Hyping Nonsense: What Happens When Artificial Intelligence Turns On Us?

Posted in culture, transhumanism on January 23rd, 2014 by Samuel Kenyon

The user(s) behind the G+ account Singularity 2045 made an appropriately skeptical post today about the latest Machines-versus-Humans “prediction,” specifically an article “What Happens When Artificial Intelligence Turns On Us” about a new book by James Barrat.

As S2045 says:

Don’t believe the hype. It is utter nonsense to think AI or robots would ever turn on humans. It is a good idea to explore in novels, films, or knee-jerk doomsday philosophizing because disaster themes sell well. Thankfully the fiction or speculation will never translate to reality because it is based upon a failure to recognize how technology erodes scarcity. Scarcity is the root of all conflict.

Smithsonian even includes a quote by the equally clueless Eliezer Yudkowsky:

In the longer term, as experts in my book argue, A.I. approaching human-level intelligence won’t be easily controlled; unfortunately, super-intelligence doesn’t imply benevolence. As A.I. theorist Eliezer Yudkowsky of MIRI [the Machine Intelligence Research Institute] puts it, “The A.I. does not love you, nor does it hate you, but you are made of atoms it can use for something else.” If ethics can’t be built into a machine, then we’ll be creating super-intelligent psychopaths, creatures without moral compasses, and we won’t be their masters for long.

In the G+ comments you can see some arguments about the evidence for or against the prediction. I would like to add a couple arguments in support of Singularity 2045’s conclusion (but not necessarily endorsing his specific arguments):

  1. Despite “future shock” (before Kurzweil and Vinge there was Toffler) from accelerating change in certain avenues, most of these worries about machines-vs-humans battles are so fictional because they assume a discrete transition point: before the machines appeared and after. The only way that could happen is if there was an massive planetary invasion of intelligent robots from another planet. In real life things happen over a period of time with transitions and various arbitrary (e.g. because of politics) diversions and fads…despite any accelerating change.
  2. We have examples of humans living in partial cooperation and simultaneously partial conflict with other species. Insects outnumber us. Millions of cats and dogs live in human homes and get better treatment than the poor and homeless in the world. Meanwhile, crows and parrots are highly intelligent animals often living in symbiosis with humans…except when they become menaces.

If we’re going to map fiction to reality, Michael Crichton techno-thrillers are a bit closer to real technological disasters, which are local specific incidences resulting from the right mixture of human errors and coincidence (and this happens in real life sometimes for instance nuclear reactor disasters). And sometimes those errors are far apart at first like somebody designing a control panel badly which assists in a bad decision by an operator 10 years later during an emergency.

And of course I’ve already talked about the Us-versus-Them dichotomy and the role of interfaces in human-robot technology in my paper “Would You Still Love Me If I Was A Robot?”

Addendum

I doubt we will have anything as clear cut as an us-vs-them new species. And if we maintain civilization (e.g. not the anti-gay anti-atheist witch-hunting segments) then new variations would not be segregated / given less rights and vice-versa they would not segregate / remove our human rights.

As far as I know, there is no such thing as a natural species on Earth that “peacefully coexists.” This may be the nature of the system, and that’s certainly easy to see when looking at the evolutionary arms races constantly happening. Anyway my point is that any attempt to appeal to nature or the mythical peaceful caveman is not the right direction. The fact that humans can even imagine never-ending peace and utopia seems to indicate that we have started to surpass nature’s “cold equations.”

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2013: Postmortem

Posted in meta on January 19th, 2014 by Samuel Kenyon

This is a personal postmortem (aka retrospective), not a report on the world at-large.

crossing a stream in the Amazon

crossing a stream in the Amazon

What Went Right

I accomplished several things of a wider diversity than I did in 2012, particularly new-to-me activities.

