Reading Passage 1 – Walking with dinosaurs

You should spend about 20 minutes on Questions 1-13, which are based on Reading Passage 1 below.​

Peter L. Falkingham and his colleagues at Manchester University are developing techniques which look set to revolutionize our understanding of how dinosaurs and other extinct animals behaved.

The media image of palaeontologists who study prehistoric life is often of field workers camped in the desert in the hot sun, carefully picking away at the rock surrounding a large dinosaur bone. But Peter Falkingham has done little of that for a while now. Instead, he devotes himself to his computer. Not because he has become inundated with paperwork, but because he is a new kind of palaeontologist: a computational palaeontologist.

What few people may consider is that uncovering a skeleton, or discovering a new species, is where the research begins, not where it ends. What we really want to understand is how the extinct animals and plants behaved in their natural habitats. Drs Bill Sellers and Phil Manning from the University of Manchester use a ‘genetic algorithm’ – a kind of computer code that can change itself and ‘evolve’ – to explore how extinct animals like dinosaurs, and our own early ancestors, walked and stalked.

The fossilized bones of a complete dinosaur skeleton can tell scientists a lot about the animal, but they do not make up the complete picture and the computer can try to fill the gap. The computer model is given a digitized skeleton, and the locations of known muscles. The model then randomly activates the muscles. This, perhaps unsurprisingly, results almost without fail in the animal falling on its face. So the computer alters the activation pattern and tries again … usually to similar effect. The modeled dinosaurs quickly ‘evolve’. If there is any improvement, the computer discards the old pattern and adopts the new one as the base for alteration. Eventually, the muscle activation pattern evolves a stable way of moving, the best possible solution is reached, and the dinosaur can walk, run, chase or graze. Assuming natural selection evolves the best possible solution too, the modeled animal should be moving in a manner similar to its now-extinct counterpart. And indeed, using the same method for living animals (humans, emu and ostriches) similar top speeds were achieved on the computer as in reality. By comparing their cyberspace results with real measurements of living species, the Manchester team of palaeontologists can be confident in the results computed showing how extinct prehistoric animals such as dinosaurs moved.

The Manchester University team have used the computer simulations to produce a model of a giant meat-eating dinosaur. lt is called an acrocanthosaurus which literally means ‘high spined lizard’ because of the spines which run along its backbone. It is not really known why they are there but scientists have speculated they could have supported a hump that stored fat and water reserves. There are also those who believe that the spines acted as a support for a sail. Of these, one half think it was used as a display and could be flushed with blood and the other half think it was used as a temperature-regulating device. It may have been a mixture of the two. The skull seems out of proportion with its thick, heavy body because it is so narrow and the jaws are delicate and fine. The feet are also worthy of note as they look surprisingly small in contrast to the animal as a whole. It has a deep broad tail and powerful leg muscles to aid locomotion. It walked on its back legs and its front legs were much shorter with powerful claws.

Falkingham himself is investigating fossilized tracks, or footprints, using computer simulations to help analyze how extinct animals moved. Modern-day trackers who study the habitats of wild animals can tell you what animal made a track, whether that animal was walking or running, sometimes even the sex of the animal. But a fossil track poses a more considerable challenge to interpret in the same way. A crucial consideration is knowing what the environment including the mud, or sediment, upon which the animal walked was like millions of years ago when the track was made. Experiments can answer these questions but the number of variables is staggering. To physically recreate each scenario with a box of mud is extremely time-consuming and difficult to repeat accurately. This is where computer simulation comes in.

Falkingham uses computational techniques to model a volume of mud and control the moisture content, consistency, and other conditions to simulate the mud of prehistoric times. A footprint is then made in the digital mud by a virtual foot. This footprint can be chopped up and viewed from any angle and stress values can be extracted and calculated from inside it. By running hundreds of these simulations simultaneously on supercomputers, Falkingham can start to understand what types of footprint would be expected if an animal moved in a certain way over a given kind of ground. Looking at the variation in the virtual tracks, researchers can make sense of fossil tracks with greater confidence. The application of computational techniques in palaeontology is becoming more prevalent every year. As computer power continues to increase, the range of problems that can be tackled and questions that can be answered will only expand.

Reading Passage 2 – The robots are coming

You should spend about 20 minutes on Questions 14-26, which are based or Reading Passage 2 below.

