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1990년, ‘인터넷’이라는 개념이 가시화되면서 이전까지 불가능했던 연결, 지식, 속도를 누릴 수 있게 됐습니다. 그로부터 27년이 지난 요즘, 인터넷의 가장 최신 형태인 사물인터넷(IoT)에 대한 질문을 많이 받습니다. IoT의 가능성이 무궁무진한 가운데 과연 우리는 1990년에 인터넷을 사용했던 것처럼 지금 IoT를 사용하고 있을까요? 당시 우린 인터넷을 충분히 누리고 있다고 생각했었지만, 지금은 어떤가요? 2012년, 인터넷과 재생
고객관계관리(CRM), 여행 산업의 핵심 경쟁력으로 떠올라 요즘 ‘먹방’과 ‘쿡방’의 인기를 무섭게 따라잡고 있는 트렌드가 있습니다. 바로 ‘여행 방송’입니다! 꽃보다 할배의 열풍을 시작으로 원나잇 푸드트립, 패키지로 세계 일주 – 뭉쳐야 뜬다, 배틀 트립 등 각종 여행 방송이 쏟아지고 있는데요. 실제 방송 후 관련 여행 상품들의 매출이 증가하면서 그 인기를 증명하고 있죠!
Ich möchte Sie etwas fragen? Sehen Sie Risikomanagement als: alleinige Aufgabe des Chief Risk Officer, als Pflichtübung, die jede Menge Papierkram – aber keine wirklichen Vorteile – bringt; oder Verantwortung von allen im Unternehmen und eine Chance, eine sichere Umgebung für heikle Geschäftsentscheidungen zu schaffen? Natürlich sind diese beiden Auffassungen
Are you caught up in the machine learning forecasting frenzy? Is it reality or more hype? There's been a lot of hype about using machine learning for forecasting. And rightfully so, given the advancements in data collection, storage, and processing along with technology improvements, such as super computers and more powerful
Deep learning made the headlines when the UK’s AlphaGo team beat Lee Sedol, holder of 18 international titles, in the Go board game. Go is more complex than other games, such as Chess, where machines have previously crushed famous players. The number of potential moves explodes exponentially so it wasn’t
The forecast is frightening: Robots will take over all manual labor and self-generating code will automatically spin out the algorithms once developed by statisticians and programmers. Humans will become obsolete. What will mere mortals do all day long? Ride captive as our self-driving cars take us on a sentimental journey
Machine learning seems to be the new hot topic these days. Everybody's talking about how machines are beating human players in chess, Jeopardy, and now even Go. In the future, artificial intelligence will drive our cars and our jobs will be taken over by robots. There’s a lot of hype,
Organizations of all kinds operate in uncertain environments with challenging natural, political, socio-economic and cultural influences. These influences may affect their ability to meet their objectives. The uncertain economic situation of the past few years is a clear, real and pragmatic example of the effect of this uncertainty, something known
‘이미지 인식(Image recognition)’, 머신러닝과 인공지능(AI)을 포함한 기술 산업 전반을 뜨겁게 달구고 있는 주요 이슈입니다. 특히 컴퓨터 비전, 즉 영상 처리 기술은 무인 자동차, 얼굴 인식, 의료 결과 예측 등 수많은 혁신 기술들을 현실화하는 핵심 요소인데요. 그 가운데 크고 작은 많은 기업들은 이미지 인식과 관련된 다양한 기반 기술과 프로세스의 가능성을 실현하기 위해
In meinem vorherigen Beitrag ging es darum, wie sich das Internet of Things (IoT) über den aktuellen Hype hinaus geschäftsfähig machen, also operationalisieren, lässt. Und um die Hürden, die Unternehmen in Sachen Analytics dafür überwinden müssen. Immer wieder spreche ich in diesem Zusammenhang mit Kunden über ein Thema, das nicht
유럽 연합(EU)의 자금세탁방지(AML; Anti-Money Laundering)와 고객 위험 평가에 대한 새로운 컴플라이언스 요구가 거세지고 있습니다. 이에 따라 앞으로 6개월 후 유럽 금융 업계는 큰 도전 과제에 부딪힐 것으로 보이는데요. 바로 유럽 연합의 ‘4차 자금세탁방지 지침(Fourth Anti-Money Laundering Directive)’ 때문입니다. 해당 지침과 관련된 모든 변경 사항들이 전면 시행되기까지 6개월도 채 남지 않았습니다.
Thanks to a new open source project from SAS, Python coders can now bring the power of SAS into their Python scripts. The project is SASPy, and it's available on the SAS Software GitHub. It works with SAS 9.4 and higher, and requires Python 3.x. I spoke with Jared Dean
Via streaming data, Jim Harris says machines can learn some amazing things without being programmed with domain knowledge.
