• Теги
    • избранные теги
    • Компании1709
      • Показать ещё
      Международные организации58
      • Показать ещё
      Страны / Регионы395
      • Показать ещё
      • Показать ещё
      • Показать ещё
      • Показать ещё
      • Показать ещё
28 июня, 22:07

Inside Story - How to stop cyber attacks? – Inside Story

Cyber security has become one of the most important aspect of life in the 21st century. Which means, keeping computer systems secure becomes paramount for governments and companies. Now, a major cyber attack that began in Ukraine has crippled computer systems around the world. It's shut down government agencies and impacted thousands of businesses from India to Denmark. It's linked to the so-called WannaCry ransomware, a virus that holds data hostage until a payment is made. No one knows who's behind this attack yet but the US says it's investigating. So, what will it take to stop attacks like these? and will it get worse before it gets better? Presenter: Richelle Carey Guests: Antonis Michalas - Head of Cyber Security Group at the Department of Computer Science at the University of Westminster Patrick Flynn - Director of National Security Programs Neil Walsh - Chief of the United Nations Global Programme on Cybercrime Subscribe to our channel http://bit.ly/AJSubscribe Follow us on Twitter https://twitter.com/AJEnglish Find us on Facebook https://www.facebook.com/aljazeera Check our website: http://www.aljazeera.com/

Выбор редакции
27 июня, 21:09

Network dynamics of social influence in the wisdom of crowds [Social Sciences]

A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton’s discovery of the “wisdom of crowds” [Galton F (1907) Nature 75:450–451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals...

27 июня, 11:42

CSRA Inc (CSRA) Up 2.1% Since Earnings Report: Can It Continue?

CSRA Inc (CSRA) reported earnings 30 days ago. What's next for the stock? We take a look at earnings estimates for some clues.

22 июня, 04:25

Home Sweet Home: The Best College Degrees for Homeownership

When will you become a homeowner? You might be surprised to know your college degree could be a factor.

20 июня, 14:11

Teradyne (TER) Appoints Marilyn Matz to Board of Directors

Teradyne Inc. (TER) recently announced changes at the management level with the appointment of Marilyn Matz to its Board of Directors, effective July 3.

Выбор редакции
19 июня, 16:12

Women and STEM -- by Shulamit Kahn, Donna Ginther

Researchers from economics, sociology, psychology, and other disciplines have studied the persistent under-representation of women in science, technology, engineering, and mathematics (STEM). This chapter summarizes this research. We argue that women's under-representation is concentrated in the math-intensive science fields of geosciences, engineering, economics, math/computer science and physical science. Our analysis concentrates on the environmental factors that influence ability, preferences, and the rewards for those choices. We examine how gendered stereotypes, culture, role models, competition, risk aversion, and interests contribute to gender STEM gap, starting at childhood, solidifying by middle school, and affecting women and men as they progress through school, higher education, and into the labor market. Our results are consistent with preferences and psychological explanations for the under-representation of women in math-intensive STEM fields.

14 июня, 16:11

Steve Scalise, Congressman Shot During Baseball Practice, Is A Top House Republican

function onPlayerReadyVidible(e){'undefined'!=typeof HPTrack&&HPTrack.Vid.Vidible_track(e)}!function(e,i){if(e.vdb_Player){if('object'==typeof commercial_video){var a='',o='m.fwsitesection='+commercial_video.site_and_category;if(a+=o,commercial_video['package']){var c='&m.fwkeyvalues=sponsorship%3D'+commercial_video['package'];a+=c}e.setAttribute('vdb_params',a)}i(e.vdb_Player)}else{var t=arguments.callee;setTimeout(function(){t(e,i)},0)}}(document.getElementById('vidible_1'),onPlayerReadyVidible); Rep. Steve Scalise (R-La.) was shot Wednesday as he practiced for the upcoming congressional baseball game.  His injuries are reportedly non life-threatening. He was shot in the hip and is being treated at the nearby GWU Hospital, The Associated Press said. Video obtained by ABC News showed Scalise on a stretcher following the incident. MORE:- Rep. Scalise's injury not life threatening- Pres. Trump briefed- so far no nexus to terrorismLIVE: https://t.co/PhFdeHl0gW pic.twitter.com/W3EEbCIyiM— Good Morning America (@GMA) June 14, 2017 The Louisiana Republican, 51, was first elected to Congress in 2008, is the House Majority Whip, the number three Republican in the House GOP leadership.  A tea party Republican, Scalise was seen as someone who could bridge the gap between the GOP establishment and its more conservative wing when he was elected to the party’s leadership in 2014. Before serving in Congress, Scalise served in the Louisiana state Senate briefly in 2008 and in the Louisiana House of Representatives from 1995 until 2007. A New Orleans native, Scalise worked as a software engineer and marketing executive before beginning his career in politics, a job that he held during almost all of his tenure in the Louisiana state house, according to The Washington Post. He graduated from Louisiana State University with a degree in computer science in 1989. In 2014, Scalise was at the center of controversy after reports he had spoken to a gathering of white supremacists in 2002. Republicans defended Scalise amid the controversy. Scalise was easily reelected to his seat in November, and represents one of the most conservative districts in Louisiana, which includes the suburbs of New Orleans. -- This feed and its contents are the property of The Huffington Post, and use is subject to our terms. It may be used for personal consumption, but may not be distributed on a website.

