Artificial Intelligence in 2017: Expands Capabilities, but Impacts the Workforce

Artificial-Intelligence-SinequaThe beginning of the new year is a good time to reflect on the events of 2016 and on their forebodings for the coming year and beyond.There has no doubt been a great deal of buzz around artificial intelligence (AI) this year. However, it’s difficult to sort through what’s hype and what’s not to determine where these technologies will actually take us in 2017. While we know the trend will continue in some form, what will be new or different next year? Here are some of my predictions: 

Artificial Intelligence is taking the industry by storm, and not just in “Westworld.” We’re entering a new phase of AI thanks to advances in computing power and volume of data. This has opened the door to solve computational problems on a scale that no human mind could approach – even in a lifetime. The result is that computers are now able to provide responses that aren’t dictated by a collection of “if A, then B” rules, offering results that can only be explained by saying that the computer “understands.” The benefit is that complex and time-consuming cognitive processes can now be automated, and we can do things at scale that were previously impossible because unlike humans, computers are not overwhelmed by volume.

We’re definitely headed in the direction of workforce displacement and I believe it’s going to happen quickly, as there are huge economic incentives to increase efficiency and to automate manual tasks. This will happen faster than we expect because we think linearly, while technology is advancing exponentially. We struggle with that perspective because it quickly outpaces what we can readily grasp, whether that be in size or speed, or both. This will bring additional challenges because the disruption will occur across the occupational spectrum (unlike the industrial revolution, which primarily impacted “low-skill” jobs). I don’t see any particular sector being hit by this tidal wave in 2017, but AI is a disruptor like we’ve never seen before and it will be here soon whether we are ready for it or not.

However, with this transformation, tasks that have been impractical because of the time/labor involved now become feasible, which means we’ll be able to do things we haven’t been able to do before. It will also free us from many mundane and repetitive tasks, enabling people to focus on new or more valuable activities. This will increase efficiency in the workplace as well as consistency, which will improve quality and safety. So while the workforce will look very different from how it looks today – certainly in 10 years and probably in five, AI and ML are going to greatly extend and expand our capabilities in ways that, for now, we can only imagine.

What are your predictions for 2017 and beyond? For a full list of my predictions on AI other topics such as machine learning and big data, check out my post in VMblog.

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What is Cognitive Search? How a New Generation of Platform is Transforming Enterprise Insights?

Despite the effort from technology vendors to deliver relevant, contextual, and actionable insights with their applications, most organizations have been slow if not reluctant to embrace these advances in search-driven experiences. In fact, a lot of companies have been burned by their past enterprise search experiences.

The good news is that something is shaking the world of Enterprise Search – some would say ‘finally.’ New industry investments and R&D effort are changing the search experience to provide more relevant results and deeper insights to users in their work context.

As we enter the era of “cognitive computing,” new search solutions combine powerful indexing technology with advanced Natural Language Processing (NLP) capabilities and Machine Learning algorithms in order to build an increasingly deep corpus of knowledge from which to feed relevant information and 360° views to users in real-time. This is what leading analyst firms call “Cognitive Search” or “Insight Engines.”These cognitively-enabled platforms interact with users in a more natural fashion, learn/progress as they gain more experience with data and user behavior, and proactively establish links between related data from various sources, both internal and external.

In a recent brief, Forrester defines cognitive search as:

“Indexing, natural language processing, and machine-learning technologies combined to create an increasingly relevant corpus of knowledge from all sources of unstructured and structured data that use naturalistic or concealed query interfaces to deliver knowledge to people via text, speech, visualizations, and/or sensory feedback.”

How does cognitive search work to deliver relevant knowledge?

