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can you please explain me both terms and what are the major differences?
How can you determine whether an intelligence is weak or strong?
Why these terms?
Q: How can you determine whether an intelligence is weak or strong?
A: There is no strong AI in existence (yet). Therefore everything is weak AI or less.
Explanation from here:
Artificial Narrow Intelligence (ANI): Sometimes referred to as Weak
AI, Artificial Narrow Intelligence is AI that specializes in one area.
There’s AI that can beat the world chess champion in chess, but that’s
the only thing it does. Ask it to figure out a better way to store
data on a hard drive, and it’ll look at you blankly.
And
Artificial General Intelligence (AGI): Sometimes referred to as Strong
AI, or Human-Level AI, Artificial General Intelligence refers to a
computer that is as smart as a human across the board—a machine that
can perform any intellectual task that a human being can. Creating AGI
is a much harder task than creating ANI, and we’re yet to do it.
Professor Linda Gottfredson describes intelligence as “a very general
mental capability that, among other things, involves the ability to
reason, plan, solve problems, think abstractly, comprehend complex
ideas, learn quickly, and learn from experience.” AGI would be able to
do all of those things as easily as you can.
Both are AI, but one is for single purpose only, so it is regarded as less powerful, hence the term weak. The other is Human level AI, so it is called strong.
But the terminology does not stop here. You also have Artificial Superintelligence, which is even better than Human AI:
Artificial Superintelligence (ASI): Oxford philosopher and leading AI
thinker Nick Bostrom defines superintelligence as “an intellect that
is much smarter than the best human brains in practically every field,
including scientific creativity, general wisdom and social skills.”
Artificial Superintelligence ranges from a computer that’s just a
little smarter than a human to one that’s trillions of times
smarter—across the board.
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I was reading an article earlier that suggests 1/3 of us are scared their job will be taken over by robots at some point. At first I was thinking (as robots have to be programmed as well) that we, as programmers, would be safe from this. That brought me to this question.
Would artificial intelligence be capable of performing (advanced) programming tasks or would they always be limited to the level they where programmed at?
What I mean by level is, for example, a scripting language as opposed to a programming language. (Would it be even possible for a scripting language to write and compile software in a programming language?)
This topic was put on hold because the answers would be based on opinions rather than facts. Just for clarity, I am expecting answers that are based on facts. An answer that simply says yes or no would be an opinion based answer, an answer that explains why is based on facts.
Okay, first of all, since you are dealing with the development of programs, it would be better suited to consider this a question based on Artificial Intelligence, rather than Robotics. It's much more simpler to develop programs to write other programs instead of developing a physical entity to type out a program.
AI has developed to such an extent, that simple games can be played much better than what normal humans can do: Wikipedia: Progress in AI.
As of now, development of complex programs is still out of the reach of AI, though it's not far off. Still, for the most part, AI may be used to assist human developers - since it's still not fully developed - rather than replace them altogether.
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Suppose I've been recruited to a university study testing a new AI, and we're going to do a traditional Turing test. I will sit at a computer and interact via text-based chat with something that is either a computer or another volunteer, and then have to guess which it is.
Suppose I've been given a particularly nice incentive to get it right. How can I improve my odds? What sort of things should I talk about?
Should I try to teach the chatter a simple new concept I just made up, and then quiz them about it?
Should I try to make jokes and innuendo and see if they "get it"?
Should I ask philosophical questions?
Should I try practical questions that humans tend to "irrationally" answer in a particular way?
Try some l33t speak, that might do it. Then again I dont even understand some of it at times. Maybe I'm a computer. Maybe you're a computer
Realize that the state of the art in chatbots is still abysmal. The biggest mistake you can make is to try to do something bizarre, in which case both a human and an AI might reasonably respond in a bizarre manner. Instead, just engage in a normal conversation and resist any significant deviations proposed by your correspondent. That should easily let you identify anything operating at the current published state of the art.
If you encounter something well beyond state of the art, I think the next threshold would be a directed inquiry into a aesthetic decision: "What's your favorite [movie|book|artist|meal]? Why? So would you say you never like...? Are there aspects of that you dislike...?" etc.
Since computer programs might be very logical and high performing, but have no experience of being a human being, I would suggest questions of type trivial everyday events that wouldn't get adequate answers on Google, likely. As questions about cleaning, small details on outfits, boring and irritating situations ...
And it might be a good idea to communicate in a spoken careless way with lots of words and sentences with clear and controversial statements of the above type only here and there.
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everybody. I am entirely new to the topic of classification algorithms, and need a few good pointers about where to start some "serious reading". I am right now in the process of finding out, whether machine learning and automated classification algorithms could be a worthwhile thing to add to some application of mine.
I already scanned through "How to Solve It: Modern heuristics" by Z. Michalewicz and D. Fogel (in particular, the chapters about linear classifiers using neuronal networks), and on the practical side, I am currently looking through the WEKA toolkit source code. My next (planned) step would be to dive into the realm of Bayesian classification algorithms.
