М. С. Хахалина

Английский язык для сферы IT и программистов

Учебное пособие

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Автор:

Хахалина М. С., доцент кафедры английского языка для профессиональной коммуникации Российского государственного педагогического университета имени А. И. Герцена.

Рецензенты:

Белоглазова Е. В., доктор филологических наук, профессор кафедры английского языка и лингвострановедения Российского государственного педагогического университета имени А. И. Герцена;

Лагутина А. А., кандидат педагогических наук, доцент кафедры английского языка для профессиональной коммуникации Российского государственного педагогического университета имени А. И. Герцена.

Данное учебное пособие предназначено для магистрантов и бакалавров старших курсов, обучающихся в сфере информационных технологий и программирования и осваивающих дисциплины «Английский язык в профессиональной коммуникации», «Научно-технический перевод» и другие специальности, связанные с иноязычной профессиональной лексикой.

Каждый модуль содержит аутентичные научно-популярные статьи по различным сферам компьютерных наук, а также серию упражнений для успешного освоения и закрепления профессиональной лексики. Благодаря наличию ключей пособие может использоваться как в аудиторном формате, так и для самостоятельной работы студентов.

УДК 811.111(075.8)

ББК 81.2Англ-923

© Хахалина М. С., 2024

© ООО «Проспект», 2024

Module 1. COMPUTER SCIENCE AND COMPUTER GAMES

Text 1. COMPUTER GAMES GIVE A BOOST TO ENGLISH

Pre-reading

Discuss in pairs or small groups

• How can computer games influence studying school disciplines?

• Can you think of ways computer games can help you learn new skills (e.g. computer skills, languages, math, etc.)?

• Can you think of ways computer games can affect your health (positively or negatively)?

Source: University of Gothenburg

Summary: If you want to make a mark in the world of computer games you had better have a good English vocabulary. It has now also been scientifically demonstrated that someone who is good at computer games has a larger English vocabulary.

The study confirms what many parents and teachers already suspected: young people who play a lot of interactive English computer games gain an advantage in terms of their English vocabulary compared with those who do not play or only play a little.

The study involved 76 young people aged 10-11. Data was collected via questionnaires and a so-called language diary. This was used to list all encounters with the English language outside school, such as using the computer and playing digital games. Among other things, the study investigated whether there was any correlation between playing digital games and motivation to learn English, self-assessed English linguistic ability and strategies used to speak English.

The results indicate that there is a major difference between the genders when it comes to computer gaming. Boys spend an average of 11.5 hours a week playing, while girls spent less than half that time, 5.1 hours. Girls instead spent far more time (11.5 hours) than boys (8 hours) on language-related activities online, primarily on social networks.

The computer games that appear to be most effective for the development of English vocabulary are those known as Massively multiplayer online role-playing games (MMORPG), a genre of role-playing computer games in which a large number of players interact with one another in a virtual world.

“As a player you simply have to be able to understand what’s being said, to read English and to interact yourself by both writing and speaking English,” says Liss Kerstin Sylvén, Associate Professor at the University of Gothenburg, who conducted the study together with Pia Sundqvist, Senior Lecturer in English at Karlstad University.

The results from the study underline the results from other studies conducted by both researchers. Regular gamers have a significantly better English vocabulary than others.

“The importance of coming into contact with English outside school, for example by reading English or, as in this case, by playing computer games, means a lot in terms of young people’s English vocabulary. It also has positive effects on what happens at school in the classroom. The subject of English at school and the English that the young people encounter and use in their leisure time are not two separate worlds,” says Liss Kerstin Sylvén.

