The impression of free-will is very believable though as the brain probably exhibits chaotic dynamics[1]. From any given state the brain is in, a slight change, however minute, could give rise to a very different outcome later on. This means that for any system with a model that’s external to an individual brain (e.g. a brain simulation if such a thing is possible), it is impossible for that model to completely predict the behaviour of the brain… eventually the brain’s state will diverge from the model. The important point is, this can happen even if the brain is completely deterministic. So even if the rules governing our cognition are unwavering instructions, which I think is unlikely, there is still the inability for a system outside of the brain to predict it’s behaviour[2].

In addition, I believe that consciousness is due to a recursive model that represents ourselves (ala Douglas Hofstadter’s book – I am a Strange Loop). As this is a model of the epiphenomenon of our “self”, it also has incomplete knowledge of the rest of the brain – this gives our conscious minds the illusion of free will as it can’t completely predict what it/we will do next. We think we are weighing up choices based on our knowledge and then making a “decision”, but that’s because we (our conscious minds) don’t have complete knowledge of the brain’s underlying hardware which ultimately leads us to that choice. This lack of knowledge in our conscious minds is what we call “free will”.

[1] Or at least I’d expect it to, I don’t have references I’ve read over, but this looks promising.

[2] That is, assuming we exclude the almost impossible ideal of having perfect knowledge of the brain’s state which would include all neurochemistry as well as structure.

This post is taken from a comment I made to Leo Parker Dirac’s post on “Free Will and Turing-completeness of the Brain”. Turns out I think it’s a relatively succinct description of what the concept of free will actually is so I thought I’d repost it here…

So without further ado, I’d like to give you my picks for the 3 open board seats…

Howard Bloom – Howard’s book “Global Brain – The Evolution of Mass Mind from the Big Bang to the 21st Century” was one of those mind-expanding books for me. I read it while I was still an undergrad at Canterbury University and it really led me to look at evolution and complex systems in a different light. In fact, I bought another copy a year ago and have since been lending it to friend after friend. As far the rest of his portfolio of skills, Chris Grayson probably sums it up as well as I can. Of note though is his extensive experience in publicity and marketing for many big-name musical artists. If Howard can do the same for the meme of transhumanism we’ll be in good hands.

Bryan Bishop – I first heard of Bryan on one of the myriad of mailing lists on transhumanism/AI/tech (I forget exactly which)… however, I later spent a lot of time browsing his wiki on DIY bio hacking and many other interesting topics. Bryan certainly knows his stuff and is very involved in the H+ community. He’s often in the IRC room (#hplusroadmap on freenode) as “kanzure” if you’d like to chat with him.

The third seat I’ll have to decide between:

• Nick Taylor – an enthusiastic explorer and IET chartered engineer. He has the right personality and mind to deal with the uncertainties facing us as the future races towards us.
• Patri Friedman – Spearheading the challenge of viable seasteading, Patri has the courage to challenge and explore the accepted wisdom and encourage other people to do the same.
• Max More – Last, but certainly not least, Max is arguably one of the fathers of modern transhumanism. Humanity+ would benefit greatly from having a “big thinker” like Max on the board.

Clearly we have some great candidates to choose from. I should also note that while I haven’t explicitly mentioned some of the other candidates I don’t mean to suggest they are not also very capable individuals that would also be assets to Humanity+.

However, there is unfortunately an exception… I feel it would be remiss to not comment on some of the internal issues that led to my frustration with being on the board. One of the most pressing issues is that Alex Lightman, the former Executive Director of Humanity+, is now running for the board. As you may know, Alex resigned as Executive Director but the particulars of the situation were not disclosed in the announcement of his resignation. The events that led up to Alex’s resignation were complex and not very pleasant, and involved a considerable majority of the Board requesting his resignation. This wasn’t done lightly and I was a signatory on said request.

