Expert System is sponsoring this year’s MediaXchange conference taking place April 30- May 2 in New Orleans, LA.
Tim Turner, from TownNews will be presenting along with Expert System on how to use Semantic & Cognitive technology to monetize news content. This 2.5-day event attracts the country’s top news media executives and marketing talent and covers a wide variety of topics from innovative technologies to content strategy and optimization.
Please stop by booth #405 for a demo and a chance to win Bluetooth headphones!
Expert System is proud to be a Platinum Sponsor at the 2017 MarkLogic World event in Chicago May 15-17. MarkLogic World is where business leaders and technologists gather to connect, learn, and discover how to get the most out of the world’s best database for integrating data.
Don’t miss Daniel Mayer, CEO Expert System Enterprise present “What’s your AI strategy? Deploying Cognitive Intelligence into your application”
In this session, we will look at Text Analytics & Cognitive Intelligence and how they open the door to unprecedented productivity gains and business insights when applied to unstructured (textual) data. We will illustrate “the art of the possible” and how you can make your applications more intelligent with AI.
Stop by the Expert System booth to see a demo of Cogito. Register here.
Expert System is a Gold sponsor at the 2017 Connected Claims conference in Chicago May 24-25.
Daniel Mayer, CEO Expert System Enterprise, will be presenting “Cognitive Claims: to efficiency and beyond”. The promise of cognitive technology has landed on the shores of Insurance. By enabling information systems to understand the meaning of text, it has opened the door to increased automation and productivity in claims handling.
This session will illustrate through current applications how cognitive technology is reshaping claims and outline the new horizons that are opening for those who are embracing it.
Visit the website to learn more
Please join Expert System on May 9, 2017 at 2pm EST and learn how insurance carriers are using AI to automate time consuming tasks such as claims assessment, validating coverage and extracting ICD codes as well as for complex decision making such as risk grading and fraud prevention in underwriting.
Listen as Daniel Mayer, CEO of Expert System Enterprise highlights how instituting AI in the claims process is saving time, money and increasing customer satisfaction. We will be showcasing real-life examples of how carriers are using AI to increase business value and save millions of dollars each year.
Register here to join!
In our discussions with customers, we have noticed a trend that is significantly impacting the way that semantic solutions fit into the corporate landscape. In the “good old days,” an employee with a question would submit it to an in-house team of information experts who would use a set of expert tools to respond (by way of feedback report or list) to the initial enquirer.
While this is still common in many places, we are noticing a tendency to rely more and more on self-service solutions that put the employee in the position the end user, accessing solutions that attempt to address the bulk of incoming questions with web-based solutions. Welcome to the world of the intelligent search engine.
There are a number of factors at play here. More and more users feel confident performing searches on their own. Encouraged by the (often misleading) impression that, because they know how to use their favorite search engine, they think that the same type of search is also effective on a professional level. Rather than consulting an in-house team of experts, the average user would just go ahead and look for themselves.
At the same time, solutions that provide quick access to relevant information–i.e. Intelligent search engines– are becoming more and more powerful and intuitive. Finally, budgets for information departments are tight and frequently reassessed.
The result is that more and more users who are experts in their specific domain – such as biology, law or engineering – find themselves exposed directly to search and analysis interfaces without the reassuring presence of an information professional to help guide or improve the experience. This places a major responsibility for performance on the part of the technology solutions and tools. While it may be acceptable for private users to engage in search without a more detailed understanding of the principles behind a search engine, the same is not true in an enterprise environment. In a corporate setting, where incomplete or misguided search results may have a much larger impact, the pressure to have appropriate tools is, of course, much higher.
As one project sponsor put it: “The aim of the solution we want is not to make 5% of our staff 100% smarter, but to make 80% of our staff 10% smarter.” After all, many users hardly ever use even Boolean search. As a result, they would not be able to gauge the full potential of thesaurus-based indexing or more advanced technologies that are available for improving search efficiency.
Read also the article about Natural Language Processing Systems in Artificial Intelligence.
At Expert System, we translate this into an increased effort to develop not only the underlying information processing engine, but also the complete application. We know that we have to be able to address the fact that intuitive interfaces and effective document processing tend to hide the complexities of what goes on “under the hood” when it comes to an intelligent search engine.
Our Biopharma Navigator is a perfect example of this approach. The information that professionals in the life sciences and pharmaceutical sector need access to is too complex, heterogenous and distributed to be digested quickly without the help of both an information professional and intelligent search technologies.
Biopharma Navigator is a web-based solution that allows domain experts who are not information professionals to conduct a set of typical scenarios such as searching for experts, news about a therapeutic area or competitor activities. Results, enriched by typical synonyms behind the scene, are presented in rich dashboards that provide immediate access to the information they’re looking for.
Today, our Biopharma Navigator is used professionally by more than 1,000 industry experts to quickly and easily access the information they need.
In a similar vein, our Analysts’ Workspace software implements the same idea: Putting powerful semantic technologies in the hands of people who need quick and comprehensive results (without having to start from scratch for each new search).
