ARTIFICIAL INTELLIGENCE – PROS & CONS
ABSTRACT
On one hand, in the recent years, Artificial Intelligence (AI) has grown from small scale laboratory science into technological and industrial success. Basic research in AI has expanded enormously during this period. In the last thirty years, AI have demonstrated that intelligence requires more than the ability to reason. It also requires a great deal of knowledge about the world. The ultimate goal of AI is the construction of programs that solve hard problems. Previously, most of the AI programs are written in LISP, PROLOG, or some specialized AI shell but now they are being written in wide variety of the programming languages as AI has spread in the mainstream computing world. On the other hand, by far the greatest danger of AI is that people conclude too early that they understand it. The problem seems to be unusually acute in Artificial Intelligence. The field of AI has a reputation for making huge promises and then failing to deliver on them. Actually, AI is not hard, but for some reason, it is very easy for people to think they know far more about Artificial Intelligence than they actually do.
INTRODUCTION
Artificial intelligence is a branch of computer science that deals with intelligence of machines. It is the study and design of intelligent agents or how to make computers do things that people do better. An intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. We can also define it as the science and engineering of making intelligent machines. Today, artificial intelligence has become an essential part of the technology industry, providing the heavy lifting for the most difficult problems in the computer science. AI research is highly technical and specialized. Subfields of AI are organized around particular problems, the application of particular tools and around long standing theoretical differences of opinion. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.
Cognitive biases potentially affecting judgement of global risk. Cognitive biases are settled science; one need simply quote the literature. Artificial Intelligence is not settled science; it belongs to the frontier. Actuarial statistics cannot be consulted to assign small annual probabilities of catastrophe. Calculations from a precise, precisely confirmed model to rule out events or place infinitesimal upper bounds on their probability, as with proposed physics disasters cannot be used.
ANTHROPOMORPHIC BIAS
Let us imagine a complex biological adaptation with ten necessary parts. If each of ten genes is independently at 50% frequency in the gene pool - each gene possessed by only half the organisms in that species - then, on average, only 1 in 1024 organisms will possess the full, functioning adaptation. If gene B depends on gene A, then gene B has no significant advantage unless gene A forms a reliable part of the genetic environment. Complex, interdependent machinery is necessarily universal within a sexually reproducing species; it cannot evolve otherwise. In every known culture, humans experience joy, sadness, disgust, anger, fear, and surprise, and indicate these emotions using the same facial expressions. Humans evolved to model other humans - to compete against and cooperate with our own co specifics. Not surprisingly, human beings often “anthropomorphize” - expect humanlike properties of that which is not human. In The Matrix, the supposed "artificial intelligence" Agent Smith initially appears utterly cool and collected his face passive and unemotional. But later, while interrogating the human Morpheus, Agent Smith gives vent to his disgust with humanity and his face shows the human-universal facial expression for disgust. The key result is that even when people consciously believe an AI is unlike a human, they still visualize scenarios as if the AI were anthropomorphic. Anthropomorphic bias can be classed as insidious: it takes place with no deliberate intent, without conscious realization, and in the face of apparent knowledge.
People need not realize they are anthropomorphizing in order for anthropomorphism to supervene on cognition. When we try to reason about other minds, each step in the reasoning process may be contaminated by assumptions so ordinary in human experience that we take no more notice of them than air or gravity. If the aliens have sufficiently advanced technology, they'll genetically engineer themselves to like soft skins instead of hard exoskeletons. Advanced aliens could reengineer themselves (genetically or otherwise) to like soft skins. An insectoid alien who likes hard skeletons will not wish to change itself to like soft skins instead - not unless natural selection has somehow produced in it a distinctly human sense of meta-sexiness. When using long, complex chains of reasoning to argue in favor of an anthropomorphic conclusion, each and every step of the reasoning is another opportunity to sneak in the error. And it is also a serious error to begin from the conclusion and search for a neutral seeming line of reasoning leading there; this is rationalization. If it is self-brain-query which produced that first fleeting mental image of an insectoid chasing a human female, then anthropomorphism is the underlying cause of that belief, and no amount of rationalization will change that.
