Panel discussion on the Future of AI at the Flemish Academy of Sciences and Arts

Frank van Harmelen

Vrije Universiteit Amsterdam

19 September 2017Creative Commons License

allowed to share amp remix

but must attribute amp non-commercial

On the nature of AI and on the relation

between symbolic and statistical AI

What is AI

bull Wrong

ldquoAI is defined as intelligence exhibited by an articifial entity (usually a computer) Research in AI is concerned with producing useful machines to automate (difficult) human tasks requiring intelligent behaviourrdquo (Wikipedia early edition)

bull Better AI is concerned with understanding the structure amp behaviourof intelligent agents (most easily studied in artificial agents)

bull AI = Advanced Informatics ldquoAI is the branch of Computer Science dealing with computationally unsolvable problemsrdquo (AI textbook)

ldquoDefinition of intelligencerdquobull carry out complex reasoning

(solve physics problems prove theorems)

bull draw plausible inferences (diagnose cars solve a murder mystery)

bull use natural language (read stories and answer questions about them carry out extended conversation)

bull solving novel complex problems (generating plans designing artifacts)

bull social activities that require a theory of mind

We do not mean that people can recognize familiar objects or execute motor skills or navigate around spaceabilities we share with dogs and cats (and fish)

But isnrsquot modern AI all about Machine Learning Two main lines of development in AI

bull symbolic representations

bull statistical representation

There have been alternativing cycles of one dominating over the other in different decades of the history of AI

Statistical vs symbolic AIvery different types of applications

statisticalbull pattern recognition (images sound shapes)

bull motor skills (robots)

bull speech generation (sound)

bull search engines

symbolicbull planning (autonomous space missions)

bull reasoning (diagnosis design decision support)

bull language generation (conversations)

bull search engines

Statistical vs symbolic AIdifferent parts of our brains

perception and recognition of auditory stimuli memory and speech

visual processing

movement orientation recognitionperception

reasoning planning parts of speechproblem solving

NEW

OLD

Statistical vs symbolic AIvery different strengths amp weaknessesbull statistical approaches get better with more data

symbolic approaches get worse

bull Statistical approaches need lots of datasymbolic approaches already work with little data

bull symbolic approaches are explainablestatistical approaches are not

bull statistical approaches are good with uncertaintysymbolic approaches are brittle

bull symbolic approaches have a good meta-theorystatistical approaches donrsquot

helliphellip

Semantic weba revolution in symbolic KR Two simple tricks()

1 Agree on a basic language for representing symbolic knowledge

2 Use web-technology to build distributed interlinked knowledge bases

This has allowed KBrsquos to scale (108- 109 statements)

() Notice that these are same two tricks that allowed the WWW to scale

Semantic weba revolution in symbolic KR

ldquoThis allows KBrsquos to scale (108- 109 statements)rdquo

This forces to abandon many simplifying assumptions

Semantic Web Kbrsquos arebull VERY largebull Heterogeneousbull Dynamicbull Inconsistentbull hellip

All this allows (amp requires) ML techniques to apply

ldquoDefinition of intelligencerdquobull carry out complex reasoning

(solve physics problems prove theorems)

bull draw plausible inferences (diagnose cars solve a murder mystery)

bull use natural language (read stories and answer questions about them carry out extended conversation)

bull solving novel complex problems (generating plans designing artifacts)

bull social activities that require a theory of mind

We do not mean that people can recognize familiar objects or execute motor skills or navigate around spaceabilities we share with dogs and cats (and fish)

But isnrsquot modern AI all about Machine Learning Two main lines of development in AI

bull symbolic representations

bull statistical representation

There have been alternativing cycles of one dominating over the other in different decades of the history of AI