A Can robots advance so far that they become the ultimate threat to our existence? Some scientists say no, and dismiss the very idea of Artificial Intelligence. The human brain, they argue, is the most complicated system ever created, and any machine designed to reproduce human thought is bound to fail. Physicist Roger Penrose of Oxford University and others believe that machines are physically incapable of human thought. Colin McGinn of Rutgers University backs this up when he says that Artificial Intelligence ‘is like sheep trying to do complicated psychoanalysis. They just don’t have the conceptual equipment they need in their limited brains’.

B Artificial Intelligence, or Al, is different from most technologies in that scientists still understand very little about how intelligence works. Physicists have a good understanding of Newtonian mechanics and the quantum theory of atoms and molecules, whereas the basic laws of intelligence remain a mystery.

But a sizable number of mathematicians and computer scientists, who are specialists in the area, are optimistic about the possibilities.

To them it is only a matter of time before a thinking machine walks out of the laboratory. Over the years, various problems have impeded all efforts to create robots. To attack these difficulties, researchers tried to use the ‘top- down approach’, using a computer in an attempt to program all the essential rules onto a single disc. By inserting this into a machine, it would then become self-aware and attain human-like intelligence.

C In the 1950s and 1960s great progress was made, but the shortcomings of these prototype robots soon became clear. They were huge and took hours to navigate across a room. Meanwhile, a fruit fly, with a brain containing only a fraction of the computing power, can effortlessly navigate in three dimensions.

Our brains, like the fruit fly’s, unconsciously recognize what we see by performing countless calculations. This unconscious awareness of patterns is exactly what computers are missing. The second problem is robots’ lack of common sense. Humans know that water is wet and that mothers are older than their daughters. But there is no mathematics that can express these truths. Children learn the intuitive laws of biology and physics by interacting with the real world. Robots know only what has been programmed into them.

D Because of the limitations of the top-down approach to Artificial Intelligence, attempts have been made to use a ‘bottom-up’ approach instead – that is, to try to imitate evolution and the way a baby learns. Rodney Brooks was the director of MIT’s Artificial Intelligence laboratory, famous for its lumbering ‘top- down’ walking robots. He changed the course of research when he explored the unorthodox idea of tiny ‘insectoid’ robots that learned to walk by bumping into things instead of computing mathematically the precise position of their feet. Today many of the descendants of Brooks’ insectoid robots are on Mars gathering data for NASA (The National Aeronautics and Space Administration), running across the dusty landscape of the planet. For all their successes in mimicking the behavior of insects, however, robots using neural networks have performed miserably when their programmers have tried to duplicate in them the behavior of higher organisms such as mammals. MIT’s Marvin Minsky summarises the problems of Al: ‘The history of Al is sort of funny because the first real accomplishments were beautiful things, like a machine that could do well in a maths course. But then we started to try to make machines that could answer questions about simple children’s stories. There’s no machine today that can do that.’

E There are people who believe that eventually there will be a combination between the top- down and bottom-up, which may provide the key to Artificial Intelligence. As adults, we blend the two approaches. It has been suggested that our emotions represent the quality that most distinguishes us as human, that it is impossible for machines ever to have emotions. Computer expert Hans Moravec thinks that in the future robots will be programmed with emotions such as fear to protect themselves so that they can signal to humans when their batteries are running low, for example. Emotions are vital in decision-making. People who have suffered a certain kind of brain injury lose the ability to experience emotions and become unable to make decisions. Without emotions to guide them, they debate endlessly over their options. Moravec points out that as robots become more intelligent and are able to make choices, they could likewise become paralysed with indecision. To aid them, robots of the future might need to have emotions hardwired into their brains.

F There is no universal consensus as to whether machines can be conscious, or even, in human terms, what consciousness means. Minsky suggests the thinking process in our brain is not localised but spread out, with different centres competing with one another at any given time. Consciousness may then be viewed as a sequence of thoughts and images issuing from these different, smaller ‘minds’, each one competing for our attention. Robots might eventually attain a ‘silicon consciousness’. Robots, in fact, might one day embody an architecture for thinking and processing information that is different from ours – but also indistinguishable. If that happens, the question of whether they really ‘understand’ becomes largely irrelevant. A robot that has perfect mastery of syntax, for all practical purposes, understands what is being said.

Reading Passage 3 – Endangered languages

You should spend about 20 minutes on Questions 27-40, which are based on Reading Passage 3 below.

