Out of curiosity, I've been reading up a bit on the field of Machine Learning, and I'm surprised at the amount of computation and mathematics involved. One book I'm reading through uses advanced concepts such as Ring Theory and PDEs (note: the only thing I know about PDEs is that they use that funny looking character). This strikes me as odd considering that mathematics itself is a hard thing to "learn."
Are there any branches of Machine Learning that use different approaches?
I would think that a approaches relying more on logic, memory, construction of unfounded assumptions, and over-generalizations would be a better way to go, since that seems more like the way animals think. Animals don't (explicitly) calculate probabilities and statistics; at least as far as I know.
The behaviour of the neurons in our brains is very complex, and requires some heavy duty math to model. So, yes we do calculate extremely complex math, but it's done in a way that we don't perceive.
I don't know whether the math you typically find in A.I. research is entirely due to the complexity of the natural neural systems being modelled. It may also be due, in part, to heuristic techniques that don't even attempt to model the mind (e.g., using convolution filters to recognise shapes).
If you want to avoid the math but do AI like stuff, you can always stick to simpler models. In 90% of the time, the simpler models will be good enough for real world problems.
I don't know of a track of AI that is completely decoupled from math though. Probability theory is the tool for handling uncertainty which plays a major role in AI. So even if there was not-so-mathematical subfield, math techniques would most be a way to improve on those methods. And thus the mathematics would be back in game. Even simple techniques like the naive Bayes and decision trees can be used without a lot of math, but the real understanding comes only through it.
Machine learning is very math heavy. It is sometimes said to be close to "computational statistics", with a little more focus on the computational side. You might want to check out "Collective Intelligence" by O'Reilly, though. I hear they have a good collection of ML techniques without math too hard.
You might find evolutionary computing approaches to machine learning a little less front-loaded with heavy-duty maths, approaches such as ant-colony optimisation or swarm intelligence.
I think you should put to one side, if you hold it as your question kind of suggests you do, the view that machine learning is trying to simulate what goes on in the brains of animals (including Homo Sapiens). A lot of the maths in modern machine learning arises from its basis in pattern recognition and matching; some of it comes from attempts to represent what is learnt as quasi-mathematical statements; some of it comes from the need to use statistical methods to compare different algorithms and approaches. And some of it comes because some of the leading practitioners come from scientific and mathematical backgrounds rather than computer science backgrounds, and they bring their toolset with them when they come.
And I'm very surprised that you are suprised that machine learning involves a lot of computation since the long history of AI has proven that it is extremely difficult to build machines which (seem to) think.
I've been thinking about this kind of stuff a lot lately.
Unfortunately, most engineer/mathematician types are so tied to their own familiar mathematical/computational worlds, they often forget to consider other paradigms.
Artists, for example, often think of the world in a very fluid way, usually untethered by mathematical models. Much of what happens in art is archetypal or symbolic, and often doesn't follow any seemingly conventional logical arrangement. There are, of course, very strong exceptions to this. Music, for instance, especially in music theory, often requires strong left brained processes and so forth. In truth, I would argue that even the most right brained activities are not devoid of left logic, but rather are more complex mathematical paradigms, like chaos theory is to the beauty of fractals. So the cross-over from left to right and back again is not a schism, but a symbiotic coupling. Humans utilize both sides of the brain.
Lately I've been thinking about a more artful representational approach to math and machine language -- even in a banal world of ones and zeroes. The world has been thinking about machine language in terms of familiar mathematical, numeric, and alphabetic conventions for a fairly long time now, and it's not exactly easy to realign the cosmos. Yet in a way, it happens naturally. Wikis, wysisygs, drafting tools, photo and sound editors, blogging tools, and so forth, all these tools do the heavy mathematical and machine code lifting behind the scenes to make for a more artful end experience for the user.
But we rarely think of doing the same lifting for coders themselves. To be sure, code is symbolic, by its very nature, lingual. But I think it is possible to turn the whole thing on its head, and adopt a visual approach. What this would look like is anyone's guess, but in a way we see it everywhere as the whole world of machine learning is abstracted more and more over time. As machines become more and more complex and can do more and more sophisticated things, there is a basic necessity to abstract and simplify those very processes, for ease of use, design, architecture, development, and...you name it.