Statistical vs symbolic AIvery different types of applications

statisticalbull pattern recognition (images sound shapes)

bull motor skills (robots)

bull speech generation (sound)

bull search engines

symbolicbull planning (autonomous space missions)

bull reasoning (diagnosis design decision support)

bull language generation (conversations)

bull search engines

Statistical vs symbolic AIdifferent parts of our brains

perception and recognition of auditory stimuli memory and speech

visual processing

movement orientation recognitionperception

reasoning planning parts of speechproblem solving

NEW

OLD

Statistical vs symbolic AIvery different strengths amp weaknessesbull statistical approaches get better with more data

symbolic approaches get worse

bull Statistical approaches need lots of datasymbolic approaches already work with little data

bull symbolic approaches are explainablestatistical approaches are not

bull statistical approaches are good with uncertaintysymbolic approaches are brittle

bull symbolic approaches have a good meta-theorystatistical approaches donrsquot

helliphellip

Semantic weba revolution in symbolic KR Two simple tricks()

1 Agree on a basic language for representing symbolic knowledge

2 Use web-technology to build distributed interlinked knowledge bases

This has allowed KBrsquos to scale (108- 109 statements)

() Notice that these are same two tricks that allowed the WWW to scale

Semantic weba revolution in symbolic KR

ldquoThis allows KBrsquos to scale (108- 109 statements)rdquo

This forces to abandon many simplifying assumptions

Semantic Web Kbrsquos arebull VERY largebull Heterogeneousbull Dynamicbull Inconsistentbull hellip

All this allows (amp requires) ML techniques to apply

But isnrsquot modern AI all about Machine Learning Two main lines of development in AI

bull symbolic representations

bull statistical representation

There have been alternativing cycles of one dominating over the other in different decades of the history of AI

Statistical vs symbolic AIvery different types of applications

statisticalbull pattern recognition (images sound shapes)

bull motor skills (robots)

bull speech generation (sound)

bull search engines

symbolicbull planning (autonomous space missions)

bull reasoning (diagnosis design decision support)

bull language generation (conversations)

bull search engines

Statistical vs symbolic AIdifferent parts of our brains

perception and recognition of auditory stimuli memory and speech

visual processing

movement orientation recognitionperception

reasoning planning parts of speechproblem solving

NEW

OLD

Statistical vs symbolic AIvery different strengths amp weaknessesbull statistical approaches get better with more data

symbolic approaches get worse

bull Statistical approaches need lots of datasymbolic approaches already work with little data

bull symbolic approaches are explainablestatistical approaches are not

bull statistical approaches are good with uncertaintysymbolic approaches are brittle

bull symbolic approaches have a good meta-theorystatistical approaches donrsquot

helliphellip

Semantic weba revolution in symbolic KR Two simple tricks()

1 Agree on a basic language for representing symbolic knowledge

2 Use web-technology to build distributed interlinked knowledge bases

This has allowed KBrsquos to scale (108- 109 statements)

() Notice that these are same two tricks that allowed the WWW to scale

Semantic weba revolution in symbolic KR

ldquoThis allows KBrsquos to scale (108- 109 statements)rdquo

This forces to abandon many simplifying assumptions

Semantic Web Kbrsquos arebull VERY largebull Heterogeneousbull Dynamicbull Inconsistentbull hellip

All this allows (amp requires) ML techniques to apply

Statistical vs symbolic AIvery different types of applications

statisticalbull pattern recognition (images sound shapes)

bull motor skills (robots)

bull speech generation (sound)

bull search engines

symbolicbull planning (autonomous space missions)

bull reasoning (diagnosis design decision support)

bull language generation (conversations)

bull search engines

Statistical vs symbolic AIdifferent parts of our brains

perception and recognition of auditory stimuli memory and speech

visual processing

movement orientation recognitionperception

reasoning planning parts of speechproblem solving

NEW

OLD

Statistical vs symbolic AIvery different strengths amp weaknessesbull statistical approaches get better with more data

symbolic approaches get worse

bull Statistical approaches need lots of datasymbolic approaches already work with little data

bull symbolic approaches are explainablestatistical approaches are not

bull statistical approaches are good with uncertaintysymbolic approaches are brittle

bull symbolic approaches have a good meta-theorystatistical approaches donrsquot

helliphellip

Semantic weba revolution in symbolic KR Two simple tricks()