14 июня, 01:00

Canada Seeks to Take Advantage of US Political Disarray

  Canada’s Tech Firms Capitalize On Immigration Anxiety In The Age Of Trump   For years, Canada’s tech industry has watched in frustration as Microsoft and Google hired the country’s top computer science grads for high-paying jobs in Seattle and Silicon Valley. Now Canada believes it has found a new way to lure American and… Read More The post Canada Seeks to Take Advantage of US Political Disarray appeared first on The Big Picture.

12 июня, 18:42

ICYMI: Ivanka Trump Previews the Trump Administration’s Workforce Development Week on Fox News’ “Fox and Friends”

“So last week… was infrastructure week. Focusing on the commitment to rebuilding this country, rebuild[ing] rural locations, which have fallen into great disrepair, repair[ing] our waterways, air traffic control. So there were a series of very important and big and far reaching initiatives on infrastructure. This coming week is about workforce development. … Ultimately, we are really focused on why the American people elected Donald Trump as their president.”  – Ivanka Trump Click to Watch IVANKA TRUMP: “So last week, while it didn't get the level of headlines, it will ultimately have a much more impact, was infrastructure week. Focusing on the commitment to rebuilding this country, rebuild rural locations, which have fallen into great disrepair, repair our waterways, air traffic control. So there were a series of very important and big and far reaching initiatives on infrastructure. This coming week is about workforce development. So with all the noise, with all the intensity of the media coverage….ultimately, we are really focused on why the American people elected Donald Trump as their president.” AINSLEY EARHARDT, FOX NEWS: “People say jobs, jobs, jobs. That's why you, your dad, the Secretary of Labor--You are going to Wisconsin tomorrow to visit with kids taking classes at technical schools?” TRUMP: “We are visiting one of the great examples of skilled based learning and skills based education technical schools in Wisconsin--which we are very excited about--to talk about the skills gap and to really highlight the fact that there is a viable path other than a four year college experience. … There are 6 million available American jobs. …[W]e're constantly hearing from CEOs that they have job openings but they don't have workers with the skill set they need to fill those jobs. Really bridging that gap and bringing experienced based education to the forefront. So apprenticeship, actually, that's the model. STEVE DOOCY, FOX NEWS: “Something your dad knows it well.” TRUMP: “He knows it very well! And it has worked throughout the world and it is something we deemphasized here in favor of four year traditional college, but they don't have to be mutually exclusive.” DOOCY: “As somebody who has run her own business, this something personal to you. I'm sure there have been situations where we love to hire people but we can't find the people who have the right skill set.” TRUMP: “It’s true, and while it's not a woman's issue, it disproportionately affects women and minorities, especially when you think out into the future where the available jobs today and [where] the future jobs are coming from. A lot of … them are in STEM-related fields, science, engineering, computer science.” DOOCY: “So teach them today for the jobs of tomorrow?” TRUMP: “…[W]omen are, for example, … 47% of the overall workforce, we only make up 23% of STEM-related occupations. So, we're moving in the wrong direction in terms of our participation and that's something ultimately we need to change. We’ll encourage … K-12, but also retraining for workers whose jobs have been displaced. So we have a huge emphasis on it this week. It's critically important and I think we can make a very big impact.” … TRUMP: “Yeah, and we need the full participation. So many people are also working jobs that are part time, and it's an enormous problem in this country. The number of part-time workers who are working two and three jobs that, collectively, they are making less than when they worked one job that's been replaced. And they don't have access to leave for vacation to holidays, to traditional benefits. So that's another problem we are very much looking address.”

