Important Tools and Perspectives for the Future of AI

Similar documents
SWARM INTELLIGENCE. Mario Pavone Department of Mathematics & Computer Science University of Catania

! The architecture of the robot control system! Also maybe some aspects of its body/motors/sensors

Neuro-Fuzzy and Soft Computing: Fuzzy Sets. Chapter 1 of Neuro-Fuzzy and Soft Computing by Jang, Sun and Mizutani

What We Talk About When We Talk About AI

Swarm Intelligence. Corey Fehr Merle Good Shawn Keown Gordon Fedoriw

Robots: Tools or Toys? Some Answers from Biorobotics, Developmental and Entertainment Robotics. AI and Robots. A History of Robots in AI

Knowledge Representation and Reasoning

ES 492: SCIENCE IN THE MOVIES

Swarm Intelligence W7: Application of Machine- Learning Techniques to Automatic Control Design and Optimization

Chapter 1: Introduction to Neuro-Fuzzy (NF) and Soft Computing (SC)

This list supersedes the one published in the November 2002 issue of CR.

Our Final Invention: Artificial Intelligence and the End of the Human Era

Cognitive Science: What Is It, and How Can I Study It at RPI?

Artificial Intelligence: An overview

Curriculum Vitae. Department of Computer and Information Sciences The Norwegian University of Science and Technology (NTNU) 7034 Trondheim Norway

INFORMATION AND COMMUNICATION TECHNOLOGIES IMPROVING EFFICIENCIES WAYFINDING SWARM CREATURES EXPLORING THE 3D DYNAMIC VIRTUAL WORLDS

What is Artificial Intelligence? Alternate Definitions (Russell + Norvig) Human intelligence

How the Body Shapes the Way We Think

biologically-inspired computing lecture 20 Informatics luis rocha 2015 biologically Inspired computing INDIANA UNIVERSITY

Artificial Intelligence

Objectives. Game AI: Collaborative Diffusion. Project: The Sims. Advance from simple game to very sophisticated games

Non-Invasive Brain-Actuated Control of a Mobile Robot

Artificial Intelligence A Very Brief Overview of a Big Field

Dipartimento di Elettronica Informazione e Bioingegneria Robotics

1) Complexity, Emergence & CA (sb) 2) Fractals and L-systems (sb) 3) Multi-agent systems (vg) 4) Swarm intelligence (vg) 5) Artificial evolution (vg)

Mind upgrade. Information technologies, methods and practices for mind enhancement

COMPUTATONAL INTELLIGENCE

Instilling Morality in MachinesMultiagent Experiments. David Burke Systems Science Seminar June 3, 2011

Digital image processing vs. computer vision Higher-level anchoring

Proposers Day Workshop

Programmable self-assembly in a thousandrobot

The MARCS Institute for Brain, Behaviour and Development

Behavior-based robotics, and Evolutionary robotics

CS 378: Autonomous Intelligent Robotics. Instructor: Jivko Sinapov

CSC384 Intro to Artificial Intelligence* *The following slides are based on Fahiem Bacchus course lecture notes.

Evolutions of communication

MA/CS 109 Computer Science Lectures. Wayne Snyder Computer Science Department Boston University

The University of Algarve Informatics Laboratory

Presented by: V.Lakshana Regd. No.: Information Technology CET, Bhubaneswar

Lecture 1 What is AI?

Responsible AI & National AI Strategies

BRAIN CONTROLLED CAR FOR DISABLED USING ARTIFICIAL INTELLIGENCE

Embodiment from Engineer s Point of View

BLUE BRAIN - The name of the world s first virtual brain. That means a machine that can function as human brain.

On Intelligence Jeff Hawkins

Lecture 1 What is AI?

AIS and Swarm Intelligence : Immune-inspired Swarm Robotics

Introduction to Artificial Intelligence. Department of Electronic Engineering 2k10 Session - Artificial Intelligence

Behaviour-Based Control. IAR Lecture 5 Barbara Webb

Collective Robotics. Marcin Pilat

Introduction to AI. What is Artificial Intelligence?

