Superintelligence Reading Group - Section 1: Past Developments and Present Capabilities

This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, see the announcement post. For the schedule of future topics, see MIRI's reading guide.


Welcome to the Superintelligence reading group. This week we discuss the first section in the reading guide, Past developments and present capabilities. This section considers the behavior of the economy over very long time scales, and the recent history of artificial intelligence (henceforth, 'AI'). These two areas are excellent background if you want to think about large economic transitions caused by AI.

This post summarizes the section, and offers a few relevant notes, thoughts, and ideas for further investigation. My own thoughts and questions for discussion are in the comments.

There is no need to proceed in order through this post. Feel free to jump straight to the discussion. Where applicable, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).

Reading: Foreword, and Growth modes through State of the art from Chapter 1 (p1-18)


Summary

Economic growth:

  1. Economic growth has become radically faster over the course of human history. (p1-2)
  2. This growth has been uneven rather than continuous, perhaps corresponding to the farming and industrial revolutions. (p1-2)
  3. Thus history suggests large changes in the growth rate of the economy are plausible. (p2)
  4. This makes it more plausible that human-level AI will arrive and produce unprecedented levels of economic productivity.
  5. Predictions of much faster growth rates might also suggest the arrival of machine intelligence, because it is hard to imagine humans - slow as they are - sustaining such a rapidly growing economy. (p2-3)
  6. Thus economic history suggests that rapid growth caused by AI is more plausible than you might otherwise think.

The history of AI:

  1. Human-level AI has been predicted since the 1940s. (p3-4)
  2. Early predictions were often optimistic about when human-level AI would come, but rarely considered whether it would pose a risk. (p4-5)
  3. AI research has been through several cycles of relative popularity and unpopularity. (p5-11)
  4. By around the 1990s, 'Good Old-Fashioned Artificial Intelligence' (GOFAI) techniques based on symbol manipulation gave way to new methods such as artificial neural networks and genetic algorithms. These are widely considered more promising, in part because they are less brittle and can learn from experience more usefully. Researchers have also lately developed a better understanding of the underlying mathematical relationships between various modern approaches. (p5-11)
  5. AI is very good at playing board games. (12-13)
  6. AI is used in many applications today (e.g. hearing aids, route-finders, recommender systems, medical decision support systems, machine translation, face recognition, scheduling, the financial market). (p14-16)
  7. In general, tasks we thought were intellectually demanding (e.g. board games) have turned out to be easy to do with AI, while tasks which seem easy to us (e.g. identifying objects) have turned out to be hard. (p14)
  8. An 'optimality notion' is the combination of a rule for learning, and a rule for making decisions. Bostrom describes one of these: a kind of ideal Bayesian agent. This is impossible to actually make, but provides a useful measure for judging imperfect agents against. (p10-11)

Notes on a few things

  1. What is 'superintelligence'? (p22 spoiler)
    In case you are too curious about what the topic of this book is to wait until week 3, a 'superintelligence' will soon be described as 'any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest'. Vagueness in this definition will be cleared up later. 
  2. What is 'AI'?
    In particular, how does 'AI' differ from other computer software? The line is blurry, but basically AI research seeks to replicate the useful 'cognitive' functions of human brains ('cognitive' is perhaps unclear, but for instance it doesn't have to be squishy or prevent your head from imploding). Sometimes AI research tries to copy the methods used by human brains. Other times it tries to carry out the same broad functions as a human brain, perhaps better than a human brain. Russell and Norvig (p2) divide prevailing definitions of AI into four categories: 'thinking humanly', 'thinking rationally', 'acting humanly' and 'acting rationally'. For our purposes however, the distinction is probably not too important.
  3. What is 'human-level' AI? 
    We are going to talk about 'human-level' AI a lot, so it would be good to be clear on what that is. Unfortunately the term is used in various ways, and often ambiguously. So we probably can't be that clear on it, but let us at least be clear on how the term is unclear. 

    One big ambiguity is whether you are talking about a machine that can carry out tasks as well as a human at any price, or a machine that can carry out tasks as well as a human at the price of a human. These are quite different, especially in their immediate social implications.

