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I have seen the future, and it is a world of unparalleled convenience, untold marketing opportunities, and zero privacy. 

To set the futuristic tone, IBM invited Peter Diamandis, founder of the nonprofit X Prize Foundation, which humbly describes itself as “a catalyst for the benefit of humanity.” To give you an idea of Diamandis’ interests, he said he is currently “prospecting” asteroids that he plans to mine for resources. He put the value of one asteroid at $5.4 trillion.

But he was there Thursday to talk about Watson, and how humankind is producing so much data these days that it can no longer make sense of it all without artificial intelligence (AI). Watson and its ilk are needed to uncover patterns in mountains of information and make decisions we can no longer arrive at through traditional programming.

This isn’t big data, it’s gargantuan data.

Take, for example, images from new fleets of satellites that can see things as small as 50 centimeters across.

“You want to know what your competitors in China are doing? You can watch them,” said Diamandis.

“Well, you couldn’t count them,” he said, “but Watson could.”

But the data gets closer to home than satellite images, and when it does there’s little room for privacy. “We’re heading towards a world of near perfect data,” Diamandis said, where “perfect” means everything that happens is recorded and available for someone to mine.

Add to that an army of miniature image-snapping drones cruising the streets, and “you’ll never get pick-pocketed and not know who did it.”

“If you thought your privacy wasn’t dead yet,” he concluded, “think again.”

If that doesn’t sound thrilling, there’s a trade-off for giving up all this data — a whole new world of convenience. And it’s already starting to emerge in the form of 100 applications that developers have built using Watson’s cognitive APIs.

Go Moment

Go Moment’s concierge app uses Watson’s AI to serve hotel guests

There are no more calls to the front desk and waiting an eternity for someone to show up at your room. Need tickets for a show? A beer and some towels by the pool? Recommendations for dinner? Ask Ivy and it shall deliver. Watson’s APIs basically allow any app developer to create their own version of Siri, with knowledge about any environment they load in.

Ivy will be available to 20 million hotel guests by the end of the year, according to Go Moment CEO Raj Singh. Hotels love it, he said, because it keeps guests happy and cuts labor costs. That’s because it can field most questions itself, and those it can’t it routes directly to the right department without tying up the front desk. “We automate two months of labor every day.” he claimed.

WayBlazer

WayBlazer is a Watson-powered Expedia on steroids

Another Watson-powered service is WayBlazer, which is basically Expedia on steroids. The service lets you input searches using natural language — “What’s the best hotel for a relaxing extended weekend?” — and spits out results based on a profile it builds over time. The data is culled from thousands of sources including social media, blogs, magazines and newspapers.

Marketers are another big target for Watson, and former Sun CEO Scott McNealy was there to show his service, WayIn. It uses Watson’s image recognition capabilities to trawl photos on social media and make them searchable, even when they don’t have tags describing their content.

“We’ll ingest and tag 200 million pictures a day,” he said, which can be filtered by demographic and other attributes. “You can’t do this without Watson.”

His presence was fitting, since it was McNealy who declared over a decade ago that “You have zero privacy, get over it.”

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The Future Of The Pc Looks Cloudy

The PC of the future is on your desk. It’s also in your pocket. It’s inside your TV, your car and your refrigerator. The PC of your future doesn’t exist as one discrete tool; instead, it’s a personal network of devices that share data and collectively represent you to the rest of the Internet. By the year 2023, we will abandon our bulky desktops in favor of remote storage, lashing together our favorite music, movies and games with a web of internet-connected devices to build a digital raft of data that will buoy us through the ebb and flow of our daily lives.

In the future you can still build a powerful desktop PC with three monitors and a 10 petabyte hard drive, but you might find it more convenient to subscribe to Google Cloud and link all your devices and digital download accounts together under a single cloud computing service.

Of course in the future, major service providers will gobble up smaller services to become the digital megacorps we’ve always been secretly dreaming of. Only now are we beginning to see the balkanization of virtual services among different mobile phone providers; platform-agnostic cloud computing services like MobileMe, Google Apps and Microsoft’s Windows Azure are just the beginning. As much as I would like to predict a warm and loving future in which we all unite beneath one free and open Internet, I think it’s far more likely that in the future you will pick one corporate camp that best matches your computing needs and stick within it.