Riding horses in Iceland

Riding horses in Iceland

Highlights:

  • Sky-dived for the first time
  • Went outdoor top-rope rock climbing for the first time
  • Submitted two artificial intelligence papers to conferences/symposia, one of which was accepted
  • Went to aforementioned symposium and presented a poster for it
  • Wrote 25 blog posts
  • Started working on developmental systems
  • Started a new job (technically started mid Dec 2012)
  • Wrote a new, improved version of my short film screenplay, Enough to be Dangerous
  • Started working on a horror film screenplay
  • Acted in a music video for Wake No More
  • Acted in a book video for Kissing Oscar Wilde
  • Acted in a a BU short comedic film called South x Southeast (screened in Dec 2012, but we’ll count it since the video appeared online in 2013)
  • Explored Iceland
  • Explored Ecuador
    • Including rising to the highest elevation I’ve ever been at
    • Explored (via a guide) the Amazon rainforest
      • Monkeys!
      • I ate a grub (it wasn’t raw–it was cooked (smoked) by the natives)
  • Learned to ride a horse (in Iceland, also went riding in Ecuador in the Andes mountains)
  • Explored the entire Freedom Tunnel in New York
  • Went sailing for the first time (part of a company forced-fun day, but it was fairly interesting)
  • Participated in No Pants Subway Ride
Laundromat cafe, Reykjavik, Iceland

Laundromat cafe, Reykjavik, Iceland

Iceland

Iceland

Iceland

A volcanic crater we climbed up to

Iceland

Iceland

Kayaking in the volcanic crater of Quilotoa, Ecuador with Emily

Kayaking in the volcanic crater of Quilotoa, Ecuador with Emily

Me holding a freshly opened jungle coconut (Amazon rainforest)

Me holding a freshly opened jungle coconut (Amazon rainforest) (photo by Emily Durrant)

What Went Wrong

AI Paper Number 2

The second papers I wrote was obviously not ready (both the paper and the research), but I submitted it anyway. However, I got excellent feedback in the rejection.

Not Enough Personal Coding

Although I worked a lot on non-work projects, I didn’t write as much code as I wanted. Still, I think there was an increase from 2012.

Not Enough Hardware

Aside from testing some little programs I wrote on a NAO robot, I didn’t do any work with real robots in 2013. I need to improve the balance of theory to implementation with my robotic and computational intelligence ideas.

Not Enough Art

I didn’t make any new drawings except for a few doodles. Also although I’ve written some notes for new music compositions, I didn’t actually generate any new music last year…although I did finally post an old composition on Soundcloud.

Skydiving

I didn’t like being squashed in a painful/awkward position on the floor of an overfull plane and without even any handles to pull myself up out of this position. It kind of ruined the whole experience until later on after the parachute was out and we were gliding—after tumbling ridiculously on exiting the plane and almost going unconscious from lack of breathing. Apparently the instructor was supposed to tell me about making sure to breath (the experience is quite new if you are not used to having your face blasted with air as you plummet from the highest location you’ve ever jumped before), but he failed to do so. I probably won’t go back to that airfield and if I do, I will make sure I’m not getting on the plane unless I can crouch or sit in a safe, un-strained, non-distracting position.

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Growing Robot Minds

Posted in artificial intelligence on January 11th, 2014 by Samuel Kenyon

One way to increase the intelligence of a robot is to train it with a series of missions, analogous to the missions (aka levels) in a video game.

mission (aka levels)

In a developmental robot, the training would not be simply learning—its brain structure would actually change. Biological development shows some extremes that a robot could go through, like starting with a small seed that constructs itself, or creating too many neural connections and then in a later phase deleting a whole bunch of them.

As another example of development vs. learning, a simple artificial neural network is trained when the weights have been changed after a series of training inputs (and error correction if it is supervised). In contradistinction, a developmental system changes itself structurally. It would be like growing completely new nodes, network layers, or new networks entirely during each training level.

Or you can imagine the difference between decorating a skyscraper (learning) and building a skyscraper (development).

construction (development) vs. interior design (learning)

construction (development) vs. interior design (learning)

What Happens to the Robot Brain in these Missions?