  • It extracts valuable information from large volumes of complex and diverse data sources. It is crucial to tap into all available enterprise data whether internal or external, both structured and unstructured, to provide deeper insights to users in order for them to make better business decisions. Cognitive search provides this connection to provide comprehensive insights.
  • It provides contextually and relevant information. Finding relevant knowledge across all available enterprise data requires cognitive systems using Natural Language Processing (NLP) capable of “understanding” what unstructured data from texts (documents, emails, social media blogs, engineering reports, market research…), and rich-media content (videos, call center recordings..), is about. Machine Learning algorithms help refine the insight gained from data. Trade and company dictionaries and ontologies help with synonyms and with relationships between different terms and concepts. That means a lot of intelligence and horse power “under the hood” of a system providing “relevant knowledge” or insight.
  • It leverages Machine Learning Capabilities to continuously improve the results relevancy. Machine Learning algorithms (amongst the most popular ones: Collaborative Filtering and Recommendations, Classification by Example, Clusterization, Similarity calculations for unstructured contents, and Predictive Analysis) provide added value by continuously refining and enhancing the search results in an effort to provide the best relevancy to users.

Thanks to new technology advancements, cognitive search brings to data-driven organizations a new generation of search enabling them to go far beyond the traditional search box, empowering its users to get immediate and relevant knowledge at the right time on the right device.

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Sinequa’s Cognitive Search & Analytics Platform Receives an Award from BigData Insider

Last night, Sinequa participated in the Readers’ Choice BigData Insider Award Gala in Augsburg, Germany. From April 19 to August 31, 2016, the readers nominated their IT Vendor of the Year across six portals: BigData insider, cloud computing Insider, Datacenter Insider, IP Insider, Security insiders and Storage insiders. In total, more than 34,000 readers voted for their favorite solutions.

As a result of the vote, Sinequa’s Cognitive Search & Analytics platform won the Silver Award in the “Big Data Management & System Tools” category. In the same category, Talend and SAS received respectively the Platinum Award and the Gold Award.

Big Data Insider Award 2016

“We are honored to receive this distinction resulting from the vote of the readers of BigData Insider comprised of customers and partners. This is a great recognition for Sinequa’s growing momentum in the DACH region,” said Laurent Fanichet, Vice President, Marketing at Sinequa.

Sinequa @ BigData-Insider-Awards-2016

Bild: Dominik Sauer / VIT
From left to right: Matthias Hintenaus, Sinequa, Andreas Gödde, SAS and Harald Weimer Talend.

 

 

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Finance & Banking: Collecting High Value from Cognitive Search and Content Analytics

In today’s rapidly changing technology climate, financial services customers expect their banks, insurance companies and asset management providers to know them. It’s expected that providers know about recent transactions, account details, and even anticipate future needs. But this can be challenging with the numerous silos of content in which customer data resides. With a 360° view of the customer through cognitive search and analytics, financial services organizations can deliver the customer experience that provides more value, drives increased sales and meets rapidly evolving customer expectations.

As an example, Crédit Agricole, one of the largest banks in the world, has launched an ambitious project to deliver a new digital workplace, offering a 360° view of customers to its representatives as well as to the customers themselves. The bank’s more than 60,000 internal users will be able to know the exact situation of the customer in front of them, to find the most relevant offerings for the customer and the corresponding procedures. The customers connecting to the bank’s online service also find themselves in a similar “work place” that allows them to know the current status of all their business with the bank including accounts, contracts, records of transactions, share portfolio and share prices, banking charges, additional services, and more.

This comprehensive “work place” is created through inclusive enterprise search and analytics of all of the bank’s data sources. From CRM and account transaction applications to external sources such as stock exchange data, corporate websites, financial and trading news-feeds, the bank can provide a complete customer picture from which to deliver robust service, new offerings and build increased customer satisfaction.

Cognitive Search and Analytics platforms index all the structured and unstructured data sources and create a semantically enriched index, optimized for performance in dealing with user search queries.  In fact, some search and analytics solutions even offer as many as 150 smart connectors, ‘out of the box,’ that can seamlessly connect multiple sources of data.  These companies integrate your company’s and industry specific dictionaries allowing the information to be integrated and indexed, putting your specific knowledge ‘under the hood’ of one platform – making it an intelligent partner for workers looking for business insight at their digital workplace.

See here how Sinequa’s Cognitive & Analytics platform brings business value to Finance and Banking organizations.

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Uncovering Business Insights Through Cognitive Search

Uncovering-Business-Insights-Through-Cognitive-Search-Sinequa

Big Data. It’s among the most pressing challenges — and opportunities — for today’s solution providers. Enterprise data, be it structured in databases and enterprise applications or unstructured textual data from documents (including contracts, letters, emails, news-feeds, websites, and more) or videos and images, contains a wealth of content that, if searched and analyzed with cognitive intelligence, can deliver valuable insights for the customers you serve.

