## Evaluating Webots

Posted in artificial intelligence, robotics on February 4th, 2014 by Samuel Kenyon

I’m trying to find a better simulator than Breve for robots and 3D physics world creation.

I have been examining the Webots simulation environment. It seems pretty useful since I could write controllers in C++ and it comes with several robot models out of the box. And I like the scene graph (or “scene tree” as they call it) approach for environments (they use VRML97 which is obsolete but at least it’s a well known standard); I only played with the interface enough to add a sphere and a box but it seems good enough so far. A lot easier than doing it completely programmatically and/or completely from scratch with raw data files. And I have made 3D models from scratch with data files in the past and it was not that efficient (compared to some ideal GUI) except for tweaking exact numbers.

They have some Nao (aka “NAO”) robot models and it would be awesome to use that for some mental development research. I’m thinking about affordances, and certainly the Nao with its 25 DOF and lots of sensors is more than sufficient for interesting affordances with real world (or simulated 3D world) environments. It may actually be overkill…

simulated Nao looks at a brick box

Not that I have access to a real Nao, although I programmed two little little test scripts last year on an actual Nao using Choregraphe, a primarily visual programming tool. Webots can run a Nao server so you can actually hook Choregraphe to the Webots simulation (Choreograph has a sim but just of the robot model, not of environmental interactions). Unfortunately I couldn’t try this out as it’s blocked for free users (the screenshot below shows an attempt to use the robot soccer world).

naoqisim denied!

And I just realized if I write a Webots Nao controller, there’s no documentation or obvious way that I can see to know what the exact actuator names are to pass to `wb_robot_get_device()` and the demo doesn’t show all motors (or maybe they haven’t implemented all motors?). [Update: I have been informed that you can get the device tags from the Robot Window which can be made visible with the menu (Robot -> Show Robot Window) or double-clicking on the robot in the sim view. Not as good as a text list but at least the info is there.] Making motion files manually would be a pain as well. Maybe I will end up making a simpler robot model from scratch.

The lack of access to the Superviser is also making me question using this for my non-funded research. I might try running some experiments in it, and just see how far I can go without a Superviser.

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## 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?”

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

### 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

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
• 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

Iceland

A volcanic crater we climbed up to

Iceland

Kayaking in the volcanic crater of Quilotoa, Ecuador with Emily

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.

## 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)

### 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.

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

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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