Inside a robotic mental development mission or stage, almost anything could go on depending on the mad scientist who made it. It could be a series of timed, purely internal structural changes. For instance:

  1. Grow set A[] of mental modules
  2. Grow mental module B
  3. Connect B to some of A[]
  4. Activate dormant function f() in all modules
  5. Add in pre-made module C and connect it to all other modules

Instead of (or in addition to) pre-planned timed changes, the stages could be in part based on environmental interactions. And I think that is actually a possibly useful tactic to make a robot adjust to its own morphology and the particular range of environments that it must operate and survive in. And that makes the stages more like the aforementioned missions as one has in computer games.

Note that learning is most likely going to be happening at the same time (unless learning abilities are turned off as part of a developmental level). In the space of all possible developmental robots, one would expect some mental change gray areas somewhere between development and learning.

Given the input and triggering roles of the environment, each development level may require a special sandbox world. The body of the robot may also undergo changes during each level.

The ordering of the levels/ sandboxes would depend on what mental structures are necessary going in to each one.

A Problem

One problem that I have been thinking about is how to prevent cross-contamination of mental changes. One mission might nullify a previous mission.

For example, let’s say that a robot can now survive in Sandbox A after making it through Mission A. Now the robot proceeds through Mission B in Sandbox B. You would expect the robot to be able to survive in a bigger new sandbox (e.g. the real world) that has elements of both Sandbox A and Sandbox B (or requires the mental structures developed during A and B). But B might have messed up A. And now you have a robot that’s good at B but not A, or even worse not good at anything.

Imagine some unstructured cookie dough. You can form a blob of it into a special shape with a cookie cutter.

cookie cutter

cookie cutter

But applying several cookie cutters in a row might result in an unrecognizable shape, maybe even no better than the original blob.

As a mathematical example, take a four stage developmental sequence where each stage is a different function, numbered 1-4. This could be represented as:

y = f_{4}(f_{3}(f_{2}(f_{1}(x))))

where x is the starting cognitive system and y is the final resulting cognitive system.

This function composition is not commutative, e.g.

f_{4}\circ f_{3}\circ f_{2}\circ f_{1} \neq f_{1}\circ f_{2}\circ f_{3}\circ f_{4}

A Commutative Approach

There is a way to make an architecture and transform function type that is commutative. You might think that will solve our problem, however it only works with certain constraints that we might not want. To explain I will show you an example of a special commutative configuration.

We could require all the development stages to have a minimal required integration program. I.e. f1(), f2(), etc. are all sub-types of m(), the master function. Or in object oriented terms:

The example here would have each mission result in a new mental module. The required default program would automatically connect this module with the same protocol to all other modules.

So in this case:

f_{4}\circ f_{3}\circ f_{2}\circ f_{1} = f_{1}\circ f_{2}\circ f_{3}\circ f_{4}

I don’t think this is a good solution since it seriously limits the cognitive architecture. We would not even be able to build a simple layered control system where each higher layer depends on the lower layers. We cannot have arbitrary links and different types of links between modules. And it does not address how conflicts are arbitrated for outputs.

However, we could add in some dynamic adaptive interfaces in each module that apply special changes. For instance, module type B might send out feelers to sense the presence of module type A, and even if A is added afterwards, eventually B will find it after all the modules have been added. But, we will not be able to actually unleash the robot into any of the environments it should be able to handle until the end, and this is bad. It removes the power of iterative development. And it means that a mission associated with a module will be severely limited.

The most damning defect with this approach is that there’s still no guarantee that a recently added module won’t interfere with previous modules as the robot interacts in a dynamic world.

A Pattern Solution

A non-commutative solution might reside in integration patterns. These would preserve the functionality from previous stages as the structure is changed.

a multipole switch to illustrate mental switching

a multipole switch to illustrate mental switching

For instance, one pattern might be to add a switching mechanism. The resulting robot mind would be partially modal—in a given context, it would activate the most appropriate developed part of its mind but not all of parts at the same time.