Not everyone can be an expert in text analysis: Some of us need to be able to cure cancer, make good investment decisions or assess political risks and opportunities while leaving the analysis of the required sources to the experts. At Expert System, this is our mission.
Senior Cognitive Scientist, Expert System
Bryan Bell, EVP Corporate Development at Expert System is speaking at the Open Data Science Confefrence and AI Expo May 3-5 in Boston, MA.
Bryan will be presenting “The Cognitive Computing Promise: Combining Deep Linguistics Analysis with Semantics to deliver on the promise.” May 4th at 3pm. Delivering on this promise of cognitive computing is not simple because language is complicated. It requires combining a set of technologies that utilize contextual understanding, pattern matching, natural language processing, linguistic analysis, and semantics to understand information in a way that is similar to the way the human brain is able connect information.
Learn more about the event here
Expert System will take part in the Critical Infrastructure Protection & Resilience Europe conference, held May 9-11 at The Hauge, Netherlands. CIPRE brings together leading stakeholders from industry, operators, agencies and governments to collaborate on securing Europe.
The unique two-track conference programme will deliver a line up of international experts to discuss securing Europe’s critical infrastructure, from both physical and cyber perspectives.
As part of the Critical Information Infrastructure Protection / Cyber Security track, Expert System’s Gianluca Sensidoni, Business Process Manager, Intelligence Division, will lead the session “Cyber Techniques and Technologies to Detect, Prevent and Protect”, on May 11 at 11.15.
Visit Expert System at stand #12 for a live demonstration of the Cogito cognitive technology or contact us to schedule a meeting during the event.
Visit the event website to learn more
– L’uomo d’affari Trump ha messo le pistole sul tavolo. E il mondo ha deciso di accettare le regole del nuovo gioco. Quanta paura dobbiamo avere dopo il lancio sull’Afghanistan della Moab, mother of all bombs, l’ordigno più potente mai immaginato dopo la bomba atomica? Quale sarà il prossimo passo? E sono le guerre ad alimentare il mercato delle armi o è il mercato delle armi ad alimentare le guerre? Dubbio antico. E se Papa Francesco ha ragione quando dice: «Fermate i signori della guerra, la violenza distrugge il mondo e a guadagnarci sono loro», allora il più cinico speculatore del pianeta è il Presidente degli Stati Uniti. E il primo ministro italiano, Paolo Gentiloni, in questa piramide nera si colloca alle sue spalle ad appena sette scalini di distanza.
Some specialists believe that machine learning applications are, on the one hand, magic boxes capable of doing whatever we want or, conversely, are alien-like solutions that are useless in everyday life. As it often happens, especially when it comes to new technologies, the truth lies somewhere in the middle.
Prisma is a photo editing app that transforms users’ photos into works of art by applying the syles of famous artists or different and original patterns. Prisma doesn’t simply apply a filter (like Instagram does) but creates new photos following a model and, as the official description states, “a unique combination of neural networks and artificial intelligence helps you turn memorable moments into timeless art.”
How can Prisma turn a normal picture into a masterpiece?
All machine learning applications (and Prisma follows the same logic) learn from information, parameters and schemes and use them to improve their algorithms independently, without being explicitly programmed.
Machine learning is more pervasive than we think: there are numerous real-world applications – self-driving cars, speech and image recognition, text classification, web search, smart robots, etc. – that are included in this sub-set of artificial intelligence. They need specific training (Prisma learns works of art by Picasso or mosaic model features) and use these examples to make a system better (Prisma completely changes the style of the picture by applying a different one).
In business, we can say that machine learning offers a sophisticated approach, but there is a limit to the level of improvement possible in analyzing unstructured information.
– Need data or models that have been prepared manually by people. And even then the process is not completely automatic. Machine learning applications do not learn on their own; someone has to teach it the differences between topics, words and concepts, etc.
– Require a large set of data and examples for training related to the field or the topic. Machine learning can understand the difference among different information only if documents about different topics and information are uploaded during the training process.
– Obtain good results only if the training is frequent (and if the data set grows). Machine learning can improve its knowledge only by adding – over and over again – more information.
– Need different patterns. Too much data of the same genre makes the system less accurate. Machine learning can distinguish between the different meanings of the same word, or politics from ecology for example, only if these meanings, or other topics like history, medicine, math, etc. are known by the system.
– Do not learn in real time. You can’t add a new concept among the options that machine learning offers.
So, you can’t hope to train machine learning to identify many different words and different pieces of information without sufficient models and training. Even an extensive knowledge base cannot help you deal with a new word if machine learning has never seen it during training.
That said, going beyond text analytics, machine learning offers many opportunities in a variety of fields, and really learning complex information possible.
In general, we could say that all machine learning applications are neither a magic boxes nor a useless solution. They cover a very broad range of fields, some very critical (for example life science and health applications)… but how far can they go? Will machines ever learn everything and automatically?
– Stefano Spaggiari, Amministratore Delegato di Expert System, è stato intervistato nel corso dell’AIM Investor Day 2017, tenutosi a Milano il 6 aprile 2017.