Give the summary of the text using the key terms. Read the following words and word combinations and use them for understanding and translation of the text:

CURRENT TRENDS

Read the following words and word combinations and use them for understanding and translation of the text:

hype- навязчивая (агрессивная) реклама, рекламная шу-миха, раскрутка

acquisitions- приобретение

buzz word- модное словцо

cognitive computing- когнитивные вычисления, познава-тельные вычисления

predictive analytics- прогнозная аналитика

two-voice counterpoint- двухголосая полифония (контра-пункт)

visual cue- визуальная подсказка

to underpin- лежать в основе

big data- большие данные, супермассив данных

to sift through- перелопатить

evidence- реальные факты, полученные сведения

to leverage- выгодно использовать, по-новому применять

relevant information- необходимая (актуальная) информация

It looks like the beginning of a new technology hype for artificial intelligence (AI). The media has started flooding the news with product announcements, acquisitions, and investments. AI is capturing the attention of tech firm and investor giants such as Google, Microsoft, and IBM. The buzz words are great too: cognitive computing, deep learning, AI2.

For those who started their careers in AI and left in disillusionment or data scientists today, the consensus is often that artificial intelligence is just a new fancy marketing term for good old predictive analytics.They point to the reality of Apple’s Siri to listen and respond to requests as adequate but more often frustrating. Or, IBM Watson’s win on Jeopardy as data loading and brute force programming.

But, is this fair? No. New AI breaks the current rule that machines must be better than humans: they must be smarter, faster analysts, or manufacture things better and cheaper.

New AI says:

· The question is sometimes more important than the answer. Suggestions don’t always need to be answers, they can be questions. Eric Horvitz of Microsoft told MIT Technology Review, “…Another possibility is to build systems that understand the value of information, meaning they can automatically compute what the next best question to ask is....”

· Improvisation is the true meaning of adaptation. Search on ‘artificial intelligence’ and ‘improvisation’ and you get a lot of examples of AI being linked to music. The head of Facebook’s AI lab and musician, Yan Lecun, says,“I have always been interested in Jazz because I have always been intrigued by the intellectual challenge of improvising music in real time”. Linking the two, he wrote a program that automatically composed two-voice counterpoint for a college artificial intelligence project.

· Collaboration produces better results. Guy Hoffman at the Media Innovation Lab, School of Communication, IDC Herzliya introduced a robot that could not only compose music independently, but also collaborate with another musician (Guy himself) to create a new piece of music. The robot provided visual cues, reacting and communicating the effect of the music and creative process for lifelike interaction between robot and composer.

This is game changing, both in how organizations operate and strategize as well as the impact on customer experience. These three principles are the foundation for customer and organizational engagement. Today AI is like a super smart magic eight ball. Tomorrow AI supports and creates a dialog between companies and customers, managers and employees, and business to business.

We’re now seeing the emergence of cognitive computing, a new era of computing systems that will understand the world in the way that humans do: through senses, learning, and experience.

These new cognitive systems will help us think. They will relate to us and answer questions using the language we humans use every day. They will learn from their interactions with data and with us, basically adapting their behavior automatically based on new knowledge.

That’s what makes this third major era of computing such a huge leap forward. The first era was made up of tabulating machines and the second of programmable computers. While the programmable era will continue perhaps indefinitely and certainly underpin the next era of computing,cognitive systems represent a whole new approach to solving complex data and information analysis problems that goes beyond just computing.

Data is available everywhere, all the time. It’s piling up, simply waiting to be used. Which is why we need computing systems that we can interact with using human language, rather than programming language. We need computers that can dish up advice, rather than waiting for commands.

How will these systems work? IBM Watson, one of the first systems built as a cognitive computing system, applies deep analytics to text and other unstructured big data sources to pull meaning out of the data by using inference, probability, and reasoning to solve complex problems. Watson is a first step toward cognitive computing, expanding the reaches of human understanding by helping us quickly and efficiently sift through massive amounts of data, pinpointing the information and insights that are now trapped within these sources.

Watson does this by using hundreds of analytics, which provide it with capabilities such as natural language processing, text analysis, and knowledge representation and reasoning to make sense of huge amounts of complex information in split seconds, rank answers based on evidence and confidence, and learn from its mistakes. And, of course, this capability is deployed in the cloud and made available to applications as a cognitive service.

One of the first domains for Watson is healthcare. Cleveland Clinic is working to explore how Watson can be used to better leverage valuable information trapped in large electronic health records. Watson’s analytics can sift through unstructured clinical notes in a patient’s health record, reason over that information, and connect it with other structured information in the health record to produce summaries, deeper insights, and faster access to relevant information.

