One of the corner stones of The Selfish Gene (Dawkins) is the spontaneous emergence of replicators, i.e. molecules capable of replicating themselves.
Has this been modeled in silico in open-ended evolutionary / artificial life simulations?
Systems like Avida or Tierra explicitly specify the replication mechanisms; other genetic algorithm/genetic programming systems explicitly search for the replication mechanisms (e.g. to simplify the von Neumann universal constructor)
Links to simulations where replicators emerge from a primordial digital soup are welcome.
The software system Amoeba by Andrew Pargellis has studying the origins of life as an explicit goal; he saw interesting patterns developing first, that eventually turned into self-replicators.
More recently (well, 2011) Evan Dorn used Avida to study a range of questions to do with the origin of life, with a focus on astrobiology. They wanted to examine how chemical distributions changed as abotic environments shifted to biotic so that astronomers would know what to look for.
For a good starting point, take a look at:
https://link.springer.com/article/10.1007/s00239-011-9429-4
Related
Assuming there is a task (e.g. an image processing method with a lot math) which is reasonable to be implemented on FPGA in sense of answer https://stackoverflow.com/a/8695228/544463
Is there any known (that you can actually name) successful application or practice for combining it with "dedicated" (designed on custom demand) super computing cluster (HPC), e.g. with Infiniband stack? I wonder if that has already been done and to which extend that was successful.
My main motivation for the question is that http://en.wikipedia.org/wiki/Reconfigurable_computing is a long term (academic) perspective for the future development of cluster computing as a distinctive alternative to cloud computing (the later concentrates more on the software (higher) flexibility level but also through possible "reconfiguration"). Is it already practical?
I would also expect somebody is doing research on this... It would be nice to learn about results.
Well, it's not FPGA, but D.E. Shaw's Anton computer for molecular dynamics is famously ASICs connected with a custom high-speed network; J. P. Morgan uses clusters of FPGAs in its risk-analysis calculations (recent Forbes article here). Convey computers has been pushing FPGA+x86+high speed networking fairly hard for the past couple of years, so presumably there's some sort of market there...
http://www.maxeler.com/ - they build racks of Intel PCs hosting custom boards stuffed with FPGAs (and - critically - the associated software and FPGA code) to speed up seismic processing, financial analysis and the like.
I think they could be regarded as successful (I gather they turn a profit) and have big customers from finance and oil companies amongst their clientele.
Is there any known (that you can actually name) successful application
or practice for combining it with "dedicated" (designed on custom
demand) super computing cluster (HPC), e.g. with Infiniband stack? I
wonder if that has already been done and to which extend that was
successful.
It's being attempted academically with Novo-G.
You might be interested in Maxwell.
I know that Cray used to have a series of supercomputers some years ago that combined AMD Opterons with Xilinx FPGAs (iirc) through a HyperTransport bus, basically allowing you to create your own specialized processor for custom workloads. According to their website though, they now seem to have dropped FPGAs in favor of GPUs.
For the current research, there's always Google Scholar...
Update: After a bit of searching, it appears to have been the Cray XT5h, which had the possibility of using FPGA coprocessors...
Some have already been mentioned (convey, cray), some not (e.g. beecube).
But one of the biggest FPGA-Clusters I ever heard of, is missing:
The Large Hadron Collider at CERN. They produce in seconds enormous amounts of data (2.7 Terabit/s). They use the FPGAs (> 100) of them to reduce and filter the data to reduce it, and make it handable.
It does not fit your request to be connected to a dedicated HPC-Cluster, but they are a HPC-Cluster on their own (as on the higher hierarchy levels the used FPGAs are FX, they include two PowerPCs and are also some kind of "normal" cluster).
There is quite a lot of published work in reconfigurable computing applications.
Here's a list of links to SRC Computers-centric published papers.
There's the Center for High-Performance Reconfigurable Computing.
Google search "FPGA" or "reconfigurable" along with these academic institution names and you'll find many published papers. Some of the papers you'll find go back to 2004.
Jackson State University
Clemson University
Catholic University
George Washington University
George Mason University
National Center for Supercomputing Applications (NCSA)
University of Illinois (UIUC)
Naval Postgraduate School (NPS)
Air Force Research Lab (AFRL)
University of Dayton Research Institute (UDRI)
University of Florida
University of Arkansas
There also was a reconfigurable-centric conference hosted by NCSA, the Reconfigurable Systems Summer Institute (RSSI).
