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AI Took My Job! Ken Jennings’ name is vaguely familiar to people, but why? Because his profound knowledge on all things trivial led to him being the unbeatable champion of a TV game show called Jeopardy! It also put him in the gunsights of IBM. They spent thousands of hours, invested millions of dollars, all just to build a machine named WATSON that could defeat him playing that TV-derived game. See how Ken deals with the consequences of coming out on the losing end of a battle with a computer that took his job – a fate which, according to this Oxford study , is likely to affect 47% of people over the next 20 years… Working Together? Synergy, says Garry Kasparov, is essential to the way humans and machines operate. As the World Chess Master for over a decade, he too became a target for IBM. They spent thousands of hours and millions of dollars to beat him at his own game. Kasparov doesn’t fret about being beaten by a machine. He worries that he might not be beaten by a machine; that human beings will choose to cower in their caves rather than work to create something greater than themselves. Machines can calculate, he says, and humans can understand; machines have instructions, and humans have purpose. When you add AI’s sheer speed to our desire to learn, it is an almost unbeatable combination. We’re better together. The question, of course, is whether Kasparov is right that there is some special, rather mysterious faculty of human “understanding” that machines cannot outperform, which many experts have come to doubt and reject... In Control Based on the real risk and virtual long-term certainty that AI will surpass humans in all domains of understanding, Sam Harris recommends a much more cautious approach. It is inevitable that we will advance our technology in all fields, and that includes AI itself. Imagine trying to get all of humanity to give up research, or to stop improving some aspect of their current lives, and you quickly see that it is infeasible. Sustained technological advancement, Harris argues, must eventually lead to superintelligent systems. He has no definite solution to the momentous risk/opportunity management question he poses, except that more of our best scientific resources should be allocated to it. We likely only have one chance to get it right… Part of the Solution (Literally)? Whereas Sam Harris hypothesizes that a safer way to create superintelligent AIs would be to incorporate them in our own biological brains, Ray Kurzweil suggests that we create a thinking-complex in the Cloud, and insert nanobots into our brains that are capable of linking to it upon need. We have 300 million “thinking modules” in our neocortex (the part of our brain most strongly associated with our abstract intelligence). Imagine if you could connect to a billion more in order to solve a problem. You may not remember the name of the two lead actors in the 1989 movie that you loved called “When Harry Met Sally.” Your memory isn’t perfect, and so you pull out your smart device and add a few thousand more computers to the task via Google’s search engine. Suddenly you are reminded that it was Meg Ryan and Billy Crystal, which opens a mental floodgate, and you recall that Carrie Fisher and Bruno Kirby were also in it, and that triggers even more memories. Being able to connect instantly to a Synthetic Neocortex would make it possible to solve immensely complex problems with relative ease. Seeing the Possibilities Fei-Fei Li talks about how AI will advance once it has significant vision capability. She and her team built a massive picture database (called ImageNet) and trained an AI to recognize images through constant exposure to millions of images that were labelled and described. Over the years the AI has been taught to analyse pictures more deeply, and learned to construct accurate sentences such as “This picture is of a large airplane sitting on a runway” when it “sees” that picture. Something a child can do automatically is often very difficult to teach to a computer. (Cf. Moravec’s Paradox , stating that contrary to initial assumptions in robotics research, some high-level reasoning requires very little computation, whereas low-level sensorimotor skills require enormous amounts of computational resources.) You have probably used the ImageNet technology with TinEye, Google Image, Root About, or Karma Decay, among many others where you can upload an image and then have its source identified, or its contents described in text, or read aloud to help people with limited vision understand images. Once AIs can accurately identify what they see, they provide us with a whole new set of insights. They can think about a million times faster than us and find patterns that escape our visual-cum-cognitive notice. Morality & the Machine: Aligning AI with Our Values The area of AI content-learning that may ultimately determine our fate the most is human morality. Despite substantial disagreement, our values as human beings are fairly universal and very close in the huge space of all possible values. Machine intelligences will have none of them by default – consider the possibility of “paperclip maximizing” superintelligences. It is up to us to ensure that, in time for the AI take-offs that are likely to happen this century, we find a reliable way of teaching the machines to value and pursue the things we value; to understand us and to seek the things we ourselves seek. In order for an AI’s decisions to be safe and beneficial, they have to pass (directly or indirectly) through the human filter. Philosopher Nick Bostrom proposes to make this problem a research priority for humanity. His TED Talk makes us edgy about the AI possibilities. But it also stresses that the AI alignment task, while momentous and enormously challenging, is not hopeless.