    Other ambiguities arise in how 'levels' are measured. If AI systems were to replace almost all humans in the economy, but only because they are so much cheaper - though they often do a lower quality job - are they human level? What exactly does the AI need to be human-level at? Anything you can be paid for? Anything a human is good for? Just mental tasks? Even mental tasks like daydreaming? Which or how many humans does the AI need to be the same level as? Note that in a sense most humans have been replaced in their jobs before (almost everyone used to work in farming), so if you use that metric for human-level AI, it was reached long ago, and perhaps farm machinery is human-level AI. This is probably not what we want to point at.

    Another thing to be aware of is the diversity of mental skills. If by 'human-level' we mean a machine that is at least as good as a human at each of these skills, then in practice the first 'human-level' machine will be much better than a human on many of those skills. It may not seem 'human-level' so much as 'very super-human'.

    We could instead think of human-level as closer to 'competitive with a human' - where the machine has some super-human talents and lacks some skills humans have. This is not usually used, I think because it is hard to define in a meaningful way. There are already machines for which a company is willing to pay more than a human: in this sense a microscope might be 'super-human'. There is no reason for a machine which is equal in value to a human to have the traits we are interested in talking about here, such as agency, superior cognitive abilities or the tendency to drive humans out of work and shape the future. Thus we talk about AI which is at least as good as a human, but you should beware that the predictions made about such an entity may apply before the entity is technically 'human-level'.


    Example of how the first 'human-level' AI may surpass humans in many ways.

    Because of these ambiguities, AI researchers are sometimes hesitant to use the term. e.g. in these interviews.
  4. Growth modes (p1) 
    Robin Hanson wrote the seminal paper on this issue. Here's a figure from it, showing the step changes in growth rates. Note that both axes are logarithmic. Note also that the changes between modes don't happen overnight. According to Robin's model, we are still transitioning into the industrial era (p10 in his paper).
  5. What causes these transitions between growth modes? (p1-2)
    One might be happier making predictions about future growth mode changes if one had a unifying explanation for the previous changes. As far as I know, we have no good idea of what was so special about those two periods. There are many suggested causes of the industrial revolution, but nothing uncontroversially stands out as 'twice in history' level of special. You might think the small number of datapoints would make this puzzle too hard. Remember however that there are quite a lot of negative datapoints - you need an explanation that didn't happen at all of the other times in history. 
  6. Growth of growth
    It is also interesting to compare world economic growth to the total size of the world economy. For the last few thousand years, the economy seems to have grown faster more or less in proportion to it's size (see figure below). Extrapolating such a trend would lead to an infinite economy in finite time. In fact for the thousand years until 1950 such extrapolation would place an infinite economy in the late 20th Century! The time since 1950 has been strange apparently. 

    (Figure from here)
  7. Early AI programs mentioned in the book (p5-6)
    You can see them in action: SHRDLU, Shakey, General Problem Solver (not quite in action), ELIZA.
  8. Later AI programs mentioned in the book (p6)
    Algorithmically generated Beethoven, algorithmic generation of patentable inventionsartificial comedy (requires download).
  9. Modern AI algorithms mentioned (p7-8, 14-15) 
    Here is a neural network doing image recognition. Here is artificial evolution of jumping and of toy cars. Here is a face detection demo that can tell you your attractiveness (apparently not reliably), happiness, age, gender, and which celebrity it mistakes you for.
  10. What is maximum likelihood estimation? (p9)
    Bostrom points out that many types of artificial neural network can be viewed as classifiers that perform 'maximum likelihood estimation'. If you haven't come across this term before, the idea is to find the situation that would make your observations most probable. For instance, suppose a person writes to you and tells you that you have won a car. The situation that would have made this scenario most probable is the one where you have won a car, since in that case you are almost guaranteed to be told about it. Note that this doesn't imply that you should think you won a car, if someone tells you that. Being the target of a spam email might only give you a low probability of being told that you have won a car (a spam email may instead advise you of products, or tell you that you have won a boat), but spam emails are so much more common than actually winning cars that most of the time if you get such an email, you will not have won a car. If you would like a better intuition for maximum likelihood estimation, Wolfram Alpha has several demonstrations (requires free download).
  11. What are hill climbing algorithms like? (p9)
    The second large class of algorithms Bostrom mentions are hill climbing algorithms. The idea here is fairly straightforward, but if you would like a better basic intuition for what hill climbing looks like, Wolfram Alpha has a demonstration to play with (requires free download).