This list is certainly not exhaustive, but it will get you started.
Disclosures: I currently work for SRC Computers, LLC, I worked at NCSA/UIUC and I chaired the RSSI conference its first two years.
Yet another great use case developed by adapteva called parallela (they have a kickstarter project).
They are developing a epoch-series of processors controlled by a two cores ARM processor (that shares the board).
I am so much anticipating to have this toy in my hands!
PS
Since it was largely inspired by ardunio (and similar ARM-like) systems, this project is still limited by 1 Gbps networking.
I've read in Wikipedia that neural-network functions defined on a field of arbitrary real/rational numbers (along with algorithmic schemas, and the speculative `transrecursive' models) have more computational power than the computers we use today. Of course it was a page of russian wikipedia (ru.wikipedia.org) and that may be not properly proven, but that's not the only source of such.. rumors
Now, the thing that I really do not understand is: How can a string-rewriting machine (NNs are exactly string-rewriting machines just as Turing machines are; only programming language is different) be more powerful than a universally capable U-machine?
Yes, the descriptive instrument is really different, but the fact is that any function of such class can be (easily or not) turned to be a legal Turing-machine. Am I wrong? Do I miss something important?
What is the cause of people saying that? I do know that the fenomenum of undecidability is widely accepted today (though not consistently proven according to what I've read), but I do not really see a smallest chance of NNs being able to solve that particular problem.
Add-in: Not consistently proven according to what I've read - I meant that you might want to take a look at A. Zenkin's (russian mathematician) papers after mid-90-s where he persuasively postulates the wrongness of G. Cantor's concepts, including transfinite sets, uncountable sets, diagonalization method (method used in the proof of undecidability by Turing) and maybe others. Even Goedel's incompletness theorems were proven in right way in only 21-st century.. That's all just to plug Zenkin's work to the post cause I don't know how widespread that knowledge is in CS community so forgive me if that did look stupid.
Thank you!
From what little research I've done, most of these claims of trans-Turing systems, or of the incorrectness of Cantor's diagonalization proof, etc. are, shall we say, "controversial" in legitimate mathematical circles. Words like "crank" get thrown around frequently.
Obviously, the strong Church-Turing thesis remains unproven, but as you pointed out there's really no good reason to believe that artificial neural networks constitute computational capabilities beyond general recursion/UTMs/lambda calculus/etc.
From a theoretical viewpoint, I think you're absolutely correct -- neural networks provide very little that's new or different.
From a practical viewpoint, neural networks are simply a way of casting solutions into a form where parallel execution is natural and easy, whereas Turing machines are sequential in nature, and executing their sequences in parallel is relatively difficult. In fact, most of what's been done in CPU development over the last few decades has basically been figuring out ways to execute code in parallel while maintaining the illusion that it's executing in sequence. A lot of the hardware in a modern CPU is devoted to maintaining that illusion, and the degree to which parallel execution has become explicit is mostly an admission that maintaining the illusion has become prohibitively expensive.
Anyone who "proves" that Cantor's diagonal method doesn't work proves only their own incompetence. Cf. Wilfred Hodges' An editor recalls some hopeless papers for a surprisingly sympathetic explanation of what kind of thing is going wrong with these attempts.
You can provide speculative descriptions of hyper-Turing neural nets, just as you can provide speculative descriptions of other kinds of hyper-Turing computers: there's nothing incoherent in the idea that hypercomputation is possible, and speculative descriptions of mechanical hypercomputers have been made where the hypercomputer is stipulated to have infinitely fine engravings that encode an oracle for the Halting machine: the existence of such a machine is consistent with Newtonian mechanics, though not quantum mechanics. Rather, the Church-Turing thesis says that they can't be constructed, and there are two reasons to believe the Church-Turing thesis is correct:
No such machines have ever been constructed; and
There's work been done connecting models of physics to models of computation, going back to work in the early 1970s by Robin Gandy, with recent work by people such as David Deutsch (e.g., Machines, Logic and Quantum Physics and John Tucker (e.g., Computations via experiments with kinematic systems) which argues that physics doesn't support hypercomputation.
The main point is that the truth of the Church-Turing thesis is an empirical fact, and not a mathematical fact. It's one that we can have confidence is true, but not certainty.
From a layman's perspective, I see that
NNs can be more effective at solving some types problems than a turing machine, but they are not compuationally more powerful.
Even if NNs were provably more powerful than TMs, execution on current hardware would render them less powerful, since current hardware is only an apporximation of a TM and can only execute problems computable by a bounded TM.