THE WIDTH OF MIND DESIGN SPACE
Evolution strongly conserves some structures. Once other genes evolve which depend on a previously existing gene, that early gene is set in concrete; it cannot mutate without breaking multiple adaptations. Homeotic genes tell many other genes when to activate. Mutating a homeotic gene can result in a fruit fly embryo that develops normally except for not having a head. As a result, homeotic genes are so strongly conserved that many of them are the same in humans and fruit flies - they have not changed since the last common ancestor of humans and bugs. Any two AI designs might be less similar to one another than you are to a petunia. The term "Artificial Intelligence" refers to a vastly greater space of possibilities than does the term "Homo sapiens". AI deals about minds in-general, or optimization processes in general. Natural selection creates complex functional machinery without mindfulness; evolution lies inside the space of optimization processes but outside the circle of minds. It is this enormous space of possibilities which outlaws anthropomorphism as legitimate reasoning.
PREDICTION AND DESIGN
It is impossible to predict whether an arbitrary computational system implements any input-output function, including, say, simple multiplication. So how is it possible to build computer chips which reliably implement multiplication? Because human engineers deliberately use designs that they can understand. Anthropomorphism leads people to believe that they can make predictions, given no more information than that something is “intelligence” - anthromorphism will go on generating predictions regardless, of the brain automatically putting itself in the shoes of the "intelligence".
One path leading to global catastrophe – to someone pressing the button with a mistaken idea of what the button does - is that Artificial Intelligence comes about through a similar accretion of working algorithms, with the researchers having no deep understanding of how the combined system works. Nonetheless, the AI will be friendly, with no strong visualization of the exact processes involved in producing friendly behavior, or any detailed understanding of what they mean by friendliness. It's mistaken belief that an AI will be friendly which implies an obvious path to global catastrophe.
UNDERESTIMATING THE POWER OF INTELLIGENCE
Individual differences of human intelligence have a standard label, Spearman's g aka gfactor,a controversial interpretation of the solid experimental result that different intelligence tests are highly correlated with each other and with real-world outcomes such as lifetime income. Spearman's g is a statistical abstraction from individual differences of intelligence between humans, who as a species are far more intelligent than lizards. Spearman's g is abstracted from millimeter height differences among a species of giants. General intelligence is a between-species difference, a complex adaptation, and a human universal found in all known cultures. There may as yet be no academic consensus on intelligence, but there is no doubt about the existence, or the power, of the thing-to-be-explained. The phrase "transhuman AI" or "artificial superintelligence" evoke images of booksmarts-in-a-box: an AI that's really good at cognitive tasks stereotypically associated with "intelligence". But not superhumanly persuasive; or far better than humans at predicting and manipulating human social situations; or inhumanly clever in formulating long-term strategies.
Artificial Intelligence is not an amazing shiny expensive gadget to advertise in the latest tech magazines. Artificial Intelligence does not belong in the same graph that shows progress in medicine, manufacturing, and energy. Artificial Intelligence is not something you can casually mix into a lumped futuristic scenario of skyscrapers and flying cars and nanotechnological red blood cells that let you hold your breath for eight hours. The catastrophic scenario which stems from underestimating the power of intelligence is that someone builds a button, and doesn't care enough what the button does, because they don't think the button is powerful enough to hurt them. Underestimating the power of intelligence implies a proportional underestimate of the potential impact of Artificial Intelligence, the group of concerned researchers and grant makers and individual philanthropists who handle existential risks on behalf of the human species, will not pay enough attention to Artificial Intelligence. Or the wider field of AI will not pay enough attention to risks of strong AI, and therefore good tools and firm foundations for friendliness will not be available when it becomes possible to build strong intelligences. Artificial Intelligence could be the powerful solution to other existential risks, and by mistake we will ignore our best hope of survival. The point about underestimating the potential impact of Artificial Intelligence is symmetrical around potential good impacts and potential bad impacts.