I believe that the E.D. of Humanity+ and any board member should hold themselves to high standards of professional and ethical behavior. I do not believe Alex Lightman is a suitable candidate, and I feel that if Alex was elected, Humanity+ would suffer as a result. This is my opinion, and I have intentionally left any further detail out of this post as I do not wish to run a smear campaign. I am however available to discuss concerns of Humanity+ members, but it should be noted that anything I discuss is my personal opinion.

Whatever you decide, please do vote!

Another post from the draft pile that I finally polished into something that isn’t a series of half formed sentences… enjoy

The human body as a closed system is not sustainable, as any closed system eventually achieves an equilibrium lacking order. Entropy would increase as the second law of thermodynamics asserts itself. Flux of energy/matter is required to maintain and build order. This is a central part of Ludwig von Bertalanffy’s paper on “general systems theory” and his theory of open systems:

“the conventional formulation of physics are, in principle, inapplicable to the living organism being open system having steady state. We may well suspect that many characteristics of living systems which are paradoxical in view of the laws of physics are a consequence of this fact.”

I think though, that a similar law applies to intelligent systems. Without stimulus the mind is not alive and eventually a lack of synaptic firing would lead to the neuronal weighting between neurons to deteriorate. This would result in a reversal to the initial states that most artificial neural networks start in (they are usually initiated with random weights)… but perhaps this reversal of weights on neurons that no longer fire isn’t a bad thing. It may lead to them being re-purposed…

As one ages, it can become more difficult to pick up new information as existing synaptic channels get reinforced and so the neuronal tributaries of our brains because less used, or require more active effort to use than taking the ready associations that come easily to our consciousness. While these tributaries may get reset to random weightings due to dis-use, this may allowed them to later get stimulated and used to store new associations.

The NY Times earlier this year posted “How to train the aging brain”:

“There’s a place for information,” Dr. Taylor says. “We need to know stuff. But we need to move beyond that and challenge our perception of the world. If you always hang around with those you agree with and read things that agree with what you already know, you’re not going to wrestle with your established brain connections.”

Such stretching is exactly what scientists say best keeps a brain in tune: get out of the comfort zone to push and nourish your brain. Do anything from learning a foreign language to taking a different route to work.

These new scenarios make the brain utilise alternative neuronal branches:

“As adults we have these well-trodden paths in our synapses,” Dr. Taylor says. “We have to crack the cognitive egg and scramble it up. And if you learn something this way, when you think of it again you’ll have an overlay of complexity you didn’t have before — and help your brain keep developing as well.”

Not only that, but if you are encouraging more interesting events in your life, especially those that push and challenge you and your preconceptions, then your perception of time expands. While in the moment it may seem like time flies, retrospectively it will seem like the past took longer. The brain collapses intervals of time where nothing much happens.

So if you don’t push your brain to learn new things, you’re cutting it off from having anything new to work with. It will also be easier to efficiently and compactly store your experiences based on what you already know. This shrinks your temporal impression of memory and, retrospectively, it will seem as though the last 5 or 10 years were but a blink. If you keep using the same arguments, and facing the same challenges, then you will become optimised and specialised at that task, but this will come at the cost of generality and breadth of understanding.

This got me thinking…

Since I’m working on NetEmpathy, which is currently focussed on analysing the sentiment of tweets, the meaning within tweets (when it exists) is very high. There’s little space for superfluous flowery text when you only have 140 characters.

Myq mentioned how academic papers are a lot like this now. The meaning is highly compressed, particularly in scientific papers. You’ve got to summarise past research, state your method so that it’s reproducible, analyse the results, etc. All in a half a dozen pages. This wasn’t always the case though. In the past academic papers would be long works which meandered their way to the point. Part of this might have to do with the amount of preexisting knowledge present in society, i.e. earlier on there was less global scientific knowledge available, so to adequately cover the background of a subject wasn’t a major difficulty and they could spend more time philosophising. That’s a topic for another post though…

What I was interested is how densely information is packed. Is this increasing?

