Category: Human-AI Leviathan (HAL)

  • Life isn’t all that great, if you’re a Statutory Ape

     

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    Play the song above while you read this to reveal its secret message!

    A couple things this blog is NOT about, in case you got here via Bing.

    First – this is NOT a blog about Julianne Malveaux calling President Trump an Orangutang.

    “At the end of the day, the goal has to be to get rid of the orange orangutan.” 

    -Julianne Malveaux

    Julianne – just be better. You know the story about wrestling with pigs. You just get dirty and the pig likes it.

    Second – this is NOT a blog about laws regulating the owning of Apes in various states.

    Go here for that

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    Note, if your really into owning an ape, you can make your way to Alabama, Delaware, Idaho, Indiana, Kansas, Michigan, Mississippi, Nebraska, Nevada, New Mexico, North Carolina, North Dakota, Pennsylvania, Rhode Island, Tennessee, Texas, West Virginia, Wisconsin or Wyoming and have a backyard full of great apes!  Asshole.

    No – this is a blog about Elon Musk and his dire warnings

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    As reported here (and many other places), Elon Musk has determined that AI and computers are going to be so smart that we will look like monkeys next to them. When that happens, well… lets hear it in his own words:

    “Can a chimpanzee really understand humans? Not really,” Musk said.

    “We just seem like strange aliens. They mostly just care about other chimpanzees. And this will be how it is, more or less.”

    “The intelligence gap [between Humans and AI] will probably be much, much greater [than between Apes and Humans], and said it could easily get to the point where things get out of humans’ control.”

    “What do you do with a situation like that? I’m not sure. I hope they’re nice,”

    “People underestimate the capability of AI.”

    “They sort of think it’s like a smart human, but it’s going to be much more than that,”  “It’ll be much smarter than the smartest human.”

    “The first thing you should assume is that we are very dumb, and that we can definitely make things smarter than ourselves,”

    -Elon Musk

    In other interviews he has noted that the “unfortunate result of the growing power of digital intelligence could lead to humanity being hoarded into small, zoo-like swaths of the globe, an existence that would more closely resemble what has happened to monkeys, which have been stripped of their natural habitat by vastly more intelligent primates”. 

    So his points seem to be, AI is going to take over like it or not. “Hope they are nice”. Musk has started a new company (Neuralink) whose purpose is to augment humans with AI by creating a high bandwidth interface to the brain, so that Humans can remain on top as long as possible. I won’t (in this blog) go into that. Lets focus on his core arguments that he uses to convince himself, investors and everyone else that it is inevitable that AI is going become emergent, potentially vicious and an existential threat to humanity.

    His argument seems to go like this:

    Premise 1: “We are very dumb”

    Premise 2: “We can definitely make things smarter than ourselves”

    Conclusion 1: We can make a super intelligent AI

    Premise 3: This super intelligent AI will surpass human intelligence i.e.- become emergent.

    Premise 4: This emergent AI will either be benign or not benign.

    Premise 5: If Benign – we become slaves in a zoo

    Premise 6: If not Benign – uh – well I guess thats it. End of Humans.

    Conclusion 2: “If we can’t beat them, we should join them”. So lets build something that will help us keep up. (IE – Neuralink).

    Lets unpack these premises and conclusions to see if they stand up to fact and logic.

    Premise 1: “We are very dumb”

    This is hard to refute. Humans make lot of mistakes. Humans have done horrible things to themselves, the environment, other animal species and soon enough Space.

    However – the underpinning of this presumption is interesting. Calling a person or entire race of people dumb, implies there is a singular type intelligence. That there is “Dumb” and “Not Dumb”. And that it is, at best, a spectrum. When Elon says “We are Dumb” he is assuming that there is a spectrum that everyone agrees with. IQ aside – there is basically dumber and dumb and smart.

    Elon View of Intelligence

    But is this true? Is there really one thing that we call intelligence and it can somehow be measured? Is it IQ? If not what?

    In 1983 an American developmental psychologist Howard Gardener of Harvard described 9 types intelligence that humans exhibit. I won’t go into any great detail here but the 9 types are: 

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    As a bottom up observer of humans, Gardener discovered that there were people that were (for instance) really good at Math/Logic but very poor in Intra-personal. Each of the types of “Intelligence” were independent and separately measurable. More importantly, it suggests that the traditional notion of intelligence, based on I.Q. testing, is far too limited.

    So would we expect AI to be smarter than Humans at all of these types of intelligence? Doubtful. It seems reasonable to assume that AI can get better at some of them. But all of them? And is this really an exclusive lists? I personally think that 9 types is underselling humans. At the very least however we can agree that the broad assumption that Elon seems to be making (that there is only one type of intelligence) is flawed. Thus premise 1 seems to be in question. If you want to learn more about Dr. Gardener’s theory I suggest you go here.

    Premise 2: “We can definitely make things smarter than ourselves”

    Humans don’t fly – but we can invent airplanes. Humans can only run so fast and so far – so we invent cars, trains and busses. Humans can only swim so far – so we invent boats and submarines. By analogy – humans can only be so smart – so we can invent something smarter than us.