CAPABILITY AND MOTIVE
When technology advances far enough we'll be able to build minds far surpassing human intelligence. Now, it's obvious that how large a cheesecake you can make depends on your intelligence. A superintelligence could build enormous cheesecakes - cheesecakes the size of cities - by golly, the future will be full of giant cheesecakes!" The question is whether the superintelligence wants to build giant cheesecakes. The vision leaps directly from capability to actuality, without considering the necessary intermediate of motive. The following chains of reasoning, considered in isolation without supporting argument, all exhibit the Fallacy of the Giant Cheesecake:
· A sufficiently powerful Artificial Intelligence could overwhelm any human resistance and wipe out humanity. [And the AI would decide to do so.] Therefore we should not build AI.
· A sufficiently powerful AI could develop new medical technologies capable of saving millions of human lives. [And the AI would decide to do so.] Therefore we should build AI.
· Once computers become cheap enough, the vast majority of jobs will be performable by Artificial Intelligence more easily than by humans. A sufficiently powerful AI would even be better than us at math, engineering, music, art, and all the other jobs we consider meaningful. [And the AI will decide to perform those jobs.] Thus after the invention of AI, humans will have nothing to do, and we'll starve or watch television.
OPTIMIZATION PROCESSES
The above deconstruction of the Fallacy of the Giant Cheesecake invokes an intrinsic anthropomorphism - the idea that motives are separable; the implicit assumption that by talking about "capability" and "motive" as separate entities, we are carving reality at its joints. This is a useful slice but an anthropomorphic one. To view the problem in more general terms, I introduce the concept of an optimization process: a system which hits small targets in large search spaces to produce coherent real-world effects. An optimization process steers the future into particular regions of the possible.
For any event one can predict the outcome of a process, without being able to predict any of the intermediate steps in the process. To hit a tiny target in configuration space requires a powerful optimization process. The notion of an "optimization process" is predictively useful because it can be easier to understand the target of an optimization process than to understand its step-by-step dynamics.
AIMING AT THE TARGET
What "AIs" will "want"? How the future will be? Predictions are that, "AIs will attack humans with marching robot armies" or "AIs will invent a cure for cancer". Complex relations between initial conditions and outcomes - that would lose the audience are not proposed. But we need relational understanding to manipulate the future, steer it into a region palatable to humankind. If we do not steer, we run the danger of ending up where we are going. The critical challenge is not to predict that "AIs" will attack humanity with marching robot armies, or alternatively invent a cure for cancer. The task is not even to make the prediction for an arbitrary individual AI design. Rather the task is choosing into existence some particular powerful optimization process whose beneficial effects can legitimately be asserted. I strongly urge my readers not to start thinking up reasons why a fully generic optimization process would be friendly. Natural selection isn't friendly, nor does it hate you, nor will it leave you alone. Thus, achieving harmony of predicted positive results and actual positive results.
FRIENDLY AI
It would be a very good thing if humanity knew how to choose into existence a powerful optimization process with a particular target. Or in more colloquial terms, it would be nice to build a nice AI. "Friendly AI" also refers to the product of technique - an AI created with specified motivations. One common reaction I encounter is for people to immediately declare that Friendly AI is impossibility, because any sufficiently powerful AI will be able to modify its own source code to break any constraints placed upon it. Any AI with free access to its own source would, in principle, possess the ability to modify its own source code in a way that changed the AI's optimization target. This does not imply the AI has the motive to change its own motives. When computer engineers prove a chip valid - a good idea if the chip has 155 million transistors and you can't issue a patch afterward - the engineers use human-guided, machine-verified formal proof.
