Isaac Asimov, confronted with the problem of imagining future intelligent machines with potentially destructive capabilities, suggested his famous three laws of robotics. The first law forbids a robot from harming a human; the second compels it to obey human commands unless they conflict with the first law; the third demands that a robot protects its existence unless it conflicts with laws one and two. Asimov expanded his set by adding an extra law (the zeroth) where the “human” in law one was replaced with “humanity”.

Asimov’s laws have been repeatedly debated to what extend they could be a basis for building “friendly” AI systems, i.e. machine intelligence that will not bring harm to its human creators. The issue of how an AI “feels” about human beings becomes of vital significance when AI surpasses human intelligence, or when AI is no longer under our control. Let us take these two cases in turn starting from the latter.

It is very likely that AI is already beyond our control. We live in a wired world where highly sophisticated computer networks, some incorporating layers of AI systems, communicate without the intervention of human operators. Forex trading is mostly done by computers. In 2010 a computer virus allegedly created by an elite Israeli unit attacked and rendered inoperable many ofIran’s nuclear facilities. The Stuxnet virus demonstrated how effective a cyberattack can be, as well as how vulnerable we are should intelligent programs “decide” without the consent or instigation of human programmers to, say, trigger a nuclear war.

Perhaps the only way to save ourselves from an impeding global disaster would be to program an AI “defense” systems. That would be, for example, roaming intelligent agents on the Internet checking for emerging patterns signaling that “dummier” systems have entered into a potentially perilous state. This “higher” protecting AI would have to be programmed so that it has humanity’s preservation as its goal. Perhaps Asimov’s laws could form a basis for this programming. Unless the people who program this “cyberpolice” are the military. They, as we will see, may have a more sinister agenda.

The second case, i.e. AI becoming smarter that humans, is much harder to analyse. How would a superintelligent machine “think”?  Will it have a code of ethics similar to ours? Will it care if people died? The most honest answer to such questions is that we cannot possibly know.

Depending on how superintelligence evolves it may or may not care about humans, or biological life in general. Even if it cared it may evolve different goals that ours. For instance, while we strive to preserve our natural environment for future generations, superintelligent AI may decide that its reason for living is to dismantle Earth and the solar system and use the energy to increase its computing power. Who is to say what the higher “ideal” is.

We are used to be the arbitrators of ethical reasoning on our planet because we have reshaped the planetary environment in order to become the top predators. Our position as top predators will be compromised in a new bio-digital environment shaped by the will, and whim, or superintelligent AIs. If this happened to what “higher authority” could we turn for justice?

Faced with such daunting technological dilemmas hoi polloi may opt for the precautionary principle and cry out:”stop AI research now!” Unfortunately, this may not be such an easy option.

“War is the father of all things“, said Heraclitus. The US military has been using drones for quite a while. They have proven their operational worth in taking out terrorist operatives in north Pakistan and elsewhere. As far as we civilians may know the current stage of drone technology is strike by remote, i.e. a human officer decides when to release a deadly weapon. Nevertheless, it makes military sense to develop and deploy semi-autonomous, or fully autonomous systems. One could persuasively argue that such systems would be less error-prone and more effective. Robot warfare has dawned.

Robotic warfare is riddled with many ethical issues. Because the most hideous cost of war is the loss of human lives a military power that can deploy a robot army will be less hesitant to do so. What interests me however in robot warfare is how military AI technology is developed away from the political and philosophical spotlights that scrutinize civilian  research.

War seems to be an intricate part of our primate nature. Our cousin species, the chimpanzees, are known to stage wars in the Congo. History and anthropology show us that warring human societies have different needs than peaceful ones. Willing to sacrifice one’s life in the battlefield may appear heroic to many but, as many poets since Euripides have shown us, it is ultimately pointless; an act of abject nihilism. Militarism is the ideology that sanctions forcing of one’s will against another not by argument or persuasion, but by the application of superior firepower. This nihilistic view of the world has no qualms to develop unfriendly AI in order to win.

Our only defense against a militaristic, anti-humanist, worldview is to strengthen the international institutions that prevent us from going to war. Indeed, we must design systems for resolving crises where war is not an option. A peaceful world where war is pointless will give us the time to determine, and direct, our technological advancement towards a humanistic future. Our future survival is a matter of political choice, and applied game theory.

How can we know if intelligent machines are getting smarter? The simple answer is by measuring their IQ. Nevertheless there are some obvious, and perhaps some less obvious, problems with such an approach. The most obvious hindrance is the plethora of AI approaches and methodologies that technologists follow in building their intelligent machines.

On one end of the spectrum are the “symbolists”, those who develop algorithms that manipulate symbols in universal Turing machines (such as your PC). Their most successful products so far are called “expert systems”. At the other end there are the “connectionists”; they mimic the human brain by building artificial neural networks. Many encouraging developments have come from connectionist architectures, mostly applied in pattern recognition and machine learning. Other technologists follow hybrid approaches that fall between those two extremes.