A similar pattern could be used for developing or learning new skills—a new skill doesn’t overwrite previous skills, it is instead added to the relevant set of skills which are triggered or selected by other mechanisms.


Image credits:

  1. Nintendo
  2. Georgia State University Library via Atlanta Time Machine
  3. dezeen
  4. crooked brains
  5. diagram by the author
  6. MyDukkan

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Content/Internalism Space and Interfacism

Posted in artificial intelligence, interfaces, philosophy on December 29th, 2013 by Samuel Kenyon

Whenever a machine or moist machine (aka animal) comes up with a solution, an observer could imagine an infinite number of alternate solutions. The observed machine, depending on its programming, may have considered many possible options before choosing one. In any case, we could imagine a 2D or 3D (or really any dimensionality) mathematical space in which to place all these different solutions.

Fake example of a design/solution or analysis space.

Fake example of a design/solution or analysis space.

Of course, you have to be careful what the axes are. What if you chose the wrong variables? We could come up with dimensions for any kind of analysis or synthesis. I want to introduce here one such space, which illuminates particular spectra, offering a particular view into the design space of cognitive architectures. In some sense, it’s still a solution space—as a thinking system I am exploring the possible solutions for making artificial thinking systems.

Keep in mind that this landscape is only one view, just one scenic route through explanationville—or is it designville?

This photo could bring to mind several relevant concepts: design vs. analysis, representations, and the illusion of reality in reflections

This photo could bring to mind several relevant concepts: design vs. analysis, representations, and the illusion of reality in reflections

Content/Internalism Space

In the following diagram I propose that these two cognitive spectra are of interest and possibly related:

  1. Content vs. no content
  2. Internal cognition vs. external cognition
Content vs. Internalism Space

Content vs. Internalism Space

What the Hell is “Content”?

I don’t have a very good definition of this, and so far I haven’t seen one from anybody else despite its common usage by philosophers. One aspect (or perhaps container) of content is representation. That is a bit easier to comprehend—an informational structure can represent something in the real world (or represent some other informational structure). It may seem obvious that humans have representations in their minds, but that is debatable. Some, such as Hutto and Myin, suggest that human minds are primarily without content, and only a few faculties require content [1]:

Some cognitive activity—plausibly, that associated with and dependent upon the mastery of language—surely involves content. Still, if our analyses are right, a surprising amount of mental life (including some canonical forms of it, such as human visual experience) may well be inherently contentless.

And the primary type of content that Hutto and Myin try to expunge is representational. It’s worth mentioning that representation can be divorced from the Computational Theory of Mind. Nothing here goes against the mind as computation. If you could pause a brain, you could point to various informational states, which in turn compose structures, and say that those structures are “representations.” But they don’t necessarily mean anything—they don’t have to be semantic. This leads to aboutness…

Another aspect of content is aboutness. “Aboutness” is an easier to use word in place of the philosophical term intentionality; “intentionality” has a different everyday meaning which can cause confusion [2]. We think about stuff. We talk about stuff. External signs are about stuff. And we all seem to have a lot of overlapping agreements on what stuff means, otherwise we wouldn’t be able to communicate at all and there wouldn’t be any sense of logic in the world.

So does this mean we all have similar representations? Does a stop sign represent something? Is that representation stored in all of our brains, thus we all know what a stop sign means? And what things would we not understand without in-brain representations? For instance, consider some sensory stimulus that sets off a chain reaction resulting in a particular behavior that most humans share. Is that internal representation, or are dynamic interfaces, however complicated, something different?