Unfortunately, I am lacking a serious theoretical foundation in this area (let alone, having used it in any way as of yet), so any hints at where to look next would be appreciated; in particular, a good introduction of available classification algorithms would be helpful. Being more a craftsman and less a theoretician, the more practical, the better...
Hints, anyone?
I've always found Andrew Moore's Tutorials to be very useful. They're grounded in solid statistical theory and will be very useful in understanding papers if you choose to read them in the future. Here's a short description:
These include classification
algorithms such as decision trees,
neural nets, Bayesian classifiers,
Support Vector Machines and
cased-based (aka non-parametric)
learning. They include regression
algorithms such as multivariate
polynomial regression, MARS, Locally
Weighted Regression, GMDH and neural
nets. And they include other data
mining operations such as clustering
(mixture models, k-means and
hierarchical), Bayesian networks and
Reinforcement Learning
The answer referring to Andrew Moore's tutorials is a good one. I'd like to augment it, however, by suggesting some reading on the need which drives the creation of many classification systems in the first place: identification of causal relationships. This is relevant to many modeling problems involving statistical inference.
The best current resource I know of for learning about causality and classifier systems (especially Bayesian classifiers) is Judea Pearl's book "Causality: models, reasoning, and inference".
Overview of Machine Learning
To get a good overview of the field, watch the video lectures of Andrew Ng's Machine Learning course.
This course (CS229) -- taught by Professor Andrew Ng -- provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.
Classifiers
As for which classifier you should use, I'd recommend first starting with Support Vector Machines (SVM) for general applied classification tasks. They'll give you state-of-the-art performance, and you don't really need to understand all of the theory behind them to just use the implementation provided by a package like WEKA.
If you have a larger data-set, you might want to try using Random Forests. There's also an implementation of this algorithm in WEKA, and they train much faster on large data. While they're less broadly used than SVMs, their accuracy tends to match or nearly match the accuracy you could get from one.
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I've recently come across Intelligent Agents by reading this book :
link text
I'm interested in finding a good book for beginners, so I can start to implement such a system.
I've also tried reading "Multiagent Systems : A modern approach to distributed artificial intelligence" (can't find it on amazon) but it's not what I'm looking for.
Thanks for the help :).
The agent view point is simply an abstraction of convenience. There is nothing magical about agents. It is a way of thinking about software processes that may be migrated from one system to another.
So, yes, if you want your agents to be intelligent, then you need to understand AI algorithms.
There is numerous classical books:
David MacKay's classic (for free here)
Norvig's AIMA, of which a new version came out recently
Bishop's Neural Networks for Pattern Recognition
Bishop's Machine Learning and Pattern Recognition
The first two are the easiest, the second one covers more than machine learning. However, there is little "pragmatic" or "engineering" stuff in there. And the math is quite demanding, but so is the whole field. I guess you will do best with O'Reilly's programming collective intelligence because it has its focus on programming.
The book you have linked is actually a collection of invited research papers, which means it is quite an advanced book if you are just starting in Intelligent Systems.
Actually, there are two interpretations of Intelligent Systems:
(a) Artificial Intelligence studied mainly by the Computer Science community. AI deals with machine learning, knowledge representation and reasoning, learning and planning methods. AI is about developing algorithms. The absolute reference to AI is: "Artificial Intelligence, A modern approach"
Although you are referring to this interpretation, in case you are interested, here is the second one:
(b) Intelligent Control Systems studied mainly by Electrical Engineers. It deals with designing intelligent systems that are able to adapt to changes in the environment, able to learn, able to make intelligent decisions, etc. Intelligent systems deals with developing mathematical models of "intelligence" that can be applied to real-world systems, so as to optimize their performance (or some other measure). The tools used are mostly adaptive control, neural networks and optimization methods. There isn't an easy to follow book on this subject, however some excellent articles are here and here. Also, an excellent reference on Neural Networks is "Neural Networks, A Comprehensive Foundation"
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Throughout my academic years in computer science I fell in love with many aspects of artificial intelligence. From expert systems, neural networks, to data mining (classification). I wonder, if I was to transform this academic passion professionally, what kind of AI-related jobs are out there?
You would be surprised at the number of domains where AI-based approaches are used. From optimal industrial control, process management and optimization, to business rules and financial modeling, to text analysis, machine translation, search engines...
Almost anywhere humans have been used to take complex decisions based on data, the amount of data modern electronic communications and acquisitions methods produce has become too much to handle without software. And only "intelligent" (or at least, less single-mindedly stupid) software can handle the complexity of the data, the complexity of the rules, and the numerous failure modes.
Professor for Artificial Intelligence courses. ;)
The most obvious answer to me are games.
I think games present a very interesting challenge for AI, because you're essentially playing to lose but in a fun way.
I know one software company in my city is using AI, that was developed as a Masters in Engineering project, to detect fraudulent bank/financial transactions. It's pretty interesting stuff. They look for strange recurring payments, or compare account numbers based on known terrorist organizations ...etc. I'm not sure how many people are doing similar work, but i'm sure with the lock-down on financial institutions these days these types of applications will become more prevalent (it's working for them).
Aside from direct application - AI people also are usually hardcore algorithms people by nature, and that kind of knowledge is sought after everywhere.