09 июня, 19:17

 Жан Понс, Ecole Normale Superieure: Мы решили базовые проблемы развития компьютерного зрения, но до широких индустриальных применений еще далеко

По подсчетам MarketsandMarkets, глобальный рынок систем компьютерного зрения (подробнее об их работе  - в материале Forbes)  к 2020 году пройдет отметку в около $12,5 млрд, показывая ежегодный рост в более чем 9%.  Аналитики компании Tractica сравнивают технологии компьютерного зрения с новичком-«квотербеком», который принес своей команде победу в чемпионате и, очевидно, принесет ей новые кубки и медали. Сегодня технологии компьютерного зрения  (все те, которые позволяют машинам  получать изображения объектов реального мира, интерпретировать их и принимать автономные решения на основе полученных данных) позволяют «видеть» промышленным роботам, первым беспилотным автомобилям, охранным системам и, например, «виртуальным примерочным», с которыми начинают экспериментировать ритейлеры. О том, как нейросети сделали компьютерное зрение одним из самых перспективных направлений искусственного интеллекта,  как машины учатся распознавать окружающую реальность, Forbes поговорил с исследователем Жаном Понсом. Понс, автор трех книг по компьютерному зрению (самая известная,  «Компьютерное зрение: современный подход», переведена на русский язык), профессор МIT и глава лаборатории computer science парижской Ecole Normale Supérieure, приехал на несколько дней в Москву на саммит «Машины могут видеть», организованный VisionLabs,  венчурным фондом Sistema_VC и «Стрелкой». Исследовательская группа Понса работает над тремя задачами. Во-первых, это разработка систем для «понимания» изображений и видео. Для этого нужно «узнать» объекты (например, отличить банан от собаки), действия (пьет ли человек воду или улыбается), элементы интерьера или экстерьера (скажем, фонари или шторы), а также научить систему «ориентироваться» — узнавать стены, улицы,небо и т.д. Во-вторых, группа Жана развивает решения для формирования 3D-изображений и моделирования  сцен. Такие системы ученые отдают, например, археологам, ведущим раскопки в Помпеях, и голливудским режиссерам — для спецэффектов и постпродакшн. Третье направление работы — восстановление изображений и видео, когда первоначальные данные повреждены или очень «зашумлены». Понс ведет исследования совместно с   исследователями других групп в использовании алгоритмов машинного обучения, рассказывает Понс. Например, распознавание естественного языка может повысить качество распознавания видео: например, идентификацию  смеха в видео сделать легче, если система определяет в качестве признака и звук, и положение рта. Пока системы компьютерного зрения определяют: на картинке — кошка, и для нее это только набор буквенных символов. Но вскоре мы сможем научить машины понимать, что «кошка» — это мяукающее существо на четырех лапах, прогуливающееся по двору. Работа исследователей машинного обучения именно с разными типами данных приведет нас к эре семантического анализа видео, уверен Понс. — Каковы достижения  технологий компьютерного зрения в последние годы?  Об их прогрессе много говорят, вспоминая, например, то, как соцсети научились идентифицировать пользователей по загруженным фото или то, как в нашу жизнь вошли жестовые интерфейсы и автономные автомобили. — Технологии искусственного интеллекта в целом переживают вторую волну развития в течение последних десяти лет. С 1960-х  они проходили взлеты и падения. На какое-то время дискуссии затихали, потом возобновлялись снова.  Но именно в последнее десятилетия мы видим огромное расширение возможностей их применения. В этом смысле то, как оценили достижения, например, Mobileye (компания разрабатывает хардверные  и софтверные решения для беспилотников, приобретена Intel за $15 млрд — Forbes) показательно. Безусловно, компании в сфере рекламных технологий и соцсети тоже внесли вклад: теперь многие из загруженных в Facebook или Instagram фотографий попадают в выборку  для обучения нейронных сетей. Системы анализируют снимки с Google Street View, спутниковые данные, информацию с камер в городах. То, что так быстро растет база изображений и видео для «тренирововк»  действительно способствует успехам компьютерного зрения. Но все же основные проблемы развития технологий все еще остаются в академическом поле. Мы имеем дело с научными проектами, а не столько со все новыми задачами, выдвигаемыми бизнесом для создания все новых готовых решений.   Мы решили базовые проблемы развития компьютерного зрения, но до широких индустриальных применений еще далеко. Корпорации открывают R&D-лаборатории, они вынуждены работать пока над исследовательскими проектами, а не сугубо коммерческими продуктами. — Что можно считать индикатором столь быстрого прогресса систем компьютерного зрения? — Если сопоставить то, что они умеют сейчас, и что они умели десять лет назад, мы увидим колоссальный рост сложности решаемых задач. В то время машины могли различать только общие контуры, разделить фон и объект. Теперь они умеют идентифицировать  объекты разных классов в очень многодетальных  изображениях. Например, теперь у нас есть методы обнаружения лиц, распознавания движений тех или иных черт лица. К тому же, сообщество разработчиков  вводит «бенчмаркинг» — количественные критерии по точности и качеству распознавания, которые устанавливают в качестве требований для прохождения тестов различные исследовательские группы.   — Какие проблемы все еще предстоит решить? — Сегодня системы компьютерного зрения используют в основном обучение с учителем. Такой подход предполагает, что для распознавания образов машины получают изображения или кадры видео, уже размеченные людьми. Нейросеть получает «правильный ответ»: действительно ли на фото, например, собака или банан, как она предположила. Далее система учитывает, верно ли она сделала вывод, — и переходит к новому фото.  Сегодня сотни тысяч изображений  предварительно вручную обрабатываются людьми — обычно с привлечением краудсорсинга. Поэтому масштабировать такой процесс сложно: нужно будет все больше людей и все больше времени. Это сдерживающий фактор для многих областей потенциального применения компьютерного зрения. Есть два пути его преодоления. Во-первых, нужно активнее развивать методы машинного обучения без учителя. Тогда системы научатся принимать решения полностью самостоятельно. Тогда мы сможем все увеличивать объем  обрабатываемых изображений, фактически он будет безлимитным.  Во-вторых, можно работать с метаданными фотографий и видео. Мы пошли во многом по второму пути и смогли перейти на обучение с частичным привлечением учителя. В этом случае мы даем добровольцам размечать только ограниченный набор изображений (в нашем примере-  около 2000) и впоследствии уже автоматически соотносим отдельные объекты. Постепенно, за счет эффективных алгоритмов, система учится все лучше и лучше устанавливать связи между объектами, изначально названными людьми, и образами.  В целом, все еще остается «вызовом»  развитие все более качественных алгоритмов для глубинного обучения и их все более  «результативных тренировок», это все еще «открытая территория». К тому же, многое предстоит сделать в разработке систем для распознавания трехмерных объектов. Сегодня большинство исследователей сосредоточены на  распознавании визуальных образов в работе с фотографиями, на повышении детализации. Но многие забывают, что мы воспринимаем мир объемно, поэтому нам нужно учить машины не сводить картинки, а «видеть» объемно.   — Технологии компьютерного зрения выходят из стен академических институтов на рынок, компании от Microsoft до Facebook открывают подразделения для работы с ними. Как  научное сообщество работает с бизнесом? — Могу сказать, что с машинным зрением активно работают не только интернет-корпорации, которые хотят улучшить в том числе качество поиска по изображениям, но и  игроки в сфере hardware  - например, Intel и Qualcomm. Но все компании все еще не могут проводить полностью самостоятельные исследования, большинство значимых научных результатов выходят из академической среды. У меня встречи с представителями тех или иных компаний примерно  раз в неделю —  кто-то спрашивает о сотрудниках для своих подразделений, кто-то хочет заказать разработки. Мы часто отправляем в лаборатории корпораций студентов и аспирантов, а вот браться за многие задания я не могу — не хватает ресурсов сотрудников, хотя  я продолжаю давать консультации. Уверен, корпорации могут использовать наши наработки, финансировать новые исследования, привлекать кадры. Я не рисую радужной картинки, это искренняя оценка: взаимодействие науки и бизнеса очень «здоровое». Кстати, к нам обращаются и стартапы. Работа моей небольшой команды (около 20 человек) уже дала жизнь четырем стартапам. Интересно то, что они не просто взяли наши готовые разработки для коммерциализации, а использовали наши глубокие базовые знания технологий компьютерного зрения, объединившись со студентами, и запустили проекты с собственными идеями. Например, одна из наших разработок для археологии стала основой для рыночного продукта. Это очень вдохновляет. Я думаю, что и дальше стартапы, инвесторы и ученые будут работать совместно. На мой взгляд, хотя венчурный капитал сосредоточен в Кремниевой Долине, перспективные стартапы будут появляться и в Европе — по крайней мере, во Франции я вижу для начинающих компаний в этой сфере много возможностей работы с инкубаторами, с частными инвесторами.  Но все же они будут продолжать работать с академическими институтами — именно здесь сосредоточены люди, глубоко понимающие технологии. — В каких сферах технологии машинного зрения сегодня применяются наиболее эффективно? Где вы видите перспективы наиболее серьезных прорывов с точки зрения внедрений? — Я ученый и, пожалуй, не смогу прогнозировать скорость внедрения этих технологий в тех или иных отраслях.  Я могу отметить, что, безусловно, мы живем  в  век умных машин и если мы хотим, чтобы они все активнее входили в жизнь, мы должны научить их восприятию. Одной из серьезнейших задач, безусловно, будет интегрировать   компьютерное зрение в автономные роботизированные системы для «дикой среды». Я имею в виду, что пока мы тренируем системы компьютерного зрения, например, для промышленных роботов — мы можем учить их в условиях, которые при моделировании максимально приближаем к тем, что они встретят, скажем, на той или иной фабрике. Создать же «тренировочный полигон» для систем компьютерного зрения для будущих роботов на улице будет намного сложнее.  Открытые пространства для машин означают очень широкий набор меняющихся параметров — разная погода, разные показатели видимости, разные шумы, разные городские ландшафты.  Нам еще предстоит разработать системы, способные подстраиваться одновременнно под самую разную обстановку.