It’s common today to have numerous silos, both on premise and in the cloud, of content in which critical data resides. From customer records and contracts to financial data and emails, data silos often take many different shapes and forms without the ability to “talk” to one another. If only a 360 degree view of this data were available at the employees’ finger tips. This could provide deeper customer insight, increased sales opportunities, and greater customer loyalty with the ability to meet rapidly evolving customer expectations.

With cognitive search and analytics, this goal can be achieved. Leveraging Machine Learning algorithms and advanced natural language processing (NLP), cognitive search and analytics solutions enable customers to embark on ambitious Big Data projects with the opportunity to extract relevant information from the volumes of content they retain.

In fact, some search and analytics solutions even offer as many as 150 smart connectors, out of the box that can seamlessly connect to multiple sources of data. This works to integrate your customers’ industry specific dictionaries allowing the information to be indexed, putting their specific knowledge under the hood of one platform — making it an intelligent partner for anyone searching for relevant information for his/her subject.

To efficiently leverage Big Data for your customers, consider an advanced search and analytics platform that delivers these five critical elements.

  1. Cognitive search with a combination of indexing, natural language processing and machine learning. For a search and analytics solution to be effective, it needs to understand the natural language as it’s spoken across ever major language. This will help to deal with unstructured content such as email and document files. It should also leverage machine learning algorithms so that it can learn as it progresses, delivering more value and insight with each new volume it analyzes. This is what one analyst firm defines as Cognitive Search which allows organizations to create an increasingly relevant corpus of knowledge from all sources of unstructured and structured data that use naturalistic or concealed query interfaces to deliver knowledge to people via text, speech, visualizations, and/or sensory feedback.
  2. Extensive connections for comprehensive indexing. To make the most of the multiple silos of data throughout the organization, a search and analytics solution needs to have a wide range of connectors so that it can support every type of data, making it easily ingested into the platform so that it’s included in a comprehensive analysis. Building connectors before starting projects will delay value extraction and make projects more expensive. From databases and enterprise applications including CRM and ERP systems such as SAP, to big data Hadoop environments, cloud applications like Office 365, GoogleApps and Salesforce, and cloud storage such as Box and Microsoft OneDrive, having a connector for every vital application in the business will ensure that the resulting insights deliver a complete view into the business.
  3. Support for the structured and unstructured. When analyzing business data, it’s critical to include unstructured data, such as email and document files, as well as structured content, like the data included in databases. Only when both are included in an analysis can true insights be revealed. Since so much valuable data is embedded in unstructured files, evaluating these contents can produce truly insightful information into the business that can’t otherwise be recognized by evaluating structured forms of content.
  4. Extensive security and access control. Today’s data is not only a critical asset, it’s also private and stringently regulated. Any solution that touches regulated data must follow strict security and compliance guidelines, ensuring that policy controls are in place. Be sure to select a search and analytics platform that supports stringent access controls, including user authentication, cross-domain security and secure communications, to assure that compliance practices are followed.
  5. Agility to support hybrid infrastructure. The cloud is quickly changing everything. When large data sets are in play, it’s very likely that much of that data is being retained in cloud-based environments. Whether retained in public cloud solutions, such as Amazon Web Services (AWS), or private cloud architectures, data still must be accessed and integrated into a comprehensive enterprise search and analytics solution to be part of a successful solution for true business insights. Here it’s critical to select a solution that will not only support any combination of a private and public cloud infrastructure as well as on-premises architectures with a hybrid approach to data analysis that will also support hundreds of millions of documents and billions of database records. This will ensure that regardless of how large the environment becomes, and wherever data may reside, it can become part of a comprehensive analysis for true and accurate results.

Big data presents a wealth of opportunity for you and your customers. By taking a holistic approach to cognitive search and analytics so that every silo of data is included in an enterprise search activity, the insights can be exceptionally revealing. These results can not only increase customer opportunities, grow sales and improve overall organizational productivity, they’ll also help you build the customer loyalty that will pay off for years to come.

 

 

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