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Bioinspired Computing Overview & Biased History Lecture 1:

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2 Bioinspired Computing Overview & Biased History Lecture 1:

3 2 Lecturing Staff Tony Cohn –Topics 1,2, 5 David Hogg –Topic 3 Eric Silverman –Topic 4 Slides based on last years –Netta Cohen, Seth Bullock and guest lecturers –Slides will be handed out –All slides currently available, but these will be updated from last years version as the weeks progress (so dont assume things will be exactly the same as last year!)

4 3 What is this module all about? Biological computation Artificial Intelligence Bio-inspired computing Examples:

5 4 The First Computer Charles Babbage (1791-1871): Inventor of difference engine – recognised as direct ancestor of the modern computer. First (non-biological) digital machine. - is it bioinspired?

6 5 Bioinspired programming Some of the best bioinspired applications to computing are the ones we would never associate with biology. Perhaps the most successful and pervasive bioinspired application is…

7 6 Alan Kay by Scott Gasch While studying at the University of Utah he learned about the innovative Sketchpad program developed by Ivan Sutherland and began programming in Simula. Borrowing ideas from this and other systems, as well as from his background in biology and mathematics, he formulated his "biological and algebraic analogy. Kay postulated that the ideal computer would function like a living organism; each "cell" would behave in accord with others to accomplish an end goal but would also be able to function autonomously. Cells could also regroup themselves in order to attack another problem or handle another function.

8 7 How it works Autonomous cells Messages (data, sender & receiver addresses) Operations (contained in message) Cell differentiation (context-dependent functionality ) What good is this? Building programs the way civil engineers design buildings. programmers can create objects to mimic generic conceptual building blocks: No need for a new language with each application. Inspiration & technical basis for the MacIntosh OS and subsequent windowing based systems (NextStep, Microsoft Windows 3.1/95/98/NT, X-Windows, Motif, etc.).

9 8 AI programming Bioinspired programming stands in stark contrast to familiar AI programming languages. In 1959, John McCarthy suggested a programming language with common sense. Lisp (List Processing): logical operations represented as manipulations of lists. Even functions and procedures are defined as lists. McCarthys goal in designing Lisp was - and still is - to make a machine that would be as intelligent as a human. Common sense: the ability to deduce for ones self a sufficiently wide class of consequences based on available information.

10 9 Good solutions benefit from appropriate representations. Good solutions rely on appropriate heuristics (rules of thumb). Principles of AI Cast your mind back to AI22: Introduction to AI… These principles date from AIs earliest beginnings… The Symbolic Search Hypothesis: A physical symbol system exercises its intelligence in problem solving by search – that is, by generating and progressively modifying symbol structures until it produces a solution structure.

11 10 The Birth of AI In the 1940s Alan Turing was already speculating on… Artificial Intelligence is an attempt to simulate reasoning as: abstract, formal, disembodied, symbol manipulation. 1.…the possibility of general computer intelligence – abstract games: good initial tasks requiring little contact with the outside world… 2.…the potential for a computer chess player – search algorithms to used to find good moves… 3.…a way of deciding whether a computer was intelligent – the Turing Test is a totally disembodied interrogation (but a somewhat situated one)… Note that Turing was also interested in BiC – The chemical basis of morphogenesis (52)

12 11 Intelligence w/out Reason Rodney Brooks (Intelligence Without Reason): Critic of the AI approach & strong proponent of embodiment and situatedness in bioinspired computing (BIC). AI, he claims, followed the abstract route due to technological gaps in the 40s & 50s. Today (1991), he says, its time to move on. Brooks recognised that life-like systems are often intelligent to some degree, yet reasoning is primarily considered to be a human attribute. Rather than modelling complicated human behaviour, why not start simple?

13 12 There are a number of key aspects characterizing this style of work [Situatedness] The robots are situated in the world - they do not deal with abstract descriptions but with the here and now of the world directly influencing the behavior of the system. [Embodiment] The robots have bodies and experience the world directly their actions are part of a dynamic with the world and have immediate feedback on their own sensations. [Intelligence] They are observed to be intelligent - but the source of intelligence is not limited to just the computational engine. It also comes from the situation in the world, the signal transformations within the sensors, and the physical coupling of the robot with the world. [Emergence] The intelligence of the system emerges from the system's interactions with the world and from sometimes indirect interactions between its components - it is sometimes hard to point to one event or place within the system and say that is why some external action was manifested. (Brooks 91)

14 13 Why Not The Whole Iguana? (Dennett 78) Traditional computing is task-oriented (vertical). To survive, animals have to be good across the board! Ant Cricket Iguana Human WalkingSexMemoryChess Horizontal Vertical Rather than build parts of human intelligence, why not build an entire much simpler intelligence?

