AI experts don't know how to create an artificial intelligence. AI researchers study how to solve various problems in CS traditionally performed by humans that humans don't solve by carrying out an algorithm by hand like Natural Language Processing, machine learning, automated reasoning, search (e.g. chess).
There are at least two flavors of A.I. "Strong A.I." seeks to build something human-like and could pass Turings immitation game test. The new movie Ex Machina explores this test. "Weak A.I." replicates just a single cognitive skill like game playing, pattern recognition, natural language or something more trivial. Most recent A.i. worked on the latter, or its theoretical background.
Actually there can be anything possible in the articles that are published by so called Tech Journalists who have no idea of the fundamentals of the tech.
What do you mean it exists? How do you definite AI? Can your project reason with you? Or is it simply an Input-Output type of program? Just because you use natural language with it instead of punch cards doesn't mean it is intelligent.
People tried to do AI in the 60s to 90s era. It is dubbed symbolic AI. It didn't work out. A good chance that it never will. Today machine learning algos and a bunch of automated statistics is called "Artificial Intelligence". It's not intelligence at all. Intelligence implies something more than I/O computation.
I tend to agree that AI today is a long way from what the founders of the subject imagined – it's become something more like "Applied Computer Science". But what's now called "Artificial General Intelligence" isn't dead, and people are still working on it.
Also, it's more tricky than you'd think to narrow down what counts as intelligence. There aren't really any hard lines between an I/O program and an intelligent agent, even though they seem pretty far apart.
Just because you don't see the hard lines doesn't mean they aren't there. We are deluding ourselves by avoiding a hard definition of intelligence so we can keep believing that we are creating AI when its really nothing of the sort.
Just because you can't see unicorns doesn't mean they aren't there, but at some point you have to give up the search. It's fine to talk about how, broadly speaking, rats are more intelligent than ants, plants or microbes (which are basically I/O rules with a body), chimpanzees more so than rats, humans more so than than chimps etc. But in general there's a ton of overlap and the qualities we associate with intelligence – memory, planning, self-awareness, tool use, whatever – are only loosely correlated continua.
There are a few more binary measures in intelligence research, such as the mirror test, but at best they're only a small piece of the puzzle. There's no sudden point where everything clicks into place.
Of course, if you have such a good definition of intelligence, feel free to enlighten me.
Well I would say that "intelligence" is learning and inference with causal models rather than just predictive or correlative models. You can then cash it all out into a few different branches of cognition, like perception (distinguishing/classifying which available causal models best match the feature data under observation), learning (taking observed feature data and using it to refine causal models for greater accuracy), inference (using causal models to make predictions under counterfactual conditions, which can include planning as a special case), and the occasional act of conceptual refinement/reduction (in which a model is found of how one model can predict the free parameters of another).
You are repeating the same mistake as before. The difference is significant between admitting we don't know what those lines are and claiming that they don't exist.
That's the plot line of a hyper critical, yet insightful, joke that came out of one of the many AI downturns in past decades, that as soon as an algo or implementation works it isn't AI anymore, its (fill in the blank specialization). So AI is just the present set of algos that don't (yet?) work.
So that fuzzy logic, thats not AI anymore, thats a footnote in the EE control systems theory class, isn't it? And the face recognition is a parallel processing assignment in FPGA class, speech recognition is an advanced section in DSP theory class, etc.
Right, it's not yet at that point for face and speech recognition, but that certainly happened to game AI (Chess), constraint solving (Sudoku), and planning.