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Artificial Intelligence: Drawing Inspiration from Human Capabilities

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@whatsaiLouis Bouchard

I explain Artificial Intelligence terms and news to non-experts.

This video is both an introduction to the recent paper Thinking Fast and Slow in AI by Francesca Rossi and her team at IBM, and to Luis Lamb's most recent paper Neurosymbolic AI: the 3rd Wave of AI.

Both these papers are drawing inspiration from human capabilities to build a future generation of artificial intelligence that would be more general and trustworthy.

Then, there are 10 important questions for the AI community to consider in their research.

Enjoy the video!

Chapters:

  • 0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.
  • 5:33 Neuro-Symbolic AI: The Third Wave
  • 11:01 Thinking Fast and Slow
  • 16:06 Thinking Fast and Slow in AI - 10 Questions for the AI Research Community

References:

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Video Transcript

(Note: this transcript is auto-generated by YouTube and may not be 100% accurate)

00:00

okay let me say let me tell you about my

00:03

experience you know in dealing with

00:05

trust and

00:06

ai ethics in general and also what i

00:09

think are some

00:11

main points so some of them are

00:12

technical and some of them are not

00:14

in really achieving this ecosystem of

00:17

trust around the ai so this is the

00:19

overall picture that i think that

00:21

many of the previous panelists put

00:23

forward you know we want the future

00:25

i would say of and with ai because of

00:28

course i don't think as ai

00:30

uh at ai as just autonomous systems but

00:33

also systems that work with us

00:34

so it's not just the future of the eye

00:36

for me but also with ai

00:38

uh and it has all these uh desirable

00:41

properties of course trustworthiness is

00:44

one but of course general collaborative

00:46

and

00:46

as already mentioned for gdp three and

00:48

language very huge launch and matter

00:50

also sustainably

00:51

uh computationally but how to focus on

00:55

this

00:55

third panel how do we build an ecosystem

00:58

of trust

00:58

and i and i talk about an ecosystem of

01:01

trust because

01:02

it has many dimensions just like

01:04

trusting other people as many dimensions

01:07

so of course we want ai system to be

01:09

accurate

01:10

and that's but beyond accuracy we really

01:13

want

01:14

a lot of desirable properties one of

01:16

them i called it and

01:18

some other people call it value

01:19

alignment which is around fairness you

01:22

know what how do we want these

01:25

machines to behave to behave according

01:27

to some values that we care about

01:28

one of them of course is fairness so we

01:31

want the bias to be

01:32

identified removed and so on but

01:35

it may have maybe other values that are

01:37

beyond fairness

01:38

robustness also you know

01:40

generalizability beyond

01:42

you know some data distribution and

01:44

explainability explainability is very

01:46

important especially in the context of

01:48

machines that work together with human

01:50

beings

01:52

now but differently from what we would

01:55

expect in building trust with another

01:56

human being

01:58

here we are not in in the presence of

02:01

another human being we are the presence

02:03

of ai systems that are created by human

02:05

beings

02:06

so just like margaret and others have

02:08

said we want

02:09

something also from those that create

02:12

that ai

02:12

system from the developers from the

02:14

deployers for those that use the ai

02:16

and one of the things that i think

02:18

margaret pointed out very clearly and

02:21

we have a very similar approach is we

02:23

want transparency we want transparency

02:25

about

02:26

the decisions that have been made during

02:28

the ai pipeline

02:30

whether their decision about the

02:31

training data or other decision and

02:34

in that very nice visual way margaret

02:37

showed that bias can be injected in many