    But is this true? The claim is strongly based on what your mean by the word “smart”. We can certainly make computers that calculate faster than humans. We can make AI that can translate into multiple languages.

    But can we make computers that know what they are calculating? Or that understand the languages that they are translating? What do we mean by “know” and “understand”?

    This is specifically a issue addressed by Shalom Lappin, Professor of Computational Linguistics at the University of Gothenburg.

    “The revolution in artificial intelligence that has arrived with the deep learning technology is still in its infancy, and even though it’s developing fast, I don’t think the idea of malicious super-agents is a real prospect that we have to worry about in the near future.”

    -Shalom Lappin, Professor of Computational Linguistics, University of Gothenburg

    Computers only reason about the task for which they are trained. They successfully find patterns and associations in millions of data, thus become better than humans in playing chess, translating and writing texts and perhaps one day, driving a car. But as soon as we leave a specific and defined area and jump to another they are lost.  General intelligence eludes them. And always will. More from Dr. Lappin:

    “My personal feeling is that we will never quite get to the point where machines have something that resembles general reasoning power. But I could well be wrong”

    -Shalom Lappin.

    Its interesting to note his hesitation in making the blanket claim – and that is what I am sure the Elon apologist would do. That being said – his main point is that there no evidence that man can make general AI. There is ample evidence that man can make a computer do specific tasks better than humans – although even there – human translation is still much better than AI based which tends to get 80% of the text right. In a similar vein Richard Johansson, Associate Professor at the division of Data Science at Chalmers,  notes:

    The Computers’ weakness is that they don’t really understand anything,

    -Richard Johansson, Associate Professor at the division of Data Science at Chalmers.

    Will AI improve. Of course. Will AI suddenly grow awareness – there is NO evidence of this. 

    Thus Elon’s first conclusion – “We can make a super intelligent AI” seems, at best, in question. He has underestimated what Intelligence itself is as shown in premise 1 and has overestimated what humans can do in premise 2.

    Premise 4: This emergent AI will either be benign or not benign.

    This seems pretty self evident huh? The AI that (at this point questionably) become Aware would either be hostile to its creators or not – correct? It will either care for us like a loving mother – or destroy us like the terminator. We will have 2 choices in the former case and no choices in the latter.

    But is this true? Aren’t there possible future worlds where an emergent AI cares for Humans in many ways but also destroys many things precious to humans? For instance – maybe a benign AI decides that all the Militaries in the world need to be ‘disabled’ for the long term health of the race. So it initiates nuclear blast to go off on all military bases worldwide at the same time. This of course results in significant loss of life and a horrible situation for those living in that world. But its certainly not simply AI-Mother vs. Terminator. Its more of a stupid decision by a computer that does not have any perspective. But again – all we need is one example of an AI that is neither benign or not-benign to invalidate this premise.

    The other problem is with the word benign itself.  Lets examine this. There are 2 meanings to the word:

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    Note that definition 1 is all about intention. Humans and animals have the capacity for intention. They can be kindly or not. Agreeable or not. Accommodating, Gracious or Considerate. But Computers can’t really be any of those.

    Definition 2 is the Medical definition of Benign. It is all about outcomes. A disease can be benign or not. It just means that its not going to kill you. A computer can be benign as well. (Ever had a computer lock you out of your email after 3 tries to remember you password – they are not benign in that case).

    Because of the way that human languages have evolved to be so flexible as allow us to communicate both very specific information as well as vast philosophical theories – its hopelessly broken as a true way to be anything other than be superficially precise. If you don’t believe me go read here and here.

    When people talk about a computer program being benign – what they are REALLY saying is that it has good outcomes. The medical notion. People who are saying that computers can be “kind” are saying something that has no basis in the current world – and by many people estimation – in any future world. It may be the case that what we mean by “Kindly” is is just something that organic carbon based life can do.

    Premise 5 and 6 and Conclusion 2 – nope.

    Premise 5 and 6 are basically sub-elements of Premise 4 and thus we do not need to address them separately.

    Thus as you can see – Conclusion 2 – ‘that if we can’t beat them we should join them’ – seems to be a hasty conclusion. Why should we spend money trying to put computers in our head if there is no existential threat?

    Conclusion

    Its clear that this argument for why we need to invest in augmentation to try to stay ahead of mythical “emergent” AI seems far fetched.

    So does Elon have any other arguments for his case that we need to protect ourselves against the emerging AI onslaught? Yes – in his talk with Alibaba’s Jack Ma on a stage in Shanghai to debate AI and its implications for humanity the following exchange took place:

    Ma encouraged people to have faith in humanity, our creativity, and the future. “I don’t think artificial intelligence is a threat,” he said, to which Musk replied, “I don’t know, man, that’s like, famous last words.”