But how do I keep my data private?

In the far-flung future of 2023, you shouldn’t have any secrets from Big Brother Google. As ex-CEO Eric Schmidt once said, “if you have something that you don’t want anyone to know, maybe you shouldn’t be doing it in the first place.”

If you think otherwise, make sure to shop around and find a cloud computing company that offers the best privacy guarantee; in the future, there will be enough competition among providers that you should have no trouble finding an encrypted cloud computing service that suits your unique needs. Accessing the Google file-sharing servers via GMail and Google Docs is already encrypted via SSL, and by 2023 we’ll see a whole host of competitors spring up as broadband internet access becomes cheaper and more ubiquitous in the global market.

What if I need to make movies, play games or perform other demanding computational tasks?

You’ll likely have to work under a bandwidth cap, but for a flat monthly fee you’ll be able to crunch numbers, play games and edit media from a netbook, tablet or even your TV. The success or failure of the OnLive game service will be our bellwether; if contemporary cloud gaming services like Gaikai and OnLive thrive, we can expect to see similar services spring up for any task that requires a high-end PC.

What if I lose network access?

Faster, cheaper broadband and the efforts of many major cities to expand municipal wireless will help, as will the spread of 4G cellular wireless networks. If nothing else works, there’s always chúng tôi the cloud!

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Removing The Shackles On Ai Is The Future Of Data Science

AI is finally living up to the hype that has surrounded it for decades. While AI is not (yet) the saviour of humanity, it has progressed from concept to reality, and practical applications are improving our environment.

However, much like Clark Kent, many of AI’s astounding exploits are veiled, and its impacts can only be seen when you look past the ordinary mask. Consider BNP Paribas Cardif, a large insurance corporation with operations in more than 30 countries. Every year, the organisation handles around 20 million client calls. They can evaluate the content of calls using speech-to-text technology and natural language processing to satisfy specific business purposes such as controlling sales quality, understanding what customers are saying and what they need, getting a sentiment barometer, and more.”

Consider AES, a leading producer of renewable energy in the United States and around the world. Renewable energy necessitates far more instruments for management and monitoring than traditional energy. AES’ next-level operational effectiveness is driven by data science and AI, which provide data-driven insights that supplement the actions and decisions of performance engineers. This guarantees that uptime requirements are met and that clients receive renewable energy as promptly, efficiently, and cost-effectively as feasible. AES, like Superman, is doing its part to save the planet.

These are only a few of the many AI applications that are already in use. They stand out because, until now, the potential of AI has been constrained by three major constraints:

  Compute Power

Traditionally, organizations lacked the computing power required to fuel AI models and keep them operational. Companies have been left wondering if they should rely only on cloud environments for the resources they require, or if they should split their computing investments between cloud and on-premise resources.

  Centralized Data

Data has traditionally been collected, processed, and stored in a centralised location, sometimes referred to as a data warehouse, in order to create a single source of truth for businesses to work from.

Maintaining a single data store simplifies regulation, monitoring, and iteration. Companies now have the option of investing in on-premises or cloud computation capability, and there has been a recent push to provide flexibility in data warehousing by decentralizing data.

Data localization regulations can make aggregating data from a spread organization unfeasible. And a fast-growing array of edge use cases for data models is undermining the concept of unique data warehouses.

  Training Data

A lack of good data has been a major impediment to the spread of AI. While we are theoretically surrounded by data, gathering and keeping it may be time-consuming, laborious, and costly. There is also the matter of bias. When designing and deploying AI models, they must be balanced and free of bias to ensure that they generate valuable insights while causing no harm. However, data, like the real world, has bias. And if you want to scale your usage of models, you’ll need a lot of data.

To address these issues, businesses are turning to synthetic data. In fact, synthetic data is skyrocketing. According to Gartner, by 2024, 60% of data for AI applications would be synthetic. The nature of the data (actual or synthetic) is unimportant to data scientists. What matters is the data’s quality. Synthetic data eliminates the possibility of prejudice. It’s also simple to scale and less expensive to obtain. Businesses can also receive pre-tagged data with synthetic data, which drastically reduces the amount of time and resources required to build and generate the feedstock to develop your models.