‘Nevermind whales, save the languages’, says Peter Monaghan, graduate of the Australian National University

Worried about the loss of rain forests and the ozone At linguistics meetings in the US, where the layer? Well, neither of those is doing any worse than endangered-language issue has of late been a large majority of the 6,000 to 7,000 languages that something of a flavour of the month, there is remain in use on Earth. One half of the survivors will growing evidence that not all approaches to the almost certainly be gone by 2050, while 40% more preservation of languages will be particularly will probably be well on their way out. In their place, helpful. Some linguists are boasting, for example, almost all humans will speak one of a handful of of more and more sophisticated means of capturing mega languages – Mandarin, English, Spanish.

Linguists know what causes languages to disappear, but less often remarked is what happens on the way to disappearance: languages’ vocabularies, grammars and expressive potential all diminish as one language is replaced by another. ‘Say a community goes over from speaking a traditional Aboriginal language to speaking a creole*,’ says Australian Nick Evans, a leading authority on Aboriginal languages, ‘you leave behind a language where there’s very fine vocabulary for the landscape. All that is gone in a creole. You’ve just got a few words like ‘gum tree’ or whatever. As speakers become less able to express the wealth of knowledge that has filled ancestors’ lives with meaning over millennia, it’s no wonder that communities tend to become demoralised.’

If the losses are so huge, why are relatively few linguists combating the situation? Australian linguists, at least, have achieved a great deal in terms of preserving traditional languages. Australian governments began in the 1970s to support an initiative that has resulted in good documentation of most of the 130 remaining Aboriginal languages. In England, another Australian, Peter Austin, has directed one of the world’s most active efforts to limit language loss, at the University of London. Austin heads a programme that has trained many documentary linguists in England as well as in language-loss hotspots such as West Africa and South America.

At linguistics meetings in the US, where the endangered-language issue has of late been something of a flavour of the month, there is growing evidence that not all approaches to the preservation of languages will be particularly helpful. Some linguists are boasting, for example, of more and more sophisticated means of capturing languages: digital recording and storage, and internet and mobile phone technologies. But these are encouraging the ‘quick dash’ style of recording trip: fly in, switch on digital recorder, fly home, download to hard drive, and store gathered material for future research. That’s not quite what some endangered-language specialists have been seeking for more than 30 years. Most loud and untiring has been Michael Krauss, of the University of Alaska. He has often complained that linguists are playing with non-essentials while most of their raw data is disappearing.

Who is to blame? That prominent linguist Noam Chomsky, say Krauss and many others. Or, more precisely, they blame those linguists who have been obsessed with his approaches. Linguists who go out into communities to study, document and describe languages, argue that theoretical linguists, who draw conclusions about how languages work, have had so much influence that linguistics has largely ignored the continuing disappearance of languages. Chomsky, from his post at the Massachusetts Institute of Technology, has been the great man of theoretical linguistics for far longer than he has been known as a political commentator. His landmark work of 1957 argues that all languages exhibit certain universal grammatical features, encoded in the human mind. American linguists, in particular, have focused largely on theoretical concerns ever since, even while doubts have mounted about Chomsky’s universals.

Austin and Co. are in no doubt that because languages are unique, even if they do tend to have common underlying features, creating dictionaries and grammars requires prolonged and dedicated work. This requires that documentary linguists observe not only languages’ structural subtleties, but also related social, historical and political factors. Such work calls for persistent funding of field scientists who may sometimes have to venture into harsh and even hazardous places. Once there, they may face difficulties such as community suspicion. As Nick Evans says, a community who speak an endangered language may have reasons to doubt or even oppose efforts to preserve it. They may have seen support and funding for such work come and go. They may have given up using the language with their children, believing they will benefit from speaking a more widely understood one. Plenty of students continue to be drawn to the intellectual thrill of linguistics field work. That’s all the more reason to clear away barriers, contend Evans, Austin and others. The highest barrier, they agree, is that the linguistics profession’s emphasis on theory gradually wears down the enthusiasm of linguists who work in communities. Chomsky disagrees. He has recently begun to speak in support of language preservation. But his linguistic, as opposed to humanitarian, argument is, let’s say, unsentimental: the loss of a language, he states, ‘is much more of a tragedy for linguists whose interests are mostly theoretical, like me, than for linguists who focus on describing specific languages, since it means the permanent loss of the most relevant data for general theoretical work’. At the moment, few institutions award doctorates for such work, and that’s the way it should be, he reasons. In linguistics, as in every other discipline, he believes that good descriptive work requires thorough theoretical understanding and should also contribute to building new theory. But that’s precisely what documentation does, objects Evans. The process of immersion in a language, to extract, analyse and sum it up, deserves a PhD because it is ‘the most demanding intellectual task a linguist can engage in’.