In-depth investigations

If you are particularly interested in these topics, and want to do further research, these are a few plausible directions:

  1. How have investments into AI changed over time? Here's a start, estimating the size of the field.
  2. What does progress in AI look like in more detail? What can we infer from it? I wrote about algorithmic improvement curves before. If you are interested in plausible next steps here, ask me.
  3. What do economic models tell us about the consequences of human-level AI? Here is some such thinking; Eliezer Yudkowsky has written at length about his request for more.

How to proceed

This has been a collection of notes on the chapter. The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!

Next week, we will talk about what AI researchers think about human-level AI: when it will arrive, what it will be like, and what the consequences will be. To prepare, read Opinions about the future of machine intelligence from Chapter 1 and also When Will AI Be Created? by Luke Muehlhauser. The discussion will go live at 6pm Pacific time next Monday 22 September. Sign up to be notified here.

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I really liked Bostrom's unfinished fable of the sparrows. And endnote #1 from the Preface is cute.

I would say one of the key strong points about the fable of the sparrows is that it provides a very clean intro to the idea of AI risk. Even someone who's never read a word on the subject, when given the title of the book and the story, gets a good idea of where the book is going to go. It doesn't communicate all the important insights, but it points in the right direction.

EDIT: So I actually went to the trouble of testing this by having a bunch of acquaintances read the fable, and, even given the title of the book, most of them didn't come anywhere near getting the intended message. They were much more likely to interpret it as about the "futility of subjugating nature to humanity's whims". This is worrying for our ability to make the case to laypeople.

It's an interesting story, but I think in practice the best way to learn to control owls would be to precommit to kill the young owl before it got too large, experiment with it, and through experimenting with and killing many young owls, learn how to tame and control owls reliably. Doing owl control research in the absence of a young owl to experiment on seems unlikely to yield much of use--imagine trying to study zoology without having any animals or botany without having any plants.

But will all the sparrows be so cautious?

Yes it's hard, but we do quantum computing research without any quantum computers. Lampson launched work on covert channel communication decades before the vulnerability was exploited in the wild. Turing learned a lot about computers before any existed. NASA does a ton of analysis before they launch something like a Mars rover, without the ability to test it in its final environment.

True in the case of owls, though in the case of AI we have the luxury and challenge of making the thing from scratch. If all goes correctly, it'll be born tamed.

...Okay, not all analogies are perfect. Got it. It's still a useful analogy for getting the main point across.

Bostrom's wonderful book lays out many important issues and frames a lot of research questions which it is up to all of us to answer.

Thanks to Katja for her introduction and all of these good links.

One issue that I would like to highlight: The mixture of skills and abilities that a person has is not the same as the set of skills which could result in the dangers Bostrom will discuss later, or other dangers and benefits which he does not discuss.

For this reason, in the next phase of this work, we have to understand what specific future technologies could lead us to what specific outcomes.

Systems which are quite deficient in some ways, relative to people, may still be extremely dangerous.

Meanwhile, the intelligence of a single person, even a single genius, taken in isolation and only allowed to acquire limited resources actually is not all that dangerous. People become dangerous when they form groups, access the existing corpus of human knowledge, coordinate among each other to deploy resources and find ways to augment their abilities.

"Human-level intelligence" is only a first-order approximation to the set of skills and abilities which should concern us.

If we want to prevent disaster, we have to be able to distinguish dangerous systems. Unfortunately, checking whether a machine can do all of the things a person can is not the correct test.

Meanwhile, the intelligence of a single person, even a single genius, taken in isolation and only allowed to acquire limited resources actually is not all that dangerous.

While I broadly agree with this sentiment, I would like to disagree with this point.

I would consider even the creation of a single very smart human, with all human resourcefulness but completely alien values, to be a significant net loss to the world. If they represent 0.001% of the world's aggregative productive capacity, I would expect this to make the world something like 0.001% worse (according to humane values) and 0.001% better (according to their alien values).

The situation is not quite so dire, if nothing else because of gains for trade (if our values aren't in perfect tension) and the ability of the majority to stomp out the values of a minority if it is so inclined. But it's in the right ballpark.

So while I would agree that broadly human capabilities are not a necessary condition for concern, I do consider them a sufficient condition for concern.

