background preloader

Future, Trends & News

Facebook Twitter

Top 9 ethical issues in artificial intelligence. Optimizing logistics, detecting fraud, composing art, conducting research, providing translations: intelligent machine systems are transforming our lives for the better. As these systems become more capable, our world becomes more efficient and consequently richer. Tech giants such as Alphabet, Amazon, Facebook, IBM and Microsoft – as well as individuals like Stephen Hawking and Elon Musk – believe that now is the right time to talk about the nearly boundless landscape of artificial intelligence. In many ways, this is just as much a new frontier for ethics and risk assessment as it is for emerging technology. So which issues and conversations keep AI experts up at night?

1. The hierarchy of labour is concerned primarily with automation. Look at trucking: it currently employs millions of individuals in the United States alone. This is where we come to the question of how we are going to spend our time. 2. 3. 4. Intelligence comes from learning, whether you’re human or machine. 5. 6. 7. Gartner Reprint. 25 February 2016 | ID:G00275472 Analyst(s): Roxane Edjlali, Mark A. Beyer Summary Disruption is accelerating in this market, with more demand for broad solutions that address multiple types of data and offer distributed processing and repository options. Against this backdrop, this report will help data, analytics and IT leaders find the right vendor for their needs. Market Definition/Description This document was revised on 29 February 2016. Organizations now require data management solutions for analytics that are capable of managing and processing internal and external data of diverse types in diverse formats, in combination with data from traditional internal sources.

Our definitions also state that: The data warehouse (see Note 1) and data management solutions for analytics (DMSAs) are systems that perform the processing required to support analytics. Magic Quadrant Figure 1. Source: Gartner (February 2016) Vendor Strengths and Cautions 1010data Strengths Cautions Actian Amazon Web Services. Thriving in an Increasingly Digital Ecosystem. References (10) 1. K. Zickuhr and L. Rainie, “E-Reading Rises as Device Ownership Jumps,”January 16, 2014, 2. 3. 4. 5. 6. 7. 8. 9. 10.

Show All References. Zukunft der Arbeit: Uns braucht es bald nur noch als Konsumenten - NZZ NZZ am Sonntag. Taxifahrer, Kassierer und Buchhalter wird es in 20 Jahren als Beruf nicht mehr geben. Eine vielzitierte Studie der Universität Oxford schätzt, dass in 20 Jahren die Hälfte der heute in den USA existierenden Jobs verschwinden wird. An ihre Stelle treten Computer – sei es physisch als Roboter oder unsichtbar als Software. Anders als bisher werden auch gut Qualifizierte betroffen sein. Auch in der Schweiz wird es mehrere 100 000 Arbeitsplätze treffen (siehe S. 27). Schon mit der Erfindung der Dampfmaschine 1712 haben Maschinen Menschen ersetzt. Doch bisher wurden immer anderswo genügend neue Jobs geschaffen. Im Zuge der digitalen Revolution streiten sich nun zwei Lager darüber, ob dies auch diesmal gelingt oder ob der rasante Zuwachs an Produktivität durch Computer unseren Kindern Massenarbeitslosigkeit beschert.

Massenarbeitslosigkeit ist bedrohlich. Früher ersetzten Maschinen Muskelkraft, heute geistige Fähigkeiten. . • Datenanalyse: Der Computer Watson von IBM erkennt Sprache und Bilder. Top 50 SAP Employees by Twitter Followers 2014. An update to last year's list of top tweeters at SAP excluding official accounts (which typically have much higher numbers of followers). Comparing Data Warehouse Solutions. We have collected the best resources from the net comparing the top data warehouse solutions such as Vertica, Aster Data, Greenplum, Netezza, Teradata, HANA, Hbase etc...

Comparison of MPP Data Warehouse Platforms The industry is moving towards open, commodity solutions Traditional database servers, such as IBM DB2, Oracle Exadata and Microsoft SQL Server, license proprietary software, but run on commodity hardware. Although the nature of SMP architecture typically favors having a few large expensive servers. But the biggest MPP data warehouse vendors all have proprietary software.

Price/Performance of HANA, Exadata, Teradata, and Greenplum Of these numbers the one that may be the furthest off is the HANA number. Vertica vs Aster Data vs Greenplum vs Netezza vs Teradata Should I Choose Netezza, Teradata or Something Else? Concurrency is a by-product of performance. Teradata vs Netezza vs Hadoop Will Hadoop Challenge Greenplum and Netezza? The Future of Analytics Depends on IBM - and Vice Versa! IBM is again at a crossroads as its stock price languishes and market analysts question if it is on the right path. The Company has doubled down on predictive analytics as a core element of its transformation strategy – however, the decline in its low growth legacy businesses is outpacing the growth in its new business initiatives.

Consequently, overall revenue has declined in each of the past fourteen quarters and earnings have continued to disappoint, resulting in the stock price and market value of the Company declining; leading to talk of activist investors, spinning off the Watson business and other perceived value creation strategies. This blog is not an analysis of the equity value of IBM’s stock, rather it is an attempt to review the progress of the Company’s investments in analytics.

Currency movements, share buybacks and other financial events will influence the Company’s value, but its core strategy will be the ultimate determinant of its value. IBM has been there before. October Microsoft BI World News. Magic Quadrant for Data Integration Tools. 29 July 2015 ID:G00269320 Analyst(s): Eric Thoo, Lakshmi Randall Enterprise buyers increasingly see data integration as a strategic requirement, for which they want comprehensive data delivery capabilities, flexible deployment models, and synergies with information and application infrastructures. To help them make the right choice, Gartner assesses 13 vendors. Market Definition/Description The discipline of data integration comprises the practices, architectural techniques and tools for achieving consistent access to, and delivery of, data across the spectrum of data subject areas and data structure types in the enterprise — to meet the data consumption requirements of all applications and business processes.

