Knowledge Worker Productivity Improvements with Machine Learning

Leveraging machine learning to enhance capabilities that can recognize context, concepts, and meaning means there are interesting new opportunities for collaboration between knowledge workers and computational power. For example, Bluedog’s experts can now provide more of their own input for training, quality control, and fine-tuning of algorithm-based outcomes. We use the computational power of our servers to augment the expertise of human collaborators — this helps to create new areas for our experts to leverage.

For example, at Bluedog, we utilize several algorithm-based tools to help us quickly assess opportunities for our clients. We extract information from Word Documents locally for multiple uses. With one tool, we take advantage of each Word document’s XML metadata. From there, we use a regex library to find each targeted word or phrase in the document, then adding them to a list. Our toll then performs for-loops to scan for relevant patterns in the XML to extract data.

Knowledge workers — the staff or consultants who reason, create, decide, and apply insight into non-routine cognitive processes — can contribute to redesigning work process roles and team member roles. Consider financial auditing, where AI is likely to become pervasive. Often, when AI offers a finding, the algorithm’s reasoning isn’t obvious to the accountant, who ultimately must offer an explanation to a client — characteristic of the “black box” problem. To improve this outcome, Bluedog recommends providing an interface so experts to enter concepts they deem important into the system and be provided with a means to test their own hypotheses. In this way, we recommend making models accessible to common sense. 

As cybersecurity concerns mount, organizations have increased the use of instruments to collect data at various points in their network to analyze threats — and to address “Internet-of-Things” (IoT) devices. However, many of these data-driven systems do not integrate data from multiple sources. Nor do they incorporate the common-sense knowledge of cybersecurity experts, who know the range and diverse motives of attackers, understand typical internal and external threats, and appreciate the degree of risk to an organization. 

Bluedog’s experts specify the use of Bayesian models — which employ probabilistic analysis to capture complex interdependence among risk factors —  combined with expert systems judgment. In cybersecurity for enterprise networks, complex factors may include large numbers and types of devices on the network. It is crucial to access the knowledge of the organization’s security experts about attackers and risk profile to better intercept cybercriminals.