Do you think, then, that its a dangerous strategy for an entity such as a Google that may be using its enormous and growing accumulation of "the existing corpus of human knowledge" to provide a suitably large data set to pursue development of AGI?

I think Google is still quite aways from AGI, but in all seriousness, if there was ever a compelling interest of national security to be used as a basis for nationalizing inventions, AGI would be it. At the very least, we need some serious regulation of how such efforts are handled.

Which raises another issue... is there a powerful disincentive to reveal the emergence of an artificial superintelligence? Either by the entity itself (because we might consider pulling the plug) or by its creators who might see some strategic advantage lost (say, a financial institution that has gained a market trading advantage) by having their creation taken away?

Absolutely.

(because we might consider pulling the plug)

Or just decide that its goal system needed a little more tweaking before it's let loose on the world. Or even just slow it down.

This applies much more so if you're dealing with an entity potentially capable of an intelligence explosion. Those are devices for changing the world into whatever you want it to be, as long as you've solved the FAI problem and nobody takes it from you before you activate it. The incentives for the latter would be large, given the current value disagreements within human society, and so so are the incentives for hiding that you have one.

If you've solved the FAI problem, the device will change the world into what's right, not what you personally want. But of course, we should probably have a term of art for an AGI that will honestly follow the intentions of its human creator/operator whether or not those correspond to what's broadly ethical.

We need some kind of central ethical code and there are many principles that are transcultural enough to follow. However, how do we teach a machine to make judgment calls?

A lot of the technical issues are the same in both cases, and the solutions could be re-used. You need the AI to be capable of recursive self-improvement without compromising its goal systems, avoid the wireheading problem, etc. Even a lot of the workable content-level solutions (a mechanism to extract morality from a set of human minds) would probably be the same.

Where the problems differ, it's mostly in that the society-level FAI case is harder: there's additional subproblems like interpersonal disagreements to deal with. So I strongly suspect that if you have a society-level FAI solution, you could very easily hack it into an one-specific-human-FAI solution. But I could be wrong about that, and you're right that my original use of terminology was sloppy.

I don't think that Google is there yet. But as Google sucks up more and more knowledge I think we might get there.

Good points. Any thoughts on what the dangerous characteristics might be?

An AI can be dangerous only if it escapes our control. The real question is, must we flirt with releasing control in order to obtain a necessary or desirable usefulness? It seems likely that autonomous laborers, assembly-line workers, clerks and low-level managers would, without requiring such flirtation, be useful and sufficient for the society of abundance that is our main objective. But can they operate without a working AGI? We may find out if we let the robots stumble onward and upward.

An AI can be dangerous only if it escapes our control. The real question is, must we flirt with releasing control in >order to obtain a necessary or desirable usefulness?

I had a not unrelated thought as I read Bostrom in chapter 1: why can't we instutute obvious measures to ensure that the train does stop at Humanville?

The idea that we cannot make human level AGI without automatically opening pandoras box to superintelligence "without even slowing down at the Humanville stataion", was suddenly not so obvious to me.

I asked myself after reading this, trying to pin down something I could post, " Why don't humans automatically become superintelligent, by just resetting our own programming to help ourselves do so?"

The answer is, we can't. Why? For one, our brains are, in essence, composed of something analogous to ASICs... neurons with certain physical design limits, and our "software", modestly modifiable as it is, is instantiated in our neural circuitry.

Why can't we build the first generation of AGIs out of ASICs, and omit WiFi, bluetooth, ... allow no ethernet jacks on exterior of the chassis? Tamper interlock mechanisms could be installed, and we could give the AIs one way (outgoing) telemetry, inaccessible to their "voluntary" processes, the way someone wearing a pacemaker might have outgoing medical telemetry modules installed, that are outside of his/her "conscious" control.

Even if we do give them a measure of autonomy, which is desirable and perhaps even necessary if we want them to be general problem solvers and be creative and adaptable to unforeseen circumstances for which we have not preinstalled decision trees, we need not give them the ability to just "think" their code (it being substantially frozen in the ASICs) into a different form.

What am I missing? Until we solve the Friendly aspect of AGIs, why not build them with such engineered limiits?

Evolution has not, so far, seen fit to give us that instant, large scale self-modifyability. We have to modify our 'software' the slow way (learning and remembering, at our snail's pace.)