The market for data integration tools includes vendors that offer software products to enable the construction and implementation of data access and data delivery infrastructure for a variety of data integration scenarios. These include: Metadata and data modeling support. Figure 1. Data Warehousing | Resources | Snowflake. Why BI isn’t ready for the cloud | VentureBeat | Cloud | by Ajeet Singh, ThoughtSpot. You’ve heard it again and again: The cloud is the new black. Only ever eclipsed by “big data” or “IoT” buzz, it remains a massive focus for all data-driven companies. But while the cloud has worked very well for business applications with low data volumes and simple security requirements (CRM, for example), it hasn’t proven as successful for analytics use. Enterprises are still chasing adoption Over the last two decades, companies have collectively spent billions of dollars on business intelligence tools.

I recently learned about a large financial services company that owns 92 analytics tools across different departments and geographies. Deployment efficiency leans heavily toward on-premise For enterprise IT, the challenge of adoption far outweighs the operational efficiency of how the technology is deployed. Here are the top 4 reasons why BI isn’t ready for the cloud: 1. 2. 3. 4. CRM, HR, and ERP cloud apps do not typically run from the same physical location.

Ready, set … wait. Top 10 2015 Predictions for Microsoft Azure | Microsoft Trends. Like Office 365, Microsoft Azure has undergone a lot of change in the past year. Throughout 2014, it seemed like there was a new service being released in either preview or production release every few weeks. Looking forward to 2015, here is list of predictions for Azure in 2015. One of Microsoft’s key differentiators as an enterprise cloud provider is its focus on security. Microsoft already has many of the key certifications, public commitments to enterprise grade security, etc. and in 2015 expect the investments to continue. Amazon is still the dominant cloud supplier and will continue to be in 2015. Also watch for data center expansion in 2015 as a way to take away market share from Amazon – Microsoft has launched now in China and Australia for example and this geographic expansion will continue as we move into 2015. 7. There were many new services launched in 2014 for Microsoft Azure in preview including: 6.

BI Manager. Aggregation and Redundancy-Free Business. I recently participated in the Very Large Data Bases Conference (VLDB), the #1 ranked academic research conference in the database field, hosted in Hangzhou, China. During this conference, Professor Dr. Hasso Plattner delivered the keynote presentation: “The Impact of Columnar In-Memory Databases on Enterprise Systems”. Hasso Plattner shared visions of future enterprise applications to be aggregation and redundancy free; fundamentally changing business applications.

“Aggregation and redundancy free” ,can be described in one single word: "Simplification". The term “simplification” doesn't necessarily mean fewer or less. The real meaning for "simplification" includes the following:1. There have been reasons to keep redundancy data, for example bad performance and reciprocal effects between transaction and analytical workloads when sharing the same disk IO. Look at Hasso Plattner’s paper on this topic. 2. Modern business applications should be dynamic. 3. SAP Shares Analytics Vision, Lumira, BI 4.2 Details at ASUG’s SABOC Event. SAP offered new product information and key BusinessObjects roadmap details—covering BI suite consolidation, HANA tie-ins, BI 4.2 and Lumira enhancements—during the first day of ASUG’s SAP Analytics & BusinessObjects Conference in Ft.

Worth, Tex. Steve Lucas, SAP’s global president platform solutions, kicked off his keynote address to more than 1,000 attendees by stating that he was here to “talk to you about great software.” Lucas presented SAP’s analytics vision, which was based on enterprise BI (with BusinessObjects at the center), agile visualization (Lumira) and predictive technologies (Predictive Analytics, with KXEN software folded in as well). Simplification of the BusinessObjects Suite is one key piece of the executing SAP’s strategy. “We know in the BusinessObjects enterprise suite that there are way too many clients,” Lucas told the crowd. “There’s going to be dramatic simplification coming in the portfolio.” Here is an overview of some of the other announcements Lucas made: MGI_big_data_exec_summary. Big Data Meets Trough Of Disillusionment: Gartner. Gartner says cloud, in-memory databases are headed into Hype Cycle "trough of disillusionment. " Productivity expected within two to five years.

Cloud computing and in-memory databases, two darlings of the big data movement, have passed the "peak of inflated expectations" and are headed into the "trough of disillusionment," according to Gartner's Hype Cycle for Big Data, 2013. Hype Cycles are Gartner's way of communicating the degree of hyperbole versus productivity associated with emerging technologies, with the peak and trough being followed by a "slope of enlightenment" and progressing to "plateau or productivity" as technologies mature.

In the case of cloud and in-memory databases, the plateau is two to five years away, according to research discussed at last month's Gartner Symposium. That could be because moving anything approaching big data scale into the cloud involves storage and data-movement costs that can add up. . [ Looking for the inside edge on big data breakthroughs?


News. IT Trends For 2013. Metatrends_bi_competence_site.pdf. Trends. Disruptive technologies: Advances that will transform life, business, and the global economy. The relentless parade of new technologies is unfolding on many fronts. Almost every advance is billed as a breakthrough, and the list of “next big things” grows ever longer. Not every emerging technology will alter the business or social landscape—but some truly do have the potential to disrupt the status quo, alter the way people live and work, and rearrange value pools.

It is therefore critical that business and policy leaders understand which technologies will matter to them and prepare accordingly. Disruptive technologies: Advances that will transform life, business, and the global economy, a report from the McKinsey Global Institute, cuts through the noise and identifies 12 technologies that could drive truly massive economic transformations and disruptions in the coming years. The report also looks at exactly how these technologies could change our world, as well as their benefits and challenges, and offers guidelines to help leaders from businesses and other institutions respond.