The Real Risks of Artificial Intelligence: A Look at the Present and Future

Reading passage for U6 Science and Technology: The real risks of artificial intelligence live a be liove some Al watches we are racing towards the Singilarity - a pointa/which astedlindianentbecomes. in and machines go on to improve ace seine towards the starlant Ye thalrapers - and its a big if - what wilbecome of shouid at fow years, several high profle voices, from Stephen Hawking to Elon Must and Blaster to noy where that. inhould be more concerned about profit voices. from Stephen be super mar Xl. And they ve put their armed whit benefi human is: Musk is among severa bissible dangerous outconnes r, an orgnisation dedicated to developing Al that will benefit But for many, such fears are overblown As Andrew Ne at Stanford University, who is also chief scientis at Chinese internet giant Baidu, puts it: fearing a rise of lIe robots is like worrying about overpopulation on Mars That's not to say our increasing reliance on Al does not carry real risks, however In fact, those risks are already lie, there is t systems become involved in ever more decisions narends ranging from healthcare to finance to criminal justice, there is a danger that important parts of our lives are being made without sufficient scrutiny. What's more, Als could have koodk are etects that we have not prepared for, such as changing our felationship with doctors to the way our neighbourhoods are What exactly is AI? Very simply, it's machines doing things that are considered to require intelligence when humans do them; understanding natural language, recognizing faces in photos, driving a car, or guessing what other books we might like based on What we have previously enjoyed reading. les in dis, render Between a mechanical arm on a factory production line programmed to repeat the same basic task over and over again, and an arm that learns through trial and error how to handle different tasks by itself. How is Al helping us? The leading approach to AI right now is machine learning, in which programs are trained to pick out and respond to patterns in large amounts of data, such as identifying a face in an image or choosing a winning move in the board game Go. This technique can be applied to all sorts of problems, such as getting computers to spot patterns in medical images, for example. Google's artificial intelligence company DeepMind are collaborating with the UK's National Health Service in a handful of projects, including ones in which their software is being taught to diagnose cancer and eye disease from patient scans. Others are using machine learning to catch early signs of conditions such as heart disease and Alzheimers. Artificial intelligence is also being used to analyse vast amounts of molecular information looking for potential new drug candidates - a process that would take humans too long to be worth doing. Indeed, machine learning could soon be indispensable to healthcare. Artificial intelligence can also help us manage highly complex systems such as global shipping networks. For example, the system at the heart of the Port Botany container terminal in Sydney manages the movement of thousands of shipping containers in and out of the port, controlling a fleet of automated, driverless straddle-carriers in a completely human-free zone. Similarly, in the mining industry, optimisation engines are increasingly being used to plan and coordinate the movement of a resource, such as iron ore, from initial transport on huge driverless mine trucks, to the freight trains that take the ore to port. Als are at work wherever you look, in industries from finance to transportation, monitoring the share market for suspicious trading activity or assisting with ground and air traffic control. They even help to keep spam out of your inbox. And this is just the beginning for artificial intelligence. As the technology advances, so too does the number of applications. So what's the problem? Rather than worrying about a future AI takeover, the real risk is that we can put too much trust in the smart systems we are building. Recall that machine learning works by training software to spot patterns in data. Once trained, it is then put to work analysing fresh, unseen data. But when the computer spits out an answer, we are typically unable to see how it got there. There are obvious problems here. A system is only as good as the data it learns from. Take a system trained to learn which patients with pneumonia had a higher risk of death, so that they might be admitted to hospital. It inadvertently classified patients with asthma as being at lower risk. This was because in normal situations, people with pneumonia and a history of asthma go straight to intensive care and therefore get the kind of treatment that significantly reduces their risk of dying. The machine learning took this to mean that asthma + pneumonia = lower risk of death. As Als are rolled out to assess everything from your credit rating to suitability for a job you are applying for to criminals' chance of reoffending, the risks that ino Readino passage fo livrons- withous us necesarly knowing - Bet worse. Reading passage for U6 Science and Technology the ca so much of the data that we fed Als is inserfee, ive should not oxpect perfect answeral the the in stice кое віто встори таловале іне но, сео збо і іпро сов уко в нил оо о от Ато песо го бе пробо сото оте мо зстийту. Sn We are building artinctal nielsenceetsion.makins process ikely to be both as briliant and as faved as we are. Questions 28-36 Complete the sentences below. Write NO MORE THAN TWO WORDS from the passage for each answer. Write your answers in boxes 28-36 on your answer sheet. 28. Singularity is the point, where AI our own machines. 29. Many people, including Stephen Hawking, Elon Musk and Bill Gates warned us about possible. of supersmart AI. 30. According to Andrew Ng, fearing a rise of is similar to worrying about overpopulation on Mars. will be without 31. There is a danger that many important parts of our lives, like healthcare, finance and sufficient scrutiny. when humans do them. 32. Simply put, AI is machines doing things that are considered to require 33. Nowadays, the main approach to AI is 34. DeepMind in collaboration with the UK's National Health Service works on many projects, including the one where software learns how to and eye disease. 35. In the nearest future machine learning could be to healthcare. 36. AI might also help in managing networks. Questions 37-40 Do the following statements agree with the information given in Reading Passage 3? In boxes 37-40 on your answer sheet, write TRUE if the statement agrees with the information FALSE if the statement contradicts the information NOT GIVEN if there is no information on this 37. Al works in many different industries nowadays. 38. We shouldn't put too much trust in AI in the future. 39. The quality of the data doesn't affect the ability of AI to learn information correctly. 40. We can get perfect answers from Al all the time. Content: 28. the point where artificial intelligence surpasses human intelligence 29. possible dangerous outcomes of artificial intelligence 30. worrying about a rise of AI is like worrying about overpopulation on Mars 31. healthcare, finance, and criminal justice are being made without sufficient scrutiny 32. intelligence when humans do them 33. machine learning 34. diagnose cancer and eye disease 35. indispensable 36. global shipping networks 37. TRUE 38. TRUE 39. FALSE 40. FALSE