1 Agree on a basic language for representing symbolic knowledge

2 Use web-technology to build distributed interlinked knowledge bases

This has allowed KBrsquos to scale (108- 109 statements)

() Notice that these are same two tricks that allowed the WWW to scale

Semantic weba revolution in symbolic KR

ldquoThis allows KBrsquos to scale (108- 109 statements)rdquo

This forces to abandon many simplifying assumptions

Semantic Web Kbrsquos arebull VERY largebull Heterogeneousbull Dynamicbull Inconsistentbull hellip

All this allows (amp requires) ML techniques to apply

Statistical vs symbolic AIdifferent parts of our brains

perception and recognition of auditory stimuli memory and speech

visual processing

movement orientation recognitionperception

reasoning planning parts of speechproblem solving

NEW

OLD

Statistical vs symbolic AIvery different strengths amp weaknessesbull statistical approaches get better with more data

symbolic approaches get worse

bull Statistical approaches need lots of datasymbolic approaches already work with little data

bull symbolic approaches are explainablestatistical approaches are not

bull statistical approaches are good with uncertaintysymbolic approaches are brittle

bull symbolic approaches have a good meta-theorystatistical approaches donrsquot

helliphellip

Semantic weba revolution in symbolic KR Two simple tricks()

1 Agree on a basic language for representing symbolic knowledge

2 Use web-technology to build distributed interlinked knowledge bases

This has allowed KBrsquos to scale (108- 109 statements)

() Notice that these are same two tricks that allowed the WWW to scale

Semantic weba revolution in symbolic KR

ldquoThis allows KBrsquos to scale (108- 109 statements)rdquo

This forces to abandon many simplifying assumptions

Semantic Web Kbrsquos arebull VERY largebull Heterogeneousbull Dynamicbull Inconsistentbull hellip

All this allows (amp requires) ML techniques to apply

Statistical vs symbolic AIvery different strengths amp weaknessesbull statistical approaches get better with more data

symbolic approaches get worse

bull Statistical approaches need lots of datasymbolic approaches already work with little data

bull symbolic approaches are explainablestatistical approaches are not

bull statistical approaches are good with uncertaintysymbolic approaches are brittle

bull symbolic approaches have a good meta-theorystatistical approaches donrsquot

helliphellip

Semantic weba revolution in symbolic KR Two simple tricks()

1 Agree on a basic language for representing symbolic knowledge

2 Use web-technology to build distributed interlinked knowledge bases

This has allowed KBrsquos to scale (108- 109 statements)

() Notice that these are same two tricks that allowed the WWW to scale

Semantic weba revolution in symbolic KR

ldquoThis allows KBrsquos to scale (108- 109 statements)rdquo

This forces to abandon many simplifying assumptions

Semantic Web Kbrsquos arebull VERY largebull Heterogeneousbull Dynamicbull Inconsistentbull hellip

All this allows (amp requires) ML techniques to apply

Semantic weba revolution in symbolic KR Two simple tricks()

1 Agree on a basic language for representing symbolic knowledge

2 Use web-technology to build distributed interlinked knowledge bases

This has allowed KBrsquos to scale (108- 109 statements)

() Notice that these are same two tricks that allowed the WWW to scale

Semantic weba revolution in symbolic KR

ldquoThis allows KBrsquos to scale (108- 109 statements)rdquo

This forces to abandon many simplifying assumptions

Semantic Web Kbrsquos arebull VERY largebull Heterogeneousbull Dynamicbull Inconsistentbull hellip

All this allows (amp requires) ML techniques to apply

Semantic weba revolution in symbolic KR

ldquoThis allows KBrsquos to scale (108- 109 statements)rdquo

This forces to abandon many simplifying assumptions

Semantic Web Kbrsquos arebull VERY largebull Heterogeneousbull Dynamicbull Inconsistentbull hellip

All this allows (amp requires) ML techniques to apply