Cloud Data Warehouse – The Road To The Future

The need to interpret the vast data is growing unprecedently in the world. With digitization taking over industries, more and more organizations are generating digital data like never before. The growing data is not only a huge asset but also presenting immense opportunities for the industries. To derive interpretations and insights from the data means going a rigorous process of collecting, transforming, loading, and finally

Bidding Goodbye to Traditional Processes

When it comes to managing data, most businesses were using the same traditional on-site infrastructure a few years back. While this worked a few years ago due to a variety of reasons, the winds of change have taken over. Enterprises looking for smarter solutions, because their data was increasing and so were the data management costs. This led to huge turbulence in the traditional data management system, which was mainly on-site. Since the on-site data warehouse was not only difficult to manage but also had more than a few issues, enterprises found their solution in the cloud. Ad as we know today, a cloud data warehouse is excessively popular among enterprises and helping them make sense of all the data. They help businesses streamline their operations and gain visibility to all departments running within. Moreover, cloud data warehouses help enterprises serve their customers and create further opportunities in the market. As businesses come up with new plans and products, data warehouses begin to play even a more important role in the process. They are becoming the new norm. Gone are the days when an enterprise had to purchase hardware, create server rooms along with hire, train, and maintain a dedicated team of staff to run it. Today, the tables have turned and everything is being managed on the cloud. But, to precisely understand why cloud data warehouses outperform traditional systems we need to dive down into their differences.  

Cloud Data Warehouses Becoming the New Norm

Today’s businesses are moving faster than ever. In other words, they are racing out too far more customers and accomplishing a lot more things. Data has become a part of their core processes. For example, banks are processing the credit and debit cards of customers at every second. Similarly, insurance companies are maintaining their customer profiles and updating them frequently with policy-related information and changes. On the other hand, we have brick and mortar stores, process in-store purchases while the online stores process the purchases made digitally. The idea behind this is that all these stores process information that is transactional in nature. They have to be written and updated frequently. Right now businesses have an online transaction processing database to take care of these. This is just one side of the coin. The other side means managing revenue, business operations, customer engagements, and many other things, that are potentially based on the transactional data. Moreover, this data is only growing and businesses need a solution for their optimization. The problem is, however, that online transaction processing systems are designed for managing and processing one small transaction at a time. When it comes to tons of data they fail to deliver the required results. This is where the solution of data warehouses emerges. They already can perform processing on large amounts of data. As a link to the traditional transactional database, they will hold a copy of it and store it safely in the cloud. Moreover, the best part of using a cloud data warehouse is that they only charge you for the services you use. For example, based on your company data, you will require a certain amount of space in the cloud. Similarly, for the number of computations, you have to perform you will need a separate computational space. In the

Author Bio

Cornell University: Shaping The Future Of Technology Through Data Science And Statistics

Cornell University was founded in 1865 in Ithaca, New York by Andrew D. White and Ezra Cornell, the latter famously stating “I would found an institution where any person can find instruction in any study.” The founders could not have envisioned the full extent of modern data science, of course, but scientific research of all types has been at the heart of Cornell’s mission since its beginning. Statistics itself – the precursor or original discipline underlying data science – first came to prominence at Cornell after World War II, with the presence of two seminal figures in the field, Jack Kiefer and Jacob Wolfowitz, as faculty members. Since then, Cornell’s Department of Statistics and Data Science (as it is now called) has hosted and continues to be the home of many prominent researchers in theoretical and applied statistical methods.  

Data Science Programs at Cornell

Cornell University offers two undergraduate degrees in statistics and data science, as well as the M.S. and Ph.D., all of which enroll numerous students who find successful careers upon graduation. But its flagship Master of Professional Studies in Applied Statistics, or M.P.S., is unique and is the only program of its type offered by an Ivy League university. The M.P.S. is a two-semester Master’s degree program that provides training in a broad array of applied statistical methods. It has several components: (i) a theoretical core focusing on the underlying mathematical theory of probability and statistical inference (with a 2-year calculus prerequisite); (ii) a wide selection of applied courses including (but not limited to), data mining, time series analysis, survey sampling, and survival analysis; (iii) certification in the SAS® programming language (required); (iv) a professional development component including in-depth training in career planning and job searching, interviewing and resume writing, professional standards and etiquette, etc.; and (v) a year-long, hands-on, start-to-finish professional data analysis “capstone” project.  