Proving a computer chip correct requires a synergy of human intelligence and computer algorithms, as currently neither suffices on its own. Perhaps a true AI could use a similar combination of abilities when modifying its own code - would have both the capability to invent large designs without being defeated by exponential explosion, and also the ability to verify its steps with extreme reliability. That is one way a true AI might remain know ably stable in its goals, even after carrying out a large number of self-modifications. “Friendly AI” is theoretically impossible, dares to quantify over every possible mind design and every possible optimization process - including human beings, who are also minds, some of whom are nice and wish they were nicer.
TECHNICAL FAILURE AND PHILOSOPHICAL FAILURE
Potential failures of attempted Friendly AI can be categorized into two informal fuzzy categories, technical failure and philosophical failure.
· Technical failure is when you try to build an AI and it doesn't work the way you think it does - you have failed to understand the true workings of your own code.
· Philosophical failure is trying to build the wrong thing, so that even if you succeeded you would still fail to help anyone or benefit humanity.
HARDWARE
People tend to think of large computers as the enabling factor for Artificial Intelligence. Outside futurists discussing Artificial Intelligence talk about hardware progress because hardware progress is easy to measure - in contrast to understanding of intelligence. Rather than thinking in terms of the "minimum" hardware "required" for Artificial Intelligence, think of a minimum level of researcher understanding that decreases as a function of hardware improvements. The better the computing hardware, the less understanding you need to build an AI. The extremal case is natural selection, which used a ridiculous amount of brute computational force to create human intelligence using no understanding, only nonchance retention of chance mutations. Increased computing power makes it easier to build AI, but there is no obvious reason why increased computing power would help make the AI Friendly. Increased computing power makes it easier to use brute force; easier to combine poorly understood techniques that work.
THREATS AND PROMISES
It is a risky intellectual endeavor to predict specifically how a benevolent AI would help humanity, or an unfriendly AI harm it. There is the risk of conjunction fallacy. There is the risk - virtually the certainty - of failure of imagination; and the risk of Giant Cheesecake Fallacy that leaps from capability to motive. Nonetheless I will try to solidify threats and promises. The future has a reputation for accomplishing feats which the past thought impossible. Future civilizations have even broken what past civilizations thought (incorrectly, of course). The three families of unreliable metaphors for imagining the capability of a smarter-than-human Artificial Intelligence:
· G-factor metaphors: Inspired by differences of individual intelligence between humans. AIs will patent new technologies, publish groundbreaking research papers, make money on the stock market, or lead political power blocs.
· History metaphors: Inspired by knowledge differences between past and future human civilizations. AIs will swiftly invent the kind of capabilities that cliché would attribute to human civilization a century or millennium from now: molecular nanotechnology; interstellar travel; computers performing 1025 operations per second.
· Species metaphors: Inspired by differences of brain architecture between species. AIs have magic. G-factor metaphors seem most common in popular futurism: when people think of "intelligence" they think of human geniuses instead of humans.
A fast, nice intelligence wielding molecular nanotechnology is power on the order of getting rid of disease, not getting rid of cancer. There is finally the family of species metaphors, based on between-species differences of intelligence. The AI has magic - not in the sense of incantations and potions, but in the sense that a wolf cannot understand how a gun works, or what sort of effort goes into making a gun, or the nature of that human power which lets us invent guns. Strong super humanity would be more than cranking up the clock speed on a human equivalent mind.
CONCLUSION
· Cognitive biases potentially affecting judgement of global risk.
· Anthropomorphic bias can be classed as insidious: it takes place with no deliberate intent, without conscious realization, and in the face of apparent knowledge.
· Enormous space of possibilities which outlaws anthropomorphism as legitimate reasoning.
· AI will be friendly which implies an obvious path to global catastrophe.
· The point about underestimating the potential impact of Artificial Intelligence is symmetrical around potential good impacts and potential bad impacts.
· When technology advances far enough we'll be able to build minds far surpassing human intelligence.
· A risky intellectual endeavor to predict specifically how a benevolent AI would help humanity.
· A fast, nice intelligence wielding molecular nanotechnology is power on the order of getting rid of disease, not getting rid of cancer.
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