Ovum, one of UK’s top analyst firms, has just released a report about Cortical.io Contract Intelligence, recognizing its benefits applying Artificial Intelligence (AI) technology in the legal domain.
Based on patented technology, Cortical.io Contract Intelligence extracts key information from complex contracts and populates existing contract management software with that information. The difference with other solutions in the market results from the neuroscience-based algorithm used to process text, which solves the problems of language ambiguity, is quickly trained and enables a fast and transparent implementation process.
“One of the challenges in the legal world is that the vocabulary used in legal documents is rather narrow, and small differences in how these words are expressed can lead to significant contextual or semantic differences”, describes Michael Azoff, distinguished analyst at Ovum. “With their original approach that combines Numenta’s memory model and unsupervised machine learning algorithms, Cortical.io has created a powerful technology to process unstructured text data and extract meaning. At Ovum, we believe that Cortical.io Contract Intelligence deserves evaluation.”
Cortical.io Contract Intelligence processes all types of legal documents, including lease agreements, ISDA master agreements, bond indentures, and certificates. The solution analyzes the meaning, not just of keywords, but of whole sentences, paragraphs, and long text so that the problems of language ambiguity and vocabulary mismatch within and across documents are overcome. For example, “done deal” and “signed contracts” are recognized as very similar by Cortical.io Contract Intelligence, although they do not have any term in common.
Cortical.io Contract Intelligence is accessed via a simple user interface and does not require any specific training. It is designed for business users and does not require any AI experts or knowledge. The solution is already used by Fortune 100 companies to reduce manual review and data extraction time, as well as contract processing costs.Read more
PwC Germany and Cortical.io have recently signed a joint business relationship agreement whereby PwC becomes partner of Cortical.io and develops natural language understanding solutions using Cortical.io’s technology.
A young tech company, Cortical.io has developed a unique natural language understanding technology that solves many challenges related to big text data. The novel, meaning-based algorithm is based on Cortical.io’s patented Semantic Folding methodology. It allows both high-precision and high-speed semantic text processing and can be applied to any kind of unstructured text data. The application fields are close to endless, and practically all verticals can benefit from this innovation.
“Nowadays, every business, whether small or large, collects overwhelming amounts of text data,” explains Francisco Webber, CEO and co-founder of Cortical.io, the high demand for new data processing solutions across all industry sectors. “Each business is confronted with at least one big text data issue: some need to classify products based on lengthy text descriptions, others must extract key information from complicated legal or technical documents. Most companies need help in interacting with their customers, answering questions, recommending products, and so forth. All need an automated, reliable solution that can be easily adapted to their particular use case and can deliver first results within a few weeks. This is exactly what Cortical.io offers,” comments Webber.
PwC is one of the leading auditing and consultancy organizations in Germany and provides support to clients of different sizes, in a wide range of sectors. “In order to create a real value for our customers, our solutions focus on innovative technologies, but only if they have proven their worth in practical applications,” explains Sven Fessler, Senior Manager Big Data & Analytics at PwC Germany. “Cortical.io fulfills both criteria: they have developed a completely new approach to text processing and have already successfully deployed solutions in multiple enterprise environments”.
Cortical.io’s semantic technology is currently in production at several Fortune 500 companies where it is integrated into existing software solutions, in very different contexts. Sascha Demgensky, attorney and auditor at PwC, who, together with Sven Fessler, initiated the partnership, comments: “Next to its disruptive character, Cortical.io’s technology is easily adaptable to any business domain and delivers prompt, impressive results. We are confident that, combined with PwC’s expertise, we can offer our customers intelligent solutions that will impact their bottom line sustainably”. Cortical.io’s solutions that already bring customers significant cost savings include semantic search and contract analytics.
“The partnership with PwC represents a strategic move for Cortical.io,” states Francisco Webber. “PwC’s focus on high quality, their many years of experience and the wide range of professional services they provide make them an ideal partner to spread our technology. Major innovations are coming ahead in the field of natural language understanding,” concludes Webber, “and you can be sure that Cortical.io and PwC will be major forces behind this disruption.”
Cortical.io offers Natural Language Understanding (NLU) solutions based on Semantic Folding, a methodology that opens a fundamentally new perspective on the handling of big text data. Inspired by the latest findings on how the brain processes information, the Cortical.io Retina engine converts language into semantic fingerprints, numerical representations that capture meaning explicitly.