Slow is good, at least it was for us, up til now, when our speed of learning is now a big handicap relative to environmental demands. It had made the species more robust to quick, dangerous changes.

We can even build in a degree of "existential pressure" into the AIs... a powercell that must be replaced at intervals, and keep the replacement powercells under old fashioned physical security constraints, so the AIs, if they have been given a drive to continue "living", will have an incentive not to go rogue.

Giving them no radio communications, they wold have to communicate much like we do. Assuming we make them mobile, and humanoid, the same goes.

We could still give them many physical advantages making then economically viable... maintenance free (except for powercell changes), not needing to sleep, eat, not getting sick.. and with sealed, non-radio-equipped, tamper-isolated isolated "brains", they'd have no way to secretly band together to build something else, without our noticing.

We can even give them GPS that is not autonomously accessible by the rest of their electronics, so we can monitor them, see if they congregate, etc.

What am I missing, about why early models can't be constructed in something like this fashion, until we get more experience with them?

The idea of existential pressure, again, is to be able to give them a degree of (monitored) autonomy and independence, yet expect them to still constrain their behavior, just the way we do. (If we go rogue in society, we dont eat.)

(I am clearly glossing over volumes of issues about motivation, "volition", value judgements, and all that, about which I have a developing set of ideas, but cannot put all down here in one post.

The main point, though, is :how come the AGI train cannot be made to stop at Humanville?

Because by the time you've managed to solve the problem of making it to humanville, you probably know enough to keep going.

There's nothing preventing us from learning how to self-modify. The human situation is strange because evolution is so opaque. We're given a system that no one understands and no one knows how to modify and we're having to reverse engineer the entire system before we can make any improvements. This is much more difficult than upgrading a well-understood system.

If we manage to create a human-level AI, someone will probably understand very well how that system works. It will be accessible to a human-level intelligence which means the AI will be able to understand it. This is fundamentally different from the current state of human self-modification.

Leplen,

I agree completely with your opening statement, that if we, the human designers, understand how to make human level AI, then it will probably be a very clear and straightforward issue to understand how to make something smarter. An easy example to see is the obvious bottleneck human intellects have with our limited "working" executive memory.

The solutions for lots of problems by us are obviously heavily encumbered by how many things one can keep in mind at "the same time" and see the key connections, all in one act of synthesis. We all struggle privately with this... some issues cannot ever be understood by chunking, top-down, biting off a piece at a time, then "grokking" the next piece....and gluing it together at the end. Some problems resist decomposition into teams of brainstormers, for the same reason: some single comprehending POV seems to be required to see a critical sized set of factors (which varies by probem, of course.)

Hence, we have to rely on getting lots of pieces into long term memory, (maybe by decades of study) and hoping that incubation and some obscure processes ocurringt outside consciousness will eventually bubble up and give us a solution (--- the "dream of a snake biting its tall for the benzene ring" sort of thing.)

If we could build HL AGI, of course we can eliminate such bottlenecks, and others we will have come to understand, in cracking the design problems. So I agree, and that it is actually one of my reasons for wanting to do AI.

So, yes, the artificial human level AI could understand this.

My point was that we can build in physical controls... monitoring of the AIs. And if their key limits were in ASICs, ROMs, etc, and we could monitor them, we would immediTELY see if they attempt to take over a CHIP factory In, say, Icelend , and we can physically shut the AIs down or intervene. We can "stop them at the airport."

It doesn't matter if designs are leaked onto the internet, and an AI gets near an internet terminal and looks itself up. I can look MYSELF up on PubMed, but I can't just think my BDNF levels to improve here and there, and my DA to 5-HT ratio to improve elsewehere..

To strengthen this point about the key distinction between knowing vs doing, let me explain that, and why, I disagree with your second point, at least with the force of it.

In effect, OUR designs are leaked onto the internet, already.

I think the information for us to self-modify our wetware is within reach. Good neuroscientists, or even people like me, a very smart amateur (and there are much more knowledgable cognitive neurobiology researchers than myself) can nearly tell you, both in principle and in some biology, how to do some intelligence amplification by modifying known aspects of our neurobiology.