02:39

different uh

02:40

places in the ai pipeline uh we don't

02:42

have the concept of a model card but a

02:44

very similar one called the ai faction

02:46

and in fact we work together with

02:48

margaret and others also within the

02:50

partnership and ai

02:51

to compare and learn from each other

02:53

these different ways to achieve

02:55

uh transparency the second point that i

02:58

want to make is that

02:59

of course we want these developers also

03:02

to work according to some ai

03:04

ethics and guidelines and principles but

03:07

principles are just the first step

03:10

in in in a corporate place where

03:13

ai is being produced and deployed so it

03:16

really needs a lot of multi-stakeholder

03:18

consultation

03:19

education training and as margaret

03:22

already mentioned diverse teams

03:24

you know to bring all many different

03:26

backgrounds it needs a lot of technical

03:28

tools

03:29

for example to detect mitigate bias to

03:31

generate explanation and so on

03:33

it needs a lot of work in helping

03:36

developers understand

03:37

how to change the way they're doing

03:39

things how to make it as easy as

03:41

possible to adopt a

03:43

new methodology and how to build an

03:45

overall governance

03:47

in a company within you know that is a

03:50

kind of an

03:50

umbrella over what developers are doing

03:53

what the business units are doing and so

03:55

on so it's really a process

03:57

and that's why i put this picture of

03:58

trust with all the cranes because it's a

04:00

process to build trust in ai

04:04

so the last point that i want to make is

04:06

that uh

04:07

for all these properties that we want in

04:09

the ai systems in order to be able to

04:12

trust them

04:12

unfortunately current ayai is not there

04:15

yet

04:16

these are the reasons why francisca

04:18

rossi and her team at ibm

04:20

published this paper proposing a

04:22

research direction to advance ai

04:24

drawing inspiration from cognitive

04:26

theories of human decision making

04:29

where the premise is if we gain insights

04:32

into human capabilities that are still

04:34

lacking in

04:35

ai such as adaptability robustness

04:38

abstraction generalizability common

04:41

sense and causal reasoning

04:43

we may obtain similar capabilities as we

04:46

have

04:46

in an ai system nobody knows yet what

04:49

will be the future of ai

04:51

will it be neural networks or do we need

04:53

to integrate machine learning with

04:55

symbolic and

04:56

logic based ai techniques the latest

04:59

is similar to the neurosymbolic learning

05:01

systems

05:02

which integrate two fundamental

05:04

phenomena of intelligent behavior

05:06

reasoning and the ability to learn from

05:09

experience

05:10

they argue that a better comprehension

05:12

of how humans have

05:14

and have evolved to obtain these

05:16

advanced capabilities

05:18

can inspire innovative ways to imbue ai

05:21

systems with these competencies

05:23

but nobody will be better placed than

05:25

lewis lamb

05:26

himself to explain this learning system

05:28

shown in his recent paper

05:30

neurosymbolic ai the third wave

05:34

what we want to do here is exactly this

05:36

convergence because one of the key

05:38

questions is to identify

05:40

the building blocks of ai and how to

05:44

make

05:44

a.i more trustworthy ai

05:48

explainable but not only explainable

05:50

interpretable as well

05:52

so in order to make ai interpretable

05:55

sound and to use the right models

05:59

right computational models so that one

06:01

can explain what's going on in ai

06:03

we need better representations we need

06:06

models that are sound

06:07

and soundness and the results that come

06:10

from logic

06:12

the correctness results and all of that

06:14

can benefit

06:15

of course the great results you are

06:17

having on deep learning so

06:19

our work corroborates this point that uh

06:22

gary marcus made and also that danny

06:25

kanema made

06:26

at tripoli i that system one i mean the