A new application of Watson, called WatsonPaths, is able to analyze complex medical scenarios and propose relationships and connections to possible diagnoses extracted from the underlying medical literature. Medical students can interact with WatsonPaths to both learn from Watson and teach Watson by grading Watson’s recommendations.

"Right now the science of cognitive computing is in the formative stages," says IBM Research's Ton Engbersen. "To become machines that can learn, computers must be able to process sensory as well as transactional input, handle uncertainty, draw inferences from their experience, modify conclusions according to feedback, and interact with people in a natural, human-like way."

Watson is a first step, but it points to what will be possible and how the age of cognitive computing will transform how we work with computers and what we expect out of them, helping remake our industries, economies and societies.

Notes:

deep learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations.[

brute force programming - программирование методом "грубой силы", неэффективный с точки зрения расходова­ния вычислительных ресурсов стиль программирования, решение "в лоб", когда программист полагается только на производительность компьютера, вместо того чтобы попы­таться упростить задачу, - поэтому программы получаются громоздкими, тяжеловесными, неэлегантными. В ряде слу­чаев такой подход оправдан, например, когда решение ра­зовой задачи нужно получить любой ценой

magic eight ball также mystic 8 ball, шар судьбы, шар во­просов и ответов, шар предсказаний — игрушка, шуточ­ный способ предсказывать будущее. Это шар, сделанный из пластмассы, обычно диаметром 10-11 см, внутри которого есть емкость с тёмной жидкостью, в которой плавает фи­гура с 20 поверхностями — икосаэдр, на которых нанесены ответы. Ответы (20 вариантов) нанесены в формате «да», «нет», «абсолютно точно», «плохие шансы», «вопрос не ясен», и т. д.

Assignments

1. Translate the sentences from the texts into Russian in writing paying attention to the underlined words and phrases:

· For those who started their careers in AI and left in disillusionment or data scientists today, the consensus is often that artificial intelligence is just a new fancy marketing term for good old predictive analytics.

· This is game changing, both in how organizations operate and strategize as well as the impact on customer experience. These three principles are the foundation for customer and organizational engagement.

· While the programmable era will continue perhaps indefinitely and certainly underpin the next era of computing,cognitive systems represent a whole new approach to solving complex data and information analysis problems that goes beyond just computing.

· Watson is a first step toward cognitive computing, expanding the reaches of human understanding by helping us quickly and efficiently sift through massive amounts of data, pinpointing the information and insights that are now trapped within these sources.

2. Answer the following questions:

1. What is the current situation in the field of AI?

2. What are the three basic principles of new AI?

3. Why is AI (in its current state) called “a super smart magic eight ball”?

4. How does Watson address complex problems?

5. Where can cognitive computing systems be applied?

3. Translate into English:

До сих пор все, что было в кибернетике и вычисли­тельной технике, базировалось, так или иначе, на моделях фон Неймана и Тьюринга. Сегодня IBM Research исследует следующее поколение вычислительных систем — когни­тивных — они, по сути, отходят от модели Тьюринга, кото­рая говорит о том, что любое вычисление может быть пред­ставлено в виде бесконечной ленты ячеек, в каждой из ко­торых находится одна простая команда.

Человеческий мозг так не работает. У нейронов, во-первых, гораздо больше связей; во-вторых, у нервной клетки больше состояний, чем 0 и 1. А в-третьих, и это са­мое важное, у традиционных кибернетических устройств есть 3 принципиально разные функции, разделенные ме­жду разными модулями. Одна функция — это память, где хранится информация, вторая функция — это устройство ввода-вывода и третья — это, собственно говоря, функция вычисления. Нейрон – устройство универсальное: он полу­чает информацию, хранит и перерабатывает ее.

Таким образом, когнитивные машины, конечно, должны не заново создавать мозг (природа один раз уже это сделала), а на основе тех знаний о физических и химиче­ских процессах, которые происходят в чипе, попытаться воспроизвести этот единый, параллельный по природе, растянутый во времени процесс познания, мышления, вос­приятия и осмысления реальности, и на этом основании выдать решение.

Пока что живые системы гораздо более эффективны, прежде всего, энергетически. Суперкомпьютер Watson,обыгравший участников телевикторины Jeopardy!, потреб­ляет 80 кВт энергии, а человеческий мозг — 20 Вт. То есть Watson в 4 тыс. раз менее энергоэффективен, чем мозг: на одинаковое по результату действие у него уходит гораздо больше энергии, чем у человека.