Journal reference:

Pia Sundqvist, Liss Kerstin Sylvén. Language-related computer use: Focus on young L2 English learners in Sweden. ReCALL, 2014; 26 (01): 3 DOI: 10.1017/S0958344013000232

EXERCISES

Mark the statements true or false

1. The study confirms that playing computer games helps teenagers significantly improve their English grammar.

2. The study was conducted by University of Gothenberg and involved 11 participants.

3. The participants had to write about their daily life in a special diary.

4. The results indicate that girls spend an average of 11.5 hours a week playing computer games.

5. Playing computer games helps improve English vocabulary.

Match the words from the text to make collocations

make
scientifically
multiplayer
self
major
conduct
come
games
difference
a study
demonstrated
into contact
a mark
assessment

Explain the words and phrases from the text with your own words

Confirm; suspect; interactive; involve; via; questionnaire; diary; encounter; correlation; indicate; average

Rephrase the abstract of the article

Translate into English

Положительное влияние, подчеркивать результаты, собирать данные, получить преимущество, значительно лучше, указывать, сталкиваться

Text 2. COMPUTER GAMES IN THE CLASSROOM: EDUCATIONAL SUCCESS DEPENDS ON THE TEACHER

Date: March 3, 2022

Source: University of Cologne

Summary: Future teachers see educational potential in computer games, study shows. Teacher training should therefore address their potential in the classroom.

New study results by a research team at the University of Cologne show that future teachers increasingly want to use computer games in the classroom. The study identifies particularly relevant aspects that should be addressed in teacher training programmes in order to support this intention. The study results have been published under the title ‘Teaching with digital games: How intentions to adopt digital game-based learning are related to personal characteristics of pre-service teachers’ in the British Journal of Educational Technology.

Computer games play a major role in the lives and media use of children and adolescents people. However, current school teaching rarely takes this medium into account. The future generation of teachers currently being trained at universities could change this. ‘In our current study, we focused on the teachers of tomorrow and how they can be better prepared to employ computer games in the classroom because computer games have great potential for teaching’, said Marco Rüth from the University of Cologne’s Psychology Department.

In previous studies, the authors had already shown that as a learning tool in the classroom, computer games can support students’ skills development. They also found that after using computer games in class, students can reflect critically and constructively on their experiences with the medium. Based on this, the researchers surveyed 402 teacher trainees from German-speaking universities online about their intention to integrate computer games as learning tools and as an object of reflection in their future school lessons. The team examined 21 personal characteristics, including perceived effectiveness of computer games, knowledge about computer games, and fear of using computer games in the classroom. ‘Above all, the perceived effectiveness of computer games and perceived connections of computer games to curricula play a central role in the intention of teacher trainees to actually want to use them in school lessons,’ Professor Kai Kaspar explained.

The current survey also revealed differences between the scenarios in which computer games are used: ‘If teacher trainees want to use computer games to promote the competencies of students, they pay particular attention to their own fear of using computer games and the extent to which people important to them think they should use computer games,’ explained Marco Rüth. ‘If, on the other hand, they want to use computer games for media-critical discussions, the focus was instead on the effort involved for them.’

Since computer games are currently rarely included as a relevant medium in teacher training programmes, the researchers recommend that, above all, insights into the effectiveness of computer games and their relevance to curricula should be included in teacher training programmes. Likewise, teacher trainees should be aware of potential pitfalls in practical implementation and be able to deal with them, so that teaching competencies with computer games are promoted in the long term. ‘This would require not only adjustments to the curriculum of the teacher training programme, but also further support services and research findings so that teachers in their later school practice know exactly when and how they can use computer games effectively in the classroom,’ said Professor Kaspar.

Journal Reference:

Marco Rüth, Adrian Birke, Kai Kaspar. Teaching with digital games: How intentions to adopt digital game‐based learning are related to personal characteristics of pre‐service teachers. British Journal of Educational Technology, 2022; DOI: 10.1111/bjet.13201

EXERCISES

True/false

1. The article analyses the use of computer games in teacher training.

2. Computer games are quite often included in teacher training programmes.

3. In previous studies, the authors had already shown that as a learning tool in the classroom, computer games can support teachers’ skills development.

4. According to the research, computer games help develop critical thinking skills.

5. 21 students took part in the research.