That all said, I do not believe machines will ever learn anything on their own without human input. But that is another debate, as to the character of religion, God, science, and the universe.
I attended a course in machine-learning last semester. The cognitive science chair at our university is very interested in symbolic machine learning (That's the stuff without mathematics or statistics ;o)). I can recommend two outstanding textbooks:
Machine Learning (Thomas Mitchell)
Artificial Intelligence: A Modern Approach (Russel and Norvig)
The first one is more focused on machine learning, but its very compact has got a very gentle learning curve. The second one is a very interesting read with many historical informations.
These two titles should give you a good overview (All aspects of machine learning not just symbolic approaches), so that you can decide for yourself which aspect you want to focus on.
Basically there is always mathematics involved but I find symbolic machine learning easier to start with because the ideas behind most approaches are often amazingly simple.
Mathematics is simply a tool in machine learning. Knowing the maths enables one to efficiently approach the modelled problems at hand. Of course it might be possible to brute force one's way through, but usually this would come with the expense of lessened understanding of the basic principles involved.
So, pick up a maths book, study the topics until it you're familiar with the concepts. No mechanical engineer is going to design a bridge without understanding the basic maths behind the system behaviour; why should this be any different in the area of machine learning?
There is a lot of stuff in Machine Learning, outside just the math..
You can build the most amazing probabilistic model using a ton of math, but fail because you aren't extracting the right features from the data (which might often require domain insight) or are having trouble figuring out what your model is failing on a particular dataset (which requires you to have a high-level understanding of what the data is giving, and what the model needs).
Without the math, you cannot build new complicated ML models by yourself, but you sure can play with existing tried-and-tested ones to analyze information and do cool things.
You still need some math knowledge to interpret the results the model gives you, but this is usually a lot easier than having to build these models on your own.
Try playing with http://www.cs.waikato.ac.nz/ml/weka/ and http://mallet.cs.umass.edu/ .. The former comes with all the standard ML algorithms along with a lot of amazing features that enable you to visualize your data and pre/post-process it to get good results.
Yes, machine learning research is now dominated by researchers trying to solve the classification problem: given positive/negative examples in an n-dimensional space, what is the best n-dimensional shape that captures the positive ones.
Another approach is taken by case-based reasoning (or case-based learning) where deduction is used alongside induction. The idea is that your program starts with a lot of knowledge about the world (say, it understands Newtonian physics) and then you show it some positive examples of the desired behavior (say, here is how the robot should kick the ball under these circumstances) then the program uses these together to extrapolate the desired behavior to all circumstances. Sort of...
firstly cased based AI, symbolic AI are all theories.. There are very few projects that have employed them in a sucessfull manner. Nowadays AI is Machine Learning. And even neural nets are also a core element in ML, which uses a gradient based optimization. U wanna do Machine learning, Linear Algebra, Optimization, etc is a must..
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I'm a bit confused about artificial intelligence.
I understand it as the capability of a machine to learn new things, or do different things without actually executing code (already written by someone).
In SO I see many threads about A.I. in games, but IMO that is not an A.I. Because if it is every software even a print command should be called A.I. In games there is just code that is executed. I would call it pseudo-AI.
Am I wrong? Should be also this considered as A.I.?
Wikipedia says this:
Artificial intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it.
AI textbooks define the field as "the study and design of intelligent agents"
[1], where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.
What you are considering is more specifically referred to as Machine Learning, which is indeed a subbranch of AI. As you can see from the second sentence above, however, the "AI" considered in games also fits perfectly well into this definition.
Of course, the actual line between what is AI, and what not, is quite blurry. This is also due to the fact, that everyone and his mother believes to know what "AI" means.
I suggest you grab yourself a more scientific book (say the classical Russel,Norvig) to get a more thorough grasp on the different fields that are present under the huge roof of what we simply refer to as "AI".
"Minsky and McCarthy, both considered founders of AI, say that artificial intelligence is anything done by a program or a machine that if a human did the same activity, we would say the human had to apply intelligence to accomplish the task."