What can evolution tell us about the feasibility of artificial intelligence? Carl Shulman Singularity Institute for Artificial Intelligence

Cognitive robots and emotional intelligence Cloud robotics Ethical, legal and social issues of robotic Construction robots Human activities in many

Copyright 2018, Technology Futures, Inc. 1

5a. Reactive Agents. COMP3411: Artificial Intelligence. Outline. History of Reactive Agents. Reactive Agents. History of Reactive Agents

One computer theorist s view of cognitive systems

Developing Frogger Player Intelligence Using NEAT and a Score Driven Fitness Function

A Genetic Algorithm-Based Controller for Decentralized Multi-Agent Robotic Systems

Artificial Intelligence

Outline. What is AI? A brief history of AI State of the art

A SURVEY OF SOCIALLY INTERACTIVE ROBOTS

AI MAGAZINE AMER ASSOC ARTIFICIAL INTELL UNITED STATES English ANNALS OF MATHEMATICS AND ARTIFICIAL

Behaviour Patterns Evolution on Individual and Group Level. Stanislav Slušný, Roman Neruda, Petra Vidnerová. CIMMACS 07, December 14, Tenerife

The Singularity. Elon Musk Compares Building Artificial Intelligence To Summoning The Demon

The Singularity. A technically informed, but very speculative critique of recent statements of e.g. Elon Musk, Stephen Hawking and Bill Gates

Outline. Introduction to AI. Artificial Intelligence. What is an AI? What is an AI? Agents Environments

Artificial Intelligence. What is AI?

Android (Child android)

Unit 1: Introduction to Autonomous Robotics

Biological Inspirations for Distributed Robotics. Dr. Daisy Tang

CPS331 Lecture: Agents and Robots last revised November 18, 2016

ARTIFICIAL INTELLIGENCE IN POWER SYSTEMS

CSCI 445 Laurent Itti. Group Robotics. Introduction to Robotics L. Itti & M. J. Mataric 1

THE FUTURE OF DATA AND INTELLIGENCE IN TRANSPORT

Unit 1: Introduction to Autonomous Robotics

The robots are coming, but the humans aren't leaving

An Introduction to Swarm Intelligence Issues

Welcome. PSYCHOLOGY 4145, Section 200. Cognitive Psychology. Fall Handouts Student Information Form Syllabus

Evolutionary Computation for Creativity and Intelligence. By Darwin Johnson, Alice Quintanilla, and Isabel Tweraser

THE MECA SAPIENS ARCHITECTURE

Lecture 1 What is AI? EECS 348 Intro to Artificial Intelligence Doug Downey

Human and Machine Intelligence: Implications for the future of education

PSYCO 457 Week 9: Collective Intelligence and Embodiment

Artificial Intelligence and Robotics Getting More Human

Co-evolution for Communication: An EHW Approach

Challenging the Future with Ubiquitous Distributed Control

Neuromorphic Analog VLSI

The Interstellar Church of Tomorrow. Dr Gavin Merrifield

Instructors: Prof. Takashi Hiyama (TH) Prof. Hassan Bevrani (HB) Syafaruddin, D.Eng (S) Time: Wednesday,

Visvesvaraya Technological University, Belagavi

II. ROBOT SYSTEMS ENGINEERING

Humanification Go Digital, Stay Human

The attribution problem in Cognitive Science. Thinking Meat?! Formal Systems. Formal Systems have a history

Swiss Re Institute. September 2018 Dr. Jeffrey R. Bohn

CS594, Section 30682:

KOVAN Dept. of Computer Eng. Middle East Technical University Ankara, Turkey

Synthetic Brains: Update

Artificial Intelligence: Implications for Autonomous Weapons. Stuart Russell University of California, Berkeley

Artificial Intelligence

CHAPTER TWELVE. The Artificial Intelligence (AI) Approach I: The Mind As Machine

Transcription:

Important Tools and Perspectives for the Future of AI The Norwegian University of Science and Technology (NTNU) Trondheim, Norway keithd@idi.ntnu.no April 1, 2011

Outline 1 Artificial Life 2 Cognitive Incrementalism 3 Swarm Intelligence 4 Cyborgs 5 The Singularity

Artificial Life Attempting to formalize the essence of living and life-like systems: a bio-logic. Properties of ALife Systems: Synthetic: Bottom-up, multiple interacting agents. Self-Organizing: Global structure emerges from local interactions. Self-Regulating: Distributed (non-global) control (self-maintaining, autopoietic) Adaptive Learning and/or evolving. Complex: On the edge of chaos; dissipative.

Cognitive Incrementalism Mindware (pg. 135), Andy Clark, 2001 This is the idea that you do indeed get full-blown human cognition by gradually adding bells and whistles to basic (embodied and embedded) strategies of relating to the present at hand. I am, therefore I think. Cornerstone belief of The New AI, a.k.a. Situated and Embodied AI (SEAI). A bottom-up, Alife-inspired approach to AI. Focus on (real and simulated) adaptive robots that learn (or evolve) to perform basic sensorimotor tasks with increasing degrees of cognitive demand.