You may be interested in S. Franklin and M. Garzon, Neural computability. There is a preview on Google. It discusses the computational power of neural nets and also states that it is rumored that neural nets are strictly more powerful than Turing machines.
I'm wondering if anyone knows of any software techniques taking advantage of biology? For example, in the robotics world, there are tons, but what about software?
Many concepts originally observed in biology have been used in software. For example Genetic Algorithm (GA).
Artificial life (AL) exposes/uses several principles of biology such as resilience to imperfect code snippets, addressing by content, imperfect reproduction (in some implementations, also sexual, i.e. multi-orginanisms-driven, reproduction) and a non-goal-driven utility function. An interesting result of AL, is the spontaneous production of macro phenomenons observed in domains such as ecology or epidemiology (domains largely influenced by biology), such as the emergence of parasites and even that of organisms which take advantage of parasites, or subtle predator-prey relationships.
Maybe software can be said to have gone "full circle" with some experiments in computing which involve real (carbon-based) DNA (or RNA) molecules! The original experiment in this area (PDF link) by Prof. Alderman (of RSA fame), who coded the various elements of a graph-related problem (an hamiltonian graph) with different DNA molecules and let the massive parallel computing power of bio-chemistry do the rest and solve the problem !
Back in the digital world, but with a strong inspiration from biology and indeed from anatomy of the cerebral cortex, and from many theoretical and clinical observations in the neuroscience field, we have Neural Networks (NN). In the area of NN, maybe worthy of a special notice, is Numenta's Hierarchical Temporal Memory model which, although it reproduces the [understanding we have of] the neo-cortex only very loosely, introduces the idea that the very same algorithm is applied in all areas and at all levels of the cognitive process powered by the brains, an idea largely supported by biological, anatomical and other forms of evidence.
If your question means "have biological ideas been used to optimize software?" then
Genetic programming (http://en.wikipedia.org/wiki/Genetic_programming) is one example. From the Wikipedia article:
In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. Therefore it is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.
If your question means "what software techniques have been inspired by biology?" then
see more generally http://en.wikipedia.org/wiki/Bio-inspired_computing. I would expect that several other methods such as ant-swarms (http://en.wikipedia.org/wiki/Ant_colony_optimization) and Neural Networks (http://en.wikipedia.org/wiki/Neural_network_software) could also be used.
Artificial Neural Networks are another classic example. The software application tends to be pattern recognition and prediction of behaviour of complex systems.
Ant colony optimization, a search / optimization method, and Artificial Life like Conway's Game of Life
Most of the answers yet talk about AI. The title of your question hints towards software that hides itself in order not to be detected.
We got viruses.
We got virus-hunters...
Me myself, I even hid some bugs in my own programs ... :(
Alan Kay (the object technology pioneer) spoke at length about the influence of biology in the OOP paradigm. He's got a series of ideas about how objects are like "cells" and that OOP scales in a similar way to the way that cells can scale to produce massive architectures...
You can follow quite a bit of this in his Turing Award Speech:
http://video.google.com/videoplay?docid=-2950949730059754521# -- Skip to about the 30:55 mark
Does anybody have any good source of software/tutorial about Genetic Engineering Simulation?
Maybe open source software about gene splicing / cloning simulation ?
Thanks
This might be up your alley:
Genetic Programming - Evolution of Mona Lisa by Roger Alsing.
Mona Lisa Source Code and binaries
Mona Lisa FAQ
All very interesting and impressive stuff.
This may not be exactly what you're after, but have you looked at Spore?
It's quite possible that there is no such thing as "serious" genetic engineering simulation software. I think it would be a very difficult concept to implement.
Excellent question!
Clearly genetic engineering can't be simulated at an analytic level - ie. modelling the mechanism involved - because some of the processes, such as protein folding, DNA transcription, etc., are too complex and haven't been modelled yet.
They are still working on a map of the human genome, and I'm sure they haven't gotten around to anything else because anything of reasonable complexity isn't that much less complex (in terms of number of genes) from a human. For example, you can save 2% in complexity, but then you have a chimp not a human.
However, there might be possible approaches based on empirical methods - i.e. "best guess" methods, or experimentation. Thus, you might look at recent work in machine learning or data mining for some possible approaches. There are a lot of data mining and machine learning methods out there, ranging from genetic algorithms, neural networks, decision trees, SVM, etc.
http://en.wikipedia.org/wiki/Data_mining
http://en.wikipedia.org/wiki/Machine_learning
I was wondering how common it is to find genetic algorithm approaches in commercial code.
