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How an AI Engine can improve your business

The huge technological progress of the last decade addressed and greatly impacted the basic human need of communication. Websites, blogs, emails, text messages, Facebook, Snapchat, Slack and smartphones on the one hand and Google on the other as the dominant intermediary, all in essence address our need to communicate.

I would refer to this as the ‘communication era’ that I believe has “plateaued” in terms of potential truly disruptive innovation.

We are now at the start of the next era, the ‘knowledge era’, where Artificial Intelligence (AI) will dominate. AI is an umbrella term that includes multiple technologies such as Machine Learning (ML), Natural Language Processing (NLP) and Knowledge Representation (KR).

AI is now the cutting-edge of technology.

How can your business use AI?

Clients and potential clients invariably ask how they can use AI to add value to their business.

The answer is by deploying an AI Engine as an implementation vehicle through which AI functionality can be leveraged, abstracting away the inherent complexity.

Composed of several distinct modules, an AI Engine can be deployed either as a service (AIaaS) or embedded within client-server, web or mobile applications.

What is an AI Engine?

An AI Engine is comprised of several fundamental modules which include a Machine Learning Module, a Natural Language Processing Module and a Knowledge Representation (Ontology) Module.

With a view to targeting specific business objectives like increasing sales, reducing costs, addressing compliance and improving cyber security, these modules are used to collect and transform data, to generate accurate predictions and insights, to read and classify text and much more.

The Fortuit AI Engine

At Fortuitapps we have developed a powerful, business-oriented AI Engine (as opposed to an equally important academically oriented counterpart).

Below I will detail the AI Engine components and describe how they can be used to improve business processes.


Machine Learning is based on Neural Networks which model biological brain function. A Neural Network is a net of neurons (brain cells) that are linked to each other through connections called synapses (think of a fishing net).

These connections have a certain strength (positive or negative) and the combination of these will result in a neuron being turned on (firing), or not, according to a certain threshold value (think of a light bulb with some power cords attached: if the cumulative power is enough the light bulb will turn on).

Human thinking is the effect of firing (or not) of neurons.

Now, take one side of a Neural Net and turn on some neurons. Then take the other side and again turn on some neurons. You have given the net an example of a given input and an expected output (think of an input of 1 plus 1, and an output of 2 so as to teach it addition).

Next, give it lots of examples and then use a specific standardized learning algorithm through which it can learn by adjusting the power of its connections and when neurons will fire. Once it stabilizes through many iterations, ask it to add numbers that you haven’t taught it: it will respond with a high probability of accuracy based on what is has learned.

In this way, given an input dataset (e.g. images) and an output data set (e.g. a description of these images), based on the patterns it ‘sees’ it can very accurately answer what a new picture contains even though it has never seen it before.

How the Machine Learning Module can improve your business

The Machine Learning Module can provide:

o descriptive analytics (what happened)

o predictive analytics (what will happen)

o prescriptive insights (what should be done)

In other words, the ML Module can analyze the current state of play and provide predictions and recommendations in respect of the future. Specific examples of Machine Learning deployments for business purposes are the following:

Inventory Demand Forecast

A national bakery wishes to accurately predict demand and accordingly adjust production. Given past performance in relation to a set of factors like time of year, weather, political situation, current events and more, an accurate ML based demand forecast can produce measurable, significant savings.

Reducing Product Returns

An online retail garment company wants to reduce returns on its products based on various factors including buyer gender, age, order history, web site visits, product interest and more.

Based on the available data, selecting an optimal ML model to provide significant insights into what factors precipitate returns can significantly impact profitability and efficiency (even to the point of “discouraging” orders in real time that have a high probability of being returned)

Product Recommendations

Perhaps the most popular applications of Machine learning are Netflix’s movie recommendation system and Amazon’s ‘customers also bought’ feature. Recommended products on “checkout” are selected given a very accurate pre-assessment of what will happen (i.e. how many proposed products will actually be purchased).

Non-Performing Loans

A huge problem lenders face, identifying potential non-performing loan candidates before they actually become non-performing and acting accordingly can clearly provide significant value.

Given enough historical data including customer transactions, profile, current events, unemployment rates, interest rates and stock market rates all fed into a selected mix of ML models can result in the identification of potential non-performing loans at an impressive level of accuracy.


Natural Language Processing (NLP) provides computers with the ability to understand and interpret human language in the way it is written or spoken. The objective is to make computers as intelligent as human beings in understanding language, recognizing speech and generating language.

Key application areas include:

o Summaries: providing text summaries that only include main concepts. Extractive summaries use the sentences or phrases already provided, whereas abstractive summaries use newly generated text.

o Sentiment analysis: identify the feeling, judgment and/or opinion in given texts (i.e. negative or positive twitter posts).

o Text classification: categorize texts (e.g. news according to domain) or compare texts with a view to identifying the group to which it may belong (e.g. this email is spam or not).

o Entity Extraction: Identify people, places, organizations and more

How the Natural Language Processing Module can help your business

Specifically, examples of deployment of the Natural Language Processing module in business are the following:

o Summaries and sentiment analysis

Scraping user comments from online blogs, news sites and forums

o Web Crawling

target blogs, forums and news sites to extract comments related to a specific product or company and then proceeding to automatically summarize and analyze related sentiment with a view to providing valuable insights in regards to products and services.

o Finding potential customers using the ‘About’ section found on may web sites

Given a list of target company web sites (for example from CrunchBase), a specialized crawler can extract texts from the ‘About’ section, summarize and classify these texts, and then provide insights in order to locate those companies from the list that may be of interest as potential customers by classifying against the ‘About’ section of current customers.

o Leveraging Email sender profile and sentiment

For each email contact, summarizing and classifying all received emails and then feeding them to a pre-trained ML Model with a view to identifying the way in which to respond that resonates to the contact’s personality and writing style.