A more modern definition is to turn this on its head:
Artificial intelligence is anything done by a program or a machine that if a human did the same activity, we would say the human did not need to apply intelligence to accomplish the task.
Intelligence is the ability to do the things that don't require reasoning. Things like understanding and generating language, sequencing your leg muscles as you walk across the floor, or enjoying a symphony. You don't stop to reason for any of those things. You Understand. INTUITIVELY, how to interpret things in your visual field, language, and all other sensory input. And you can do the right thing without reasoning. You can easily prepare all of your breakfast without any reasoning. :-)
Doing things that "require thought" or reasoning, like playing chess or solving integrals are things that computers can already do.
This misunderstanding about what intelligence really is has cost us 60 years and a million man-years of banging our head against the wall.
Deep learning is the currently most popular expression of an alternative path to a "better kind of AI". Artificial Intuition is a special branch of Deep Learning tailored at understanding text.
The easiest way to know whether you are dealing with classical (futile) or modern AI is whether the system requires you to supply any models of the world (MOTW). Any MOTW means the AI is limited to operate in the domain specified by the MOTW and is therefore not a general intelligence. Also, anything with a MOTW is typically not designed to extend that model; this is a very difficult task.
Better to start from a Model of the Mind (MOTM) or a Model of Learning. These can be derived either from neuroscience (difficult) or from epistemology (much easier). A well done MOTM can then learn anything it needs to know to solve problems in any domain.
The main problem for most is to find what's called "a domain-independent method for determining saliency". In other words, all intelligences, natural or artificial, have to spend most of their time answering the question "what matters".
Google my name and "AI" for more.
Minsky and McCarthy, both considered founders of AI, say that artificial intelligence is anything done by a program or a machine that if a human did the same activity, we would say the human had to apply intelligence to accomplish the task.
Frank and Kirt sum up the academic field of AI pretty well. Any difficulty there is defining AI reflects the more general problem of defining real intelligence. If AI has proved anything, it's that we have precious little idea what intelligence is, how amazing organisms are at solving problems, and how difficult it is to get machines to achieve the same results.
As for the use of the term AI in the video games industry, your confusion justified. The prospect of intelligent characters in games is so compelling, that the term long ago took on a life of its own as marketing jargon. However, AI is really just a poorly chosen name for the solving of problems that computers find hard and people find easy. And in that sense, there is plenty of genuine AI work going on in the games industry.
Take a look at AIGameDev.com for a taste of what is currently considered noteworthy in AI game development.
The most important aspect of AI as I believe is 'curiosity'. Intelligence comes from this very fact that it is a result of curiosity.
There is no precise definition of AI because intelligence itself is relative and hard to define, this is due to the fact that many fields (ancient and modern) such as Philosophy and Neuroscience serve as the foundations of AI. It depends on what your AI is expected to do.
Artificial Intelligence is the attempt to create intelligence from a computer program.
Regardless if its a toy program or neural science, as long as a program is able to mimic human problem-solving skills or even go beyond it, is called Artificial Intelligence.
Of course, the expectation of computer scientists on how capable a program (or machine) is to solve problems in time increases. Playing tic-tac-toe programs before is considered intelligent until chess programs where invented. Then now we are attempting to mimic how human brain through neural networks.
A.I for layman's now a day's is applied in most computer games. It's also used in most machines, like in airplane for autopilot, NASA's explorer on Mars called curiosity (2012), who's able to detect terrain obstacles and move around it.
Very tricky stuff A.I. Its like if you design a mind that replies to all the right questions with all the answers, is it A.I. Or just a talking encyclopedia. What if you can teach the A.I. by simply talking to it, do you then consider that A.I. with a mind, or again just a program. Perhaps the question or answer is if someone someday makes a machine that looks human, acts human, and thinks its human. And then see if others feel the same, that its human, if they dont know its not. And then what if it passes that test. You see its not really about is the machine conscious, or does it have a mind as those will never be truly answered. Its all about does it "seem conscious" act conscious, act like it has a mind, given thats as close as humanity will ever get to understanding that riddle. If a machine acts like it cares, and does helpful things thats all that really matters, not the rest of unseen picture. We just half to get this far in the first place. By the way check out Webo a teachable A.I.Webo a teachable A.I.
