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Seeking the human in
human-like computing
Alan Dix
Talis and University of Birmingham
http://alandix.com/academic/papers/mi20-human-like-2016 /
Human-Like Computing Machine Intelligence (MI20-HLC)
clear intersections
Human-Like Computing
tentative EPSRC definition:
“offering the prospect of
computation which is akin to
that of humans, where
learning and making sense of
information about the world
around us can match our
human performance”
Human–Computer Interaction
studies the ways in which
people engage with
technology and the ways in
which technology can be
designed to work for and
alongside people
four topics
(iv) learning about
human cognition
(also social, ethical
consequences, etc.)
(iii) because it is a good
model to emulate
(new human-inspired algorithms)
(ii) new interaction
paradigms
(traditional focus has
human in control)
(i) for interacting
with people
(so need to understand
people!)
main goals for HLC (and HCI)
AI/ML – secondary benefits – HCI/CogSci
existing/past examples
(iv) learning about
human cognition
(iii) because it is a good
model to emulate
(ii) new interaction
paradigms
(i) for interacting
with people
1992 (!) black-box algorithms and
gender/ethnic discrimination
existing/past examples
(iv) learning about
human cognition
(iii) because it is a good
model to emulate
(ii) new interaction
paradigms
(i) for interacting
with people
cognitive and computational modelling
of regret and self
future challenges
(ii) new interaction
paradigms
(i) for interacting
with people
apply HCI knowledge,
theories and methods
for rich human
behaviour
build on existing
formal & informal
understanding of
human interaction with
active computational
partners

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Seeking the Human in Human-Like Computing

  • 1. Seeking the human in human-like computing Alan Dix Talis and University of Birmingham http://alandix.com/academic/papers/mi20-human-like-2016 / Human-Like Computing Machine Intelligence (MI20-HLC)
  • 2. clear intersections Human-Like Computing tentative EPSRC definition: “offering the prospect of computation which is akin to that of humans, where learning and making sense of information about the world around us can match our human performance” Human–Computer Interaction studies the ways in which people engage with technology and the ways in which technology can be designed to work for and alongside people
  • 3. four topics (iv) learning about human cognition (also social, ethical consequences, etc.) (iii) because it is a good model to emulate (new human-inspired algorithms) (ii) new interaction paradigms (traditional focus has human in control) (i) for interacting with people (so need to understand people!) main goals for HLC (and HCI) AI/ML – secondary benefits – HCI/CogSci
  • 4. existing/past examples (iv) learning about human cognition (iii) because it is a good model to emulate (ii) new interaction paradigms (i) for interacting with people 1992 (!) black-box algorithms and gender/ethnic discrimination
  • 5. existing/past examples (iv) learning about human cognition (iii) because it is a good model to emulate (ii) new interaction paradigms (i) for interacting with people cognitive and computational modelling of regret and self
  • 6. future challenges (ii) new interaction paradigms (i) for interacting with people apply HCI knowledge, theories and methods for rich human behaviour build on existing formal & informal understanding of human interaction with active computational partners

Editor's Notes

  1. Alan is sorry not to be here in person to speak, but he sends his best wishes and is looking forward to catching up with the outcomes. Alan’s own interest in this area stretches back more than 25 years, and this short presentation looks broadly at the connections between human-like computing and human-computer interaction illustrated by examples from Alan’s own work.
  2. Human-like computing seeks to understand and develop ways in which intelligent algorithms and systems can operate in ways closer to a human; and human–computer interaction seeks to understand the ways humans work with technology and how systems can be designed to make this better. There are clear intersections as recognised both by the EPRC human-like computing workshop earlier this year and the call for this workshop.
  3. Alan reported about the EPSRC human-like computing workshop to the HCI community and identified four main topics or goals. Two of these, to interact more appropriately with people and develop new interaction paradigms for HLC, are the core aims of HLC and HCI’s contribution to HLC. The other two, human inspired algorithms and improving our understanding of human cognitive and social processes are additional secondary benefits to the AI/ML and HCI/CogSci communities respectively.
  4. Existing work demonstrates some of the potential synergies. Very early work in applications of AI and pattern recognition in HCI (1992!) identified potential legal and ethical issues of black-box algorithms including gender and ethnic bias. However, this then led to investigating potential (partial) solutions including variants of ID3 and novel database querying techniques.
  5. More recent work informed by issues of emotion in user interfaces, looked at cognitive models of regret and consciousness of self. This has implications for algorithm design, and in the case of regret has gone full circle, with improvements to machine learning and then lessons from this for qualitative understanding of emptions.
  6. Focusing on the two core goal there are key future challenges. We need to apply the knowledge, theory and methods developed by 35 years of HCI research to help provide further the HLC agenda. However, perhaps most challenging will be to move beyond more instrumental modes of interaction design towards interaction where the computer has a more active role; this can build on existing work in areas such as virtual agents and human–robot interaction as well as more theoretical research including Alan’s work on formally modelling this kind of system.