Выбор редакции
09 июня, 16:33

Where are the Women in Computer Science?

Only 29.1% of CS Undergraduate and 30.1% of CS Master students at Columbia University are female illustrating the disparity in academia for women in Computer Science.

08 июня, 13:00

3 Things Are Holding Back Your Analytics, and Technology Isn’t One of Them

During the past decade, business analytics platforms have evolved from supporting IT and finance functions to enabling business users across the enterprise. But many firms find themselves struggling to take advantage of its promise. We’ve found three main obstacles to realizing analytics’ full value, and all of them are related to people, not technology: the organization’s structure, culture, and approach to problem solving. Structure Structurally, analytics departments can range between two opposite but equally challenging extremes. On the one hand are data science groups that are too independent of the business. These tend to produce impressive and complex models that prove few actionable insights. Consider the experience of one retail financial services firm. There, the analytics function was comprised of employees who used specialized software packages exclusively and specified complicated functional forms whenever possible. At the same time, the group eschewed traditional business norms such as checking in with clients, presenting results graphically, explaining analytic results in the context of the business, and connecting complex findings to conventional wisdom. The result was an isolated department that business partners viewed as unresponsive, unreliable, and not to be trusted with critical initiatives. Insight Center Putting Data to Work Sponsored by Accenture Analytics are critical to companies’ performance. On the other hand, analysts who are too deeply embedded in business functions tend to be biased toward the status quo or leadership’s thinking. At a leading rental car agency, for instance, we watched fleet team analysts present intelligence purportedly showing that the fleet should skew toward newer cars. Lower maintenance costs more than compensated for the higher depreciation costs, they said. This aligned with the fleet vice president’s preference for a younger fleet. But it turned out that the analysts had selected a biased sample of older cars with higher-than-average maintenance costs among cars of the same age. An analysis of an unbiased sample (or the entire population) would have yielded a different result. (Of course there might have been other motivations to keep a younger fleet—customer satisfaction and brand perception, to name two—but cost reduction was not one of them.) Culture Culturally, organizations that are too data-driven (yes, they exist) will blindly follow the implications of flawed models even if they defy common sense or run counter to business goals. That’s what happened at a financial services firm where management was mulling a change to its commission structure. They wanted to switch the basis of its salesforce compensation from raw results to performance relative to the potential of each salesperson’s market. In response, analysts developed an admirable data envelopment model. The model simultaneously compared sales of different types of products with local demographic and financial statistics to come up with a single efficiency measure for each salesperson relative to their peers. Indeed, this seemed to have made compensation more equitable. But it reduced the compensation of salespeople who were less efficient but ultimately more valuable—causing them to defect to competitors. Alternatively, organizations that rely too heavily on gut instinct resist adjusting their assumptions even when the data clearly indicates that those assumptions are wrong. The aforementioned rental car agency, for example, was extremely reluctant to change course even after discovering that the data didn’t support their cost reduction claims. Methodology The dichotomy continues when it comes to methodology. At one extreme, we see analytics groups that create overly complex models with long lead times and limited adaptability to changing inputs. An example of this was the data science team, made up of repurposed actuaries, at a national auto insurance company. This team created a highly impressive model to predict whether a car would be a total or partial loss after the first accident report. The ensemble model consisted of a random forest, a principal component analysis, and a Bayes classifier. Unfortunately, the company lacked the infrastructure to directly implement the trained model in a production setting. The model was also too complex for the IT team to reproduce. As it turned out, a simple logistic regression was almost as effective. Then again, some teams create models that are too simplistic and fail to capture the nuances of the problems they’re trying to solve. A large fashion retailer ran into this issue. The company noticed that certain promotions were highly correlated with increased foot traffic. They failed to realize that they tended to run those promotions on Fridays and Saturdays, when foot traffic was substantially higher than other days of the week to begin with. Elements of an Effective Analytics Organization In light of these obstacle, we believe an effective business analytics organization balances functional knowledge, business instinct, and data analysis, with an operating philosophy to add complexity only when the additional insights justify it. This kind of organization includes: An analytics nerve center. Ideally, a small team of independent, highly skilled data scientists—typically with advanced degrees in statistics, mathematics, computer science and the like—serves as the analytics nerve center of an organization. Also, reporting into this nerve center are analytics generalists embedded in each major business function. In this setup, the embedded generalists gain the deep functional know-how they need to initiate and develop actionable analyses. They rely on the nerve center for additional support, model validation, and training. Knowing the languages of analytics and business, the embedded generalists also serve as liaisons between the independent data scientists and the business partners in their functions. Representation at the top. Heading up the nerve center? A chief analytics officer who brings the voice of analytics straight to the C-suite, where instinct tends to rule. When analytics and instinct come together, a strategy becomes more powerful. When the two disagree, research can reveal whether it’s the model or the business assumption that’s flawed. A champion-challenger approach. To manage the complexity-insight tradeoff in this ideal structure, analysts initially focus on creating a type of minimum viable product, or MVP. Here, an MVP is a model that solves the problem to a minimum acceptable level as simply as possible. This MVP becomes the “champion” and touches off challenges to add complexity and unseat it. A challenger can replace the champion, but only if all stakeholders agree that its benefits are worth the increased complexity. An interesting byproduct of this approach is that it nearly always produces something usable, even if the process is interrupted. Modern business analytics has made it possible to extract new types of insights from vast volumes of data. The result is that analytics has become crucial to any large organization’s ability to make decisions. Getting this capability right means creating an analytics organization with the structure, culture, and problem-solving methodology to reveal the actionable insights that business leaders need to compete.

05 июня, 17:30

STEPHEN CARTER: The Second Time I Learned To Read: Mrs. Dickey taught me to read. Not to read. T…

STEPHEN CARTER: The Second Time I Learned To Read: Mrs. Dickey taught me to read. Not to read. To read. I arrived in Ithaca reasonably well educated in every field but literature. Oh, I had read a bit of Shakespeare and Dickens, but only because my 8th and 9th grade English courses required it. Over […]