Swarm Intelligence Properties of Swarms Simple agents, following simple behavioral rules, interacting to form complex structures. Driven by what often appears to be random activity, but with the accentuation of some behaviors/trends (positive feedback), and the damping of others (negative feedback). Stigmergic - agents signal one another indirectly, via their changes to the environment. Advantages 1 Flexible - easily adapts to environmental change. 2 Robust - tolerates the failure of one or many components/agents. 3 Distributed Control - no need to centralize decision making. Can rely on emergence.

Stigmergy: Emergent Structure from Indirect Signals. Pheremones from the termites rub off on the dirt balls. Positive Feedback: Pheremone concentration in middle gets higher and higher as more dirt balls are added.

Swarms

Swarm Projects at DIS Genome 10 10 00 11 01 Fitness Annular Sorting Clone Simulate Annular Sorting: Vegard Hartmann(2005) & Andre Heie Vik(2005) Templated Collective Construction: Jrgen Braseth (2007) Swarm Robotics: Jannik Berg & Camilla Karud (2011)

The Intelligence and Utility of Swarms Mistaken Genius: In emergent systems, intelligence is often in the eye of the observer (who sees the global pattern), but not in the brain of the agent, which only understands local interactions. Unfortunately, given a desired global pattern, it is very hard to reverse engineer the necessary set of local interactions. Evolutionary algorithms are very helpful here. Thus, the rules themselves emerge from an evolutionary process. Applications: animation, telecommunications, internet routing, passenger and freight scheduling, assembly-line task selection, and many more General Implications: Intelligent behavior need not involve intelligent planning and coordination. Emergence, when properly harnessed, can do sophisticated things.

Cybernetic Organism (Cyborg) Organisms with artificial devices to assist in various regulatory, perceptive or motor activities. The deeper the intrusion, the more impressive. Hearing aid -vs- cochlear implant -vs- pacemaker -vs- prosthetics wired to neural circuits. Figure: (CW from upper left) Kevin Warwick; Original cyborg image; Kate Moss (youth as cyborgs via tight ties to social media); Pentagon cyborg insect spies.

Brain-Computer Interfaces (BCI) Scalp EEG Neural Ensembles Neural Context Action 1 Ask subject to think about an activity (e.g. moving joystick left) 2 Register brain activity (EEG waves - non-invasive) or (Neural ensembles - invasive) 3 ANN training case = (brain readings, joystick motion) Sample applications (Millan, in Handbook of Brain Theory and NNs, 2003) Keyboards (3 keystrokes per minute) Artificial (prosthetic) hands Wheelchairs Computer games

Natural Born Cyborgs (Andy Clark, 2003) But why this (somewhat superficial) requirement of connecting under the skin? Humans have many forms of physical aids, some external: eyeglasses, crutches, body armor, etc. What about cognitive aids?? Clark, pg. 26 What the human brain is best at is learning to be a team player in a problem-solving field populated by an incredible variety of nonbiological props, scaffoldings, instruments and resources. In this way, ours are essentially the brains of natural-born cyborgs, ever eager to dovetail their activity to the increasingly complex technological envelopes in which they develop, mature and operate.

Cognitive Scaffolding Human brain massively parallel pattern recognition system powerful sensory processor sophisticated motor controller highly plastic but with a very small working memory (WM). Scaffolding (via paper+pencil, computing devices, etc.) makes up for WM deficit. Brain s plasticity enables fruitful coupling with the scaffolding, i.e. via symbols (which require some minimum amount of WM and attention). Solving puzzles, math problems, etc. = Stigmergic communication of brain with itself via the scaffolding.

Scaffolding and the Bootstrapping of Intelligence Body Brain Problem Solving Cognitive Scaffold Intelligence Use of Scaffolding Symbols, Language, Communication Culture Today, the world-wide web provides an unprecedented information resource, lending a huge intelligence boost to any organism that can properly interface with it.

Could AI Systems Bootstrap Intelligence? AI systems have huge working memories (and thus need no scaffolding) and interface seamlessly with the WWW, but currently trail the brain w.r.t. pattern recognition and motor control. But this could change, since computer technology is evolving a lot faster than human brains. Soon, they may be evolving together... at least until machines get tired of helping us. * Cognitive Incrementalism disputes an AI s ability to become ultra-smart simply by surfing the web.

The Singularity Vernor Vinge (1983) and Ray Kurzweil (2005) Human Intelligence Machine Intelligence + Hardware Performance Improvements AI Algorithm Improvements + Machine Intelligence + + Improved Kwg of Brain Cyborgs + Human Intelligence Humans become 2nd class intellects AI designs AI Machine Intelligence