Match the words to make collocations

educational
teacher
particularly
media
take into
employ
learning
promote
practical
the competencies
account
implementation
use
relevant
computer games
tool
training
potential

Explain the words and phrases from the text with your own words

Relevant, support, adolescents, survey, reflect, curriculum, scenario

Rephrase the abstract of the article

Translate into English

Успех в обучении, результаты исследования, будущие поколения, основная роль, лучше подготовленный, предыдущие исследования, развитие навыков, критически осмыслять свой опыт, выявленная эффективность, подвох

Text 3. BOYS WHO PLAY VIDEO GAMES HAVE LOWER DEPRESSION RISK

Read the article and answer the questions

1. How many people participated in the research?

2. What caused the girls depressive disorder?

3. Who conducted the research?

4. What symptoms were the participants asked about?

5. What factors did the research team consider in the survey?

Boys who regularly play video games at age 11 are less likely to develop depressive symptoms three years later, finds a new study led by a UCL researcher. The study, published in Psychological Medicine, also found that girls who spend more time on social media appear to develop more depressive symptoms.

Taken together, the findings demonstrate how different types of screen time can positively or negatively influence young people’s mental health, and may also impact boys and girls differently. Lead author, PhD student Aaron Kandola (UCL Psychiatry) said: “Screens allow us to engage in a wide range of activities. Guidelines and recommendations about screen time should be based on our understanding of how these different activities might influence mental health and whether that influence is meaningful.

“While we cannot confirm whether playing video games actually improves mental health, it didn’t appear harmful in our study and may have some benefits. Particularly during the pandemic, video games have been an important social platform for young people. “We need to reduce how much time children — and adults — spend sitting down, for their physical and mental health, but that doesn’t mean that screen use is inherently harmful.”

Kandola has previously led studies finding that sedentary behaviour (sitting still) appeared to increase the risk of depression and anxiety in adolescents. To gain more insight into what drives that relationship, he and colleagues chose to investigate screen time as it is responsible for much of sedentary behaviour in adolescents. Other studies have found mixed results, and many did not differentiate between different types of screen time, compare between genders, or follow such a large group of young people over multiple years.

The research team from UCL, Karolinska Institutet (Sweden) and the Baker Heart and Diabetes Institute (Australia) reviewed data from 11,341 adolescents who are part of the Millennium Cohort Study, a nationally representative sample of young people who have been involved in research since they were born in the UK in 2000-2002.

The study participants had all answered questions about their time spent on social media, playing video games, or using the internet, at age 11, and also answered questions about depressive symptoms, such as low mood, loss of pleasure and poor concentration, at age 14. The clinical questionnaire measures depressive symptoms and their severity on a spectrum, rather than providing a clinical diagnosis.

In the analysis, the research team accounted for other factors that might have explained the results, such as socioeconomic status, physical activity levels, reports of bullying, and prior emotional symptoms.

The researchers found that boys who played video games most days had 24% fewer depressive symptoms, three years later, than boys who played video games less than once a month, although this effect was only significant among boys with low physical activity levels, and was not found among girls. The researchers say this might suggest that less active boys could derive more enjoyment and social interaction from video games.

While their study cannot confirm if the relationship is causal, the researchers say there are some positive aspects of video games which could support mental health, such as problem-solving, and social, cooperative and engaging elements. There may also be other explanations for the link between video games and depression, such as differences in social contact or parenting styles, which the researchers did not have data for. They also did not have data on hours of screen time per day, so they cannot confirm whether multiple hours of screen time each day could impact depression risks.

The researchers found that girls (but not boys) who used social media most days at age 11 had 13% more depressive symptoms three years later than those who used social media less than once a month, although they did not find an association for more moderate use of social media. Other studies have previously found similar trends, and researchers have suggested that frequent social media use could increase feelings of social isolation.

Screen use patterns between boys and girls may have influenced the findings, as boys in the study played video games more often than girls and used social media less frequently.

The researchers did not find clear associations between general internet use and depressive symptoms in either gender. Senior author Dr Mats Hallgren (Karolinska Institutet) has conducted other studies in adults finding that mentally-active types of screen time, such as playing video games or working at a computer, might not affect depression risk in the way that more passive forms of screen time appear to do.