I'm starting to study machine learning and bayesian inference applied to computer vision and affective computing.
If I understand right, there is a big discussion between
classical IA, ontology, semantic web researchers
and machine learning and bayesian guys
I think it is usually referred as strong AI vs weak AI related also to philosophical issues like functional psychology (brain as black box set) and cognitive psychology (theory of mind, mirror neuron), but this is not the point in a programming forum like this.
I'd like to understand the differences between the two points of view. Ideally, answers will reference examples and academic papers where one approach get good results and the other fails. I am also interested in the historical trends: why approaches fell out of favour and a newer approaches began to rise up. For example, I know that Bayesian inference is computationally intractable, problem in NP, and that's why for a long time probabilistic models was not favoured in information technology world. However, they've began to rise up in econometrics.
I think you have got several ideas mixed up together. It's true that there is a distinction that gets drawn between rule-based and probabilistic approaches to 'AI' tasks, however it has nothing to do with strong or weak AI, very little to do with psychology and it's not nearly as clear cut as being a battle between two opposing sides. Also, I think saying Bayesian inference was not used in computer science because inference is NP complete in general is a bit misleading. That result often doesn't matter that much in practice and most machine learning algorithms don't do real Bayesian inference anyway.
Having said all that, the history of Natural Language Processing went from rule-based systems in the 80s and early 90s to machine learning systems up to the present day. Look at the history of the MUC conferences to see the early approaches to information extraction task. Compare that with the current state-of-the-art in named entity recognition and parsing (the ACL wiki is a good source for this) which are all based on machine learning methods.
As far as specific references, I doubt you'll find anyone writing an academic paper that says 'statistical systems are better than rule-based systems' because it's often very hard to make a definite statement like that. A quick Google for 'statistical vs. rule based' yields papers like this which looks at machine translation and recommends using both approaches, according to their strengths and weaknesses. I think you'll find that this is pretty typical of academic papers. The only thing I've read that really makes a stand on the issue is 'The Unreasonable Effectiveness of Data' which is a good read.
As for the "rule-based" vs. " probabilistic" thing you can go for the classic book by Judea Pearl - "Probabilistic Reasoning in Intelligent Systems. Pearl writes very biased towards what he calls "intensional systems" which is basically the counter-part to rule-based stuff. I think this book is what set off the whole probabilistic thing in AI (you can also argue the time was due, but then it was THE book of that time).
I think machine-learning is a different story (though it's nearer to probabilistic AI than to logics).
There are many papers about ranged combat artificial intelligences, like Killzones's (see this paper), or Halo. But I've not been able to find much about a fighting IA except for this work, which uses neural networs to learn how to fight, which is not exactly what I'm looking for.
Occidental AI in games is heavily focused on FPS, it seems! Does anyone know which techniques are used to implement a decent fighting AI? Hierarchical Finite State Machines? Decision Trees? They could end up being pretty predictable.
In our research labs, we are using AI planning technology for games. AI Planning is used by NASA to build semi-autonomous robots. Planning can produce less predictable behavior than state machines, but planning is a highly complex problem, that is, solving planning problems has a huge computational complexity.
AI Planning is an old but interesting field. Particularly for gaming only recently people have started using planning to run their engines. The expressiveness is still limited in the current implementations, but in theory the expressiveness is limited "only by our imagination".
Russel and Norvig have devoted 4 chapters on AI Planning in their book on Artificial Intelligence. Other related terms you might be interested in are: Markov Decision Processes, Bayesian Networks. These topics are also provided sufficient exposure in this book.
If you are looking for some ready-made engine to easily start using, I guess using AI Planning would be a gross overkill. I don't know of any AI Planning engine for games but we are developing one. If you are interested in the long term, we can talk separately about it.
You seem to know already the techniques for planning and executing. Another thing that you need to do is predict the opponent's next move and maximize the expected reward of your response. I wrote a blog article about this: http://www.masterbaboon.com/2009/05/my-ai-reads-your-mind-and-kicks-your-ass-part-2/ and http://www.masterbaboon.com/2009/09/my-ai-reads-your-mind-extensions-part-3/ . The game I consider is very simple, but I think the main ideas from Bayesian decision theory might be useful for your project.