My immediate thoughts were: text compression! and measure the entropy!.

Basically, information theory dictates that text that contains less information can be represented in fewer bytes. This is why it’s possible to create lossless compression. You assign frequent symbols to be represented by smaller ones. For example, because ‘the’ is one of the most common English words, you might replace it with ’1′ (and crudely, you could replace ’1′ with ‘the’ so that you could still use ’1′ normally). This way, you’ve reduced the size of that symbol by two thirds without loss of information. Obviously this wouldn’t improve your compression factor and a spreadsheet full of numbers though.

A guy called Douglas Biber has apparently already investigated this information content historically, but from a more linguistic and manual investigation.

What I’d like to do one day is examine the compression factors of early scientific journals, recent journals, tweets, txt messages, wikipedia, etc. and see just how the theoretical information content has changed, if at all.

Another project for when I’m independently wealthy.

Things like racism or sexism, or over-generalising on a class of people is one of the more socially inappropriate things you can do. However, depending on how your logic system works, it’s not an entirely unreasonable method of thinking (the word “unreasonable” chosen purposefully) – and for any other subject, where the things being reasoned about are not humans, we wouldn’t particularly care. In fact, certain subjects like religion and spirituality are held to less strict standards of reasoning… there’s actually more defense in being racist/sexist then being a practitioner of certain religions. Perhaps this is why these occasionally go hand in hand[1].

So what do I actually mean by this? I’m going to use two methods of reasoning, deduction and induction, and then explain them in terms of uncertain truth. Nothing in this world is ultimately absolute[2] and so it behooves us to include probabilistic uncertainty in to any conclusion or relationship within our logic set.

Deduction can be summarised as inferring the specific from the general. e.g.

Note that obviously there are exceptions to the starting generalisation (1) of four-legged cats. Poor Aristotle could have been in a car accident and had a leg amputated making (2) a false conclusion. That’s where the probability and uncertain truth comes in…

Here we’ll use two parts to truth values (TVs), strength and confidence[3]. The first, strength, is how often the relationship holds true. E.g. with strength 0.9 for the we’d expect approximately 90% of cats we saw to have four legs. Confidence however, indicates how sure we are of the strength. The exact semantics of confidence are not important for the current discussion, but basically if the confidence is low then we’re not very sure about the number of legs cats have – perhaps because we haven’t seen many.

On the other hand induction is used to infer the general from the specific. e.g.

In other words, if, within all our experience, all cats we’ve seen have four legs it’s sensible to assume all cats do. With such a small sample size of only 3 cats, and without any other background knowledge (like animals of a particular species generally all have the same number of legs) we’d generally not afford much confidence in the result of this deduction[5].

So where does the sex/race/other-isms come in? Well if you haven’t worked it out yet, it’s when you use too small a sample set to do induction or you adopt generalisations from others without using your own evidence[4]. Or perhaps you do know lots of one sex that all fit your model, in that case the problem is using a single generalisation to deduct information about a new member of that sex that you know nothing about. Essentially sexism/racism is due to too large a bias towards sex/race determining an overall conclusion or understanding of the other.

I guess one thing should be clarified, the relationship between generalisation of sexism/racism. When does a generalisation turn into one of these categories. I’m not sure I really know, after all if you made generalisations about the basic anatomy of males/female or say something superficial like the colour of someone’s skin, then there wouldn’t be a whole lot of controversy there[6] but I guess it’s when you use that information to fully define everyone that falls into a category. The thing that I’m grappling with, is that, in the absence of any extra information on someone it’s sensible to use what you have to start trying to represent and understand someone new. In fact, if you didn’t, you’re demonstrating you don’t care enough about the other person to think about them at all!

And then we come to insurance brokers. They make huge generalizations. Law usually prevents them from using race as a factor, but for some reason they are still allowed to use your sex to determine the premium you’ll pay. They are also allowed to generalise on your age among many other things. In fact, pretty much the entire way the insurance industry works is through generalisations.

