Timo Honkela: Artificial Intelligence and Machine Learning in the Service of the Good

TIMO HONKELA: AI & ML IN THE SERVICE OF THE GOOD

2 Nov 2015

Timo Honkela

AI & ML IN THE SERVICE OF THE GOOD

[email protected]


Anticipation,prediction,time series

analysis

Some human skills being simulated

Patternrecognition,

machinevision,speech

processing

Naturallanguage

understandingand

generationSymbolic,

probabilisticand

intuitivereasoning

Problemsolving

andcreativity

Emotionalpresence

andsocialactivity


Why a new situation?

● Data, data, bid data – data-driven approaches that enable going even far beyond the understanding of a single person or even of a traditional human research community

● Machine-learning-based artificial intelligent systems have become core modules in many services provided by companies like Google, IBM, Facebook, Amazon, etc.

● The majority of the original ideas and innovations stem from university research, though (whether we consider IBM Watson, Google Translate, etc.)


AI paradigms dichotomized

● Theory-driven versusdata-driven

● Deductive versusinductive

● Logical reasoning versus statistical/ probabilistic/neural reasoning

● Modeling explicit rational thinking versus intuitive and implicit thinking and problem solving

● Symbolic representation versus pattern/vector representations

● Ontologies versusword and document vector spaces


Pattern recognition

Kurimo et al.

Speech recognition

http

://w

ww

.cis

.hut

.fi/c

is/p

roje

cts/

spe

ech

/


Pattern classification

Vuori & Laaksonen 2002

Recognition of handwritten characters


Pattern recognition

Koskela, Sjöberg & Laaksonen 2007

Features clustering


Pattern Generation

http://googleresearch.blogspot.fi/2015/06/inceptionism-going-deeper-into-neural.html


Building and using vast corpora

http://www.csc.fi/kielipankki/https://korp.csc.fi K. Lindén et al.


Classical example: Learning meaning from context:

Maps of words in Grimm fairy tales

Honkela, Pulkki & Kohonen 1995

Predatestopic

modeling


Multimodally Grounded Language Technology

A project funded by Academy of Finland2011-2014

Klaus Förfer's thesis:

https://aaltodoc.aalto.fi/handle/123456789/17857

http://cogsys.blogspot.fi/2015/10/klaus-forger-from-motion-capture-to.html


Honkela, Korhonen, Lagus & Saarinen: Five-dimensional sentiment analysis of corpora, documents and words,Proceedings of WSOM 2014

Sentiment analysis


Machine translation

http://www.aclweb.org/anthology/W/W15/W15-18.pdf#page=219

Tiedemann 2015

Aligning linguistics expressions


Timo Honkela

AI & ML IN THE SERVICE OF THE GOOD

IN THE SERVICE OF THE GOODIN THE SERVICE OF THE GOOD

2 Nov 2015


Life saving systems


Robotic cars could save one million lives each year? (*)

“Tesla CEO Elon Musk referred to each Model S owners as an “expert trainer” – meaning that each driver will train the autonomous features of the system to feed the collective network intelligence of the fleet by simply driving the electric vehicle on Autopilot.”

(*) https://en.wikipedia.org/wiki/List_of_countries_by_traffic-related_death_rate

http://electrek.co/2015/10/30/the-autopilot-is-learning-fast-model-s-owners-are-already-reporting-that-teslas-autopilot-is-self-improving/


An example:

Resolving TransferinCancer Classification

PR Adhikari & J HollménDiscovery Science, 1-8

Jaakko Hollmén's talk

New approaches to diagnosis and treatment


Towards Peaceand Prosperity


Jörg Tiedemann's talk

Building bridges overlanguage boundaries


Understandingsubjectivity and contextuality

of human knowledge

Grounded IntersubjecityConcept Analysis

Towards Within-Language TranslationTranslation


Understanding historical developmentsthrough the analysis oflarge historical corpora

difi

.kan

salli

skirj

asto

.fi

OCR problems?An AI & MLchallenge!


Understanding people andthe society through the analysis

of large social media corporasuch as Suomi24

Krista Lagus' talk


Increased understandingof complex

socio-economical systemsthrough crowdsourcing,

data analysisand simulation


InformationSociety for All


Liisa Tiittula &Mikko Kurimo' talk

Jorma Laaksonen's talk


Conclusions


● Many positive developments aretaking place and will be possible

● Negative effects and scenarios have to be taken into account, too

● Interdisciplinary theoretical and practical work on philosophical questions, ethical concerns, societal impacts (privacy, work), economics (fairness), complex themes related to humanities, etc. is necessary

● In general, I am optimistic that the world can be made to be a better place by appropriate and wise use of artificial intelligence and machine learning


P.S. Recently professor Bengt Holmström

reminded that at MIT studies inhumanities and social sciencesare obligatory for all students

The “two cultures” to work together for the best for all


Thank youfor your attention!


Discussion forum:

https://goo.gl/ixgW8c

Timo Honkela: Artificial Intelligence and Machine Learning in the Service of the Good

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