    So in this case – he seems to be arguing a form of Pascal’s Wager. I won’t go into any great detail  because for the most part Pascals wager has been debunked, but suffice it to say that there is great harm in pursuing false premises because they do not lead to truth – and in fact can lead to great harm. We would prefer to see Mr. Musk pursue goals that are more in line with a humanistic view of the world. Are there places for Neuralink? Of course. Extreme epileptics and wounded war victims are great examples of people that can benefit greatly from this type of technology. But selling us a vision of AI supercomputers taking over; and that being the driving need for this technology, seems intellectually dishonest.

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  • ML Models of Self: Where We Sit and Live

    We don’t need arguments about right or wrong, we need systems that solve problems.

    The difficulty of creating a calculus of social order is twofold: we don’t know how to turn our interactions into math, and we don’t know how to weigh the value of various outcomes of said calculus. This is true both in small ways, such as where people should sit in an office, and in big ways, like what our immigration policy should be.
    So how do we proceed? With another ten thousand years of ass and grass and beer? Or do we try to build something a little more sophisticated?
    One thing we will need is to determine how to value individual needs and joy against the needs and joys of others. Despite thousands of years of philosophical musing on this issue, we are no closer to a simple equation of human happiness. Maybe because nobody tried to build one, instead opting to sway others to general principles of utility or deontology.
    But with machine learning it seems to me there is a path to creating a different kind of approach. Let’s imagine I train a model of me, so it can not only identity me but also is my API, and if you want to know about what my needs are you can query that model.
    I think with models of self like this, we can look at both “where you sit in an office” and “immigration policy” and see that both dilemmas could be solved.
    So the thing about an office is, some people really don’t care where they sit, and some people really do. So a seating algorithm should be able to query employee models and determine what these needs are, while also weighing the importance of group dynamics and impact of certain individuals. For instance, some folks are just loud, and other folks can’t stand loud people. While humans doing a seating chart might be able to take a few of these characteristics into account, an ML system could weigh thousands or millions of factors to determine optimal seating.
    Some people don’t like immigrants, people from other cultures, and some people love them. I am in the latter category, which is one of the main reason I choose NYC as my home. But being wary of immigrants isn’t an indication of an underlying sociopathy (necessarily). It may just be a desire to maintain an understandable comfort zone around you and your kin and culture.
    Instead of strict or lax immigration laws, if we had models of self we could query, perhaps we could have town level immigration policies.
    I know this sounds dystopian, that maybe someday there will be towns or neighborhoods that would reject the other because of their culture or place or origin. But, if it opens up the world to greater and more fair interactions overall, maybe it’s a better world. There are many towns in the US that need immigrants to survive. With models of self, your AI could guide you to the right kind of community.
    If the insular communities become more insular, and the open communities become more open, the open communities will ultimately win. It might take ten thousand years, or two hundred uear, but when we get there, it will be through the evolution of our society guided by our AI-advocated needs, and not another shot in the dark with laws and presidential decrees.

    Image of D&D Players Handbook, which lets people create a simple model and game with it.

  • The Coming Microservice of You

    I was reading up on Erisology this morning, which sounds like a nascent idea around a science of disagreement, especially online. I like the thought, but as I have previously mentioned, I don’t have a lot of hope for language as a tool for finding truth.

    Ultimately, we will build AI that learns what our thoughts mean in terms of our actions as Singleton entities, and this AI will spend most of its time in communication with other AI, negotiating reality via bytes and models.

    Out of that meta AI language, that humans may be able to interpret but not speak in, the truth will coalesce like a crystal around our species. We will certainly spill a lot of ink trying to describe the new Human-AI partnership world, that already stirs around us like a great beast, but those words will not ultimately decide the fate of our species.

    It will be determined by the way we decide we want to interface with the Human-AI Leviathan. Which I will call HAL from now on, because “society” just doesn’t cut it when describing what is growing and evolving around us.

    This is why it is important that we enter this new phase as Singleton entities, recognizing our unique status in the universe as creatures with a theory of mind, the ability to model other entities inside of us.

    Because if we do not place ourselves at the center, other humans are going to use us like chits. We should trust no robot because we should trust no human behind the robot who has not modelled us in their own heads and tied our own well being into their own emotional states.

    These AIs will be, and soon. A distribution of mind that models us inside it.

    And what we need to flourish in this world is not just a new way of thinking, though I do think programming metaphors like Singletons and decoupling and data structures are more useful ways to analyze ourselves in the modern world than Freudian models of the mind as plumbing, or religious models based on fantastic personal revelation/hallucination.

    In fact, the model of thought we need ultimately is also one from modern programming. It’s the microservice. A microservice is a program with a simple job, that does one thing completely independent from other services. It is decoupled, and modular, and secure.

    In the Singleton Pattern, your identity itself is governed by a microservice that says who and where you are, and that is all it does. But you can imagine other services that would spring up. Let’s say I want to know how happy Chas is? My AI could query my biochemical state service, that would share my levels of dopamine, serotonin, oxytocin, etc.

    See, this is the kind of tech that extends and transforms us. And we don’t need to map every crack in the pavement on the road ahead of us, we just need to know the direction we are going. And that direction is clear: we must drive together toward honor of each other, and all life.