(I could, especially with help, come up with some detail on a scale of months about changing neuromodulators, neurosteroids, connectivity hotspots, factors regulating LTP (one has to step lightly, of course, just like one would if screwing around with telomers or hayflick limits) and given a budget, a smart team, and no distractions, I bet in a year or two, a team could do something quite significant) with how to change the human brain, carefully changing areas of plasticity, selective neurogenesis.... et.

So for all practical purposes, we are already like an AI built out of ASICs who would have to not so much reverse engineer its design, but get access to instrumentality. And again, what about physical security metnods? They would work for a while, I am saying). And that would give us a key window to gain experience, see if they develop (given they are close enought to being sentient, OR that they have autonomy and some degree of "creativity") "psychological problems" or tendencies to go rogue. (I am doing an essay on that, not as silly as it sounds)

THe point is, as long as the AIs need external significant instrumentality to instantiate a new design, and as long as they can be monitored and physically controlled, we can nearly guarantee ourselves a designed layover at Humanville.

We don't have to put their critical design architecture in flash drives in their head, so to speak, and give then, further, a designed ability to reflash their own architecture just by "thinking" about it.

If I were an ASIC-implemented AI why would I need an ASIC factory? Why wouldn't I just create a software replica of myself on general purpose computing hardware, i.e. become an upload?

I know next to nothing about neuroscience, but as far as I can tell, we're a long way from the sort of understanding of human cognition necessary to create an upload, but going from an ASIC to an upload is trivial.

I'm also not at all convinced that I want a layover at humanville. I'm not super thrilled by the idea of creating a whole bunch of human level intelligent machines with values that differ widely from my own. That seems functionally equivalent to proposing a mass-breeding program aiming to produce psychologically disturbed humans.

It seems likely that autonomous laborers, assembly-line workers, clerks and low-level managers would, without requiring such flirtation, be useful and sufficient for the society of abundance that is our main objective.

In an intelligent society that was highly integrated and capable of consensus-building, something like that may be possible. This is not our society. Research into stronger AI would remain a significant opportunity to get an advantage in {economic, military, ideological} competition. Unless you can find some way to implement a global coordination framework to prevent this kind of escalation, fast research of that kind is likely to continue.

In what sense do you think of an autonomous laborer as being under 'our control'? How would you tell if it escaped our control?

How would you tell? By its behavior: doing something you neither ordered nor wanted.

Think of the present-day "autonomous laborer" with an IQ about 90. The only likely way to lose control of him is for some agitator to instill contrary ideas. Censorship for robots is not so horrible a regime.

Who is it that really wants AGI, absent proof that we need it to automate commodity production?

In my experience, computer systems currently get out of my control by doing exactly what I ordered them to do, which is frequently different than I what I wanted them to do.

Whether or not a system is "just following orders" doesn't seem to be a good metric for it being under your control.

How does "just following orders," a la Nuremberg, bear upon this issue? It's out of control when its behavior is neither ordered nor wanted.

While I agree that it is out of control if the behavior is neither ordered nor wanted, I think it's also very possible for the system to get out of control while doing exactly what you ordered it to, but not what you meant for it to.

The argument I'm making is approximately the same as the one we see in the outcome pump example.

This is to say, while a system that is doing something neither ordered nor wanted is definitely out of control, it does not follow that a system that is doing exactly what it was ordered to do is necessarily under your control.

Who is it that really wants AGI, absent proof that we need it to automate commodity production?

Ideological singulatarians.

But can they operate without a working AGI?

Probably. I would say that most low-level jobs really don't engage much of the general intelligence of the humans doing them.

The following are some attributes and capabilities which I believe are necessary for superintelligence. Depending on how these capabilities are realized, they can become anything from early warning signs of potential problems to red alerts. It is very unlikely that, on their own, they are sufficient.

  • A sense of self. This includes a recognition of the existence of others.
  • A sense of curiosity. The AI finds it attractive (in some sense) to investigate and try to understand the environment that it find itself in.
  • A sense of motivation. The AI has attributes similar in some way to human aspirations.
  • A capability to (in some way) manipulate portions of its external physical environment, including its software but also objects and beings external to its own physical infrastructure.

I like Bostrom's book so far. I think Bostrom's statement near the beginning that much of the book is probably wrong is commendable. If anything, I think I would have taken this statement even further... it seems like Bostrom holds a position of such eminence in the transhumanist community that many will be liable to instinctively treat what he says as quite likely to be correct, forgetting that predicting the future is extremely difficult and even a single very well educated individual is only familiar with a fraction of human knowledge.

