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Tag : Algorithm AI-Cognition

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by   -   April 10, 2014

PatrickvanderSmagtGuest talk in the ShanghAI Lectures, 2010-10-21

In this guest presentation, Patrick van der Smagt talks about biomimetic approaches to robot control, kinematics, grasping, and ways to use the human body to control robots.

by   -   April 7, 2014


Lecture 9: Towards a theory of intelligence

This lecture, which I hosted at the the University Carlos III (Madrid, Spain), suggests principles and design guidelines for the development of embodied intelligent systems that are more similar to animal-like intelligence than what has been proposed so far. How to build a self-organizing embodied intelligence?

by   -   March 21, 2014


The Blind Robot by Louis-Philippe Demers is a reference to the works of Merleau-Ponty and his example of the body extension of the blind man’s cane, where the cane not only senses the world but also reveals the blind man as blind. Photo credit: Louis-Philippe Demers

Guest talk in the ShanghAI Lectures, 2010-12-16


Embodied agents have a material existence whilst audiences share this very same world and space. This lecture explores the far-reaching and often surprising implications of embodiment, both from the perspective of the audience and of the robotic agents in Robotic Arts, which empower intangible contributions from the cultural context, the suspension of disbelief and the attribution of intention towards any outside physical objects acting upon the world.

by   -   March 18, 2014

Lecture 8-II: Education and Industry Session

This lecture hosted by Prof. Samia Nefti-Meziani from the University of Salford, Manchester, UK, is about higher education and industrial impact of the ‘ShanghAI paradigm’. You should not be surprised to see early glimpses of the next industrial revolution enabled by intelligent machines in Manchester – this was the iconic example of industrial city during the very first industrial revolution.

“ITN Marie Curie Network SMART-E: Advanced Robotics for Sustainable Manufacturing in Europe” by  Samia Nefti-Meziani

by   -   March 14, 2014


The lay notion of how human communication works is, basically, that we exchange packages of information that are encoded and then decoded in our heads using language. But it just takes a little observation to see that this “computer metaphor” just doesn’t apply to the human way of communicating. We can’t reduce communication to a transfer of abstract information, and the same happens to be true of human-machine communication if we expect it to be of any use.

by   -   March 13, 2014

Tamim_AsfourGuest talk in the ShanghAI Lectures, 2010-12-16

The design of cognitive situated robots able to learn to operate in the real world and to interact and communicate with humans, must model and reflectively reason about their perceptions and actions in order to learn, act, predict and react appropriately. Such capabilities can only be attained by embodied agents through physical interaction with and exploration of the real world and requires the simultaneous consideration of perception and action. Representations built from such interactions are much better adapted to guiding behaviour than human crafted rules and allow embodied agents to gradually extend their cognitive horizon.

by and   -   March 10, 2014

My workday is over. What do I want to do now? I picture calling my wife to suggest dinner at that nice Italian restaurant and imagine the taste of gnocchi quattro formaggi. Then I remember promising to look after the grandchildren that evening. My colleague at the desk next to me has no idea of this rich world unfolding in my head. This is the world that I call my consciousness, my mental state, my mind, or my thoughts. It is comfortably private, and I cannot imagine having an intelligent life without it. My behavior and pronouncements are but the tip of the iceberg that is my consciousness. Is this consciousness something that can be replicated? In our quest to build smarter computers and robots, will we one day develop machines that have an internal mental life comparable to our own?

by   -   March 10, 2014

Lecture 8-I: Ontogenetic development: From locomotion to cognition


In this lecture, hosted at the University Carlos III of Madrid in Spain, I show how it is possible to ground ‘understanding’ on data streams coming from the physical interaction of an agent in the environment, and discuss the many open issues that remain. For example, setting information theory in an embodied framework is challenging, but the road ahead looks promising. So, what’s the link between locomotion to cognition? Is there one?

by   -   March 7, 2014


Statue of Alan Turing. Photo credit: Neil Crosby

In the last few days we’ve seen a spate of headlines like 2029: the year when robots will have the power to outsmart their makers, all occasioned by an Observer interview with Google’s newest director of engineering Ray Kurzweil.

Much as I respect Kurzweil’s achievements as an inventor, I think he is profoundly wrong.

by   -   March 6, 2014

The incoming second wave of contextual agents

RobotsEiffelTowerThere’s a virtual lobby of Intelligent Virtual Assistants (IVAs) waiting to help us these days. These multi-million dollar systems include Yahoo’s Donna, Samsung’s SAMI, Google’s Now, Nuance’s Nina, Motorola’s Assist, Microsoft’s Cortana and of course Apple’s Siri. They can give you driving directions, book a dinner table, launch an app, tell a joke, take a memo, send a text, post a tweet, ring a phone, update Facebook, check stocks, search the web, turn off the lights when you go to bed, and set an alarm to wake you up in the morning. They can do incredible things, but they’re not very valuable for one weird and very general reason.

by   -   March 6, 2014

Shuhei_MiyashitaGuest talk in the ShanghAI Lectures, 2010-12-02

Self-assembly is of crucial importance in the biological realm at all scales. This talk introduces a series of self-assembling robots developed in our project, and discusses key features that such robots are expected to possess. The robots give rise to unique insight into the interdependencies between the components’ morphology, systems’ stochasticity, and the emerged behaviors, and cast light on the design principle of self-assembling components.

by   -   March 5, 2014


Bayes’ Theorem in neon. Image source: Wikimedia Commons

The ‘system’ behind the Google robotic cars … which have driven themselves for hundreds of thousands of miles on the streets of several US states without being involved in an accident, or violating any traffic law, all the while analyzing enormous quantities of data fed to a central onboard computer from radar sensors, cameras and laser-range finders and taking the most optimal, efficient and cost effective route … is built upon the 18th-century math theorem known as Bayes’ Rule.

by   -   February 27, 2014

Serge_KernbachGuest talk in the ShanghAI Lectures, 2010-12-02

Collective systems play very important role on Earth, and we encounter them in all sizes, scales and forms; in biological and technological areas; in ocean, air and on the ground. Examples include viruses, different colloidal systems, nano- and micro-scale particles, huge world of social insects and animals; collective systems in robotics vary from nano- up to large space exploration robots. To some extent, collective systems are ubiquitous. Such a prevalence and diversity and can be explained by several unique properties: scalability, reliability, flexibility, self-developmental capabilities. This guest lecture introduces the area of collective robotics and answers the questions “what and why”. Special attention is given to reconfigurable robotics, we discuses a big vision of “universal modularity” and several ways of its achieving.

by   -   February 25, 2014


I am often asked which jobs will thrive as we move into the next phase of the robot revolution. My answer is that people will need to be multi-skilled. They will need critical thinking and design skills, they will need to be able to think statistically, and they will need a deep knowledge of human behavior.

by and   -   February 25, 2014

humanoid_thinking_thinkerWhen IBM’s Watson supercomputer triumphed over two top Jeopardy champions in February 2011, the media buzzed with talk of artificial intelligence (AI), just as it had fourteen years earlier when Watson’s predecessor, IBM’s Deep Blue, won a match with world chess champion Garry Kasparov. Bloggers, journalists, and radio hosts were asking a question as old as the field of computer science itself: When will computing machines surpass human intelligence?