Breadcrumbs

BS DC Import ID
node:33414
BS DC Import Time
Design - Head - Display
default
Design - Head - Layout
default
Design - Head - Color
default
Sub-Menu - Display - Design
default

#MachineLearning

#ArtificialIntelligence #Cybernetics #PatternRecognition #AutonomousSystems #SelfDrivingCars #Drones #Robots 

Import ID
r17_text:215901
Admin Title
D7 Paragraph: r17_text / GPC_ID: 5658
Layout
flex-row-9-3 reverse

In computer science #ArtificialIntelligence (AI) determines the operations of intelligent agents using forms of mechanical or “formal” reasoning. AI was founded on the idea that a machine can simulate human intelligence. Alan Turing’s theory of computation suggested that it was possible to represent logical operations by modifying simple symbols such as 0 and 1. Turing assumed that reasoning can be formalized as distinctive sequences of mechanical operations based on cause and effect – in other words, discrete sequences of logical steps based on a set of rules (#Algorithm, see key area #Encoding). [1] What came to be known as the classical symbolic approach to AI considers machine cognition as rule-governed manipulation of formal symbols with a centralized control mechanism. It was the attempt to code knowledge about the world in formal mathematical language. This approach was successful for so-called expert systems, which were able to carry out complex tasks, such as medical diagnosis, or planning and configuration at the level of human experts. However, they proved difficult to program since one simple error sometimes caused the whole system to fail. But most importantly the systems were not able to inherently learn. [2] By 1980 the approach was no longer pursued as it became clear that a mere simulation of thought does not amount to real understanding; therefore, that syntactic manipulation of symbols does not suffice for cognition. [3]

Import ID
r17_text:215906
Admin Title
D7 Paragraph: r17_text / GPC_ID: 5663
Layout
flex-row-9-3 reverse

A more flexible and adaptive approach to machine cognition came from the field of neuroscience and #Cybernetics, where artificial intelligence was not treated in terms of rules and representations but as dynamic systems. Warren S. McCulloch and Walter Pitts’ ground-breaking research was the first work that treated the brain as a computational apparatus. [4] Together with Donald O. Hebb’s work on associative learning deriving from the firing of nodes that produce synaptic interrelations, [5] Frank Rosenblatt developed the foundation for machine learning. [6] #MachineLearning is a field of AI that explores forms of computation which allow programs to change and adjust its internal parameters automatically, that is, without hand engineering the algorithms, in order to process data. The algorithmic structure is constituted as an artificial neural network, whose reasoning is executed by thousands of neurons, arranged into hundreds of intricately interconnected layers breaking up causal relations. Neural computation is based on modelling an adaptive system that evolves through the capturing of environmental data, which is fed back into the system. [7] Crucially, the networks’ output constitutes an approximation, a statistical likelihood for the most probable outcome.

Since 2006, machine learning has made huge leaps forward as a consequence of a steady increase in computational power coupled with the vast expansion of data capturing mechanisms and the enlargement of the physical IT infrastructure. [8] In its practical application machine learning algorithms are heavily employed for #PatternRecognition; visual object recognition and object detection particularly relevant for #AutonomousSystems such as #SelfDrivingCars, #Drones, and #Robots. In essence, machine learning reconstitutes what thinking means and raises many ethical and legal questions with regard to automated decision-making, machine bias, liability, and accountability.

Yasemin Keskintepe

 

[1] See Alan Turing, »On Computable Numbers, with an Application to the Entscheidungsproblem,« in: »Proceedings of the London Mathematical Society,« ser. 2, vol. 42, no. 1, January 1937, pp. 230–265.

[2] See David Davenport, »The Two (Computational) Faces of AI,« in: »Philosophy and Theory of Artificial Intelligence,« ed. Vincent C. Müller, Studies in Applied Philosophy, Epistemology and Rational Ethics vol. 5, Springer, Heidelberg, 2013, pp. 43–58, here p. 44.

[3] See John R. Searle, »Minds, Brains, and Programs,« in: »Behavioral and Brain Sciences,« vol. 3, no. 3, September 1980, pp. 417–424.

[4] Warren S. McCulloch and Walter Pitts, »A Logical Calculus of the Ideas Immanent in Nervous Activity,« in: »Bulletin of Mathematical Biophysics, vol. 5, no. 4, December 1943, pp. 115–133.

[5] Donald O. Hebb, »The Organization of Behavior: A Neuropsychological Theory,« Wiley, New York, Chapman and Hall, London, 1949.

[6] Frank Rosenblatt, »The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain,« in: »Psychological Review,« vol. 65, no. 6, 1958, pp. 386–408.

