Homo ergaster

A A huge debate rages about human origins, but the broad consensus among scientists is that all the different species of human (hominid) that have ever existed were descended from ape-like creatures that walked upright in Africa more than six million years ago. These creatures had many descendants, most of which became extinct, but the first creature we would recognise as human first appeared in Africa almost two million years ago. Homo ergaster, meaning ‘working human’, evolved during an accelerated period of global cooling and drying that cleared more and more tropical rainforest from Africa, and created desert in the northern half of the continent.

B One of the best sources of information about Homo ergaster is a skeleton found in 1984 by Alan Walker and Kamoya Kimeu at Nariokotome in West Turkana, Kenya. The remains turned out to be those of a young boy, who was between 11 and 13 when he died. Around 1.5 million years ago, the boy’s body sank into the marsh where he died, and became fossilised. His teeth showed that he had an abscess where his milk teeth fell out, indicating that he may have died from blood poisoning. Nariokotome Boy, as he has been dubbed, was about 160 centimetres (5 feet 3 inches) tall, and would have stood at 185 centimetres (6 feet 1 inch) had he reached adulthood.

C He was clearly a strong boy, with a body shape that was perfectly adapted to an active life in the sun. Human populations living on equatorial grasslands today, such as the Masai in Kenya, have the same tall, linear physique. This body shape creates a large surface area over which the body can cool itself more easily, preventing Nariokotome Boy from overheating under the blazing sun. This hominid was probably the first to regulate its temperature through sweating. For creatures that must remain active at midday, in a sunny, dry habitat, sweating is the most effective mechanism for maintaining safe body and brain temperatures. Homo ergaster’s body was probably smooth and largely hairless, since heat loss through sweating occurs most efficiently through naked skin. Its skin was almost certainly dark, to protect it from the sun’s harmful rays.

D Homo ergaster travelled long distances on foot, as it worked hard to scavenge enough meat to feed its body and brain. In order to increase the energy efficiency of muscles involved in upright walking, Homo ergaster developed a narrower pelvis. But its slim hips came at a price. Firstly, the narrowing of the pelvis caused the lower part of the rib cage to narrow. In order to prevent constriction of the lungs, the upper part of Homo ergaster’s rib cage expanded, giving its chest a human barrel shape. Secondly, and more importantly, the narrowing of the pelvis constricted the female birth canal. This single anatomical change seems to have had profound consequences for human society.

E As brains increased in size, mothers had to push increasingly big-brained infants through an already tight pelvis, and that caused problems. The eventual solution was a trade-off. While chimpanzees are born with their brains almost fully mature, humans are born with a comparatively immature brain. This makes human babies helpless and vulnerable during their first year of life as their brains make vital neural connections. As a result, human mothers need to be well nourished to keep up with the demands of their babies, making them increasingly reliant on the support of their male partner and other members of their group. Many experts regard this shift as the beginning of the nuclear family with its distinctively human pattern of sharing and cooperation between males and females.

F Nariokotome Boy had a thick, bony ridge across his eyes. A pair of front teeth stuck out at an angle from a large, projecting mouth below a long, wide nose. His anatomy does not seem developed enough to have given him the control over his breathing needed for complex speech, so Homo ergaster probably communicated using hand movements supplemented by a limited range of sounds. The other relatively small teeth of Nariokotome Boy suggest that Homo ergaster relied more than his ancestors on stone tools for cutting up meat before eating. To begin with, Homo ergaster used primitive implements, which were little more than chipped rocks with sharp edges. But around 1.6 million years ago, Homo ergaster developed symmetrical, heart-shaped handaxes, which gave the hominid greater control over the butchering of meat.

G Shortly after Homo ergaster appeared 1.9 million years ago, humans began to leave Africa for the first time and to move onto other continents. Archaeological records show they spread over an area ranging from Turkey into Asia, although the population may not have been very large. This newfound desire to travel may have been dictated by an increasing reliance on meat for food. Carnivores generally need much larger ranges than similar-sized herbivores, because carnivores have fewer total calories, in the form of animal prey, available to them per unit area of their territory.

