Discuss different types of artiﬁcial intelligence (AI)
Human-level artiﬁcial general intelligence (AGI)
Human cognitive architecture as a basis for AGI
Introduce a new cognitive robotics project
Allay your fears of a possible AI invasion and/or enslavement
by super-intelligent AI systems in the foreseeable future
Enthuse you about the prospect of human-level AI
To entertain you with amusing and informative videos
Some recent headlines. . .
Artiﬁcial intelligence: We’re like children playing with a
bomb. Sentient machines are a greater threat to humanity
than climate change... (Observer. June, 2016)
Will democracy survive big data and artiﬁcial
intelligence? (Scientiﬁc American. Feb, 2017)
The AI threat isn’t SkyNet, it’s the end of the middle class
(Wired Business. Feb, 2017)
Stephen Hawking, Elon Musk, and Bill Gates warn about
artiﬁcial intelligence (Observer. Aug, 2015)
DESTROYING OURSELVES: Artiﬁcial intelligence poses
THREAT to humanity – shock report (Express. Jan, 2017)
Are intelligent machines really upon us?
It depends what you mean by “machine intelligence”
Three types of AI:
Artiﬁcial Narrow Intelligence (ANI). Machine intelligence
that equals or exceeds human intelligence or efﬁciency, but
only in one speciﬁc area.
Artiﬁcial General Intelligence (AGI). Machines with general
human intelligence, capable of sustaining long-term goals and
intent, which can successfully perform any task that a human
Artiﬁcial Super-intelligence (ASI). Machine intelligence that
is much smarter than the best human brains in practically
every ﬁeld, including scientiﬁc creativity, general wisdom and
social skills (Bostrom, 2014).
The importance of distinguishing AI
The three types of AI are often conﬂated or confused in the
press and public mind.
All of the recent developments are in Narrow AI due to:
Faster hardware (GPUs)
Huge amounts of data
New statistical machine learning methods (e.g., deep learning
in neural networks)
Main machine learning methods
Supervised learning (classiﬁcation and regression)
Unsupervised learning (clustering)
Reinforcement learning (optimisation via reward signal)
Rapid conversion of unstructured information into useful and
Narrow AI will provide tools to automate and optimise this
process in science, medicine, business etc.
Elimination of jobs (mainly white-collar knowledge work).
Concentrations of knowledge, power and resources may
‘Black box’ systems where humans don’t know why AI
systems are making decisions
Require extensive training
Each system is speciﬁc to the data set it’s trained on. One
slight change requires retraining
Artiﬁcial General Intelligence
“Machines with general human intelligence, capable of sustaining
long-term goals and intent, which can successfully perform any
task that a human might do”.
More interesting for cognitive science
Much more complex and harder to achieve than narrow AI
Some recent examples from Hollywood. . .
Ex Machina (2014) Robot and Frank (2012) Prometheus (2012)
Criteria for advanced autonomous agents
US NSF $16.5M Smart and Autonomous Systems (2016)
Speciﬁed ﬁve desired capabilities:
Cognisant. Exhibit high-level cognition and awareness
beyond primitive actions.
Taskable. Users can specify desired behaviours and
outcomes in a natural and concise (possibly vague) manner
Reﬂective. Can adapt behaviour and learn new behaviour
from experience, observation, and interaction with others.
Ethical. Adhere to a system of societal and legal rules. Do
not violate accepted ethical norms.
Knowledge-rich. Semantic, probabilistic and commonsense
reasoning and meta-reasoning about uncertain, dynamic
environments. Able to acquire and understand knowledge.
Advanced autonomous agents
Long-term autonomy (able to operate unaided over time)
Flexible assistants and agents in applications such as
manufacturing, military, agriculture, health, space etc.
Robots that can be trained for new task using natural
language rather than having to be re-programmed.
Robots that can perform complex task planning without
NASA Mars Exploration Rover NASA Valkyrie robot
A key human skill – knowledge transfer
Originated in 1950s but active research programme in 1980s.
Cognitive science – differs from mainstream “narrow” AI and
traditional “divide and conquer” approach of experimental
Theories of the core, immutable structures and processes of
the human cognitive system.
Integration of multiple components of cognition.
Human cognitive architecture
Example 1 – Interactive task learning
Rosie (Univ. Michigan). Soar agent able to learn new tasks
speciﬁed in natural language and interactive demonstration.
Complex skill integrating natural language processing, vision,
logical reasoning, search and motor control.
V1: Learns several different games and puzzles through
natural language descriptions of the legal actions and goals of
V2. Learns new goals from single examples, either by being
shown a visual example of the goal or from executing a series
of actions to achieve the goal.
Agent’s hypotheses are reﬁned and corrected by language
input from human instructor.
Video: Simple task learning
Video: Tower of Hanoi puzzle
Example 2 – Mind reading and mental simulation
Cognitive architecture installed on a MDS (Mobile / Dexterous
/ Social) robot
Aim: AA that can construct accurate mental representations
of other agents’ mental states and perspectives in order to
reason about their beliefs and intentions. In order to:
Coordinate actions with teammates effectively
Anticipate teammates’ (potentially ambiguous) actions
Understand shared team goals of the team
Communicate more naturally.
Current tasks include gaze following, hide and seek, task
interruption and resumption, theory of mind.
Agent creates mental model of teammates’ beliefs and
(explicitly stated) goals and predicts what they will do next.
Video: Gaze and gesture interpretation
The ‘robot with a human mind’ project
A ﬂexible research robot
Video: NAO robot
Video: Robot football
The ‘robot with a human mind’ project
ACT-R cognitive architecture
Aims of the project
Embodied cognition constrained by perceptuo-motor
Goal directed behaviour. Including self generated goals.
Cognitive control and resource management over different
time scales (including long-term).
High-level cognition. Planning, reasoning, problem solving,
decision making, reﬂection and metacognition, analogy.
Verbal and nonverbal communication. Interaction and
adaptation through natural language understanding (speech,
Constant adaptation and learning. Human scale (i.e.,
relatively fast and not requiring thousands of training trials).
Integration of cognitive functions
Take home messages
Types of AI
Narrow AI is going to change the world radically but is limited
AGI is a much more challenging problem and is a long way off
Super-intelligent AI is a very long way off
Requires the integration of many aspects of intelligence
(learning, reasoning, planning, self-monitoring etc.)
Will require insights from cognitive science as human
cognition is currently our best example of AGI
Cognitive architectures are a potentially very useful approach
to developing systems with AGI
Talk for the 2017 University of Huddersfield 'Engage' research festival