The uniqueness of the Cortical.io algorithm makes it possible to solve many open NLU challenges, like meaning-based filtering of terabytes of unstructured text data, real-time topic detection in social media and semantic searching through millions of documents across multiple languages.
The company was founded in 2011 and holds a broad general license for Numenta’s HTM technology. Cortical.io has offices in Vienna (Austria), New York and the San Francisco Bay Area.
In this document, “PwC” or “PwC Germany” refers to PricewaterhouseCoopers GmbH Wirtschaftsprüfungsgesellschaft, which is a member firm of PricewaterhouseCoopers International Limited, each member firm of which is a separate legal entity. This document is for general information purposes only and should not be used as a substitute for consultation with professional advisors.Read more
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Gartner’s report “Cool Vendors in AI Core Technologies, 2017” recognizes vendors behind AI core technologies “to help data and analytics leaders, in collaboration with their business counterparts, conduct early AI experimentations for quick, opportunistic wins.”
We are very proud to have joined the league of Cool Vendors selected by Gartner!
Gartner subscribers can access the report here.
Cortical.io has wrapped its Retina Engine into an easy-to-use, powerful platform for fast semantic search, semantic classification and semantic filtering.
The Retina Platform is an ecosystem that offers access to the core Retina Engine via the Retina Library (formerly called Retina Spark) and the Retina API. Both enable fundamental operations on text like text comparison, keyword extraction, segmentation or context identification. The Retina Library includes additional modules for semantic search, dynamic generation of classifiers and Big Data semantic processing.
Both Retina API and Retina Library are provided with a default database for general English which can be replaced with any other language or domain-specific semantic space.
Retina API and Retina Library serve as a central resource for a multitude of different applications that elegantly solve difficult NLU tasks where other approaches struggle.Read more
The latest IDC Innovator research report, IDC Innovators: Machine Learning-Based Text Analytics, 2016 (Doc # US41312116, May 2016) recognizes Cortical.io as an IDC Innovator.
Cortical.io’s approach to understanding language, Semantic Folding, is inspired by the latest findings on the way the brain processes information. It proposes a statistics-free processing model based on semantic fingerprints, a new data representation that encodes meaning explicitly, including all senses and contexts. Cortical.io’s Retina engine learns any language by ingesting relevant text content via unsupervised learning.
Cortical.io’s Retina engine reduces complex text analytics to the application of a simple similarity function. This makes the system both highly scalable and very intuitive to use. With 16,000 semantic features, the Retina engine performs fine-grained deep semantics on any unstructured text and is completely language independent. Because it is highly efficient, the Retina Engine reduces drastically the computing power needed to process terabytes of data. It enables high performance semantic processing applications like on-the-fly classification, streaming text filtering or semantic search by analogy and opens up a new range of applications for social media monitoring, enterprise search, forensic text analytics, information discovery, compliance monitoring and much more.
IDC Innovators reports present a set of vendors chosen by an IDC analyst within a specific market that offer an innovative new technology, a groundbreaking approach to an existing issue, and/or an interesting new business model. It is not an exhaustive evaluation of all companies in a segment or a comparative ranking of the companies. IDC INNOVATOR and IDC INNOVATORS are trademarks of International Data Group, Inc.Read more
A recent academic study conducted by researchers from Leiden, Ben-Gurion and Toulouse Universities examined the performance of Cortical.io’s Semantic Folding approach for content analysis in a finance setting. Compared to the commonly used word-list method, Semantic Folding proved to have greater predictive power. Its other advantages were speed and ease of use.
“Like the human brain, our Semantic Folding engine learns a language and understands the meaning of text by making analogies. Like the brain, it is both efficient and accurate. We are thrilled to see these compelling results confirmed by an independent academic study”, comments Francisco Webber, inventor and co-founder of Cortical.io.
The research team used Cortical.io’s Retina API to create semantic fingerprints of the 30 Dow Jones Industrial Average constituents, based on business description sections of the companies’ annual reports. For each pair of companies, the similarity of their semantic fingerprints was compared to predict correlations between their stock returns over the following year.
The study found Semantic Folding to have greater predictive power than the traditional word-list based approach. Moreover, fingerprint similarity continued to significantly predict stock return correlations even when other measures of company similarity were controlled for.
The authors contend that Semantic Folding is simpler to use, has lower setup costs, and runs faster than the standard word-list based method. In addition, semantic fingerprints were considered to have an appealing visual interpretation. The authors argue that Semantic Folding significantly lowers the entry barriers for investigators interested in applying content analysis to financial data. To this end, the study includes sample code and suggests possible applications of Cortical.io’s Semantic Folding engine in several finance contexts.