Internal vs. External

This is about the prevailing cognitive science assumption that anything of interest cognitively is neural. Indeed, most would go even further than neural and limit themselves just to the brain. The brain is just one part of your nervous system. Although human brain evolution and development seem to be the cause of our supposed mental advantages over other animals, we should be careful not to discard all the supporting and/or interacting structures. If we take the magic glass elevator a bit down and sideways, we might want to consider our insect cousins in which the brain is not the only critical part of the nervous system—and insects can still operate in some fashion for days or weeks without their brains.

I’m not saying here that the brain focus is wrong; I’m merely saying that one can have a spectrum. For instance, a particular ALife experiment could be analyzed from the point of view of anywhere on that axis. Or you could design an ALife situation on any point, e.g. just by focusing on the internal controller that is analogous to a brain (internalist) vs. focusing on the entire system of brain-body-environment (externalist).

Interfacism

Since there has to be an “ism” for everything, there is of course representationalism. Another philosophical stance that is sometimes pitted against representationalism is direct realism.

Direct realism seems to be kind of sloppy. It could simply mean that at some levels of abstraction in the mind, real world objects are experienced as whole objects, not as the various mental middlemen which were involved in constructing the representation of that object. E.g., we don’t see a chair by consciously and painstakingly sorting through various raw sensory data chunks—we have an evolved and developed system for becoming aware of a chair as an object “directly.”

Or, perhaps, in an enactivism or dynamic system sense, one could say that regardless of information processing or representations, real world objects are the primary cause of information patterns that propagate through the system which lead to experience of the object.

My middle ground between direct and indirect realism would, perhaps, be called “interfacism,” which is a form of representationalism that is enactivism-compatible. Perhaps most enactivists already think that way, although I don’t recall seeing any enactivist descriptions of mental representation in terms of interfaces.

What I definitely do not concede is any form of cognitive architecture which requires veridical, aka truthful, accounts anywhere in the mind. What I do propose is that any concept of an organism can be seen as interactions. The organism itself is a bunch of cellular interactions, and that blob interacts with other blobs and elements of the environment, some of which may be tools or cognitively-extensive information processors. Whenever you try to look at a particular interaction, there is an interface. Zooming into that interface reveals yet more interfaces, and so on. To say anything is direct, in that sense, is false.

For example, an interfacism description of a human becoming aware of a glass of beer would acknowledge that the human as an animate object and the beer glass as an inanimate object are arbitrary abstractions or slices of reality. At that level in that slice, we can say there is an interface between the human and the glass of beer, presumably involving the mind attributed to the human.

Human-beer interface

Human-beer interface

But, if we zoom into the interface, there will be more interfaces.

Zooming in to the human-beer interface reveals more interfaces.

Zooming in to the human-beer interface reveals more interfaces.

And semantics will probably require links to other things, for instance we don’t just see what is in front of us—we can be primed or biased, or hallucinate, or dream, etc. How sensory data comes to mean anything at all almost certainly involves evolutionary history and ontogeny (life history) and current brain states at least as much as any immediate perceptual trigger. And our perception is just a contraption of evolution, so we aren’t really seeing true reality ever—it’s a nonsensical concept.

I think interfacism is possibly a good alternate way to look at how cognition, be it wide or narrow—at any given cognitive granularity, there is no direct connection between two “nodes” or objects. There is just an interface, and anything “direct” is at a level below, recursively. It’s also compatible with non-truthful representations and/or perception.

Some might say that representations have to be truthful or that there are representations, for instance in animal behaviors, because there is some truthful mapping between the real world and the behavior. With an interface point of view we can throw truth out the window. Mappings can be arbitrary. There may be consistent and/or accurate mappings. But they don’t necessarily have to be truthful in any sense aside from that.


References
[1]D. D. Hutto and E. Myin, Radicalizing enactivism: basic minds without content. Cambridge, Mass.: MIT Press, 2013.
[2]D. C. Dennett, Intuition Pumps and Other Tools for Thinking. W.W. Norton & Company, 2013.


Image credits
Figures by Samuel H. Kenyon.
Photo by Dimosthenis Kapa

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