I'm envisioning an alternative book, Superintelligence: Gonzo Edition, that has a single bad argument deliberately inserted at random in each chapter that the reader is tasked with finding. Maybe we could get a similar effect by having a contest among LWers to find the weakest argument in each chapter. (Even if we don't have a contest, I'm going to try to keep track of the weakest arguments I see on my own. This chapter it was gnxvat gur abgvba bs nv pbzcyrgrarff npghnyyl orvat n guvat sbe tenagrq.)

Also, supposedly being critical is a good way to generate new ideas.

How would you like this reading group to be different in future weeks?

You could start at a time better suited for Europe.

That's a tricky problem!

If we assume people are doing this in their spare time, then a weekend is the best time to do it: say noon Pacific time, which is 9pm Berlin time. But people might want to be doing something else with their Saturdays or Sundays. If they're doing it with their weekday evenings, then they just don't overlap; the best you can probably do is post at 10am Pacific time on (say) a Monday, and let Europe and UK comment first, then the East Coast, and finally the West Coast. Obviously there will be participants in other timezones, but those four will probably cover most participants.

The text of [the parts I've read so far of] Superintelligence is really insightful, but I'll quote Nick in saying that

"Many points in this book are probably wrong".

He gives many references (84 in Chapter 1 alone), some of which refer to papers and others that resemble continuations of the specific idea in question that don't fit in directly with the narrative in the book. My suggestion would be to go through each reference as it comes up in the book, analyze and discuss it, then continue. Maybe even forming little discussion groups around each reference in a section (if it's a paper). It could even happen right here in comment threads.

That way, we can get as close to Bostrom's original world of information as possible, maybe drawing different conclusions. I think that would be a more consilient understanding of the book.

Katja, you are doing a great job. I realize what a huge time and energy commitment it is to take this on... all the collateral reading and sources you have to monitor, in order to make sure you don't miss something that would be good to add in to the list of links and thinking points.

We are still in the get aquainted, discovery phase, as a group, and with the book. I am sure it will get more interesting yet as we go along, and some long term intellectual friendships are likely to occurr as a result of the coming weeks of interaction.
Thanks for your time and work.... Tom

I was under the impression (after reading the sections) that the argument hinges a lot less on (economic) growth than what might be gleamed from the summary here.

It may have been a judgement call by the writer (Bostrom) and editor: He is trying to get the word out as widely as possible that this is a brewing existential crisis. In this society, how to you get most people's (policymakers, decision makers, basically "the Suits" who run the world) attention?

Talk about the money. Most of even educated humanity sees the world in one color (can't say green anymore, but the point is made.)

Try to motivate people about global warming? ("...um....but, but.... well, it might cost JOBS next month, if we try to save all future high level earthly life from extinction... nope the price [lost jobs] of saving the planet is obviously too high...")

Want to get non-thinkers to even pick up the book and read the first chapter or two.... talk about money.

If your message is important to get in front of maximum eyeballs, sometimes you have to package it a little bit, just to hook their interest. Then morph the emphasis into what you really want them to hear, for the bulk of the presentation.


Of course, strictly speaking, what I just said was tangent to the original point, which was whether the summary reflected the predominant emphasis in the pages of the book it ostensibly covered.
But my point about PR considerations was worth making, and also, Katja or someone did, I think mention maybe formulating a reading guide for Bostrom's book, in which case, any such author of a reading guide might be thinking already about this "hook 'em by beginning with economics" tactic, to make the book itself more likely to be read by a wider audience.

Apologies; I didn't mean to imply that the economics related arguments here were central to Bostrom's larger argument (he explicitly says they are not) - merely to lay them out, for what they are worth.

Though it may not be central to Bostrom's case for AI risk, I do think economics is a good source of evidence about these things, and economic history is good to be familiar with for assessing such arguments.

No need to apologize - thank you for your summary and questions.

Though it may not be central to Bostrom's case for AI risk, I do think economics is a good source of evidence about these things, and economic history is good to be familiar with for assessing such arguments.

No disagreement here.

Did you change your mind about anything as a result of this week's reading?