05 июня, 14:00

How to Prepare the Next Generation for Jobs in the AI Economy

Most of us regard self-driving cars, voice assistants, and other artificially intelligent technologies as revolutionary. For the next generation, however, these wonders will have always existed. AI for them will be more than a tool; in many cases, AI will be their co-worker and a ubiquitous part of their lives. If the next generation is to use AI and big data effectively – if they’re to understand their inherent limitations, and build even better platforms and intelligent systems — we need to prepare them now. That will mean some adjustments in elementary education and some major, long-overdue upgrades in computer science instruction at the secondary level. For example, consider how kids are currently interacting with AI and automated technologies: Right now, it might seem magical to tell Siri, “Show me photos of celebrities in orange dresses,” and see a photo of Taylor Swift pop up on a smartphone less than a second later. But it’s clearly not magic. People design AI systems by carefully decomposing a problem into lots of small problems, and enabling the solutions to the small problems to communicate with each other. In this example, the AI program divides the audio into chunks, sends them into the cloud, analyzes them to determine their probable meaning and translates the result into a set of search queries. Then millions of possible answers to those queries are sorted and ranked. Thanks to the scalability of the cloud, this takes just a few dozen milliseconds. Insight Center Putting Data to Work Sponsored by Accenture Analytics are critical to companies’ performance. This isn’t rocket science. But it requires a lot of components – waveform analysis to interpret the audio, machine learning to teach a machine how to recognize a dress, encryption to protect the information, etc. While many are standard components that are used and re-used in any number of applications,  it’s not something a solitary genius cooks up in a garage. People who create this type of technology must be able to build teams, work in teams, and integrate solutions created by other teams. These are the skills that we need to be teaching the next generation. Also, with AI taking over routine information and manual tasks in the workplace, we need additional emphasis on qualities that differentiate human workers from AI — creativity, adaptability, and interpersonal skills. At the elementary level, that means that we need to emphasize exercises that encourage problem solving and teach children how to work cooperatively in teams. Happily, there is a lot of interest in inquiry-based or project-based learning at the K-8 level, though it’s hard to know how many districts are pursuing this approach. Ethics also deserves more attention at every educational level. AI technologies face ethical dilemmas all the time — for example, how to exclude racial, ethnic, and gender prejudices from automated decisions; how a self-driving car balances the lives of its occupants with those of pedestrians, etc. — and we need people and programmers who can make well-thought-out contributions to those decision making processes. We’re not obsessed about teaching coding at the elementary levels. It’s fine to do so, especially if the kids enjoy it, and languages such as Snap! and Scratch are useful. But coding is something kids can pick up later on in their education. However, the notion that you don’t need to worry at all about learning to program is misguided. With the world becoming increasingly digital, computer science is as vital in the arts and sciences as writing and math are. Whether a person chooses to become a computer scientist or not, coding is something that will help a person do more in whatever field they choose. That’s why we believe a basic computer programming course should be required at the 9th grade level. Only about 40% of U.S. schools now teach programming and the quality and rigor of these courses varies widely. The number of students taking Advanced Placement exams in computer science is growing dramatically, but the 58,000 students taking the AP Computer Science A (APCS-A) test last year still pales in comparison to the 308,000 who took the AP Calculus AB test.  