06:29

fast system one that's associated with

06:32

concepts like deep learning

06:34

certainly knows language as daniel

06:36

kahneman said and system

06:37

2 which is more reflective certainly

06:40

does involve

06:41

certain manipulation of symbols so this

06:44

analogy of system 1 and 2

06:46

leads us to build the ideas that are

06:50

the inspiration the inspiration that

06:52

gary brought in his book the algebraic

06:54

the algebraic mind and also that we

06:56

formalized in several

06:58

neural symbolic systems since the early

07:01

2000s

07:02

and some of them several of them

07:04

temporal reasoning model reasoning

07:06

reason about knowledge are formalized in

07:08

this book and

07:09

of course we have been evolving this

07:11

concept so that we one can deal with

07:13

combinatory explosion

07:14

and several other symbolic problems

07:17

within a neurosymbolic framework

07:20

and so the approach that we have been

07:22

defending over the years

07:24

is that we need a foundational approach

07:26

for neurosymbolic computing

07:28

neurosymbolic ai

07:29

that's based both on logical

07:31

formalization and we have francesca here

07:34

judy pearl that have been that have been

07:36

outstanding results on symbolic ai

07:39

and machine learning and we use logic

07:42

and knowledge representation to

07:43

represent the reasoning process

07:45

that is integrated with machine learning

07:47

systems

07:48

so that we can also effective

07:50

effectively perform

07:52

neural learning using deep learning

07:54

machinery so our approach has been

07:56

tested in training

07:58

assessment simulators by tno which is a

08:01

dutch subsidiary of the government it

08:03

has been applied in robotics and ai

08:06

and several other applications but what

08:08

we offer here

08:09

is a sound way including some formal

08:12

results

08:13

that our neurosymbolic systems in order

08:16

so that we can have more effective and

08:18

more trustful

08:19

ai we need to have models

08:23

interpretive models that are based on

08:25

sound logical models

08:27

and in this way we can explain what the

08:29

neural learning process is doing

08:32

at the same way that we can prove that

08:35

the results that we obtain via machine

08:38

learning

08:39

can even be related to the formal

08:41

results that one typically expects from

08:43

symbolic logic for instance here

08:46

in a system that we call connections

08:47

connectionist modal logic

08:49

which was by the way published in the

08:51

same issue of neuro computation that

08:54

jeff hinton published one of his

08:56

inflation paper

08:57

on deep belief nets we proved that model

09:01

and temporal logic programs

09:02

can be computed soundly in neural

09:06

network models so in this way what we

09:08

provide

09:09

in a way is a way of providing neural

09:12

networks

09:12

as a learning system which can also

09:15

learn to compute

09:16

in a deep way the evolution of knowledge

09:19

in time

09:20

and this is what we explained in several

09:23

of our papers

09:24

and also in recent work that we

09:26

published gary in

09:27

um in tripoli 2018

09:31

and now each guy 2020 where we present a

09:34

survey paper

09:36

so the the the final message here

09:39

is that there have been some

09:41

developments including

09:43

uh the ai debate the great ai debate

09:46

between banjo and

09:47

gary marcus last year which we saw also

09:50

at triple i 2020 that we need more

09:52

convergency

09:53

towards building more effective ai

09:56

systems

09:57

and ai systems that most people can

09:59

trust since ai

10:00

is becoming a lingua franca for science

10:03

these days

10:04

neurosymbolic is basically another type

10:06

of ai system that is trying to use a

10:09

well-funded knowledge representation

10:11

and reasoning it integrates neural

10:13

network-based learning

10:15

with symbolic knowledge representation

10:17

and logical reasoning

10:18

with the goal of creating an ai system

10:20

