It's not that a white male can't lead. I've done it. It's that we all benefit by being exposed to a diverse cohort of people, working in a diverse community. Because if you're in a leadership position, you're not leading just people who look like you.
Данная статья написана в силу возмущения тем, что в наших ВУЗах студентов простому разбору математических выражений обучают на основе как раз Обратной Польской Нотации (ОПН), что является откровенным извращением нормальной человеческой логики. Источником описания ОПН будет описание из Лафоре Р.: Л29 Структуры данных и алгоритмы в Java. Классика Computers Science. 2-е изд. — СПб.: Питер, 2013. — 704 с, рекомендованное как наиболее популярное и адекватное по этому вопросу, впрочем как и по другим часто применяемым алгоритмам. Ну то есть сравниваем разные алгоритмы с разной идеологией. Читать дальше →
Authored by Trent Lapinski via Hackernoon.com, Just over a year ago I wrote, “Did Donald Trump Use Artificial Intelligence To Win The Election?”, which was an article about Cambridge Analytica. If you haven’t been paying attention lately, Facebook just banned Cambridge Analytica from their platform, including the whistleblower that blew the whistle on how Cambridge Analytica was potentially misusing data. Meanwhile, the mainstream media has gone into a frenzy, crashing Facebook’s stock, forcing executives out of the company, and calling for social media reform. This story is seriously a year old, and although I was not the first journalist to write about this, I did bring it to the attention of about 6,300+ readers. While it is new that we have a whistleblower, and the number “50 million accounts”, what the media isn’t telling you is how Cambridge Analytica actually pulled this off. The fact of the matter is the data that Cambridge Analytica acquired from Facebook, much of which they obtained legally and as Facebook intended them to, was entirely useless without machine learning. Machine learning is one of the baby steps required for building artificial intelligence, it is a field of computer science that gives computer systems the ability to “learn” with data without being programmed by a person. It was this technology that Cambridge Analytica used to analyze tens of millions of users profiles using data they acquired from Facebook, and put together psychological profiles of users. They then used Facebook’s targeted advertising system to display ads and content at those users geared towards their own psychological profile. Think of it as persuasive arguing on steroids hyper targeted by your own beliefs about the world. For example, I let Cambridge Analytica analyze my data and they pegged me as a liberal. I know this may be a shocking revelation to all of my haters who believe I’m a conservative Russian agent conspiracy theorist who sells tinfoil hats for a living, but according to my Facebook data I’m a liberal. They also determined I’m more intelligent than 96% of Facebook users, and I somehow lost intelligence points because I like the Beatles (mostly a John Lennon fan), but that is a subject for an entirely different article. What this means is Cambridge Analytica knows I am probably not going to vote for Trump based on my psychological profile. However, that is actually more valuable to them than not. There is little point to spending advertising dollars on people who you know will vote for your candidate, the person you want to target is actually the person who will not vote for your candidate and instead convince them to not vote. This is what I believe Cambridge Analytica did, I believe they targeted liberals in swing states and promoted anti-Hillary content in an attempt to convince people not to vote. It means they had to target fewer people, spend less money, and ultimately accomplish their goal of winning the election. All Cambridge Analytica had to do was figure out who was a liberal, and convince them not to show up on election day. By doing so they could ignore everyone who couldn’t vote for whatever reason, and only spend a small portion of their budget on making sure Trump supporters showed up to vote. A no-vote was actually more powerful in swing states, where guess what? Liberal voter turnout was lower for Hillary than it was for Obama. Granted, I still think Hillary blew it and didn’t campaign properly. I am not excusing her ineptitude, she was a horrible candidate and I did not vote for her (Yes, I voted for the female doctor instead, and no I don’t regret it, lets just not talk about it, okay?). Perhaps this is a just a giant coincidence, but I don’t think it is. Given Donald Trump’s ad spend, I’m fairly certain this is exactly what happened. The reason I believe this is because