KR is a field of AI dedicated to representing information about the world: a conceptual characterization of the fundamental nature of reality in a form that a computer system can use and understand.

This can be implemented in the form of an “ontology”, which is a knowledge representation formalism comprised of a hierarchical set of concepts and categories along with their properties and the relations between them. In essence our knowledge of the world is an internal ontology.

The core of an AI Engine is implemented as an ontology which may be populated by default with a huge amount of general knowledge (e.g. the whole of Wikipedia in the form of DBPedia), or specific domain knowledge, like e-commerce or healthcare.

Leveraging the Knowledge Representation Module in business

Domain specific ontologies may provide significant commercial impact. Specific examples include:

o E-commerce

On e-commerce web sites, users may search for products by names or features, but they do not support queries such as “how to lose weight” or “how to get rid of ants” although they may sell products for such situations. What they need is an ontology through which concepts expressed in natural language are mapped to products.

Similarly, customer support could leverage the ontology using a chatbot to address queries automatically with improved accuracy and efficiency.

o Healthcare

In Healthcare, an ontology that links symptoms, conditions, treatments, and medications could provide significant added value to daily health care business processes.

o Centralizing company knowledge

Most companies have a set of commonly used documents stored in a shared directory, and also a set of documents that reside locally on employee pc’s and laptops. These documents, perhaps word, pdf or text documents, contain significant information that in total essentially comprise a significant digital asset.

Through an NLP/NLU system this knowledge could be gathered, pre-processed and semantically analyzed with a view to creating a company centralized ontology / knowledge base that can then be expanded, searched or updated.


NLG is the very important next step forward of the ‘knowledge era’ referred to above. At Fortuitapps we are now working on adding an NLG module to the AI Engine described above.

As this is part of our commercial proprietary work I will not present exact details but rather in the context of this article describe our efforts in more general terms.

What is Natural Language Generation (NLG)?

NLG is the intelligent process of generating text in natural language in response to a query or a specific user request. In order to do this, an NLG module has to model the hardly trivial capability that makes humans intelligent: common sense.

Without a substantial amount of everyday knowledge about the world (or at least about a particular domain of application), generation of natural language is not possible at the product level.

Consequently, the core of any NLG module has to be composed of a knowledge base in the form of an ontology that provides the ‘knowledge’ that will eventually by produced as a ‘text’ response.

How do we create an NLG Core Knowledge base?

As humans learn by reading documents in any form (such as books, articles, blogs newspapers, web sites and more) accordingly an NLG Core Knowledge base has to be populated by machine ‘reading’ of documents.

From a machine point of view, documents exhibit a sequential structure with multiple levels of abstraction such as headings, sentences, paragraphs and sections. These hierarchical abstractions provide the context through which we infer the meaning of words and sentences of text.

So, to populate an NLG core knowledge base we need software that can read and understand corpora (e.g. Google News texts) and leverage the vast amount of general knowledge available through Wikipedia (texts) and DBPedia (ontology).

How can a machine read and understand texts?

In a broader sense there are two main approaches to natural language understanding, both of which we use and which are complementary to each other within the context of a higher level controlling module. The two approaches are described below.

Machine Learning based approach

As described above in regards to Neural Networks, an LSTM, or Long Short-Term Memory Network, is a kind of recurrent neural network which has the capability to give itself feedback from past “lessons”.

An LSTM is an extremely powerful algorithm that can classify, cluster and make predictions about data and particularly time series and text.

We incorporate contextual features (topics) into the model to achieve specific NLP tasks like next word or character prediction, next sentence selection, and sentence topic prediction. The model we use helps us answer questions, complete sentences, generate paraphrases and provide next utterance predictions in dialog systems.

In addition to the above deep learning models, we use “shallow” models as implemented in Word2vec. Word2vec is a tool that gives us a “mental map” of words, their meanings and interactions with other words. Each word is mapped to a set of numbers in a “number space” which are called “word embeddings”.

Similar words are close to each other in this number space, and non-similar words are far apart

Semantic Frame based approach

A semantic frame is a structured model of related concepts that together provide knowledge of all of them and without which there could not be complete knowledge of any of them.

An example would be the query “apple price forecast”: are we referring to the price of the fruit apple or the company Apple Inc and a forecast in relation to its stock price? In order to answer this, we must model how humans do this, and that is by essentially using “Bayesian Inference“.

Bayesian Inference is a statistical theory in which the evidence about the true state of the world is calculated in terms of degrees of belief known as Bayesian probabilities.

In this context of Bayesian probabilities, in order to select the correct conceptual frame related to the query “apple price forecast” and so know what we are talking about we use a “Prior Primality Distribution” simply known as a ‘prior’ of an uncertain quantity.

A prior is the probability that would express one's beliefs about this quantity before some evidence is taken into account.

Although there are no guarantees, given priors and structures we select the semantic frame (either fruit price or stock price) that we deem relevant to the query with a view to producing an appropriate response.

In our example we are essentially asking what people usually mean by this, and so we use the stock price semantic frame.

Generating Natural Language Text

Based on our core NLG ontological knowledge base, and combining both approaches described above, given a query or command we proceed to generate natural language text based on the following three main steps:

o Create a document plan: select concepts that will form the knowledge map of the text to be produced

o Transform the concepts into sentences

o Filter results: Conceptual, semantic and syntactic review and adjustments

There is more to come of great interest in the field of NLG!

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