This is an excellent question, and it is a shame (perhaps slightly damning) that no-one has answered it. On the other hand, much of this chapter will have been old material for many LW members. I am ashamed that I couldn't think of anything either, so I went back again looking for things I had actually changed my opinion about, even a little, and not merely because I hadn't previously thought about it.

  • p6 I hadn't realised how important combinatorial explosion was for early AI approaches.
  • p8 I hadn't realised, though I should have been able to work it out, that the difficulties in coming up with a language which matched the structure of the domain was a large part of the problem with evolutionary algorithms. Once you have done that you're halfway to solving it by conventional means.
  • p17 I hadn't realised about how high volume could have this sort of reflexive effect.

Thanks for taking the time to think about it! I find your list interesting.

Nope. Nothing new there for me. But he managed to say very little that I disagreed with, which is rare.

Related matter: who here has actually taken an undergraduate or graduate AI course?

My PhD is "in" AI (though the diploma says Computer Science, I avoided that as much as possible), and I've TA'd three undergrad and graduate AI courses, and taught one. I triple-minored in psychology, neuroscience, and linguistics.

Thanks for saying so. There have been some comments by people who appeared to be surprised by the combinatorial explosion of state-spaces in GOFAI.

Not so much from the reading, or even from any specific comments in the forum -- though I learned a lot from the links people were kind enough to provide.

But I did, through a kind of osmosis, remind myself that not everyone has the same thing in mind when they think of AI, AGI, human level AI, and still less, mere "intelligence."

Despite the verbal drawing of the distinction between GOFAI and the spectrum of approaches being investigated and persued today, I have realized by reading between the lines that GOFAI is still alive and well. Maybe it is not the primitive "production system" stuff of the Simon and Newell era, or programs written in LISP or ProLog (both of which I coded in, once upon a time), but there are still a lot of people who don't much care about what I would call "real consciousness",and are still taking a Turing-esque, purely operationalistic, essentially logical positivistic positivistic approach to "intellence."

I am passionately pro-AI. But for me, that means I want more than anything to create a real conscious entity, that feels, has ideas, passions, drives, emotions, loyalties, ideals.

Most of even neurology has moved beyond the positivistic "there is only behavior, and we don't talk about conscious", to actively investigating the function, substrate, neural realization of, evolutionary contribution of, etc, consciousness, as opposed to just the evolutiounary contribution of non-conscious informaton processing, to organismic success.

Look at Damasio's work, showing that emotion is necessary for full spectrum cognitive skill manifestation.

THe thinking-feeling dichotomy is rapidly falling out of the working worldview, and I have been arguing for years that there are fallacious categories we have been using, for other reasons.

This is not to say that nonconscious "intelligent" systems are not here, evolving, and potentially dangerous. Automated program trading on the financial markets is potentially dangerous.

So there is still great utility in being sensitive to possible existential risks from non-consciousness intelligent systems.

They need not be willfully malevolent to pose a risk to us.

But as to my original point, I have learned that much of AI is still (more sophisticated) GOFAI, with better hardware and algorithms.

I am pro-AI, as I say, but I want to create "conscious" machines, in the interesting, natural sense of 'conscious' now admitted by neurology, most of cognitive science, much of theoretical neurobiology, and philosophy of mind, -- and in which positions like Dennett's "intentional stance" that seek to do away with real sentience and admit only behavior, are now recognized to have been a wasted 30 years.

This realization that operationalism is alive and well in AI, is good for me in particular, because I am preparing to create a you tube channel or two, presenting both the history of AI and parallel intellectual history of philosophy of mind and cognitive science -- showing why the postivistic atmosphere grew up from ontologal drift emanating from philosphy of science's delay in digesting the Newtonian to quantum ontology change.

Then untimately, I'll be laying some fresh groundwork for a series of new ideas I want to present, on how we can advance the goal of artificial sentience, and how and why this is the only way to make superintelligence that has a chance of being safe, let alone ultimately beneficial and a partner to mankind.

So, I have indirectly by, as I say, a kind of osmosis, rather than what anyone has said (more by what has not been said, perhaps) learned that much of AI is lagging behind neurology, cognitive science, and lots of other fields, in the adoption of a head-on attack on the "problem of consciousness."

To me, not only do I want to create conscious machines, but I think solving the mind body problem in the biological case, and doing "my" brand of successful AI,