Journal Reference:

A. Kandola [Opens in a new window], N. Owen, D. W. Dunstan, M. Hallgren. Prospective relationships of adolescents’ screen-based sedentary behaviour with depressive symptoms: the Millennium Cohort Study. Psychological Medicine, 2021. Vol. 52. Issue 15. https://www.cambridge.org/core/journals/psychological-medicine/article/abs/prospective-relationships-of-adolescents-screenbased-sedentary-behaviour-with-depressive-symptoms-the-millennium-cohort-study/1DE7A90DFF566E36742B141724673B47

EXERCISES

Match the synonyms:

benefit
impact
investigate
participant
moderate
confirm
adolescents
mixed
anxiety
research; study
worrying a lot
advantage
influence; effect
prove something right
people taking part in smth
ambiguous; unclear
limited; not too much
teenagers

Match the words to make collocations

screen
mental
poor
social
moderate
mixed
develop
a wide range
concentration
media
time
use
results
health
symptoms
of activities

Translate from Russian into English

Экранное время, плохая концентрация, социальные сети, умеренное использование, психическое здоровье, советы и рекомендации, значимый

Module 2. MACHINE LEARNING AND AI

Text 1. RESEARCHERS EXPAND ABILITY OF ROBOTS TO LEARN FROM VIDEOS

ROBOTS ABLE TO ACCOMPLISH TASKS AFTER WATCHING PEOPLE PERFORM THEM IN ANY ENVIRONMENT

Pre-reading

Discuss in pairs

• What are your preferable ways of learning new skills? (listening podcasts, reading books and journals, watching videos, etc.)

• What is the most efficient way of learning new skills for humans and for AI?

Source: Carnegie Mellon University

Summary: New work has enabled robots to learn household chores by watching videos of people performing everyday tasks in their homes. Vision-Robotics Bridge, or VRB for short, uses the concept of affordances to teach the robot how to interact with an object. Affordances have their roots in psychology and refer to what an environment offers an individual. The concept has been extended to design and human-computer interaction to refer to potential actions perceived by an individual. With VRB, two robots successfully learned 12 tasks including opening a drawer, oven door and lid; taking a pot off the stove; and picking up a telephone, vegetable or can of soup.

The research could help improve the utility of robots in the home, allowing them to assist people with tasks like cooking and cleaning. Two robots successfully learned 12 tasks including opening a drawer, oven door and lid; taking a pot off the stove; and picking up a telephone, vegetable or can of soup.

“The robot can learn where and how humans interact with different objects through watching videos,” said Deepak Pathak, an assistant professor in the Robotics Institute at CMU’s School of Computer Science. “From this knowledge, we can train a model that enables two robots to complete similar tasks in varied environments.”

Current methods of training robots require either the manual demonstration of tasks by humans or extensive training in a simulated environment. Both are time consuming and prone to failure. Past research by Pathak and his students demonstrated a novel method in which robots learn from observing humans complete tasks. However, WHIRL, short for In-the-Wild Human Imitating Robot Learning, required the human to complete the task in the same environment as the robot.

Pathak’s latest work, Vision-Robotics Bridge, or VRB for short, builds on and improves WHIRL. The new model eliminates the necessity of human demonstrations as well as the need for the robot to operate within an identical environment. Like WHIRL, the robot still requires practice to master a task. The team’s research showed it can learn a new task in as little as 25 minutes.

“We were able to take robots around campus and do all sorts of tasks,” said Shikhar Bahl, a Ph.D. student in robotics. “Robots can use this model to curiously explore the world around them. Instead of just flailing its arms, a robot can be more direct with how it interacts.”

To teach the robot how to interact with an object, the team applied the concept of affordances. Affordances have their roots in psychology and refer to what an environment offers an individual. The concept has been extended to design and human-computer interaction to refer to potential actions perceived by an individual.

For VRB, affordances define where and how a robot might interact with an object based on human behavior. For example, as a robot watches a human open a drawer, it identifies the contact points — the handle — and the direction of the drawer’s movement — straight out from the starting location. After watching several videos of humans opening drawers, the robot can determine how to open any drawer.

The team used videos from large datasets such as Ego4D and Epic Kitchens. Ego4D has nearly 4,000 hours of egocentric videos of daily activities from across the world. Researchers at CMU helped collect some of these videos. Epic Kitchens features similar videos capturing cooking, cleaning and other kitchen tasks. Both datasets are intended to help train computer vision models.

“We are using these datasets in a new and different way,” Bahl said. “This work could enable robots to learn from the vast amount of internet and YouTube videos available.”