A third of our states don’t even count computer science course credits toward graduation requirements. The U.S. is woefully behind many of our peer nations. Israel notably has integrated computer science into its pre-college curriculum. The UK has made good progress lately with its Computing at School program and Germany and Russia have leapt ahead as well. President Obama’s Computer Science for All initiative, announced in his 2016 State of the Union, was a belated step in the right direction, but may flounder amid budget cuts proposed by the Trump administration. Expanding computer science at the high school level not only benefits the students, but could help the field of computer science by encouraging more students — and a more diverse group of students — to consider computer science as a career. Though we were thrilled last fall when almost half of our incoming first-year class at Carnegie Mellon was female, the field of computer science is still struggling to increase the number of women and minorities. Engineering intelligence into systems, and finding insights in a ubiquitous sea of data, is a task that cries out for a diverse workforce. To be successful, however, it is critical that we update the way programming is taught. We’re too often teaching programming as if it were still the 90s, when the details of coding (think Visual Basic) were considered the heart of computer science. If you can slog through programming language details, you might learn something, but it’s still a slog — and it shouldn’t be. Coding is a creative activity, so developing a programming course that is fun and exciting is eminently doable. In New York City, for instance, The Girl Scouts have a program that teaches girls to use Javascript to create and enhance videos — an activity that kids already want to do because it’s fun and relevant to their lives. Why can’t our schools follow suit? Beyond 9th grade, we believe schools should provide electives such as robotics, computational math, and computational art to nurture students who have the interest and the talent to become computer scientists, or who will need computers to enhance their work in other fields. Few U.S. high schools now go beyond the core training necessary to prepare for the APCS-A exam, though we have a few stunning success stories — Stuyvesant High School in New York City, Thomas Jefferson High School for Science and Technology in Alexandria, Virginia, and TAG (The School for the Talented and Gifted) in Dallas, among others. These schools all boast committed faculty members who have a background or training in computer science. We also urge high school math departments to place less emphasis on continuous math, including advanced calculus, and more on the math that is directly relevant to computer science, such as statistics, probability, graph theory and logic.  Those will be the most useful skills for tomorrow’s data-driven workforce. A major hurdle is that our schools face a severe shortage of teachers who are trained in computer science. This is where U.S. tech companies could help immensely. Microsoft, for instance, sponsors the TEALS program, which pairs computer professionals with high school teachers for a few hours a week. But we need thousands of educators teaching millions of students. Even greater commitments will be necessary going forward. On the academic side, The University of Texas at Austin’s UTeach program is a model for preparing STEM teachers and has expanded to 44 universities in 21 states and the District of Columbia. Much more is needed. As with science and math, we need governmental standards driving K-12 computer science education, along with textbooks, courses and ultimately a highly trained national cadre of computer science teachers that are tied to those standards. The Computer Science Teachers Association has been a leader in this area, promulgating a standards framework and an interim set of standards. Investing in how the next generation understand and interacts with big data and AI is an investment that will pay off in the long run for all of us.