Demand for professionals in data science and analytics is expected to rise significantly over the next years (cf.  this study  by IBM). In order to keep track of future job trends, we started the DataCareer Job Market Index (DJMI) in July 2017. We track job openings on the biggest online job board,  Indeed , in the fields of data science and analytics, data engineering, business intelligence, artificial intelligence and statistics.  Sign up  for our newsletter if you’d like to receive a monthly summary of the results. Methods We searched for jobs on Indeed using the following search terms: Data Scientist, Data Analyst, Data Engineer, Big Data, Machine Learning, Artificial Intelligence, Business Analyst, Business Intelligence, Statistics, Quantitative. Using the  jobbR  package , we downloaded the jobs into R for further processing. Since the search returns some results we'd like to omit from the job index (e.g., general Scientist or Software Engineer jobs), we filtered these jobs out. The index only counts jobs that contain at least one of the following expressions in the job title: Data, Analy, Business Intelligence, Machine Learning, Deep Learning, Artificial Intelligence, AI, Computer Vision, Neural Net, Natural Language Processing, NLP,  Hadoop, SQL, Oracle, Statisti, Quant, Bioinform. This filters almost all jobs correctly, although it will miss a few data jobs that lack these keywords in the title (e.g., some general PhD student ads). The search is performed at the end of each month, with only jobs from the last 30 days being included. Using this method, how accurate can our job market estimates be expected to be? While we are likely to underestimate the absolute number of jobs, the relative changes we record over time should provide a very accurate measure of job trends. We therefore indicate relative changes compared to the reference point of July 2017, which is set to 100 in our market index. Results In Germany, job ads increased by 36% between July 2017 and January 2018, with the biggest rise occurring in January (+17%). Interestingly, we did not observe a drop in job openings during December. In July 2017 we counted a total of 2’492 jobs. In Switzerland, data job ads increased by 28% between July 2017 and January 2018. The drop in December (-13%) can likely be explained as a seasonal fluctuation, given that companies publish less job openings during the holiday season. During our reference month, July 2017, the index included 544 jobs. In the biggest data job market, the US, jobs remained relatively stable between July and December 2017. In January 2018 we observed a 12% increase in jobs. The total number of US data jobs was 35’947 in July 2017. In the UK, jobs increased by 24% in January 2018 after a drop of 18% in December 2017, . Again, we expect this to be an effect of the holiday season (which apparently leads to either a significant decline or stagnation). In July 2017, we counted 7’633 jobs for the UK. If we consider the job counts for January 2018 and relate them to population size, we get the following number of jobs per million inhabitants: 125 for the US, 125 for the UK, 81 for Switzerland, and 41 for Germany. However, these numbers should be taken with caution, as the percentage of jobs that are published on Indeed may differ between the countries. We’re currently conducting research into open questions such as the popularity of Indeed in each country and the factor by which Indeed-based estimates underestimate the total number of jobs. For questions, comments or if you’d like to collaborate on our research, don’t hesitate to send us an Email at