Story Source:

Materials provided by Carnegie Mellon University. Original written by Aaron Aupperlee. Note: Content may be edited for style and length.

EXERCISES

True/false

1. Vision-Robotics Bridge, or VRB for short, uses the concept of reinforcement to teach the robot how to interact with an object.

2. In the research, 12 robots successfully learned numerous household chores.

3. At present, training robots requires either the manual demonstration of tasks by humans or extensive training in a simulated environment.

4. The research group based their work on a previous study.

5. In this research, affordances define where and how a robot might interact with humans.

6. The team used large datasets which included more than 4,000 videos.

Match the words to make collocations

household
perform
successfully
manual
extensive
starting
vast
learn
amount
location
training
demonstration
chores
tasks

Explain the words and phrases from the text with your own words

To enable, a concept, to extend, utility, to assist, time consuming, failure, to eliminate, to master, to collect

Rephrase the abstract of the article

Translate into English

Выполнить задачу, возможность действовать, настоящие методы, большие наборы данных, ответить на телефонный звонок, взаимодействовать с объектом

Text 2. A FAR-SIGHTED APPROACH TO MACHINE LEARNING

NEW SYSTEM CAN TEACH A GROUP OF COOPERATIVE OR COMPETITIVE AI AGENTS TO FIND AN OPTIMAL LONG-TERM SOLUTION

Read the article and complete the gaps with the missing phrases

Date: November 23, 2022

Source: Massachusetts Institute of Technology

Summary: a new technique enables artificial intelligence agents to think much farther into the future when considering how their behaviors can influence the behaviors of other AI agents, toward the completion of a task. This approach improves long-term performance of cooperative or competitive AI agents.

Picture two teams squaring off on a football field. The players can cooperate to achieve an objective, and compete against other players with conflicting interests. That’s how the game works.

Creating artificial intelligence agents that can learn to compete and cooperate as effectively as humans remains a thorny problem. _______ (1).

Because of the complexity of this problem, current approaches tend to be myopic; the agents can only guess the next few moves of their teammates or competitors, which leads to poor performance in the long run. Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere have developed a new approach that gives AI agents a farsighted perspective. Their machine-learning framework enables cooperative or competitive AI agents to consider what other agents will do as time approaches infinity, not just over a few next steps. _________ (2).

This framework could be used by a group of autonomous drones working together to find a lost hiker in a thick forest, or by self-driving cars that strive to keep passengers safe by anticipating future moves of other vehicles driving on a busy highway.

“When AI agents are cooperating or competing, what matters most is when their behaviors converge at some point in the future. __________ (3). Reaching this converged behavior is what we really care about, and we now have a mathematical way to enable that,” says Dong-Ki Kim, a graduate student in the MIT Laboratory for Information and Decision Systems (LIDS) and lead author of a paper describing this framework.

More agents, more problems

The researchers focused on a problem known as multiagent reinforcement learning. Reinforcement learning is a form of machine learning in which an AI agent learns by trial and error. Researchers give the agent a reward for “good” behaviors that help it achieve a goal. The agent adapts its behavior to maximize that reward until it eventually becomes an expert at a task.

___________________ (4). As agents consider more future steps of their fellow agents, and how their own behavior influences others, the problem soon requires far too much computational power to solve efficiently. This is why other approaches only focus on the short term.

“The AIs really want to think about the end of the game, but they don’t know when the game will end. They need to think about how to keep adapting their behavior into infinity so they can win at some far time in the future. Our paper essentially proposes a new objective that enables an AI to think about infinity,” says Kim.

___________________ (5), the researchers designed their system so agents focus on a future point where their behavior will converge with that of other agents, known as equilibrium. An equilibrium point determines the long-term performance of agents, and multiple equilibria can exist in a multiagent scenario. Therefore, an effective agent actively influences the future behaviors of other agents in such a way that they reach a desirable equilibrium from the agent’s perspective. If all agents influence each other, they converge to a general concept that the researchers call an “active equilibrium.”

The machine-learning framework they developed, known as FURTHER (which stands for FUlly Reinforcing acTive influence witH averagE Reward), __________ (6).