that is both interpretable

10:22

and trustworthy this is where francesca

10:25

rossi's work comes into play

10:27

with their purpose called thinking fast

10:29

and slow in ai

10:30

as the name suggests they focus on

10:32

daniel kahneman's theory regarding the

10:35

two systems

10:36

explained in his book thinking fast and

10:38

slow

10:39

an attempt to connect them into a

10:41

unified theory

10:43

with the aim to identify some of the

10:45

roots of the desired human capabilities

10:48

here is a quick extract taken from the

10:50

ai debate 2

10:51

organized by montreal ai where daniel

10:54

cantman himself clearly explained these

10:56

two systems

10:58

and their link with artificial

10:59

intelligence

11:01

i seem to be identified with the idea of

11:04

two systems system one and system two

11:06

although they're not my idea but i did

11:08

write a book

11:09

that described them and

11:12

as quite a few of you sure know

11:16

we talk about the contrast between one

11:18

system that works fast

11:20

another that works slow uh

11:23

but the main difference between system

11:25

one and system two as i described them

11:27

was that system one is something that

11:30

happens to you

11:32

you are not active the thought that

11:35

the words come to you the ideas come to

11:38

you the emotions come to you they happen

11:40

to you

11:40

you do not do them and the essential

11:43

distinction that i was drawing

11:45

between the two systems was really that

11:47

one

11:48

something that happens to you and

11:50

something that you do

11:52

high level skills in my description of

11:55

of things

11:56

were absolutely in system one anything

11:58

that they can do automatically

12:00

anything that happens associatively is

12:03

in system one

12:06

another distinction between system one

12:08

and system two

12:09

as psychologists see them in that

12:12

operation the system one tend to be

12:14

parallel

12:14

operations or system two tend to be

12:16

serial

12:18

so it's true that anything

12:21

any activity that we would describe as

12:23

non-symbolic

12:25

i think does belong to system one

12:28

that system one i think cannot be

12:30

described

12:31

as a non-symbolic system for one thing

12:35

uh it's it's much too complicated and

12:39

rich for that

12:40

it knows language for one thing

12:42

intuitive thoughts

12:44

are in language uh the most interesting

12:47

component

12:48

of system one the basic component as i

12:51

conceive of that

12:52

notion is that it holds a representation

12:54

of the world

12:56

and and the representation that actually

12:59

allows something that resembles the

13:01

simulation of the world

13:03

as i describe it we we live

13:06

with that representation of the world

13:08

and most of the time

13:10

we are in what i call the valley of the

13:12

normal

13:13

there are events that we positively

13:15

expect

13:16

there are events that surprise us but

13:19

most of what happens to us

13:21

neither surprises us nor is it expected

13:24

what i'm going to do

13:25

to say next will not surprise you but

13:28

you didn't actually expect it

13:30

so there is that model that compares

13:33

that

13:34

accepts many many

13:38

events as normal continuations of what

13:41

happens

13:41

but it rejects some and it distinguishes

13:45

what is surprising from what is normal

13:51

that's very difficult to describe in

13:54

terms of symbolic or non-symbolic

13:56

certainly what happens is a lot of

13:58

counter factual thinking

14:00

is in fact system one thinking because

14:04

surprise is something that happens

14:07

automatically

14:08

you're surprised when something that

14:10

happens

14:11

is not normal is not expected

14:14

and that forces common sense

14:17

and causality to be in system one and

14:20

not in system two

14:22

in short can man explains that humans

14:25

decisions

14:26

are guided by two main kinds of

14:28

capabilities

14:29

or systems which he referred to as

14:32

system 1 and system 2.