Big Data, AI and Machine Learning are today's buzzwords. Data nerds, business executives and politicians alike are talking about data-related opportunities and potential risks. But since when has this been the case and how have data-related interests developed over time? We've looked into this question using Google Trends data.  Google searches reveal people's interests Google search queries have become a powerful tool to capture the moving interests in our society. Across languages, the verb  to google  has become a commonly used term for finding information on a topic online. Google Trends offers insights into the 3.5 billion queries the search engine is processing each day. In order to analyze the development of interest in data science, we looked at the search interest in five data-related keywords (Artificial Intelligence, Big Data, Business Intelligence, Data Science, Machine Learning) and their trends since 2009. The first figure below shows quarterly data of the development in Germany over the past 8 years. Using quarters instead of months or weeks reduces the noise, and trends get easier to spot. Importantly, Google Trends states all its numbers relative to the search volume of the most popular search term, where 100 reflects the highest search volume in the observed time period. The data therefore allows us to analyze trends and compare volumes for different queries, but it does not offer any insights on the absolute search traffic.  Machine Learning on the rise The graph shows a large increase in search queries for the term “Big Data.” It took much longer for other common terms in the field of data science to take off. Interestingly, Machine Learning has become the top search query, overtaking even Big Data in 2017. Data Science and Artificial Intelligence seem to be on the rise but remain at a lower level.   How does the German development compare to worldwide search queries? The figure below shows the results for the same keywords based on global search queries. Overall, the trends run very similarly. Big Data took off in 2012 and Machine Learning saw rapid growth in the past years. An interesting difference can be observed for Artificial Intelligence: While the global trend over the last two years matches the German trend, the global level of AI search queries had reached high levels in 2009 already. The graphs confirm the general impression that data science has been and continues to be on the rise. Machine Learning especially is seeing strong growth and even managed to outpace the more generic search term Big Data.  Google Trends is  easily accessible . If you'd like to import the data directly into R, the gtrendsR package will be helpful. For Python, PyTrends  enables automated downloading.
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RatePAY GmbH Berlin, Germany
RatePAY ist ein Fintech-Unternehmen, das sichere und einfache Bezahlsysteme für den Onlinehandel anbietet. Seit unserer Gründung 2009 haben wir uns zu einem der Marktführer im Payment-Bereich entwickelt. Arbeiten bei RatePAY heißt, den Payment-Markt aufzumischen. Unsere Branche verändert sich ständig, daher hat jeder bei uns viel Spielraum für eigene Ideen und Entscheidungen. Wir bieten ein Arbeitsumfeld mit einem tollen Team, attraktiver Bezahlung und einer guten Work-Life-Balance. Unsere umfangreichen Benefits lernst Du im Vorstellungsgespräch kennen. Deine Aufgaben Als Werkstudent Business Intelligence (m/w) bist Du die treibende Kraft hinter unseren Dashboards Du arbeitest Dich in Deiner neuen Aufgabe durch Deine Zahlenaffinität schnell in unsere komplexen Daten ein und wirst das Data Science Team mit ansprechenden Dashboards versorgen, die Du mit Tableau erstellt hast Du hast dabei auch keine Angst, Herausforderungen in unseren Daten zu finden und zusammen mit Deinem Team anzupacken Dein Profil Du bist immatrikulierter Student und kannst uns mindestens ein Jahr unterstützen. Bestenfalls studierst Du Wirtschaftsinformatik, allerdings sind Deine Lust auf Zahlen und Visualisierung wichtiger als Dein Studiengang Du bist ein Meister in der Nutzung von Excel, SQL Grundkenntnisse konntest Du bereits sammeln und wenn Du zusätzlich bereits Tableau kennst, sind wir begeistert Du weißt, dass Data Visualization im Bereich Data Science wichtig ist und daher motiviert Dich jeder neue Erfolg in diesem Bereich Du bist offen und kommunikativ, und das sowohl auf Deutsch als auch auf Englisch fließend Du hast Lust, bei RatePAY durchzustarten? Dann bewirb Dich direkt, wir freuen uns auf Dich!