15 14 AI vs BIC Overlap between AI and BIC is considerable, but there are also distinctions. BIC might be the follower of AI. However, it could also be regarded as a rebel. building life-like systems (utilising cartoon models of biological systems, tasks & concepts). using them to improve our understanding of biology BIC building (esp. human-like) intelligent machines with common sense & decision making that naively belies deductive logic. using them to improve our understanding of human intelligence. AI

16 15 Timetabling This module has two lecture slots Mondays 12:00 – 13:00 Roger Stevens LT24 Thursdays 15:00 – 16:00 Roger Stevens LT06 Demonstration slot allocated Mondays 13:00 – 14:00 Roger Stevens LT03 Demonstration sessions will only be given some weeks - notified in advance in lecture and/or newsgroup. WATCH THE MODULE NEWSGROUP FOR UPDATES

17 16 SHRDLU By 1972, Terry Winograds invented a simulated robot arm surrounded by blocks of various shapes and colours on a table. SHRDLU could… In Winograds words, it was a computer program which understands language in a limited domain. SHRDLUs algorithms carried out model building, search, planning and logical deduction. understand natural language instructions ask questions to disambiguate these instructions act & reason in a simulated blocksworld environment answer questions about the world & its own reasoning See for example:

18 17 A Conversation with SHRDLU (adapted from Dreyfus 1979 What Computers Cant Do) > Pick up a big red block. OKAY. > Grasp the pyramid. I DONT KNOW WHICH PYRAMID YOU MEAN. > What does the box contain? THE BLUE PYRAMID AND THE BLUE BLOCK. > I own blocks which are not red, but I dont own anything which supports a pyramid. I UNDERSTAND. > Do I own the box? NO.

19 18 Is that really how our brain works? John Von Neumann, father of modern high-speed computers also thought about neuro-computation and tried, for the first time, to construct a meaningful comparison between brain and computer power. Von Neumann argued that the brain must employ digital computation. Figuring in the number of neurons, connections, and estimates of computational speed and statistical noise in the brain, he then concluded that the brain could not be explained by logic alone. In fact, he apparently postulated (and began writing) an alternative theory but died soon after.

20 19 The Language of the Brain is Not the Language of Mathematics … whatever language the central nervous system is using, it is characterized by less logical and arithmetical depth than what we are normally used to … Consequently, there exist here different logical structures from the ones we are ordinarily used to... … whatever the system is, it cannot fail to differ considerably from what we consciously and explicitly consider as mathematics. (John Von Neumann, The Computer and the Brain, 1958.) The manuscript (published post mortem) ends as follows: Von Neumann challenged the validity of the underlying conceptualizations we use to study the brain and compare it with computers. Yet, what is surprising, given the great esteem for John Von Neumann, is that no one has taken up on his argument and fully developed its consequences in the mind versus artificial intelligence arguments that had been waging the last few years. In a recent commentary, Harold Morowitz writes:

21 20 Chess vs. Football Chess Discrete Full Information Single Opponents Turn Taking Limited Options per Turn Intellectual, disembodied Optimal Strategy Exists? Demands General Intelligence? Formal, Analytical, Symbolic Can the problems faced by footballers be solved through symbol processing and heuristic search? Commentary v. playing? Football Continuous Partial Information Heterogeneous Teams Continuous Confrontation Unlimited Options Physical, embodied No Optimal Strategy? Demands Specialist Skills? Dynamic, Physical, Reactive

22 21 Autonomous Mobile Robots 1990s: roboticists turn to building simple, robust, insect- like robots geared towards performing tasks that belie their mediocre brains. Brooks autonomous mobots embody the new philosophy: – embedded, embodied, and unencumbered by intellect – fast, cheap, and out of control Recent developments

23 22 Take home message... Biological computation Artificial Intelligence Bio-inspired computing or What is BIC and what does it want to achieve?

24 23 Additional Papers Intelligence without reason – Brooks (1991). Popular Reading John McCarthy: The uncommon logician of common sense, in Shasha & Lazere (1995). Alan Kay: A clear romantic vision, ibid. Computers and Brains, in Morowitz (1997). At home Reading: Brooks (for first Demonstration Session)

25 24 Module Structure Key Topics include –Multi-agent systems and swarm Intelligence –Artificial neural networks –Evolutionary design and genetic algorithms –Co-evolutionary design –Artificial life –Robotics and control –Interfacing biology with silicon

26 25 Some basics This module is for you. Think, read, ask questions, tinker, and have fun! Warning: Some of the material covered is, or recently was, cutting edge. There will be programming, some biology, some philosophy. There is a lot of material to cover in lectures, but… lecture slides will not tell the whole story. You must attend lectures and demo sessions The module courseware – BEAST – will be introduced over the coming weeks. Help with C++, installation etc. will be given in demo sessions. Originality and innovation will be rewarded. Show enthusiasm, play, contribute original code.

27 26 Resources Course Website: –Lecture Slides –Module Outline –Reading Lists –Assignments –Useful Links Newsgroups: local.modules.ai23 Reading available in library, or via links on course page. Each other –Talk about the material. –Talk about the assignments. –Help each other; feel free to work together, but –Submit only you own personal original work.

28 27 2-Hour Exam (60%) - answer 3 questions of 4 2 assignments (40%) of which 1 st assignment – due 9am, 17 March (15%). 2 nd assignment includes BEAST analysis & programming project. Due 9am, 28 April (25%). Additional exercises may be given in demo sessions. Assignments & exam must be passed to pass module. Assessment

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