I have reverse engineered the routines related to the AI subsystem within the Street Figher II series of games. It does not incorporate any of the techniques mentioned above. It is entirely reactive and involves no planning, learning or goals. Interestingly, there is no "technique weight" system that you mention, either. They don't use global weights for decisions to decide the frequency of attack versus block, for example. When taking apart the routines related to how "difficulty" is made to seem to increase, I did expect to find something like that. Alas, it relates to a number of smaller decisions that could potentially affect those ratios in an emergent way.
Another route to consider is the so called Ghost AI as described here & here. As the name suggests you basically extract rules from actual game play, first paper does it offline and the second extends the methodology for online real time learning.
Check out also the guy's webpage, there are a number of other papers on fighting games that are interesting.
http://www.ice.ci.ritsumei.ac.jp/~ftgaic/index-R.html
its old but here are some examples
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Our brain consists of billions of neurons which basically work with all the incoming data from our senses, handle our consciousness, emotions and creativity as well as our hormone system, etc.
So I'm completely new to this topic but doesn't each neuron have a fixed function? E.g.: If a signal of strength x enters, if the last signal was x ms ago, redirect it.
From what I've learned in biology about our nerves system which includes our brain because both consist of simple neurons, it seems to me as our brain is one big, complicated computer.
Maybe so complicated that things such as intelligence and cognition become possible?
As the most complicated things about a neuron pretty much are the chemical aspects on generating an electric singal, keeping itself alive, and eventually segmenting itself, it should be pretty easy emulating some on a computer, or?
You won't have to worry about keeping your virtual neuron alive, or?
If you can emulate a single neuron on a computer, which shouldn't be too hard, could you theoretically emulate more than 1000 billions of them, recreating intelligence, cognition and maybe even creativity?
In my question I'm leaving out the following aspects:
Speed of our current (super) computers
Actually writing a program for emulating neurons
I don't know much about this topic, please tell me if I got anything wrong :)
(My secret goal: Make a copy of my brain and store it on some 10 million TB HDD and make someone start it up in the future)
A neuron-like circuit can be built with a handful of transistors. Let's say it takes about a dozen transistors on average. (See http://diwww.epfl.ch/lami/team/vschaik/eap/neurons.html for an example.)
A brain-sized circuit would require 100 billion such neurons (more or less).
That's 1.2 trillion transistors.
A quad-core Itanium has 2 billion transistors.
You'd need a server rack with 600 quad-core processors to be brain-sized. Think $15M US to purchase the servers. You'll need power management and cooling plus real-estate to support this mess.
One significant issue in simulating the brain is scale. The actual brain only dissipates a few watts. Power consumption is 3 square meals per day. A pint of gin. Maintenance is 8 hours of downtime. Real estate is a 42-foot sailboat (22 Net Tons of volume as ships are measured) and a place to drop the hook.
A server cage with 600 quad-core processors uses a lot more energy, cooling and maintenance. It would require two full-time people to keep this "brain-sized" server farm running.
It seems simpler to just teach the two people what you know and skip the hardware investment.
Roger Penrose presents the argument that human consciousness is non-algorithmic, and thus is not capable of being modeled by a conventional Turing machine-type of digital computer. If it's like that you can forget about building a brain with a computer...
Simulating a neuron is possible and therefore theoretically simulating a brain is possible.
The two things that always stump me as an issue is input and output though.
We have a very large number of nerve endings that all provide input to the brain. Without them the brain is useless. How can we simulate something as complicated as the human brain without also simulating the entire human body!?!
Output, once the brain has "dealt" with all of the inputs that it gets, what is then the output from it? How could you say that the "copy" of your brain was actually you without again hooking it up to a real human body that could speak and tell you?
All in all, a fascinating subject!!!!
The key problem with simulating neural networks (and human brain is a neural network) is that they function continuously, while digital computers function in cycles. So in a neural network different neurons function independently in parallel while in a computer you only simulate discrete system states.
That's why adequately simulating real neural networks is very problematic at the moment and we're very far from it.
Yes, the Blue Brain Project is getting close, and I believe Moore's Law has a $1000 computer getting there by 2049.
