By Andreas Pusch1
1Founder and CEO, YUKKA Lab AG
We are drowning in an endless sea of new information. Google reported that the number of web pages has grown from 1 trillion in 2008 to a whopping 130 trillion in 2016.1 Even though a majority of these might be irrelevant to your or your company’s interests and operations, it is equally true that well-informed decisions will have a major impact on the success of any project or investment. While the amount of information is growing, the human ability to read and digest information has stayed rather stagnant in absolute terms, and even decreased in relative terms. Research has shown that an average professional reads 5–15 articles per day from 1–3 sources. Not only is this a negligible amount compared to the actual volume of news published each day, but there is also an undeniable information bias driven by personal preferences and valuable time lost reading nonsense. It is thus easy to get lost in this growing sea of information, which leads to wasted time and prevents well-informed decision-making.
But what if we could be one step ahead? What if we could objectively analyse hundreds of thousands of articles from thousands of professional sources within a matter of minutes? This would not only give us the opportunity to understand an industry, a firm or another entity in greater depth while minimizing information bias. It would also enable us to save multiple hours per day that are otherwise “wasted” reading news or researching disclosures from a company. The solution to this exact problem can be found in augmented human intelligence and specifically in two key underlying techniques, which can be used to increase process efficiency in many information driven industries, such as financial services and management consulting.
In order to extract the most value out of incoming data for it to be useful for future purposes, we need to first analyse and make sense out of it. Here, NLP comes into play and performs a linguistic analysis of the text at different levels of increasing complexity. At the lowest level, NLP performs actions to make sentences and words digestible, understandable and comparable. Initially, information is used to obtain a syntactic-semantic representation of the sentences (a representation of their meaning). The ultimate goal is for the system is to gain a deeper understanding of individual words and sentences (similar to a child learning to speak). Furthermore, NLP needs to be able to detect entities that are mentioned implicitly through pronouns or general expressions (such as “the company”). Building upon this, an application-dependent analysis can be performed such as through sentiment and target recognition, which allows the NLP system to detect the polarity of sentences (positive, neutral, negative) and the respective target entity. This entity recognition not only expands to company names, but also to C-level executives, subsidiaries, etc., as discussed in the next section.
In so doing, deep learning and neural networks set the baseline for this type of next-generation machine learning. Neural networks can almost limitlessly expand their learning capability without requiring significant pre-processing, since they are able to learn language structures from sentences and their context alone. The readily available and continuous data flow from numerous news organizations enables the algorithm to make use of a vast resource pool.
As previously discussed, it is not enough to merely sort and analyse data according to their explicitly named entity. In this context, ontologies are used to extract an exhaustive set of meaningful and valuable information. Ontologies represent a working model designed to provide classification of the relations between various concepts in a particular knowledge domain. Ontologies are all around us and are not only used by major firms (such as Amazon, to classify products into categories) but are also wired into our understanding of language. For example, if a person mentions “Mount Everest” the first thing that pops into your mind is probably “mountain” or “high”. Similarly, for “iPhone” this is likely to be “Apple” or “smartphone”. In essence, this reflects the simple fact that our brains have a tendency to categorize raw information, so that we can remember it and draw connections between certain subjects. Writers make use of this: a headline stating “Sales forecast for Model X lowered” is enough for us to assume that this is bad news for Tesla. AI ontologies replicate these connections and use them to understand relations to the same extent as we do.
NLP and ontologies, along with several other related techniques, will have a huge impact on how we process daily information. AI allows businesses to gain both an information and time advantage, as news articles are analysed, categorized and updated in real time. Up until now, getting an overview of the current situation of an entity was a labour-intensive task. Simultaneously, feeling like you were always up to date with the latest trends was near impossible due to the rapid inflow of fresh information. Completing these tasks with the help of an augmented human intelligence offers the benefit of staying on top of the news while simultaneously freeing up time and making better informed business decisions.
While previously “getting an overview” meant reading the first 2–3 articles in the newspaper, in the future it means looking at insightful visual analytics such as Tag Clouds, trend signals or data networks. Important information can be spotted within seconds, while still offering the capability to delve deeper into topics of your interest. Information bias is minimized, as keywords and sentiments are curated and computed from thousands of trusted, global sources and insights can be explained and shared more easily. All of this is possible within minutes, since as humans, we tend to remember and recall information better when it is presented visually rather than through plain black-white walls of text.
While augmented human intelligence can have an impact in many verticals, the most profound impact will be in research-intensive sectors. In these verticals, much time is spent trying to assess companies’ past operations and spotting trends going forward. If this time can be saved and thus reallocated from less repetitive work to more high-value, unstructured business processes, there will be a measurable increase in productivity for the individual user and for the company.
Augmented human intelligence thus acts as a technology compass, helping firms reach their goals in the most efficient manner, free from any unnecessary information distractions along the path.