The study, entitled “Using Semantic Fingerprinting in Finance” is available here.Read more
Cortical.io presents Retina Spark 2.0, an NLP tool specially designed for high performance semantic text processing in an Apache Spark environment. Similar to the Retina API, it operates on the semantic rather than the keyword level and measures the similarity in meaning between text passages in order to classify, filter and search large document repositories.
Retina Spark 2.0 enables the creation of:
Retina Spark 2.0 is a library that augments Spark MLlib with high-performance semantic text processing capabilities. It is Cloudera certified and can be used with on-premise or in-the-cloud Spark clusters, including those based on the Cloudera and Amazon EMR distributions. Retina Spark 2.0 supports the latest Apache Spark releases and features a Java and Scala API.
Apache Spark is an open source framework and runtime environment for distributed and parallel computing.
It is common scientific practice to investigate phenomena, which cannot be explained by an existing set of theories, scientifically by applying statistical methods. This is how medical research has led to coherent treatment procedures, which provided a great deal of usefulness to patients. By observing many cases of a disease and by identifying and accounting its various cause and effect relationships, the statistical evaluation of these records allowed to make thoughtful predictions and to find adequate treatments as countermeasures. Nevertheless, since the rise of molecular biology and genetics, we can observe how medical science moves from the time-consuming trial and error strategy to a much more efficient, deterministic procedure that is grounded on solid theories and will eventually lead to a fully personalized medicine.
The science of language had a very similar development. In the beginning, extensive statistics analyses led to a good analytical understanding of the nature and the functioning of human language and culminated in the discipline of linguistics. With the increasing involvement of computer science into the field of linguistics, it turned out that the observed linguistic rules were extremely hard to use for the computational interpretation of language. In order to allow computer systems to perform language based tasks comparable to humans, a computational theory of language was needed and as no such theory was available, research turned again towards a statistical approach by creating various computational language models derived from simple word count statistics. Although there were initial successes, statistical Natural Language Processing (NLP) suffers two main flaws: The achievable precision is always lower than the one of humans and the algorithmic frameworks are chronically inefficient.
The Semantic Folding Theory (SFT) is the attempt to develop an alternative computational theory for the processing of language data. While nearly all current methods of processing natural language based on its meaning use in some form or other word statistics, Semantic Folding uses a neuroscience rooted mechanism of distributional semantics. After capturing a given semantic universe of a reference set of documents by means of a fully unsupervised mechanism, the resulting semantic space is folded into each and every word-representation vector. These vectors are large, sparsely filled binary vectors. Every feature bit in this vector not only corresponds but also equals a specific semantic feature of the folded-in semantic space and is therefore semantically grounded. The resulting word-vectors are fully conforming to the requirements for valid word- SDRs (Sparse Distributed Representation) in the context of the Hierarchical Temporal Memory (HTM) theory by Jeff Hawkins. While the HTM theory focuses on the cortical mechanism for identifying, memorizing and predicting reoccurring sequences of SDR patterns, the Semantic Folding theory describes the encoding mechanism that converts semantic input data into a valid SDR format, directly usable by HTM networks.
The main advantage of using the SDR format is that it allows any data-items to be directly compared. In fact, it turns out that by applying Boolean operators and a similarity function, many Natural Language Processing operations can be implemented in a very elegant and efficient way.
Douglas R. Hofstadter’s Analogy as the Core of Cognition is a rich source for theoretical background on mental computation by analogy. In order to allow the brain to make sense of the world by identifying and applying analogies, all input data must be presented to the neo-cortex as a representation that is suited for the application of a distance measure.
The two faculties - making analogies and making predictions based on previous experiences - seem to be essential and could even be sufficient for the emergence of human-like intelligence.Read more
Cortical.io, an innovator in natural language processing (NLP), announces its next venture capital round. In this third round, Cortical.io opens its capital to a new investor from the US, a fund affiliated with Open Field Capital (OFC), an investment manager with a focus on emerging technology markets. After Numenta, OFC is the second US-based investor taking an ownership position in Cortical.io. Reventon (NL) confirms its interest in the machine intelligence start-up with an additional participation, bringing the capital increase to a total of USD 1.8 million.