FURTHER does this using two machine-learning modules. The first, an inference module, enables an agent to guess the future behaviors of other agents and the learning algorithms they use, based solely on their prior actions. This information is fed into the reinforcement learning module, which the agent uses to adapt its behavior and influence other agents in a way that maximizes its reward.

Winning in the long run

The researchers tested their approach against other multiagent reinforcement learning frameworks in several different scenarios, including a pair of robots fighting sumo-style and a battle pitting two 25-agent teams against one another. __________ (7).

Since their approach is decentralized, which means the agents learn to win the games independently, it is also more scalable than other methods that require a central computer to control the agents, Kim explains.

The researchers used games to test their approach, but FURTHER could be used to tackle any kind of multiagent problem. For instance, _____________ (8).

Journal Reference:

Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How. Influencing Long-Term Behavior in Multiagent Reinforcement Learning. arXiv.org, 2022; DOI: 10.48550/arXiv.2203.03535

READING TASKS

Read and complete the article with the missing sentences

a. There are a lot of transient behaviors along the way that don’t matter very much in the long run.

b. The agents then adapt their behaviors accordingly to influence other agents’ future behaviors and arrive at an optimal, long-term solution.

c. A key challenge is enabling AI agents to anticipate future behaviors of other agents when they are all learning simultaneously

d. it could be applied by economists seeking to develop sound policy in situations where many interacting entitles have behaviors and interests that change over time.

e. But since it is impossible to plug infinity into an algorithm,

f. enables agents to learn how to adapt their behaviors as they interact with other agents to achieve this active equilibrium.

g. But when many cooperative or competing agents are simultaneously learning, things become increasingly complex.

h. In both instances, the AI agents using FURTHER won the games more often.

True/false

1. The researchers have developed a new technique which enables AI to cooperate or compete with other agents.

2. Their system can anticipate a few next steps that other agents will make.

3. The system calculates all the the transient behaviours of other agent as well as the converged behaviour.

4. Reinforcement learning is a kind of learning used in machine learning which rewards AI agents for achieving their goals.

5. FURTHER framework consists of three main machine-learning modules.

VOCABULARY EXERCISES

Match the words to make phrases

current
approach
adapt
reinforcement
increasingly
computational
equilibrium
tackle
long-term
maximize
power
point
a problem
complex
approaches
learning
the reward
the behaviour
solution
infinity

Match the words with their synonyms

enable
behaviour
anticipate
simultaneously
myopic
converge
transient
inference
process of making a conclusion
lasting for a short time
make smth possible
come towards each other and meet at a certain point
the way smb acts
at the same time
predict
short-sighted

Rephrase the abstract of the article

Translate into English

Одновременно, современные подходы, приспособить свое поведение, точка равновесия, награждать, обучение с подкреплением, соревноваться, сотрудничать

Text 3. NEW CYBER SOFTWARE CAN VERIFY HOW MUCH KNOWLEDGE AI REALLY KNOWS

Date: April 4, 2023

Source: University of Surrey

Summary: With a growing interest in generative artificial intelligence (AI) systems worldwide, researchers have created software that is able to verify how much information an AI farmed from an organization’s digital database.

Surrey’s verification software can be used as part of a company’s online security protocol, helping an organisation understand whether an AI has learned too much or even accessed sensitive data.

The software is also capable of identifying whether AI has identified and is capable of exploiting flaws in software code. For example, in an online gaming context, it could identify whether an AI has learned to always win in online poker by exploiting a coding fault.

Dr Solofomampionona Fortunat Rajaona is Research Fellow in formal verification of privacy at the University of Surrey and the lead author of the paper. He said:

“In many applications, AI systems interact with each other or with humans, such as self-driving cars in a highway or hospital robots. Working out what an intelligent AI data system knows is an ongoing problem which we have taken years to find a working solution for.

“Our verification software can deduce how much AI can learn from their interaction, whether they have enough knowledge that enable successful cooperation, and whether they have too much knowledge that will break privacy. Through the ability to verify what AI has learned, we can give organisations the confidence to safely unleash the power of AI into secure settings.”

The study about Surrey’s software won the best paper award at the 25th International Symposium on Formal Methods.