So what makes it okay for businesses to blatantly be sexist and ageist among many other categorical assumptions? I don’t know.

Ahem… got a little side-tracked there. Sorry…

So when is this generalization okay? If every interaction you have with people that fall into a category supports the conclusion then your confidence will increase – and you’ll have no examples of any alternative. Few people actually have such a life history to conclude this. I suspect many people who display racism or sexist tendencies probably get this strong bias from another human or information source. If it’s someone they highly respect, such as a parent, or someone who’s the social leader amongst a group, then they may adopt conclusions with high confidence without having the experience to conclude things themselves.

How can we protect against broad conclusions about people?

• By making it exceedingly difficult to increase our confidence of very broad generalizations. Realise that when making any large scale conclusion that confidence must be limited – especially when reasoning about dynamic entities like humans.
• Favour the use of more specific categories when reasoning. This can be hard, as when you don’t know someone and all you have is their apparent race and sex then any generalisations you’ve made will be your sole source of information. You can argue that this shouldn’t be the case, but any pre-consolidated knowledge you have will come to the fore until you learn more about the person to develop a more complete representation of them.

Why is it hard to change the behavior or views of people who are already displaying ism-ness?

• I guess that confidence in belief generally erodes slowly, unless presented with directly conflicting evidence. And sometimes conflicting evidence just makes people stop listening to you because it radically challenges their world view.
• Perhaps it’s physically more effort/energy to update generalisations on large groups? If you have to adjust your confidence of conclusion on a large group, then they’ll be many many connections from the neuron that potentially represents that group and the instances and associations within that group. Since the brain is massively connected, larger groups probably require more effort to shift. Think of all those tens of thousands of synapses having to reconfigure themselves or die.
• Generalisations may be self-reinforcing. While someone might not fit your generalisation, that may slightly alter your confidence in it, but not enough to completely remove it from your mind.

I hope I’ve raised some questions in the readers head. I don’t like sexism or racism, but at the same time I can (kind of) understand the logical reasoning that might lead to it and I haven’t completely resolved how it’d sit in a fully functional artificial intelligence. After all, I really don’t want OpenCog to turn into a racist bigot!

[1] I was going to link to an appropriate example here, but couldn’t easily think of one I could back up, feel free to suggest one.
[2] If people think otherwise, I suggest they study enough quantum physics or Gödel’s incompleteness theorem.
[3] This is based on the simple truth value type of OpenCog.
[4] Not that accepting generalizations from others is bad thing. Everyone does it because we cannot experience everything directly.
[5] Although if your sum experience of the universe you live in is merely these three cats then you’d be very confident and not only that but induct that everything has four legs (and is a cat).
[6] Yeah yeah, I know, some people are transgendered. I’m keeping this simple okay?

It is my belief that empathy is a a prerequisite to consciousness.

I recently read Hofstadter’s I am a strange loop, whose central themes are around recursive representations of self leading to our perception of consciousness. For some, the idea that our consciousness is somewhat of an illusion might be hard to swallow – but then, quite likely, so are all the other qualia. They seem real to us, because our mind makes it real. To me, it’s not a huge hurdle to believe. I find the idea that our minds are infinitely representing themselves via self-reflection kind of beautiful in simplicity. You can get some very strange things happening when things start self-reflecting.

For example, Gödel’s incompleteness theorem originally broke Principia Mathematica and can do the same for any sufficiently expressive formal system when you force that formal system to reason about itself. One day I’ll commit to explaining this in a post, but people write entire books about the idea to make Godel’s theorem and it’s consequences easy to understand!

And as an example of self-reflection and recursion being beautiful, I merely have to point to fractals which exhibit self-similarity at arbitrary levels of recursion. Or perhaps the recursive and repeating hallucinations induced by psychedelics give us some clue about the recursive structures within the brain.

