good even harder is a
hierarchical planning ability if you
have a hierarchical plan it means you
have a top level goal a slightly lower
level goal and even lower level goals
and furthermore the really lowlevel
goals don't have to be worked out in
detail in their entirety for example
let's say my goal is to make a sandwich
for myself but I don't have the
ingredients for the sandwich so a lower
level thing is I need to go to the
grocery store to get those ingredients
and lower level than that is I need to
figure out which direction I should go
in order to walk to the grocery store
like which path would be best to take
but in order to do that I need to figure
out what the weather is to decide if I
should wear a raincoat or just go out as
I am but in order to do that I need to
get up out of my chair and actually go
to that room to check the weather so as
soon as I think to myself wait I need to
make a sandwich then that hierarchical
plan can just form itself in my mind and
suddenly I'm standing up to go check the
weather and of course I didn't have to
plan out all the Tiny Steps in advance
once I step into that other room and
figure out what the weather is by I
don't know asking my Amazon device for
example I didn't have to map out in my
head all of the individual steps I'm
going to take all the way to the grocery
store or whatever because my brain can
Define the plan as time goes on also
important in this discussion is a
hierarchical world model you can only do
planning at different levels in a
hierarchy if you have a model of the
world that is actually accurate at
different levels hierarchical planning
seems very natural to humans but if you
have certain conditions then it can be
really hard for example if you have ADHD
you might go out to see a friend and
forget your jacket and get soaked by
rain along the way it's easy to get
fixated on a goal and Overlook the small
things or to live in the current moment
and not really plan ahead too much for
ADHD it can depend on the energy level
that you have whether or not you think
about all the necessary steps and I
think it's a skill we have to learn and
consciously practice as well in fact
when a professor first asked me a
problem in other words at the equivalent
of how do I get to the grocery store I
didn't know how to answer him I was just
like well you figure out the closest
grocery store and you go there but he
was like no no no in programming you
first have to figure out get up from
your chair take one step to the right
Etc and of course course now if I'm
doing a coding task or anything like
that I'm engaged in many many levels of
hierarchical planning so it will be
interesting to see when AI systems can
really engage in planning at similar
levels or even more advanced levels
because I think that's one of the key
components that's missing when you think
about can an AI system write code
competently for example and the way this
relates to everything else in this video
is that you want hierarchical planning
but that needs a hierarchical world
model and I think it's difficult to have
a hierarchical world model model if you
just have one reasoning component in
your system that only understands
language but if you have Incorporated
one or more of those other six core
knowledge systems then I think it's much
easier to have a hierarchical world
model my spatial reasoning capabilities
can tell me how to get up out of this
chair and over to the other room my
episodic or long-term memory can tell me
which path which turns I have to take to
get to the grocery store my memory of
what food tastes like can help me
remember which ingredients I need to
make my sandwich and my number
representation system can help me figure
out if the grocery store is overcharging
me for things or not so it will be very
interesting to see what AI systems will
be capable of once they start
integrating hierarchical World models
hierarchical planning and more of those
six core competencies that humans and
other animals have as a sort of
cognitive Baseline finally in conclusion
we talked about how Professor s spel
work on studying child psychology has
helped us figure out what babies know we
discovered that babies can start to
learn about the number of items they can
start to engage their social muscles
they start to engage language learning
and word segmentation even before
they're born and a lot of this learning
even happens while they're asleep the
professor also identified six core areas
in which humans seem to share a lot of
similarities with animals and those six
core knowledge systems do not interact
with each other if the brain is focusing
on one then the other types of reasoning
are shut off humans have a seventh
reasoning system which is what deal
deals with language and this seventh
system is what enables humans to think
abstractly to reason and to do far more
than other animals can in fact this
seventh system is a cognitive Catalyst
because it enables those six other core
systems to actually work together you
can use some of this bit of reasoning
along with some of that and combine it
together with language we're using
language somewhat Loosely here because
structured thought recursive thought and
compositional thought is actually the
mechanism that enables these different
types of reasoning to be used together
and then once you have thoughts in a
structured and whatever manner then by
using language processing parts of your
brain you can turn it into speech in
terms of AI research we talked about s
LM which takes a really small language
model that is not capable of numeric
reasoning and combines it with a
symbolic solver this enables the
combined system to solve arithmetic
problems it's directly analogous to one
of those six core systems the one that
deals with numbers getting combined with
a language processing module finally we
also talked about hierarchical planning
which is something that existing AI
systems like large language models are
not very proficient at hierarchical
planning seems possible once you have
hierarchical World representations that
could come about for example because
you're representing more of those six
core knowledge systems so next time you
walk out of the house without your
wallet or your keys you know that you
can blame your brain's hierarchical
planning system if you liked this video
please check out this previous one I
made where I talked about different
definitions of intelligence and how we
could think about whether an AI system
is intelligent or not outside of the
human lens well that's all I have for
today thank you very much for watching
bye
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