So I watched the movie Ex Machina. I started thinking and started wondering. Is something in that level of AI possible in the near future? Maybe not her body, but her mind.

And how far off is her body?

I do apologize if I am asking silly questions, but... they got me thinking...

If we take it a step further. I am a C# developer and I was wondering, if you had to guess, do you think it would be possible to make something that advanced using a language such as C#. Or which language would you think is best suitable for this task?

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    $\begingroup$ Welcome to the site! I'd like to point out that your second question probably isn't a good place for this site, since it's about developing software. That being said, yes, it could be done in C#. It could be done in just about any language, but that doesn't mean it should be done in a lot of languages. I wouldn't want to do AI programming in a bash script, but you could. $\endgroup$ – Frostfyre Jul 5 '15 at 5:16
  • $\begingroup$ Thank you for reply. and thank you, this site is pretty cool. That makes sense because it leans towards software dev, but I was just following a trail. $\endgroup$ – Ali El Jul 5 '15 at 6:36
  • $\begingroup$ It isn't the language but pathfinding, object oriented programming pulls stored data when necessary but requires it be affixed with an address first. Our trillions of neurons somehow managed to solve the salesmen paradox without running on turbocharged nuclear reactor, more importantly it have to be energy efficient and I do not mean idle state. Note a single neuron is capable of branching its dendrites to allow more ways for routing I can imagine a synthesized electronic nanites mimicking exactly in near future and although still long way off being conscious but we are making progress. $\endgroup$ – user6760 Jul 5 '15 at 8:08
  • $\begingroup$ @user6760 what does that have to do with the Traveling Salesman problem? Or is the "salesman paradox" something else? $\endgroup$ – JDługosz Jul 5 '15 at 8:42
  • $\begingroup$ @JDługosz yes I'm referring to the traveling salesman problem and all i'm saying is you can't rearrange the beads on the abacus and it will becomes self-conscious. $\endgroup$ – user6760 Jul 5 '15 at 9:31

At this point in time, we don't know enough about brains and high level cognition to understand what is missing and still needs to be done in order to make a recognisable and generally accepted "true" AI.

It could be that we need several key breakthroughs in understanding. Those could happen at any time, making it possible that we are now within a decade of creating an Ex-Machina type brain. But equally it could be a century or more, we simply don't know how hard these problems are.

Alternatively, it might "just" be a matter of increased computing power and engineering using already-understood components - given that there are successful simulations of nervous systems in very basic creatures such as nematode worms and the visual processing of the bee. Scaling these up to human-level brain power (ignoring whether or not this in itself will lead to an intelligent system) is an engineering feat that will likely take many decades. Bullish predictions by Ray Kurzweil based on Moores law suggest 2040 as date where we might have the raw computing power available.

Probably we need both things to happen. Simply scaling up our existing work will allow better resolution and faster training for vision processes, but for example a classifier that can recognise objects is not an intelligent being. Also, just combining a bunch of AI code that we already have and letting it run faster with more memory and better sensors doesn't seem that promising - it is very likely that we will need time to learn how to combine all the parts to be successful, even assuming we have most of the individual parts understood by the time it is possible.

Here are links to some recent AI projects that might be thought-provoking, and give some sense of how far we've come since the early days of computing:

  • Robobees - drones can be flown using a vision system based on analysis of real bees' neural networks.

  • Describing images in English - a neural network can describe the contents of an image using natural language (note this is not the same as understanding such an image and the network has no "agency").

  • Deep dreaming, a neural network vision system run in reverse with feedback, a bit of fun, but also gives a sense of how robotic vision systems work. Although there are analogs to human perception (perhaps in this case involving LSD), this and other analyses of state-of-the-art vision systems are showing that we've got something a bit wrong. Computer vision networks seem to require different architecture to biological ones, and can fail in different ways, implying we have missed something about how real brains work.

  • "Big Dog" robot is an advanced design for military kit-carrying device that can work in rough terrain alongside infantry. Gives a sense of how robotic movement and locomotion are doing.

  • COG is a research robot looking at many aspects of robotics and artificial intelligence. Take a look at the capabilities page to get a sense of the level at which the research looks into components of AI.

  • The Mitsuki chatbot is 2013 winner of the Loebner Prize. A quick conversation with it shows that although it can figure out a realistic response to single questions, it has real trouble with memory, common sense logic and following a conversation beyond sentence-by-sentence responses (e.g. I told it my favourite colour was a mix of red and blue, and asked it what colour that was, and it said 'Orange?')

I am not an AI researcher, so I have picked the above examples purely because I have heard about them recently. If any other project demonstrates a completely different AI or robotic capability, add a comment and I'd be pleased to add it to the list.

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  • $\begingroup$ The raw computing power is already available, but expensive, for functional simulation. In a few years the power needed for spiking neural network will be affordable atnthe corporate or university level. (These are explained in my old Answer which is woefully lacking in upvotes for such a lucid elucidation). The reverse-engineering is laging behind the computer power. $\endgroup$ – JDługosz Jul 5 '15 at 8:47
  • $\begingroup$ Woaw... that was informative. It's true that we don't have all the puzzle pieces yet, but at the rate we are expanding technology, like you said, we will never know what is next, when. Who knows, maybe somewhere right now, the answer is on someone's napkin or whiteboard. Thank you, that was a very good read. $\endgroup$ – Ali El Jul 5 '15 at 9:14

I have a complete answer already posted here. The question there was how powerful of a computer but I covered the timeframe as well, as being the real part of the question.

To summarize,

Without understanding the emergent behavior, just simulating the neurons would take $10^{18}$ to $10^{19}$ FLOPS and 10,000 Terabytes of memory, expected to cost a million dollars in 2019.

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