04 июня, 13:53

Asia-Pacific countries call for concerted efforts to address rising non-traditional threats to security

NON-TRADITIONAL threats to security in the Asia-Pacific region had taken the center stage of the 16th Shangri-La Dialogue which concluded here on Sunday, with participants calling for concerted efforts

Выбор редакции
02 июня, 18:33

Liberal arts majors can get high-paying jobs, too

Read full story for latest details.

31 мая, 07:29

Why are there so few computer science majors?

That is a long and very interesting post by Dan Wang, it is hard to summarize, here is one tiny excerpt but better to read the whole thing: 2. You don’t need a CS degree to be a developer. This is another valid statement that I don’t think explains behaviors on the margin. Yes, I […] The post Why are there so few computer science majors? appeared first on Marginal REVOLUTION.

Выбор редакции
30 мая, 19:39

Information socialtaxis and efficient collective behavior emerging in groups of information-seeking agents [Ecology]

Individual behavior, in biology, economics, and computer science, is often described in terms of balancing exploration and exploitation. Foraging has been a canonical setting for studying reward seeking and information gathering, from bacteria to humans, mostly focusing on individual behavior. Inspired by the gradient-climbing nature of chemotaxis, the infotaxis algorithm...

Выбор редакции
30 мая, 19:36

Курсы Computer Science клуба, весна 2017, часть вторая

Продолжаем выкладывать видеозаписи курсов Computer Science клуба при ПОМИ РАН. Первая часть здесь. В этой подборке четыре курса: «Коммуникационная сложность», «Экспандеры и их применения», «Машинный перевод» и «Избранные главы теории потоков». Читать дальше →

30 мая, 14:21

What Makes DXC Technology (DXC) a Potential Pick Right Now?

DXC Technology Company (DXC) generated high returns for investors in the last one year.

25 апреля 2014, 07:36

Тенденции в ИТ секторе США

С этой Украиной народ совсем все запустил. Учитывая степень накала можно предположить, что ньюсмейкеры искусственно нагоняют истерию, чтобы отвлечь внимание от более глобальных тенденций, как например развал Еврозоны, провал «японского чуда» и политики Абе, затяжная рецессия в США, очередной провал корпоративных отчетов. Кстати, в последнее время говорят о чем угодно, но только не о последних результатах крупнейших мировых гигантов. Что там с ними?  Из 30 наиболее крупных ИТ компаний в США 11 компаний сокращают годовую выручку по сравнению к 2013 году. Это HPQ, IBM, Intel, Western Digital, Computer Sciences, Seagate Technology, Texas Instruments и другие. Наибольшее годовое сокращение выручки у Seagate Technology – почти 15%. С оценкой 5 летних тенденций, то в наихудшем положении Hewlett-Packard, IBM, Computer Sciences и Texas Instruments, у которых выручка находится на 5 летних минимумах. В таблице данные, как сумма за 4 квартала. Но есть и те, кто вырываются вперед – Microsoft, Google, Ingram Micro, Qualcomm. Apple замедляет в росте и переходит в фазу стагнации с последующим сокрушительным обвалом на фоне роста конкуренции. Intel в стагнации, как 3 года. Данные за 1 квартал предварительные, т.к. еще далеко не все отчитались. Но общие тенденции нащупать можно. Примерно 35-40% крупных компаний сокращают бизнес активность, 25-35% компаний в стагнации и еще столько же растут. Отмечу, что рост отмечает в отрасли, связанной так или иначе с мобильными девайсами – либо производство софта, либо реклама на них, или поставки аппаратной части, как Qualcomm. По прибыли.  Здесь еще хуже. Мало компаний, показывающих приращение эффективности. Около 60% компаний сокращают прибыль, либо стагнируют. Относительно стабильный тренд увеличения прибыли у Google, Oracle, Qualcomm. Хотя темпы прироста наименьшие за 3 года.