if I were hired to help win an election and had the tools and data Cambridge Analytica had, it is exactly what I would do. I don’t have anything to prove my theory unfortunately, but given what is being reported I am fairly certain I am on to something, and first came up with this theory a year ago before the media went into freak out mode. This is why psychological profiling, privacy, machine learning, and artificial intelligence is so frightening when put in the wrong hands. It isn’t the machines we need to fear, it is the people who are in control of those machines and our data that we need to worry about. Machines are just tools, but people are fickle and with the right incentive easily corrupted. Facebook, Not Russia, Helped Trump Win The Election We know from Wikileaks that John Podesta was in touch with Sheryl Sandberg, COO of Facebook, and even thanked her for helping them “elect the first woman President of the United States.” However, Hillary didn’t win despite the fact Facebook was trying to get her elected alongside Google who was censoring search results, possibly running their own bots, and Twitter who was censoring the truth and purposely hiding Wikileaks tweets which are 100% factual (as they later admitted to congress). The reason Hillary Clinton did not win despite the media and social media companies doing everything they could to rig the election in her favor is because Facebook double dipped and allowed Cambridge Analytica to use their surveying tools to collect user data on tens of millions of users. This data was then used to target tens of millions of users with political advertising using Facebook’s ad platform based on psycholgoical profiles from data they bought or acquired from Facebook. Facebook is basically responsible for feeding the analytics system that enabled Cambridge Analytica and the Trump campaign to be so targeted and effective with a minimal budget. They ultimately won Donald Trump the swing states and the election. As well as subverted democracy, and likely made Facebook a bunch of money. That’s what happened, that’s how Trump won. It wasn’t the Russians, it was our own social media companies who sold our data to the Trump campaign which they then likely used to convince liberals not to vote in swing states. It’s both horrifying, and cleverly brilliant at the same time. The funny thing is, Obama did something similar in 2012 and liberals celebrated. Not so funny when the other team takes your trick and executes it more effectively now is it? * * * If you like this article feel free to send some Bitcoin.
Susceptibility of brain atrophy to TRIB3 in Alzheimer’s disease, evidence from functional prioritization in imaging genetics [Computer Sciences]
The joint modeling of brain imaging information and genetic data is a promising research avenue to highlight the functional role of genes in determining the pathophysiological mechanisms of Alzheimer’s disease (AD). However, since genome-wide association (GWA) studies are essentially limited to the exploration of statistical correlations between genetic variants and...
The app that makes Photoshop-style retouching easy is wildly popular with celebrities but has prompted a body image debateThe Oxford English Dictionary chose “selfie” as its word of the year at the end of 2013. At around the same time, four Israeli computer science PhD students and a supreme court clerk had an idea for an app that that would allow regular people to do Photoshop-style retouching of their smartphones photos. That app was FaceTune. Continue reading...
DXC looks to repay part of its existing credit facility with the proceeds of its new senior notes offering worth GBP 250 million due 2025.
Whether it’s a giant infrastructure plan or a humble kitchen renovation, it’ll inevitably take way too long and cost way too much. That’s because you suffer from “the planning fallacy.” (You also have an “optimism bias” and a bad case of overconfidence.) But don’t worry: we’ve got the solution. The post Here’s Why All Your Projects Are Always Late — and What to Do About It appeared first on Freakonomics.
Заголовок получился, конечно, желтушный. Сразу за него извиняюсь. Сегодня всего лишь хочу поделиться одним занимательным буклетом, который был выпущен институтом computer sciences академии наук СССР в (предположительно) 1989 году. Читать дальше →
DHAKA (Reuters) - Bangladeshi police arrested three men on Sunday as they investigate a knife attack on Zafar Iqbal, a popular author and professor of computer science and engineering.
Инженеры из Массачусетского технологического института (MIT Computer Science) и Лаборатории искусственного интеллекта (CSAIL) создали робота для нарезки мебельных заготовок.