The Dynamic Leadership

Dr. John Bunge is the founding director of the M.P.S., in 1999-2000, and served in that role for 12 years. The position was then held by another Statistics professor, and at the end of his (6-year) term Dr. Bunge again became Director and will continue through 2023. Dr. Bunge has witnessed the program growth from an initial enrollment of 6 students to its current steady-state of 60, which is about the institute’s maximum capacity. Interestingly, the number of M.P.S. applications seems to continue to increase so that the demand for the available spaces becomes ever more intense. “We are content with many of the decisions we made in designing the program (as long ago as the 1990’s), but we continue to monitor professional trends in data science and to adapt our program accordingly,” Dr. Bunge said. “In particular in the past decade we have added a second “concentration” to the M.P.S., so that students may now specialize more in classical (and modern) statistical data analysis; or (the second concentration) in more computationally oriented data science, including topics such as Python programming, database management and SAS, and big data management and analysis.”  

Prominent Features of the Program

   

Offering Extraordinary Industry Exposure

The main type of practical exposure offered to M.P.S. students is the M.P.S. project. During the fall semester, the faculty identifies a number of current applied research projects, some within Cornell or from Weill Cornell Medicine (the university’s medical school in New York City), some from external clients in the private or nonprofit sectors. The M.P.S. class is then divided randomly into teams of 3 or 4 students, and each team ranks the available projects by preference. The faculty then assigns projects to teams, attempting to accommodate preference as well as possible (this is known as the “fair item assignment” problem). Teams then have until the end of the spring semester to complete their projects. In the course of this, the team must communicate continuously with the client; formulate and re-formulate the problem in statistical terms; organize and manage relevant data (provided by the client); carry out statistical analyses using suitable computational methods and software; and finally provide both a written and an oral presentation of the results. Upon completion, the projects are evaluated by the students themselves, the clients, and the faculty, and each year one or two “best project” awards are made. This is the closest experience to actual on-the-job statistical consulting that can be obtained within the academy, and it is very effective both as a learning process and as proof of competency for M.P.S. graduates. In addition, Cornell allows M.P.S. students to elect to take an additional semester of study, which then introduces the opportunity for an internship in the intervening summer, another form of practical exposure for students.  

Overcoming Academic and Industry Challenges

Dr. Bunge feels the most significant challenge is simple, and characteristic of any aspect of the technological or scientific enterprise: keeping abreast, or preferably ahead, of current developments. In practical terms, for example, what software will the students need to be familiar with? SAS® is still important but R is increasingly so, not to mention scripting languages such as Python, and big data resources or environments such as Hadoop. It is a major undertaking to stay current with developments in these areas much less to predict their future directions, and academics, while experts in their own fields, are less conversant with trends in industry, government, banking and so forth. From a broader perspective, what will be the industries of the future, and how will they apply data science? A forward-looking program cannot ignore, to take just three examples, quantum computing, genome editing (CRISPR), and for-profit space exploration (e.g., asteroid mining). These may seem like science fiction at present, but in no time at all, we will be sending our data science graduates to work in these fields, and we must prepare them accordingly, he said.  

Remarkable Accomplishments of the University

Ibm Infosphere: Product Overview And Insight

Any enterprise that uses other IBM database or analytics software will likely be interested in the IBM InfoSphere Information Server. It’s a full-featured platform that unites data integration capabilities with data quality and data governance. Gartner estimates that approximately 10,700 organizations worldwide use the product.