Two minutes in, I knew the 2017 SAS Global Forum Technology Connection would not be typical or average. For one thing, it was SAS Chief Technology Officer Oliver Schabenberger's show, and nothing he does is ever typical or average. The first surprise of the morning was his entrance. He zoomed onto the stage on a Segway, using slight
인공지능 vs. 인간, 세기의 대결 이후 1년 전 세계가 주목했던 인공지능(AI)과 인간의 바둑 승부! 인공지능의 놀라운 가능성을 확인했던 세기의 대결 이후 벌써 1년이 지났습니다. 그렇지만 인공지능에 대한 관심은 여전히 뜨거운데요. 이제 머신러닝과 딥러닝까지 가세해 대기업은 물론 스타트업과 벤처캐피탈 그리고 정부 정책에 이르기까지 글로벌 이슈를 이끌고 있습니다. 실제 구글, 마이크로소프트, 아마존,
미국의 최고 권위의 과학자인 버니바 부시(Vannevar Bush)가 기계가 생각하고 학습하는 미래를 예측한 것이 1945년이었습니다. 당시까지만 해도 과학적 공상으로만 보였던 것이 이제는 Google 검색 결과 같이 평범한 사물까지도 머신러닝의 산물이 되고 있습니다. 넷플릭스(Netflix)는 머신러닝을 사용하여 개인 맞춤형 영화 추천 서비스를 제공하고 있습니다. eHarmony는 머신러닝을 통해 사랑까지 수량화하여 예측합니다. 은행들은 사이버 감시를 비롯해 사기 및 악용
Image recognition is a hot and hyped topic in machine learning, artificial intelligence and other technology circles. Computer vision technology is essential for realizing advancements like driverless cars, face recognition, medical outcomes predictions, and a host of other breakthrough innovations. Amidst the hype, organizations large and small are trying to understand the
“Un uomo può considerarsi veramente soddisfatto solo quando sa di aver servito appieno il suo padrone” (cit. Mr. Stevens, “Quel che resta del giorno” di Kazuo Ishiguro) Parecchi anni fa, ai tempi dei miei studi universitari, mi occupavo di teatro. Un giorno, nel ripetere un esercizio legato all’interpretazione di un
In 2011, Loughran and McDonald applied a general sentiment word list to accounting and finance topics, and this led to a high rate of misclassification. They found that about three-fourths of the negative words in the Harvard IV TagNeg dictionary of negative words are typically not negative in a financial
Seit 2009 sollen der Gesundheitsfonds und der morbiditätsorientierte Risikostrukturausgleich in der deutschen gesetzlichen Krankenversicherung (GKV) für eine ausgewogenere Verteilung der Einnahmen bei den Kassen sorgen. Ziel ist ein sozialer Ausgleich für unterschiedliche Einkommensstruktur und Krankheitslasten bei den Mitgliedern. Über einen sehr interessanten Nebeneffekt, den dieses regulatorische System ungewollt ausgelöst hat,
Machine learning is a type of artificial intelligence that uses algorithms to iteratively learn from data and finds hidden insights in data without being explicitly programmed where to look or how to find the answer. Here at SAS, we hear questions every day about machine learning: what it is, how it compares to
People come from all over the world to attend this highlight of the season. It’s been a tradition for decades. Hotels book months in advance. Traffic is horrendous in the city center. The coveted tickets can cost thousands of dollars, but tens of thousands of people are lucky enough to score them. In
I've long been fascinated by both science and the natural world around us, inspired by the amazing Sir David Attenborough with his ever-engaging documentaries and boundless enthusiasm for nature, and also by the late, great Carl Sagan and his ground-breaking documentary series, COSMOS. The relationships between the creatures, plants and
Mal ehrlich, wenn ich Sie fragen würde, worüber die Kandidaten im diesjährigen US-Wahlkampf in ihren Aufeinandertreffen debattiert haben – welche Kernthemen würden Sie mir spontan (abseits von Skandalen und Affären) nennen? Und könnten Sie diese Kernthemen den einzelnen Kandidaten zuordnen? Als ich mir diese Frage stellte, war die Antwort –
W czwartym wpisie z serii 10 przykazań Ekonometrii Stosowanej poruszyliśmy kwestie związane z zachowaniem prostoty i jasności stosowanych rozwiązań oraz weryfikacją modeli. Dziś przedstawię kolejne przykazanie, które wiąże się z wykorzystaniem technik data mining. 7. Data mining stosuj z rozwagą. Data mining jest w środowisku ekonometrycznym pojęciem kontrowersyjnym i budzącym
Machine learning is taking a significant role in many big data initiatives today. Large retailers and consumer packaged goods (CPG) companies are using machine learning combined with predictive analytics to help them enhance consumer engagement and create more accurate demand forecasts as they expand into new sales channels like the
When shopping for a new TV, with many sets next to each other across a store wall, it is easy to compare the picture quality and brightness. What is not immediately evident and expected is the difference between how the set looked in the store and how it looks in your
“What we are experiencing from analytics today is nothing short of a revolution,” said CEO Jim Goodnight, who spoke at Analytics Experience 2016 and set the stage for the conference’s executive panel. “Right now, my primary mission is to ensure people understand the limitless possibilities that lie before us, given
Who says machine learning can't be fun? A crew of us from SAS went to San Francisco for the recent KDD conference, which bills itself as "a premier interdisciplinary conference, [which]brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data." We brought
Time series machine learning techniques show great promise for the analysis of health care wearable data. As our busy lifestyles render continuous monitoring more and more essential, the need to analyze data to find correlations between these data streams becomes even more important, because they can provide important cues to