The Long Road to Artificial Intelligence

In 1950, the computing pioneer Alan Turing published a paper in the journal Mind entitled ‘Computing Machinery and Intelligence’. It was the first serious, scholarly treatment of the concept of Artificial Intelligence (AI). Turing predicted that by the year 2000 the majority of people would be able to speak of machines thinking without expecting to be contradicted, envisaging a world very different from the one he lived in.

Despite Turing’s prediction, human-level artificial intelligence had not been achieved by the year 2000. Nevertheless, one significant milestone in artificial intelligence had recently been attained. In 1997 ‘Deep Blue’, a computer developed by IBM, defeated the world chess champion Garry Kasparov. Kasparov had beaten previous computer chess programs, which to him seemed predictable and mechanical, but he allegedly said he sensed an ‘alien intelligence’ on the other side of the board when he played against Deep Blue.

It is instructive to stand back and ponder this moment in the history of AI. The field had accomplished something that half a century beforehand might have been considered its crowning achievement. Humanity had been outstripped by a machine. Of course, a car can move faster than the fastest human sprinter, and a crane can carry far more than a champion weight-lifter. But intellectual prowess is what sets human beings apart from the rest of the animals, and chess is a supremely intellectual pursuit.

Yet somehow we seemed no nearer to human-level AI than in Turing’s time. How could this be? The problem with Deep Blue was that it was a specialist. All it could do was play chess. Contrast this with a typical human adult. Take the office worker who has just walked past the window of the café where I am sitting with my laptop. Her day has no doubt been a busy patchwork of activities -- making a packed lunch, reviewing the children’s homework, driving to work, composing emails, fixing the photocopier, and so on. Each of these activities requires the exercise of multiple sensorimotor skills. Consider the task of making a packed lunch. This involves retrieving utensils and ingredients from various places, opening packets, chopping, cutting, spreading, and so on.

In short, a human being is a generalist. A human chess champion can do a whole lot more than just play chess. Moreover, a human being is adaptive. Fixing photocopiers is not an innate capability. It is learned. Had the office worker been born in a different century or a different culture, she would have acquired a different set of skills. And if she has the misfortune to lose her present job, she can re-train for another one. The achievements of AI research in a variety of specialist domains (chess being just one among many success stories) contrast starkly with the field’s failure to produce a machine with general purpose, adaptive intelligence. So our basic question is, how could we produce artificial general intelligence?

An essential feature of biological intelligence is embodiment. Unlike Deep Blue, a human being is an animal with a body, and its brain is part of that body. The brain of an animal has evolved to prevent any harm from coming to that body and to perpetuate the genes that it carries. The body has muscles, enabling it to move, and senses, so that its actions can be made to react to the state of the environment, the better to achieve its goal. The brain shapes the animal’s actions according to what it perceives. Human intelligence, for all its glorious achievements, is fundamentally an extension of animal intelligence, and the human capacities for language, reason, and creativity all rest on a sensorimotor foundation.

So while the endeavour to create artificial general intelligence might succeed without much that is essential to biological life, such as metabolism and reproduction, perhaps embodiment is a methodological necessity. Perhaps the need to engage with a messy, dynamic, physical environment full of complex and varied objects, both animate and inanimate, is at the root of intelligence. The only way to form a reliable judgment of the intelligence of an artifact is to observe its behaviour in an environment like our own. And the only way to achieve human-level AI, according to this way of thinking, is through robotics. Our basic question can then be reformulated: How can we endow a robot with general intelligence?

One possibility is that general intelligence is simply the sum of many specialist sensorimotor skills, and up to now, AI hasn’t replicated enough of them. So when robots have been given a certain critical mass of skills, general intelligence will somehow emerge. But even if we gloss over the many engineering questions this proposal begs, it remains unconvincing. The products of such an approach might briefly give the appearance of general intelligence. But nobody would be fooled for very long. The multi-specialist robot is going to get stuck as soon as it has to face a problem that is outside its areas of expertise, an inevitable occurrence in an ever-changing world.

Perhaps the capacity to learn is enough to plug the gap here. In an unfamiliar situation, a new skill can be learned. Indeed, learning in its various forms is the backdrop to all intelligence. But learning is time-consuming and risky. The hallmark of general intelligence in a robot is therefore the ability to adapt an existing set of behaviours to new challenges, and to do so without recourse to trial and error or to training by a third party. Only when that can be achieved will artificial intelligence truly match human intelligence.