Jump to: IBM InfoSphere Features Table

Founded in 1911, IBM is one of the largest, oldest, and most well-respected technology companies in the in the world. Nicknamed Big Blue, the company has its headquarters in Armonk, New York, and has more than 380,000 employees globally. Last year, it reported revenue of $79.139 billion and net income of $5.753 billion. It is traded on the New York Stock Exchange under the symbol IBM, and it is a component of the Dow Jones Industrial Average and the S&P 500.

The company offers a wide array of different products and services, including mainframe systems, analytics, automation, blockchain, cloud computing, collaboration, IoT, IT infrastructure, mobility, security and artificial intelligence solutions. It serves customers in 177 different countries worldwide.

IBM acquired the ETL technology that became the InfoSphere platform in 2005. Today, the company describes the IBM InfoSphere Information Server as “a market-leading data integration platform which includes a family of products that enable you to understand, cleanse, monitor, transform, and deliver data, as well as to collaborate to bridge the gap between business and IT.”

InfoSphere’s key capabilities include data integration, data quality and data governance. Its massively parallel processing provides fast performance and scalability, and it integrates with other IBM products for analytics, data warehousing, master data management, and more. The software is available in four different editions: InfoSphere Information Server for Data Integration, InfoSphere Information Server for Data Quality, InfoSphere Information Server on Cloud and InfoSphere Information Server Enterprise Edition, which provides end-to-end data management capabilities. IBM also offers a an integration platform as a service (iPaaS) solution called Application Integration Suite on Cloud.

Gartner places IBM in the Leaders quadrant for data integration tools and the Visionary quadrant for iPaaS.

Cloud or on-premises

Proprietary

The on-premises version of IBM InfoSphere Information Server runs on Linux, Windows or AIX. Exact system requirements vary based on the size and scope of the deployment. In general, the server requires 2 GB to 6 GB RAM and 3 GB to 5 GB of storage space. The desktop client requires 2 GB RAM and 2 GB hard drive space. Some installations may require processors with eight or more cores.

InfoSphere Information Server connects to most relational and mainframe databases, ERP, CRM, OLAP, performance management and analytics applications. Dozens of connectors are available for AWS, Cognos, Greenplum, Hive, DB2, Informix, Microsoft SQL Server, Oracle, Salesforce, SAP, Sybase, Teradata and many other applications.

Information governance

Data integration

Data quality

Parallel processing

Hadoop support

Cloud support

Native API connectivity

Training, certification, support and other professional services available.

$7,800 per month and up for the cloud version of IBM Information Server. $2,750 per month and up for the IBM Application Integration Suite. Pricing for on-premises version not available.

Features IBM InfoSphere

Deployment Cloud or on-premises

System Requirements

Operating System Linux, Windows, AIX

Processor Some deployments require 8-core processors

RAM 2 GB to 6 GB

Storage 3 GB to 5 GB

Software Depends on deployment details

Connectors AWS, Cognos, Greenplum, Hive, DB2, Informix, Microsoft SQL Server, Oracle, Salesforce, SAP, Sybase, Teradata, others

Design and Development Environment Graphic environment, web-based or Windows thick client

Key Capabilities

ELT Yes

ETL Yes

CDC Yes

Data Quality Yes

Data Governance Yes

Others Parallel processing

Support and Services Training, certification, support and other professional services

Gartner Magic Quadrant Rating Leader (data integration); Visionary (iPaaS)

Price $7,800 and up for cloud version; pricing not disclosed for on-prem version

Features IBM InfoSphere

Deployment Cloud or on-premises

System Requirements

Operating System Linux, Windows, AIX

Processor Some deployments require 8-core processors

RAM 2 GB to 6 GB

Storage 3 GB to 5 GB

Software Depends on deployment details

Connectors AWS, Cognos, Greenplum, Hive, DB2, Informix, Microsoft SQL Server, Oracle, Salesforce, SAP, Sybase, Teradata, others

Design and Development Environment Graphic environment, web-based or Windows thick client

Key Capabilities

ELT Yes

ETL Yes

CDC Yes

Data Quality Yes

Data Governance Yes

Others Parallel processing

Support and Services Training, certification, support and other professional services

Gartner Magic Quadrant Rating Leader (data integration); Visionary (iPaaS)

Price $7,800 and up for cloud version; pricing not disclosed for on-prem version

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