14:34

the first provides us tools for

14:36

intuitive fast and unconscious

14:38

decisions which could be viewed as

14:41

thinking

14:41

fast while the second system handles

14:44

complex situations

14:46

where we need rational thinking and

14:48

logic to make a decision

14:50

here viewed as thinking slow

14:53

if we come back to the thinking fast and

14:55

slow in ai paper

14:56

scratch frachanska russi and her team

14:58

argue that we can make a very loose

15:00

comparison

15:01

between these two systems one and two

15:04

and the two main lines of work in ai

15:06

which are machine learning and symbolic

15:09

logic reasoning

15:10

or rather data-driven versus knowledge

15:13

driven ai

15:14

systems where the comparison between

15:17

kanman's

15:18

system one and machine learning is that

15:20

both seem to be able to build

15:22

models from sensory data such as

15:25

seeing and reading where both system 1

15:28

and machine learning produce

15:29

possibly imprecise and biased results

15:32

indeed what we call deep learning is

15:35

actually not

15:36

deep enough to be explainable similarly

15:38

to system one

15:39

however the main difference is that

15:41

current machine learning algorithms

15:43

lack basic notions of causality and

15:46

common sense

15:47

reasoning compared to our system one

15:50

we can also see a comparison between the

15:52

system 2 and ai techniques

15:54

based on logic search optimization and

15:57

planning

15:58

techniques that are not using deep

16:00

learning rather employing explicit

16:02

knowledge

16:03

symbols and high level concepts to make

16:05

decisions

16:06

this is the similarity highlighted

16:08

between the humans decision making

16:10

system

16:11

and current artificial intelligence

16:13

systems i want to remind you that as

16:15

they state

16:16

the goal of this paper is mainly to

16:18

stimulate the ai research community to

16:20

define

16:21

try and evaluate new methodologies

16:24

frameworks and evaluation metrics in the

16:27

spirit of achieving a better

16:29

understanding

16:30

of both human and machine intelligence

16:33

they intend to do that by asking the ai

16:36

community to study

16:37

10 important questions and try to find

16:40

appropriate answers or at least

16:42

think about these questions here i will

16:44

only quickly list

16:45

these 10 important questions to be

16:47

considered in future research

16:50

but feel free to read their paper for

16:52

much more information regarding these

16:54

questions

16:54

and discuss it in the comments below so

16:57

here it goes

16:58

should we clearly identify the ai system

17:01

1 and system 2 capabilities

17:04

is the sequentiality of system 2 a bug

17:06

or a feature

17:07

should we carry it over to machines or

17:09

should we exploit parallel threads

17:12

performing system 2 reasoning will this

17:14

together with the greater computing

17:16

power of machines compared to humans

17:19

compensate the lack of other

17:20

capabilities in ai

17:23

what are the metrics to evaluate the

17:25

quality of a hybrid system 1 system 2

17:28

ai system should these matrix be

17:31

different

17:32

for different tasks and combination

17:34

approaches

17:35

how do we define ai's introspection in

17:38

terms of eye consciousness and

17:40

m consciousness how do we model the

17:43

governance of system 1 and

17:45

system 2 in an ai when do we switch or

17:48

combine them

17:49

which factors trigger the switch how can

17:53

we leverage a model based on system 1

17:55

and system 2 in ai to understand and

17:58

reason

17:58

in complex environments when we have

18:01

competing priorities

18:03

which capabilities are needed to perform

18:05

various forms of moral judging and

18:08

decision making

18:09

how do we model and deploy possibly

18:12

conflicting normative ethical theories

18:14

in ai

18:15

are the various ethics theories tied to

18:18

either system 1 or system 2

18:21

how do we know what to forget from the

18:23

input data during the abstraction step

18:26

should we keep knowledge at various

18:28

levels of abstraction

18:30

or just raw data and fully explicit

18:32

high-level knowledge

18:34

in a multi-agent view of several ai

18:37

systems communicating and learning from

18:39

each other

18:40

how to exploit adapt current results on

18:43

epistemic reasoning

18:44

and planning to build learn models of

18:47

the world and of others

18:49

and finally what architectural choices

18:52

best support the above vision of the

18:55

future of ai

18:58

feel free to discuss any of these

18:59

questions in the comments below

19:01

i would love to have your take on these

19:03

questions and debate over them

19:05

i definitely invite you to read the

19:07

thinking fast and slow in ai paper

19:10

as well as daniel canman's book thinking

19:13

fast and slow if you'd like to have more

19:15

information about this

19:17

theory if this subject interests you i

19:20

would also strongly recommend you to

19:22

follow the research of yeshua benjo

19:24

addressing consciousness priors and a

19:27

huge thanks to montreal ai for

19:29

organizing

19:29

this ai debate 2 providing a lot of

19:32

valuable information

19:33

for the ai community all the documents

19:36

discussed

19:37

in this video are linked in the

19:38

description below please

19:40

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19:42

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19:44

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clearly explained

19:52

thank you for watching

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@whatsaiLouis Bouchard

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