The problem with comparing the IQ of these variable machines is this: if one assumes I to be the input of information in a machine and O the output, one lacks a common T, where T is the transformation of I into O. Proposing a universal method for testing the IQ of machines must include a caveat that the method will apply to all machines, irrespective of their “internal” T. This means that we agree to test for intelligence irrespective of what happens “inside” the machine. This is equivalent to testing the IQ of biological intelligent beings who have evolved in different planets.

The second, equally profound stumbling block for a universal IQ test has to do with definitions. What do we mean by the word “intelligence” anyway? Various people mean various things so we must be specific. To overcome semantics of intelligence is helpful to remember what the original aims of AI are. Generally speaking, AI aims to achieve four broad objectives for intelligent machines:

1. Thinking humanly, i.e. to be conscious of thinking

2. Acting humanly, i.e. to make decisions and take actions by applying evolved moral reasoning, as well as appear to be “human-like” in the action

3. Thinking rationally, i.e. processing information in a rational manner.

4. Acting rationally, i.e. producing outcomes that comply with rational reasoning.

Most serious philosophical arguments bedevil the first two objectives, while a few mild ones have issues with the third. The fourth one however, the purely behavioral one – wisely chosen by Alan Turing when he proposed his famous test- is where AI delivers its best. A machine may be said to act rationally if it appears to do so to human observers. It follows that if we endeavor to apply a universal method for testing machine IQ we must ignore “how” the machine works. If we do not we will fall prey to the philosophical wrangling of objectives 1 to 3.

So in order to arrive at a universal IQ test we must (a) ignore the internal mechanism of the machine that transforms inputs to outputs, and (b) measure only the degree of rational outcomes. So the next question is: how bad is that? It turns out that it is not bad at all. To see why, let us see what happens when human beings test for IQ.

The measurement of human intelligence was conceived in 1905 by French psychologists Alfred Binet and his assistant Theodore Simon. The French government of the time wished to ensure that adequate education was given to mentally handicapped children, so the two psychologists were commissioned to find a way to measure the “beautiful pure intelligence” of the children. Binet observed that children solved problems in the same way that younger, “normal”, children did. So he tested the possibility that intelligence was related to age. The tests that he and Simon developed were thus adapted to age: if a child was able to answer the questions that were answerable by the majority of children age 8, but unable to answer the respective questions for children age 9, she was said to have the “mental age” of 8.

IQ (Intelligence Quotient) was therefore defined as: IQ=100 x (mental age/chronological age)

Plotting this equation (IQ measurements over number of individuals tested, for each chronological age) one gets a “bell curve” with most individuals falling in the middle (the middle area of the curve defined as “normal”).

Modern tests of human IQ follow the same principles determined by Binet and Simon. They ignore internal brain mechanisms (the “T” of intelligent machines) and are only interested in outcomes (the answers to the questions). Developing a universal machine IQ test that only tests and compares rational outcomes we simply do what humans do for themselves.

Nevertheless human IQ testing is riddled with controversy. Since its inception it was noted that defining “normal” depends heavily on the statistical sample chosen for the measurement. For example white, middle class European children are better fed and better educated than poor black children in rural Africa. This difference in lifestyle impacts IQ measurements because IQ testing does not factor in social circumstances, proven by modern neuroscience to have enormous impact in brain development.

Notably, Binet and Simon’s approach was first criticized by the Russian psychologist L.S. Vygotsky who made the distinction between “really developed mental functions” and “potentially developed human functions”. IQ tests measure mostly the former. Since Vygotsky many have had issues with IQ testing, most notably H. Gardner who suggested not one but seven different types of human intelligence including linguistic, musical, mathematical, etc.

Measuring machine IQ may stumble upon disputable definitions of “normalcy”. As machines develop further issues of cultural influence may also creep in. Will Japanese robots score higher marks because the Japanese culture is more robot-friendly?

An interesting approach for a universal test of machine intelligence has been proposed by Shane Legg and Marcus Hutter. Trying to measure machine intelligence in a pure and abstract form the two researchers have suggested measuring outcomes of intelligent agents’ performance in a probability game based on what strategies should yield the best results and the biggest rewards over time.

Their suggestion appears to be viable in the context already defined, namely that we must be satisfied with measuring rational outcomes only, and not ask the difficult “how” question. Sticking to AI objective 4 we can agree about defining “universal intelligence” for machines in terms of acting rationally only.

Their proposition encapsulates an evolutionary dimension too: living creatures tend to seek rewards (food, mates, authority) while seeking the best strategies over time. Applying Legg and Hutter’s probability game at various stages of development in machine intelligence one can compare various machines now, as well as monitor the development of machines over time. If you worry about machines becoming more “intelligent” than humans in the future Legg and Hutter’s measurements should provide ample warning for the forthcoming “Singularity”.
Reference: Shane Lee and Marcus Hutter, Universal Intelligence: a definition of machine intelligence, work supported by NSF grant 200020-107616.