Professor Adrian Hilton, Director of the Institute for People-Centred AI at the University of Surrey, said:

“Over the past few months there has been a huge surge of public and industry interest in generative AI models fuelled by advances in large language models such as ChatGPT. Creation of tools that can verify the performance of generative AI is essential to underpin their safe and responsible deployment. This research is an important step towards maintaining the privacy and integrity of datasets used in training.”

Journal Reference:

University of Surrey. “New cyber software can verify how much knowledge AI really knows.” ScienceDaily. ScienceDaily, 4 April 2023. <www.sciencedaily.com/releases/2023/04/230404114204.htm>.

EXERCISES

True/false

1. The research group have developed an AI that can access sensitive data.

2. They created software which can identify flaws in software code.

3. Self-driving cars are equipped with AI.

4. The project won a 25 thousand $ prize at a scientific symposium.

5. The research is important for the future use of AI.

Match the words to make collocations

generative
exploit
coding
self-driving
verification
safe
deployment
flaws
software
fault
AI system
cars

Explain the words and phrases from the text with your own words

To verify; to farm (from a database); a flaw; privacy; application; to deduce; performance

Rephrase the abstract of the article

Translate into English

Протокол безопасности, конфиденциальные данные, успешное сотрудничество, нарушать безопасность, громадный всплеск интереса, поддерживать безопасность баз данных

Text 4. HOW ARTIFICIAL INTELLIGENCE CAN EXPLAIN ITS DECISIONS

Pre-reading

Discuss in pairs or small groups and read the article

• How can AI be used for medical research? Think of examples.

• What issues might arise when applying AI in medicine?

Date: September 2, 2022

Source: Ruhr-University Bochum

Summary: Today, when an algorithm identifies a tumor in a tissue sample, it doesn’t reveal how it arrived at this result. And that does not inspire trust. Researchers are therefore pursuing a new approach.

Artificial intelligence (AI) can be trained to recognise whether a tissue image contains a tumour. However, exactly how it makes its decision has remained a mystery until now. a team from the Research Center for Protein Diagnostics (PRODI) at Ruhr-Universität Bochum is developing a new approach that will render an AI’s decision transparent and thus trustworthy. The researchers led by Professor Axel Mosig describe the approach in the journal Medical Image Analysis, published online on 24 August 2022.

For the study, bioinformatics scientist Axel Mosig cooperated with Professor Andrea Tannapfel, head of the Institute of Pathology, oncologist Professor Anke Reinacher-Schick from the Ruhr-Universität’s St. Josef Hospital, and biophysicist and PRODI founding director Professor Klaus Gerwert. The group developed a neural network, i.e. an AI, that can classify whether a tissue sample contains tumour or not. To this end, they fed the AI a large number of microscopic tissue images, some of which contained tumours, while others were tumour-free.

“Neural networks are initially a black box: it’s unclear which identifying features a network learns from the training data,” explains Axel Mosig. Unlike human experts, they lack the ability to explain their decisions. “However, for medical applications in particular, it’s important that the AI is capable of explanation and thus trustworthy,” adds bioinformatics scientist David Schuhmacher, who collaborated on the study.

The Bochum team’s explainable AI is therefore based on the only kind of meaningful statements known to science: on falsifiable hypotheses. If a hypothesis is false, this fact must be demonstrable through an experiment. Artificial intelligence usually follows the principle of inductive reasoning: using concrete observations, i.e. the training data, the AI creates a general model on the basis of which it evaluates all further observations.

The underlying problem had been described by philosopher David Hume 250 years ago and can be easily illustrated: No matter how many white swans we observe, we could never conclude from this data that all swans are white and that no black swans exist whatsoever. Science therefore makes use of so-called deductive logic. In this approach, a general hypothesis is the starting point. For example, the hypothesis that all swans are white is falsified when a black swan is spotted.

“At first glance, inductive AI and the deductive scientific method seem almost incompatible,” says Stephanie Schörner, a physicist who likewise contributed to the study. But the researchers found a way. Their novel neural network not only provides a classification of whether a tissue sample contains a tumour or is tumour-free, it also generates an activation map of the microscopic tissue image.

The activation map is based on a falsifiable hypothesis, namely