FILIADATA Karlsruhe, Germany
Full time
Rund 700 Mitarbeiter gestalten bei FILIADATA täglich die digitale Zukunft von dm-drogerie markt. Im Klartext bedeutet das: Die Entwicklung und Betreuung der gesamten Informationssysteme von über 3.500 dm-Märkten in zwölf europäischen Ländern, des dm Online-Shops sowie der Zentralen und Verteilzentren. Bei FILIADATA sind Ihre Ideen gefragt. Das, was Sie gemeinsam im Team mit den unterschiedlichsten Experten planen und entwickeln, wird jeden Tag von unseren Mitarbeitern und Millionen von Kunden genutzt. Und das Beste: Entwicklung spielt bei uns nicht nur auf technischer, sondern auch auf persönlicher Ebene eine große Rolle. Zur Verstärkung des CRM-Teams im Bereich Marketing & E-Commerce suchen wir Data Scientists (w/m), die mit der Aufbereitung, Analyse und Visualisierung von Daten helfen, unsere Kunden besser verstehen und optimal betreuen zu können. Dabei sind Sie nicht nur in der Lage, routiniert mit Daten in einer Big-Data-Umgebung zu arbeiten, diese aufzubereiten und wichtige Erkenntnisse zu generieren, sondern auch selbstständig neue Analyse-Modelle und KPIs zu entwickeln, die Ergebnisse zielgruppengerecht aufzubereiten und zu kommunizieren. IHRE AUFGABEN SIND initiative Gestaltung und Weiterentwicklung der Big Data Customer-Analytics-Landschaft für alle Länder der dm-Gruppe Durchführen von Analytics-Projekten mit neuesten Technologien (Python, Scala, R, Hadoop, SAS etc.) in Themenfeldern wie beispielsweise Machine Learning, KI, Predictive Analytics Aufbereiten und Kommunizieren statistischer Kennzahlen durch geeignete Daten­visualisierungs­tech­niken gemein­sam bzw. in Abstimmung mit Fach-Anwendern SIE BRINGEN MIT sehr gute Programmierkenntnisse in einer der folgenden statistischen Programmiersprachen: R, Python, SAS, Scala / Spark, SQL und entsprechender Clients (z. B. RStudio, Jupyter, Zeppelin) sowie Erfahrungen im Arbeiten mit großen Datenmengen gute Kenntnisse in multivariaten statistischen Verfahren, insbesondere Regression, idealerweise auch Verfahren des maschinellen Lernens (Entscheidungsbaum-Algorithmen, neuronale Netze u. a.) und Zeitreihenanalyse Erfahrungen in der visuellen und nutzergerechten Darstellung von statistischen Kennzahlen und Analyse-Ergebnissen die Bereitschaft, Themen initiativ zu gestalten, klar zu strukturieren und eigenverantwortlich voranzutreiben WIR ERMÖGLICHEN IHNEN flache Organisationsstrukturen viel Raum für eigenverantwortliches Handeln und kreative Ideen vielfältige Weiterbildungsangebote für Ihre fachliche und persönliche Entwicklung flexible und individuelle Arbeitszeit- und Home-Office-Modelle VERTRAGSART Unbefristet, (37,5 Std./Wo.), ab sofort IHRE BEWERBUNG Bitte bewerben Sie sich mit Angabe Ihrer Gehaltsvorstellung und Ihres frühestmöglichen Eintrittstermins online unter . Bei Fragen wenden Sie sich an Marius Mai, Telefon 0721 5592-2221.