It always seemed to me that some kinds of schedulers could benefit from a GA engine, as a supplement to the main algorithm.
Genetic Algorithms have been widely used commercially. Optimizing train routing was an early application. More recently fighter planes have used GAs to optimize wing designs. I have used GAs extensively at work to generate solutions to problems that have an extremely large search space.
Many problems are unlikely to benefit from GAs. I disagree with Thomas that they are too hard to understand. A GA is actually very simple. We found that there is a huge amount of knowledge to be gained from optimizing the GA to a particular problem that might be difficult and as always managing large amounts of parallel computation continue to be a problem for many programmers.
A problem that would benefit from a GA is going to have the following characteristics:
A good way to encode potential solutions
A way to compute an a numerical score to evaluate the quality of the solution
A large multi-dimensional search space where the answer is non-obvious
A good solution is good enough and a perfect solution is not required
There are many problems that could probably benefit from GAs and in the future they will probably be more widely deployed. I believe that GAs are used in cutting edge engineering more than people think however most people (like my company does) guards those secrets extremely closely. It is only long after the fact that it is revealed that GAs were used.
Most people that deal with "normal" applications probably don't have much use for them though.
If you want to find an example, look at Postgres's Query Planner. It uses many techniques, and one just so happens to be genetic.
http://developer.postgresql.org/pgdocs/postgres/geqo-pg-intro.html
I used GA in my Master's thesis, but after that I haven't found anything in my daily work a GA could solve that I couldn't solve faster with some other Algorithm.
I don't think it is particularly common to find genetic algorithms in everyday-commercial code. They are more commonly found in academic/research code where the need to find the "best algorithm" is less important than the need to just find a good solution to a problem.
Nonetheless, I have consulted on a couple of commercial projects that do use GAs (chiefly as a result of my involvement with GAUL). I think the most interesting example was at a Biotech company. They used the GA to optimise scoring functions that were used for virtual screening, as part of their drug discovery application.
Earlier this year, with my current company, I added a new feature to one of our products that uses another GA. I think we might be marketing this from next month. Basically, the GA is used to explore molecules that have the potential for binding to a protein, and could therefore be further investigated as drugs targeting that protein. A competing product that also uses a GA is EA inventor.
As part of my thesis I wrote a generic java framework for the multi-objective optimisation algorithm mPOEMS (Multiobjective prototype optimization with evolved improvement steps), which is a GA using evolutionary concepts. It is generic in a way that all problem-independent parts have been separated from the problem-dependent parts, and an interface is povided to use the framework with only adding the problem-dependent parts. Thus one who wants to use the algorithm does not have to begin from zero, and it facilitates work a lot.
You can find the code here.
The solutions which you can find with this algorithm have been compared in a scientific work with state-of-the-art algorithms SPEA-2 and NSGA, and it has been proven that
the algorithm performes comparable or even better, depending on the metrics you take to measure the performance, and especially depending on the optimization-problem you are looking on.
You can find it here.
Also as part of my thesis and proof of work I applied this framework to the project selection problem found in portfolio management. It is about selecting the projects which add the most value to the company, support most the strategy of the company or support any other arbitrary goal. E.g. selection of a certain number of projects from a specific category, or maximization of project synergies, ...
My thesis which applies this framework to the project selection problem:
http://www.ub.tuwien.ac.at/dipl/2008/AC05038968.pdf
After that I worked in a portfolio management department in one of the fortune 500, where they used a commercial software which also applied a GA to the project selection problem / portfolio optimization.
Further resources:
The documentation of the framework:
http://thomaskremmel.com/mpoems/mpoems_in_java_documentation.pdf
mPOEMS presentation paper:
http://portal.acm.org/citation.cfm?id=1792634.1792653
Actually with a bit of enthusiasm everybody could easily adapt the code of the generic framework to an arbitrary multi-objective optimisation problem.
I haven't but I've heard of this company (can't remember their name) which uses mutating, genetic algos to calculate placements and lengths of antennas (or something) from a friend of mine. And they're supposed to (according to my friend) have huge success with this. I guess GA is just too complex for "average Joe developer" to become mainstream. Kind of like Map Reduce - spectacularly cool, but WAY too advanced to hit the "mainstream"...
LibreOffice Calc uses it in its Solver module.