Prevailing AI technology for analytics prefer the use of statistical science as the foundation for machine learning (ML) on historical data to distill knowledge and experience. Whether it be supervised or unsupervised, the result is then incorporated into playback engines to analyze new data. These methods and procedures work well for predictable scenarios with known outcomes and known variables.
What if the variables are unknown, and the outcome is not predictable by past use-cases? When presented with this scenario, AI built upon the above premises will fail fast when compare to human experts. The “instinct” of a human expert – an art, rather than a science – enables them to adapt and discover the unknown. For this reason, it comes as no surprise that some human chess players can find ways to beat their machine counter-part.
The reality of textual analytics
With textual analytics, the objective will drive the solution preference. Conventional means of using ML on historical data takes on a different challenge. For example, if the objective is to understand sentiment, the goal is ranking the sentiment gradient. On the other hand, if the objective is user dialogue engagement, the goal is to analyze the question and to formulate an answer based on the question from a knowledge system. These applications are typically domain specific and will require a dictionary and ontology of the specific domain. However, if the purpose of textual analytics is to help a user gain insight into a dynamic range of subjects such as financial news across different industries, quarterly earnings reports and financial statements, customer reviews found in e-commerce sites, or media coverage of products and services then one may find most prevailing textual analytics inadequate. This is because the dynamic nature of subjects invalidates the above solutions. For this reason, my team turned to symbolic logic and propositional calculus to look for solutions.
Our quest for a new solution
The Internet has created a hyper converged digital world where business intelligence is everywhere. Media revelations that can impact the outcome of business decisions are streaming over high-speed pipes to business decision makers 24-7. Enterprise can no longer rely on traditional AI and ML to render solutions, equally, enterprise find it extremely difficult to find adequate human experts to simultaneously process voluminous business intelligence and devise Tactical Strategic Plans that can beat the competition and capture market opportunities. My team devised a solution to fuse the competitive advantages of both – to extend ML with symbolic logic and propositional logic so as to elevate the intelligence of human experts in solving complex problems. In the process, enterprise will be able to take advantage of the latest BI in advancing its business.
A new approach to artificial intelligence for textual analytics
While the details of this technology is beyond of scope of this writing, the general concept is not difficult to understand. We call this technology “Context Discriminant”. It is based on first order logic with symbols to infer, associate, prove or disprove premises using theorem-proving algorithms such as “resolution principle”. The idea behind this technology is to equip a software system with the ability to master a language such as English to the equivalent of a graduate student or researcher who can learn a core subject from a lecture or research medium. In this scenario, the medium uses English to introduce new subjects. In the process of knowledge transfer, the medium draws relationships between subjects and expresses the properties of the underlying context. The researcher, using English as a medium, can learn any subject and acquire new knowledge by listening to lectures. In a similar manner, we implemented a software system to use the English language as the medium to learn any domain specific subjects in a set of documents. The software system uses visual charts to depict the discovered subjects, relationships, underlying context, properties, and references to source documents. When a user navigates through these properties, together with human thinking, it forms a bond of bionic fusion which enables the user to gain insights by drawing inference from these visuals.
The magic of Context Discriminant
My team developed this novel approach while searching for an automatic solution analyzing financial news. We have examined supervised and unsupervised machine learning in conjunction with financial news analytics and concluded that the pre-process and prerequisites of ML make it extremely difficult, if not impossible, to scale across various industries despite the commonality of business goals (i.e. supply & demand, competition, shareholder value, economy, outlook and revenue). Our technology evolved from a fully-automated solution into a universal tool that can be used to elevate the performance of any expert in any field. We draw parallels with contemporary man-machine fusion in bionic principles – information gathering, processing and optics such as those found in air traffic control radar or computerized tomography (CT) scans in medical applications. These devices gather information in real-time and provide continuous display to experts who can use it to perform a better job that otherwise would be impossible. We see this integration of mind and machine as the first step towards developing bionic systems elevating the capability and capacity of the human mind. Our system has been validated in the prediction of financial market forces and public company conference call transcripts. The results have been astounding.