Akelius Berlin, Germany
Full time
Akelius buys, upgrades and manages residential properties. The company owns 50,000 apartments in Sweden, Denmark, Germany, France, Canada, England and the United States. We are a rapidly growing international company with six hundred employees around the world. An integral part of our company is the IT and Administration department. The Development team consists of more than eighty employees, mostly based in Berlin. Our focus is First Class service for our worldwide business. Your tasks Translate business requirements to technical specifications; Design, build and deploy BI solutions for our reporting system with QlikView and QlikSense; Evaluate and improve existing QlikView and QlikSense reports; Executing queries upon data requests; Provide data-driven decision support; Presenting information through reports and visualization. Your profile Experience as BI Developer or Data Scientist; Data modelling; Machine learning; Usage of BI Tools like QlikView, QlikSense, Tableau or MS Power BI; Scripting knowledge in Python, R or SQL; Degree in business informatics or any other technical or mathematical area; Profound communication skills; Problem solving mindset; Speaking German or Swedish is a plus. We offer Working with a motivated and very cooperative team with a personal vocational adjustment. We offer company guidelines for employee training and support for personal and professional development. You find further information on Please send your application to
AMEOS Gruppe Germany
Full time
Werden auch Sie Teil der AMEOS Gruppe und leben mit uns die Mission: „Wir arbeiten für Ihre Gesundheit“. In unseren insgesamt 77 Einrichtungen – Krankenhäusern, Poliklinika, Pflege- und Eingliederungseinrichtungen – sorgen bereits 13.000 Mitarbeitende für das Wohlergehen unserer Patienten und Bewohner. AMEOS legt grossen Wert auf die dezentrale Struktur und die Gewährleistung der Gesundheitsversorgung in den AMEOS Regionen. Gleichzeitig dürfen die Einrichtungen auf die fachliche Unterstützung eines zentralen Kompetenzteams zählen. So tragen die rund 70 zentralen Mitarbeitenden rund um den Vorstand in Zürich erheblich zur strategischen Entwicklung der Gruppe bei. Für das  Corporate Office im Herzen der Stadt Zürich  suchen wir zum nächstmöglichen Zeitpunkt einen Business Intelligence Senior Specialist (m/w) Schwerpunkt SAP Ihre Vorteile Eine abwechslungsreiche, selbstständige wie verantwortungsvolle Tätigkeit in einem innovativen, qualitäts-, zukunfts- und serviceorientierten Unternehmen Sie erwartet ein spannendes Tätigkeitsfeld in einem kollegialen Umfeld Aufgabengebiete zum selbstständigen Erarbeiten von Zielen und deren Umsetzung Ihre Aufgaben Schnittstellenfunktion zwischen den Finanzbereichen (Accounting/Tax, Controlling, Treasury) einerseits und der IT andererseits im Hinblick auf die Optimierung und Weiterentwicklung der Geschäftsprozesse, der Systemlandschaft und der Integration von neuen Gesellschaften Unterstützung und fachliche Leitung bei operativen Projekten zur Weiterentwicklung insbesondere des SAP Systems (Schwerpunkt FI, aber auch CO, SD und MM) sowie anderer betriebswirtschaftlicher Systeme (z. B. Hanse Orga, LucaNet) Unterstützung und Beratung der verschiedenen Bereiche im Hinblick auf die optimierte Nutzung der bestehenden Systemlandschaft Harmonisierung, Pflege und Optimierung der Stammdaten, Strukturen sowie Prozesse bzw. der damit verbundenen Systemeinstellungen (Schwerpunkt SAP FI, aber auch CO, SD und MM) Proaktive fachliche Führung von SAP (Teil-)Projekten und Zusammenarbeit mit externen Beratern Operative Umsetzung und Optimierung von systemseitigen Standards in SAP (Varianten, Berichtsversionen, Umsetzung von Anforderungen aus den Finanzbereichen, Aufbau und Pflege von Standardberichten) Ihr Profil Abgeschlossenes Studium der Wirtschaftwissenschaften, der Wirtschaftsinformatik oder eines vergleichbaren Studiengangs Ausgeprägte Buchhaltungs- sowie Controllingkenntnisse Langjährige SAP-Erfahrung in Finanzbuchhaltung (FI), weitere Module wünschenswert (z. B. CO, SD, MM) Branchenkenntnisse aus dem Gesundheitsdienstleistungssektor (D oder CH) wünschenswert Teamorientiert und vernetzt denkend Souveränes Auftreten und ausgeprägte kommunikative Kompetenzen Starkes Projektmanagement-Knowhow, bestenfalls erste Erfahrungen als Projektleiter Fliessende Deutschkenntnisse Sehr gute Englischkenntnisse von Vorteil Detaillierte Auskünfte erteilt Ihnen gern Herr Alexander Hüttig, Leiter BI & Financial Reporting, unter Tel. +41 (0)58 268 56 70.Wir freuen uns auf Ihre aussagekräftige Bewerbung, vorzugsweise über unser Online-Bewerberportal. Jetzt bewerben

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