Gartner Named Sinequa a Leader in Its Magic Quadrant for Insight Engines

As the CEO of Sinequa, I am proud that Sinequa was recognized as a leader in the recently released Magic Quadrant for Insight Engines 2017. Being a Gartner Leader, once again, underlines our continued progress that has led to this renewed leadership position in a Gartner Magic Quadrant. (We have previously been positioned as a leader in the Magic Quadrant for Enterprise Search.)  Gartner selects leaders for their “Completeness of Vision” and their “Ability to Execute” Good to see that others find our vision convincing and believe in our ability to realize it!

More reassuring still is the testimonial of our customers that led Gartner to state that “reference customers regarded Sinequa’s roadmap and future vision for its software to be particularly attractive. All indicated that those were significant reasons for choosing the software.”

As an established Cognitive Search platform, we’re continuing to evolve our vision and invest in enabling the largest organizations such as Airbus, AstraZeneca, Bristol Myers Squibb, Credit Agricole, and Siemens around the globe to get more value from their ever growing and diverse Enterprise data, as well as broadening the impact of search and analytics within the digital workplace of their employees.

According to Gartner:

“Insight engines apply relevancy methods to describe, discover, organize and analyze data. This allows existing or synthesized information to be delivered proactively or interactively, and in the context of digital workers, customers or constituents at timely business moments.”

Gartner-Magic-Quadrant-for-Insight-Engines-2017-Sinequa

Get your copy of the full report here and see why Sinequa is among the 3 leaders over the 13 vendors who participated in this Magic Quadrant.

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Machine Learning Becomes Legit, but Not Mainstream in 2017

ML-Sinequa-Predictions-2017

There has been a lot of hype around machine learning lately. Over the past decades, we’ve heard about various concepts around machine intelligence that in most cases didn’t get anywhere. But more and more frequently, organizations are learning how to bring together all the ingredients needed to leverage machine learning, and there is a simple reason for that: according to Moore’s law, the performance of microprocessors has increased since 1980 be a factor of more than 16 million! A program that ran on a 1980 computer for more than half a year today delivers its results in one second!

That is why I think Machine Learning will be the story for 2017. We’ll see it move from a mystical, over-hyped holy grail, to more real-world, successful applications. Those who dismiss it as hocus-pocus will finally understand it’s real; those who distrust it will come to see its potential; and companies that apply ML to appropriate use cases will achieve real business benefit without the high cost of entry that was common in years past. In 2017 it will be clear that it has a credible place in the business toolkit.

The four necessary enablers for machine learning – huge parallel processing resources, cheap storage, large and appropriate data sets, and accessible machine learning algorithms – are all now mainstream. Most large organizations have readily-available access to all these components (appropriate data sets are potentially the only open question, as they are always business- and use-case-specific), which makes machine learning a real possibility to reduce risk, increase customer satisfaction and loyalty, create new business models, identify patterns, and optimize complex systems.

One area where machine learning is growing rapidly and already showing success is for cognitive search and analytics applications. It won’t take over core algorithms anytime soon, but ML is already supplementing and enhancing search results based on user actions and smart analysis of content.

I don’t foresee machine learning achieving “mainstream” status in 2017, but it will within the next few years because the technology is advancing exponentially, quickly enabling its use in broader contexts.

For more on my complete prediction on machine learning, check out this article in Virtual Strategy Magazine.

 

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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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5 Ways Machine Learning Makes Your Search Cognitive

5 Ways Machine Learning Makes Your Search Cognitive

Artificial intelligence, machine learning, deep learning, cognitive computing…no doubt there is a lot of buzz out there but quite a bit of confusion too in terms of expectations and pre-requisites. We often hear customers and prospects say: “I want an AI assistant that tells me what to expect and what to do next,” or “It takes a lot of time to train an AI assistant to make him an expert in my field, doesn’t it? I want something that fits in my budget and I want it now.” Also, users often have many questions regarding the potential of machine learning for end users in their work environment. In this blog post, I’m sharing our initial thoughts about machine learning algorithms and how they empower cognitive search and analytics platforms to deliver better insights in relevant work context.

Machine learning algorithms often operate in two phases: the learning phase and the model application phase. In the learning phase, the data is analyzed iteratively to extract a model from manually classified data. While in the model application phase, the extracted model is applied to further inputs to predict a result.

Machine learning algorithms depend strongly on the quality of data, which is correlated to the quality of results. Cognitive search and analytics platforms can use natural language processing (NLP) and other analytics to enrich structured and unstructured data from different sources (entity extraction, detection of relationships within the data, etc.). This “data pre-processing” stage allows machine learning algorithms to start from enriched data and deliver relevant results much faster. These results continuously enrich the index/logical data warehouse and thus make it easier to answer users’ queries in real-time.

A performant cognitive search and analytics platform must integrate machine learning algorithms with its NLP and other analytics capabilities to deliver the most intelligent and relevant search results to users. Below are five ways machine learning makes search cognitive:

  • Classification by example – a supervised learning algorithm used to extract rules (create a model) to predict labels for new data given a training set composed of pre-labeled data. For example, in bioinformatics, we can classify proteins according to their structures and/or sequences. In medicine, classification can be used to predict the type of a tumor to determine if it’s harmful or not. Marketers can also use classification by example algorithms to help them predict if customers will respond to a promotional campaign by analyzing how they reacted to similar campaigns in the past.
  • Clustering – an unsupervised learning algorithm whereby we aim to group subsets of documents by similarity. Sinequa uses clustering when we don’t necessarily want to run a search query on the whole index. The idea is to limit our search to a specific group of documents in each cluster. Unlike classification, the groups are not known beforehand, making this an unsupervised task. Clustering is often used for exploratory analysis. For example, marketing professionals can use clustering to discover different groups in their customer/prospect database and use these insights to develop targeted marketing campaigns. In the case of pharmaceutical research, we can cluster R&D project reports based on similar drugs, diseases, molecules and/or side effects cited in these reports.
  • Regression – a supervised algorithm that predicts continuous numeric values from data by learning the relationship between input and output variables. For example, in the financial world, regression is used to predict stock prices according to the influence of factors like economic growth, trends or demographics. Regression can also be used to create applications that predict traffic-flow conditions depending on the weather.
  • Similarity – not a machine learning algorithm but simply a heavy computing process that helps build a matrix synthesizing the interaction of each sample of data with another one. This process often serves as a basis for the algorithms cited above, and can be used to identify similarities between people in a given group. For example, pharmaceutical R&D can rely on similarity applications to constitute worldwide teams of experts for a research project based on their skills and their footprints in previous research reports and/or scientific publications.
  • Recommendation –  one of the various use cases consists of merging several basic algorithms to create a recommendation engine proposing contents that might be of interest to users. This is called “content-based recommendation,” which offers personalized recommendations to users by matching their interest with the description and attributes of documents.

All the algorithms above need to be executed in a fast and scalable computing environment to deliver the most precise results. Currently, the Spark distributed computing platform offers the most powerful capabilities to execute machine learning algorithms efficiently. It is indeed designed to scale up from single servers to thousands of machines and it runs much faster than simple Hadoop frameworks.

Our recent contribution in the KM World Whitepaper “Best Practices in Cognitive Computing” highlights concrete use cases, describing how cognitive information systems are capable of extracting relevant information from big and diverse data sets for users in their work context. Get your copy here.

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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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