A recent study in machine learning reported a high degree of accuracy in machines understanding the character and intentions of humans. Mario Rojas and colleagues at Barcelona University together with researchers at the Department of Psychology at Princeton University developed software that can learn to “read” human traits from human faces. The researchers trained their algorithm on nine categories (attractive, competent, trustworthy, dominant, mean, frightening, extroverted, threatening and likable). The highest degree (over 90%) of successful recognition of the shown trait was for “dominant”, “mean” and “threatening”. Notably, these traits seem to play the most crucial role in structuring hierarchies in human societies. Quoting from Science Daily Mr Rojas said: “The perception of dominance has been shown to be an important part of social roles at different stages of life, and to play a role in mate selection.”

Making machines understand human traits will help develop better interactive systems. When taken to their apogee, embedded systems such as these will equip humanoid robots of the future to interact naturally with humans, facilitating the harmonious incorporation of artificial beings into human societies. Or won’t they?

This latest research news is part of a large worldwide research effort that aims to move Artificial Intelligence beyond logical reasoning and problem-solving and into the realm of emotional understanding. This is very crucial. Human beings are ruled much more by emotion than pure logic. Notwithstanding Plato’s aphorism that poets should be banned from a perfect society (he took exception for Homer only), our lives and thoughts are better expressed through poetic vision and works of art – or crimes of passion if you prefer a darker side – rather than cool “Mr. Spock-like” reasoning. In a future world where artificial and non-artificial beings intermingle to form complex relationships understanding each other is a sine qua non.

But there is also a caveat. Furnishing artificial beings with emotional reasoning assumes a sort of “mirror” that reflects our traits unto them. However, there are several issues with mirrors which must not be overlooked (pun not intended). Looking at a robot whilst pulling a face of, say, meanness we would expect it to understand the trait and react accordingly; hopefully by trying to appease us; to curl away and wag its tail if possible. This expectation assumes that we programmed the robot with a “dog instinct” so that it knows a priori who the master is and who the slave. But what happens if we forget to do so? What happens if the robot, a mirror of our humanity, pulls the same face back?

Journal Reference: Mario Rojas Q., David Masip, Alexander Todorov, Jordi Vitria. Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models. PLoS ONE, 2011; 6 (8): e23323 DOI: 10.1371/journal.pone.0023323

Robonaut R2, the first human-like astronaut robot was awakened at the International Space Station in August 2011, and happily started tweeting to its thousands of human followers on Earth. The humanoid robot sports a torso with two human arms and hands, wears a golden helmet with a visor and looks unnervingly similar to a Cylon from Battlestar Galactica or a Clone from Star Wars.

NASA and contractor General Motors have began testing their robot on the ISS, with a view to soon having it work side by side with its human colleagues, its masters.

The robot’s torso can be attached to a single leg that R2 will use in the near future in order to move along the ISS corridors. Further into the future the descendents of R2 could explore the Lunar or Martian surfaces strapped on a four-wheeler aptly named “Centaur”. R2 is a revolutionary development for space exploration. Its engineering is remarkable:  the robot is furnished with limb and finger dexterity beyond the capabilities of a human astronaut clad in a heave space suit.

NASA’s communication experts have circulated a number of R2 photos in the media showing a mechanical wonderpiece to be more or less expected: a muscular, masculine, he-robot. I find  this projected macho image particularly interesting, as well as the ancient idea that seems to have spawned it.

R2′s image bodes well with the archetype robotic space slave first seen in the classic 1956 sci-fi movie Forbidden Planet. Robby the Robot, the creation of Dr. Morbius, was not just another “tin can” but a full member of the cast with a distinct personality and a high level of moral judgment. Like R2 Robby was a very muscular and very masculine creature, and the movie promoters did not miss the opportunity to project this.

Space robots in the late 1950s, faced with the harsh and unforgiving conditions of space, could not be anything else but hardy men. As if feminism never happened, this idea appears to have persisted well into the 21^st century: R2 is unmistakably male – or isn’t he?

Robonaut2, very much like his fictional predecessor Robby (and a host of other fictional “male” space robots, e.g. Cylons, Clones, the Lost in Space Robot, etc.) lacks genitalia. Their creators – engineers and art directors alike – chose not to permit their artificial offspring the faculty of procreation. Why? Partly, I presume, because issues of robot procreation were certain to raise a few eyebrows at NASA, not to mention at MGM Studios in the 50s. There are deep ethical issues with regards to robot sex, either between robots or between humans and robots. R2′s mission is to serve in space, not in sensual, exploration. Who needs a horny robot on the ISS?

So R2 was manufactured with all the characteristics of a man except the critical three: a penis and a pair of testes . From a human perspective R2 is a eunuch.

Eunuchs have been around history for millennia. Considered loyal and indispensible they served their royal masters in a variety of tasks, domestic as well as pubic, some rising to prominence in administration or the army. Their lack of genitalia was considered an asset. Roman patricians trusted their wives to them. Byzantine emperors ushered them to the highest echelons of palace hierarchy. Ottoman Sultans used them as guardians of their precious harems. We in the 21^st century trust them with our destiny, to explore new worlds for us and prepare them for our future.

“Those electrons feel GOOD!”, said R2 in his first tweet. “One small step for man, one giant leap for tinman kind.”

Perhaps “he” was being ironic.