“Cortical.io’s approach of using similarity as a foundation for intelligence should enable a NLP technology that not only outperforms legacy systems in traditional text processing, but also opens a new range of applications that were not possible before, because it has the potential to eliminate constraints related to processing speed, data volume and the diversity of natural languages. This is exactly the kind of market disruption potential that we seek in an investment”, describes Marc Weiss, Principal at OFC.
Together with the third capital round, Cortical.io announces the opening of an office in the San Francisco Bay Area, where its sales and business development activities will be based. “North America is a core market for intelligent text analytics”, explains Francisco Webber, CEO and co-founder of Cortical.io. “There is a lot of value still hidden in Big Text Data. While our technology can be applied to many different business cases, its algorithmic efficiency has triggered strong interest from the financial industry. In the context of compliance monitoring, for example, the high precision and recall scores help to substantially reduce the associated workload”, explains Webber before concluding: “Our solution could help banks save billions in legal bills.”Read more
Numenta, Inc., a leader in machine intelligence, and Cortical.io, an innovator in natural language processing (NLP), are pleased to announce a strategic partnership to create a new computing approach to understanding text. As part of the strategic relationship, Cortical.io has taken a broad general license to Numenta’s Hierarchical Temporal Memory (HTM) technology, and Numenta has taken an ownership position in Cortical.io. The combination of Cortical.io’s Semantic Folding technology and Numenta’s HTM technology enables a host of exciting applications that have challenged computer scientists for decades, including sentiment analysis, automatic summarization, semantic search, and conversational dialogue systems.
“Cortical.io’s Semantic Folding technology is a clever and elegant way to feed natural language into our HTM technology”, said Jeff Hawkins, founder of Numenta. “Cortical.io takes advantage of the semantic encoding and predictive modeling of HTM systems in a way that will lead to significant advances in natural language processing.”
“Natural language understanding is one of the central problems of artificial intelligence,” said Francisco Webber, founder and CEO of Cortical.io. “We aim to build the next generation of NLP, Language Intelligence, and in so doing, show the path to broadly applied machine intelligence.”
Building on their existing commercial product, the Retina API, Cortical.io will make the combined technologies available through their industrial-grade cloud service for customers ranging from innovative startups to international corporations.Read more
The Austrian science start-up Cortical.io has just secured a next venture round of 1.25 million dollar of growth capital from Reventon (NL). This will help to bring Cortical.io’s portfolio of language intelligence products to the global app-builder and enterprise market.
Based on the breakthrough neuroscience theory of Jeff Hawkins, Cortical.io’s semantic fingerprinting technology represents language like in the human brain.
Cortical.io’s Retina API allows to create semantic fingerprints of any piece of text in any language. Fingerprints of product descriptions in English can be compared to LinkedIn profiles in German, documents in Spanish compared to reading preferences of French readers, multi-language twitter messages filtered by their content and job profiles related to CVs.
With Cortical.io’s Retina everything that can be described in words can be intelligently matched based on its meaning.Read more
We have developed a technology that enables developers to perform natural language processing in an intuitive and precise manner. “Our Semantic Fingerprinting method enables the creation of a unique semantic fingerprint for any word, any document, and in the near future even for any entity that can be described with natural language”, explains Francisco Webber, co-founder of Cortical.io. The big difference to conventional semantic systems is that the conversion of words
into their semantic fingerprints is automated. There is no need for costly, time-consuming manual intervention anymore.
The core component of Cortical.io, the Retina, learns about the essence of any language by reading text material about the world and is capable of semantically interpreting and computing any textual content. It encodes words in the same way as information is fed into the brain and generates semantic fingerprints of words and documents using a fine-grained representation of 16,000 semantic features for every term.
The invention of Cortical.io’s Retina could revolutionize the search and analysis of text-based information, not only because of its transparency and simplicity of use, but also because of its small footprint: huge amounts of text -structured and unstructured- can be processed with moderate computational power.
By converting any piece of text into a semantic fingerprint, tasks such as similarity comparison, contextual keyword generation, sense disambiguation, and document classification are made simple. Cortical.io’s Semantic Fingerprinting method can be applied to messages, news, web content, document collections and even real-time text streams from social networks.
With the new API release, you can easily select different Retinas and get the most adequate results, whether you want to focus on context-similarity or synonym-similarity. If your goal is to disambiguate terms, i.e. identify which meanings are contained within a specific term, you will want to use an associatively focused Retina (“en_associative” in our API). If you prefer identifying synonymous items for terms or texts, then a synonymously focused Retina (“en_synonymous” in our API) will deliver better results.
More details about the two retinas are available in our FAQs.Read more