Hofstadter also later in the book delves into slightly murky mystical waters, which I find quite entertaining and not without merit. He says that, due to us modelling of the behaviour of others, we also start representing their consciousness too. The eventual conclusion, which is explained in much greater and philosophical detail in his book, is that our “consciousness” isn’t just the sum of what’s in our head but is a holistic total of ourselves and everyone’s representation of us in their heads.

I don’t think the Turing test will really be complete until a machine can model humans as individual and make insightful comments on their motivations. Ok, so that wouldn’t formally be the Turing test any more, but I think that as a judgement of conscious intelligence, the artificial agent needs to at least be able to reflect the motivations of others and understand the representation of itself within others. Lots of recursive representations!

The development of consciousness within AI via empathy is what, in my opinion, will allow us to create friendly AI. Formal proofs won’t work due to computational irreducibility of complex systems. In an admittedly strained analogy this is similar to trying to formally prove where a toy sailboat will end up after dropping it in a river upstream. Trying to prove that it won’t get caught in an eddy before it reaches the ocean of friendliness (or perhaps if you’re pessimistic and you view the eddy as the small space of possibilities for friendly AI). Sure computers and silicon act deterministically (for the most part), but any useful intelligence will interact with an uncertain universe. It will also have to model humans out of necessity as humans are one of the primary agents on the Earth that will need to interact with… perhaps not if it becomes all-powerful but certainly initially. By modelling humans, it’s effectively empathising with our motivations and causing parts of our consciousness to be represented inside it[1].

Given that machine could increase it’s computationally capacity exponentially via Moore’s law (not to mention via potentially large investment and subsequently rapid datacenter expansion) it could eventually model many more individuals than any one human does. So if the AI had a large number of simulated human minds, which would, if accurately modelled, probably bawk at killing the original, then any actions the AI performed would likely benefit the largest number of individuals.

Or perhaps the AI would become neurotic trying to satisfy the desires and wants of conflicting opinions.

In some ways this is similar to Eliezer’s Collected Extrapolated Volition (as I remember it at least… It was a long time ago that I read it. I should do so again to see how/if it fits with what I’ve said here).

[1] People might claim that this won’t be an issue because digital minds designed from scratch will be able to box up individual representations to prevent a bleed through of beliefs. Unfortunately, I don’t think this is a tractable design for AI, even if it was desirable. AI is about efficiency of computation and representation, so these concepts and beliefs will blend. Besides, conceptual blending is quite likely a strong source of new ideas and hypotheses in the human brain.

How is “Advanced Intelligence” different from general AI? The release says:

Typical features of Advanced Intelligence include: (1) Close interaction and coordination between Natural Intelligence and Artificial Intelligence, (2) Ideas and applications that push the frontiers of both Artificial Intelligence and Natural Intelligence, (3) Large-scale Distributed Intelligence and Web Intelligence

FWIW, I’m not sure I agree with the name, since “advanced” is a relative term, and the field of AGI (artificial general intelligence) is already fragmented enough without adding additional labels. Having said that, I’ve been invited to be an assistant co-chair, so I cordially invite you to submit interesting papers since then they’ll be a greater chance of me getting interesting papers to review

I knew it must be possible in linux somehow. For one thing, there’s wmctrl, a command line program for scripting window positions and I found some scripts made by others in the Ubuntu forums that act similar to the way I wanted.

However, it turns out there is something already available if you’re using Compiz as your display manager.

To change to using Compiz and get the required config tool, run:

And then open the menu System → Preferences → Appearance. Go to the Visual Effects tab and choose “Extra”.

Then fire up the CompizConfig Settings Manager that’s also under System → Preferences. When the dialog loads, go to the filter and type “grid”. This is the module of Compiz that gives you almost the same behaviour as SizeUp (you can get the rest of the behaviour using other modules in the “Window Management” category.