The main issue is that our brains are based largely on controlling a human body, which means that our language comprehension and production, the basis of our high-level reasoning and semantic object recognition, is strongly tied to its potential and practiced outputs to a larynx, tongue, and face muscles. Further, our reward systems are tied to signals that indicate sustenance and social approval, which are not the goals we generally want a brain-based AI to have.
An exact simulation of the human brain will be useful in studying the effects of drugs and other chemicals, but I think that the next steps will be in isolating pathways that let us do things that are hard for computers (e.g. visual system, fusiform gyrus, face recognition), and developing new or modifying known structures for representing concepts.
Short: yes we will surely be able to reproduce artificial brains, but no it maybe won't be with our current computers models (Turing machines), because we simply don't know yet enough about the brain to know if we need new computers (super-Turing or biologically engineered brains) or if current computers (with more power/storage space) are enough to simulate a whole brain.
Long:
Disclaimer: I am working in computational neuroscience research and I am interested both by the neurobiological side and the computational (artificial intelligence) side.
Most of the answers assume as true OP's postulate that simulating neurons is enough to save the whole brain state and thus simulate a whole brain.
That's not true.
The brain is more than just neurons.
First, there is the connectivity, the synapses, that is of paramount importance, even maybe more than neurons.
Secondly, there are glial cells such as astrocytes and oligodendrocytes that also possess their own connectivity and communication system.
Thirdly, neurons are heterogenous, which means that there is not just one template model of a neuron that we could just scale up to the required amount to simulate a brain, we also have to define multiple types of neurons and place them pertinently at the right places. Plus, the types can be continuous, so in fact you can have neurons that are half way between 3 different types...
Fourthly, we don't know much about the rules of brain's information processing and management. Sure, we discovered that the cerebellum works pretty much like an artificial neural network using stochastic gradient descent, and that the dopaminergic system works like TD-learning, but then we have no clue about the rest of the brain, even memory is out of reach (although we guess it's something close to a Hopfield network, but there's no precise model yet).
Fifthly, there are so many other examples from current research in neurobiology and computational neuroscience showing the complexity of brain's objects and networks dynamics that this list can go on and on.
So in the end, your question cannot be answered, because we simply do not know yet enough about the brain to know if our current computers (Turing machines) are enough to reproduce the complexity of biological brains to give rise to the full spectrum of cognitive functions.
However, biology field is getting closer and closer to computer science field, as you can see with biologically engineered viruses and cells that are programmed pretty much like you develop a computer program, and genetical therapies that basically re-engineer a living system based on its "class" template (the genome). So I dare to say that once we know enough about the brain's architecture and dynamics, the in-silico reproduction won't be an issue: if our current computers cannot reproduce the brain because of theoretical constraints, we will devise new computers. And if only biological systems can reproduce the brain, we will be able to program an artificial biological brain (we can already 3D-print functional bladders and skin and veins and hearts etc.).
So I would dare say (even if it can be controversial, this is here my own claim) that yes, artificial brains will surely be possible someday, but whether it will be as a Turing machine computer, a super-Turing computer or a biologically engineered brain remain to be seen depending on our progress in the knowledge of brain's mechanisms.
I don't think they are remotely close enough to understanding the human brain to even begin thinking about replicating it.
Scientists would have you think we are nearly there, but with regards to the brain we're not much further along than Dr. Frankenstein.
What is your goal? Do you want a program that can make intelligent decisions or a program that provides a realistic model of how the human brain actually works? Artificial intelligence can be approached from the perspective of psychology, where the goal is to simulate the brain and thereby get a better understanding of how humans think, or from the perspective of mathematics, optimization theory, decision theory, information theory, and computer science, in which case the goal is to create a program that is capable of making intelligent decisions in a computationally efficient manner. The latter, I would say is pretty much solved, although advances are definitely still being made. When it comes to a realistic simulation of the brain, I think we were only recently able to simulate a brain of cat semi-realistically; when it comes to humans, it would not be very computationally feasible at present.
Researchers far smarter than most recon so, see Blue Brain from IBM and others.