[7] See Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, »Deep Learning,« in: »Nature,« vol. 521, May 2015, pp. 436–444.

[8] See Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh, »A Fast Learning Algorithm for Deep Belief Nets,« in: »Neural Computation,« vol. 18, no. 7, July 2006, pp. 1527–1554.

Import ID
r17_list:215918
Admin Title
D7 Paragraph: r17_list / GPC_ID: 24022
Display
list-plain
Layout
undefined
Import ID
r17_list:215918:gpc_content.field_gpc_cntnr_hd_rl_gpc_cntnt
Admin Title
D7 Paragraph: r17_list / GPC_ID: 24009
Display
default
Layout
default-12

Articles

  1. Display
    default
    Layout
    default-12
  2. Display
    default
    Layout
    default-12

    William Ross Ashby

  3. Display
    default
    Layout
    default-12

    Warren S. McCulloch, Walter Pitts

  4. Display
    default
    Layout
    default-12
  5. Display
    default
    Layout
    default-12
  6. Display
    default
    Layout
    default-12

    Geoffrey E. Hinton, Simon Osinder

  7. Display
    default
    Layout
    default-12
  8. Display
    default
    Layout
    default-12

    Yann LeCunn et al.

  9. Display
    default
    Layout
    default-12

    Ray Kurzweil

  10. Display
    default
    Layout
    default-12

    (A History of Computer Chess)

  11. Display
    default
    Layout
    default-12

    Vincent C. Müller, Nick Bostrom

Import ID
r17_list:216314
Admin Title
D7 Paragraph: r17_list / GPC_ID: 24025
Display
list-plain
Layout
undefined
Import ID
r17_list:216314:gpc_content.field_gpc_cntnr_hd_rl_gpc_cntnt
Admin Title
D7 Paragraph: r17_list / GPC_ID: 24023
Display
default
Layout
default-12

Videos

Import ID
r17_media:215919
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5664
Display
media
Layout
flex-row-6-6

Google DeepMind's Deep Q-learning playing Atari Breakout

1:42 min.

Import ID
r17_media:215920
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5665
Display
media
Layout
flex-row-6-6

What is Machine Learning?

5:22 min.

Import ID
r17_media:215921
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5666
Display
media
Layout
flex-row-6-6

The Rise of Artificial Intelligence through Deep Learning | Yoshua Bengio | TEDxMontreal

17:53 min.

Import ID
r17_media:215922
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5667
Display
media
Layout
flex-row-6-6

The AI Race – Documentary ABC TV

53:49 min.

Import ID
r17_media:215923
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5668
Display
media
Layout
flex-row-6-6

How To Create A Mind: Ray Kurzweil at TEDxSiliconAlley

16:50 min.

Import ID
r17_media:215924
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5669
Display
media
Layout
flex-row-6-6

How To Create A Mind: Ray Kurzweil at TEDxSiliconAlley

21:39 min.

Import ID
r17_media:215925
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5670
Display
media
Layout
flex-row-6-6

Watson and the Jeopardy! Challenge

3:45 min.

Import ID
r17_media:215926
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5671
Display
media
Layout
flex-row-6-6

Timo Honkela: A history of machine learning and neural networks research

20:11 min.

Import ID
r17_media:215927
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5672
Display
media
Layout
flex-row-6-6

Artificial Intelligence, the History and Future – with Chris Bishop

61:21 min.

Import ID
r17_media:215928
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5673
Display
media
Layout
flex-row-6-6

Machine learning & art – Google I/O 2016

42:35 min.

Import ID
r17_media:215929
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5674
Display
media
Layout
flex-row-6-6

Past, Present and Future of AI / Machine Learning (Google I/O '17)

44:32 min.

Import ID
r17_media:215930
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5675
Display
media
Layout
flex-row-6-6

IBM: Games, A.I. & the Future of Cognitive Computing

3:32 min.

Import ID
r17_media:215931
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5676
Display
media
Layout
flex-row-6-6

Achieving Immortality by 2045 with AI and Biotech, Google Director Ray Kurzweil

52:24 min.

Import ID
r17_media:215933
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5677
Display
media
Layout
flex-row-6-6

Science Documentary: Creating Brain Systems, Quantum Computing, Quantum mechanics and Consciousness

129:29 min.

Import ID
r17_media:215934
Admin Title
D7 Paragraph: r17_media / GPC_ID: 5678
Display
media
Layout
flex-row-6-6

Deep Learning: Intelligence from Big Data

84:16 min.

Footer

ZKM | Center for Art and Media

Lorenzstraße 19
76135 Karlsruhe

+49 (0) 721 - 8100 - 1200
info@zkm.de

Organization

Dialog