Authored by Toni Airaksinen via CampusReform.org, The Kennesaw State University LBGT Resource Center recently produced a new pamphlet that adds “ne,” “ve,” “ey,” “ze,” and “xe” to the list of gender neutral pronouns. The “Gender Neutral Pronouns” pamphlet, a copy of which was obtained by Campus Reform, tells students that “some people don’t feel like traditional gender pronouns fit their gender identities,” and thus lists alternatives that students can use instead. These pronouns are accompanied by a conjugation chart listing how they might be used as a subject, object, possessive, possessive pronoun, and reflexive. For example, to refer to a student who identifies as “ne,” one could say “Ne laughed” or “That is nirs.” To refer to a student who identifies as “ve,” the pamphlet explains that one would say “Vis eyes gleam” or “I called ver.” The pamphlet - which lists seven different types of gender neutral pronouns - encourages students to ask their friends, classmates, and coworkers how they identify before making any assumptions. The guide does warn, however, that students “may change their pronouns without changing their name, appearance, or gender identity,” and suggests that preferred pronouns be re-confirmed regularly during “check-ins at meetings or in class.” “It can be tough to remember pronouns at first,” the guide notes. “Correct pronoun use is an easy step toward showing respect for people of every gender.” The guide was first discovered by Francis Hayes, a freshman studying computer science at Kennesaw State, who told Campus Reform that the pamphlet was distributed Tuesday by administrators in the school’s Student Center. "It is a disgrace, because I thought that my school was one of the few schools left that weren't teaching these things. But when I found this, I felt really disappointed,” he told Campus Reform. “Why is this university entertaining something as useless as this?" Hayes also criticized the pamphlet for potentially confusing impressionable students, claiming that Kennesaw State is “in the early stages of Cultural Marxification.” “The guide will confuse students regarding what gender they are,” he speculated, adding that “none of those pronouns exist in the English language, so it's pretty much ridiculous that they're trying to teach this." Kennesaw State media officer Tammy Demel acknowledged a request for comment from Campus Reform, but did not respond in time for publication.
Outside of the commotion in Washington, Trump's cabinet leaders are reshaping federal policy.
Indian graduate students enrolled in computer science and engineering in the U.S. dropped 21% in 2017. Are Trump policies to blame?
Can Innovators be Created? Experimental Evidence from an Innovation Contest -- by Joshua S. Graff Zivin, Elizabeth Lyons
Existing theories and empirical research on how innovation occurs largely assume that innovativeness is an inherent characteristic of the individual and that people with this innate ability select into jobs that require it. In this paper, we investigate whether people who do not self-select into being innovators can be induced to innovate, and whether they innovate differently than those who do self-select into innovating. To test these questions, we designed and implemented an innovation contest for engineering and computer science students which allowed us to differentiate between those who self-select into innovative activities and those who are willing to undertake them only after receiving an additional incentive for doing so. We also randomly offer encouragement to subsets of both the induced and self-selected contest participants in order to examine the importance of confidence-building interventions on each sample. We find that while induced participants have different observable characteristics than those that were 'innately' drawn to the competition, on average, the success of induced participants was statistically indistinguishable from their self-selected counterparts and encouragement does not change this result. Heterogeneity in treatment effects suggests an important role for the use of targeted interventions.
Computer science professor and Clinc CEO Dr. Jason Mars provided me with some insights about how artificial intelligence will affect the financial industry this year.
Lawmakers of both parties want more scrutiny of the companies whose equipment and software does everything from store voter data to record the vote.
Machine learning means learning from positive and negative examples. Yoram Singer, Professor of Computer Science at Princeton University, explains how information flows through neural networks – and how this enables applications such as self-driving cars. http://www.weforum.org/
We are still in a primitive stage of using data science and machine learning to detect and treat cancer. Regina Barzilay explains how MIT’s Computer Science and Artificial Intelligence Laboratory is training deep learning models to outperform human oncologists. http://www.weforum.org/
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.