The Blue Brain Project is the first
comprehensive attempt to
reverse-engineer the mammalian brain,
in order to understand brain function
and dysfunction through detailed
simulations.
Theoretically the brain can be modeled using a computer (as software and hard/wetware are compatible or mutually expressible). The question isn't a theoretical one as far as computer science goes, but a philosophical one:
Can we model the (chaotic) way in which a brain develops. Is a brains power it's hardware or the environment that shapes the development and emergent properties of that hardware as it learns
Even more mental:
If I, with 100% accuracy modeled my own brain, then started the simulation. And that brain had my memories (as it has my brain's physical form) ... is it me? If not, what do I have that it doesn't?
I think that if we are ever in a position to emulate the brain, we should have been working on logical system based on biological principles with better applications than the brain itself.
We all have a brain, and we all have access to it's amazing power already ;)
A word of caution. Current projects on brain simulation work on a model of a human brain. Your idea about storing your mind on a hard-disk is crazy: if you want a replica of your mind you'll need two things. First, another "blank" brain. Second, devise a method to perfectly transfer all the information contained in your brain: down to the quantum states of every atom in it.
Good luck with that :)
EDIT: The dog ate part of my text.
Has anyone worked with the programming language Church? Can anyone recommend practical applications? I just discovered it, and while it sounds like it addresses some long-standing problems in AI and machine-learning, I'm skeptical. I had never heard of it, and was surprised to find it's actually been around for a few years, having been announced in the paper Church: a language for generative models.
I'm not sure what to say about the matter of practical applications. Does modeling cognitive abilities with generative models constitute a "practical application" in your mind?
The key importance of Church (at least right now) is that it allows those of us working with probabilistic inference solutions to AI problems a simpler way to model. It's essentially a subset of Lisp.
I disagree with Chris S that it is at all a toy language. While some of these inference problems can be replicated in other languages (I've built several in Matlab) they generally aren't very reusable and you really have to love working in 4 and 5 for loops deep (I hate it).
Instead of tackling the problem that way, Church uses the recursive advantages of lamda calaculus and also allows for something called memoization which is really useful for generative models since your generative model is often not the same one trial after trial--though for testing you really need this.
I would say that if what you're doing has anything to do with Bayesian Networks, Hierarchical Bayesian Models, probabilistic solutions to POMDPs or Dynamic Bayesian Networks then I think Church is a great help. For what it's worth, I've worked with both Noah and Josh (two of Church's authors) and no one has a better handle on probabilistic inference right now (IMHO).
Church is part of the family of probabilistic programming languages that allows the separation of the estimation of a model from its definition. This makes probabilistic modeling and inference a lot more accessible to people that want to apply machine learning but who are not themselves hardcore machine learning researchers.
For a long time, probabilistic programming meant you'd have to come up with a model for your data and derive the estimation of the model yourself: you have some observed values, and you want to learn the parameters. The structure of the model is closely related to how you estimate the parameters, and you'd have to be pretty advanced knowledge of machine learning to do the computations correctly. The recent probabilistic programming languages are an attempt to address that and make things more accessible for data scientists or people doing work that applies machine learning.
As an analogy, consider the following:
You are a programmer and you want to run some code on a computer. Back in the 1970s, you had to write assembly language on punch cards and feed them into a mainframe (for which you had to book time on) in order to run your program. It is now 2014, and there are high-level, simple to learn languages that you can write code in even with no knowledge of how computer architecture works. It's still helpful to understand how computers work to write in those languages, but you don't have to, and many more people write code than if you had to program with punch cards.
Probabilistic programming languages do the same for machine learning with statistical models. Also, Church isn't the only choice for this. If you aren't a functional programming devotee, you can also check out the following frameworks for Bayesian inference in graphical models:
Infer.NET, written in C# by the Microsoft Research lab in Cambridge, UK
stan, written in C++ by the Statistics department at Columbia
You know what does a better job of describing Church than what I said? This MIT article: http://web.mit.edu/newsoffice/2010/ai-unification.html
It's slightly more hyperbolic, but then, I'm not immune to the optimism present in this article.
Likely, the article was intended to be published on April Fool's Day.
Here's another article dated late march of last year. http://dspace.mit.edu/handle/1721.1/44963