Explore the results of years of academic research that powers decision augmentation and Hybrid Intelligence®.
The article introduces DeepFeatSimulate, a deep learning model for business simulations that uses cutting-edge technology to simulate complex and non-linear business scenarios. The model combines various techniques, including variational autoencoder, GANs, autoregressive models, and Gaussian copula, to learn the data generation process of business dynamics. One of the key advantages of DeepFeatSimulate is its ability to learn the context and relation between financial value drivers, enabling simulation of complex, non-linear business scenarios. The technology is even able to simulate novel and innovative scenarios that have not happened in the past, allowing businesses to explore new and previously untested ideas. With its ability to represent the relevant business world with over 91% accuracy, DeepFeatSimulate has the potential to revolutionize the way that businesses make decisions.
The current method of financial forecasting, which relies on manual, expensive, and time-consuming spreadsheet-based processes, is broken. That is why we created DeepFeatTime, our cutting-edge multimodal deep learning model for probabilistic forecasting. It outperforms even the top investment banks by a significant margin, providing results quickly and at a low cost to make the best decision-making accessible to everyone. This technology democratizes access to outperforming decision augmentation to companies of any size. Our technical report provides full transparency into the scientific evaluation of our model.
Learn why you should go from yearly planning and roadmaps to fast and continuous decisions, and get started with augmented decision-making today.
vencortex is excited to announce that our team received another grant funding for R&D of our decision augmentation technology by the Distr@l program as part of #hessendigital strategy.
Our research advisor and collaborator Jacob Sherson, founding director of the Center for #hybridintelligence and ScienceAtHome and professor at Aarhus University discusses how these two challenges may potentially be overcome by the second trend
Citizen science and artificial intelligence (AI) have the potential to transform our understanding of the world around us, and their use in scientific research will be examined in this paper.
Advances in AI technology affect knowledge work in diverse fields, including healthcare, engineering, and management. Although automation can increase efficiency and lower costs, it can also, as an unintended consequence, deskill workers, who lose valuable skills that would otherwise be maintained as part of their daily work. Such deskilling has a wide range of negative effects on multiple stakeholders –– employees, organizations, and society at large. Hybrid Intelligence moves beyond traditional AI approaches by envisioning socio-technical systems in which humans and machine learning algorithms frame and solve tasks together. This essay discusses how Hybrid Intelligence could help manage the risk of deskilling human experts by focusing design and implementation efforts on hybrid intelligent systems enabling upskilling workers. We argue that Hybrid Intelligence may not only lower costs and improve performance, but also prevent management from creating unintended organization-wide deskilling.
The concept of business model design gained significant attention over the last years, as companies like Apple and Uber disrupted whole industries and generated tremendous returns offering not just new products or services but designing new concepts of doing business.
In the era of digital economy, IT is becoming the enabler of novel products, serv might reduce an entrepreneur’s chances to receive reasonable feedback and persuade a reasonable number of stakeholders of the viability of the opportunity to gain access to further valuable resources that support the entrepreneur in enacting the opportunity (Alvarez et al. 2013). Furthermore, the demand-side knowledge of potential customers is frequently not accessible (Nambisan and Zahra 2016).
By comparing different configurations that result in high and low risk, this research identified nine patterns that describe the role of environmental hazards and app architecture in shaping risk. From these patterns, I derive the role of technological and market uncertainty as core drivers of risk. Furthermore, my findings reveal that behavioural uncertainty and platform specificity are not drivers of risk per se. However, their absence is required to achieve low levels of risk. In addition, I detect the role of app architecture as a control mechanism for third-party innovation. As the absence of app modularity is always implying a high level of risk, it is a necessary condition for minimizing risk.
This research introduces a novel filtering approach that combines the strengths of both machines and humans in evaluating creative opportunities by using ML approaches to assign the right user with the required solution knowledge to a corresponding idea. To this end, I propose tentative DPs that I validated in the field with experts on crowdsourcing and system engineering.
Determining business models for start-ups is a highly challenging and uncertain task for entrepreneurs and requires various decisions regarding the design of the business model. Due to limitations of individual human decision-makers, this process is frequently tainted by poor decision-making, leading to substantive consequences and sometimes even failure of the new venture. As most DSS for business model validation rely on simulations or modelling rather than human intuition, there is an obvious gap in literature on such systems.
One key conceptualization highlighted by scholars to understand the mechanisms through which entrepreneurs can successfully commercialize new technologies is the business model concept
One other approach beyond trying to replicate human level intelligence and related learning mechanisms is to combine human and artificial intelligence. The basic rational behind this is the combination of complementary heterogeneous intelligences (i.e. human and artificial agents) into a socio-technological ensemble that is able to overcome the current limitations of artificial intelligence. This approach is neither focusing on the human in the loop of AI nor automating simple tasks through machine learning but on solving complex problems using the deliberate allocation of tasks among different heterogeneous algorithmic and human agents. Both the human and the artificial agents of such systems can then co-evolve by learning and achieve a superior outcome on the system level.
The aim of this research is to develop a method to predict the probability of success of early stage start-ups. Therefore, I follow a DSR approach (Hevner 2007; Gregor and Hevner 2013) to develop a Hybrid Intelligence method that combines the strength of both machine intelligence such as ML techniques to access, process, and structure large amount of information as well as collective intelligence, which uses the intuition and creative potential of individuals while reducing systematic errors through statistical averaging in an ensemble approach (Shmueli and Koppius 2011). I, thus, intend to show that a hybrid approach improves predictions for the success of start-ups under extreme uncertainty compared to machine or human only methods.
Within this paper I propose a taxonomy for design knowledge for hybrid intelligence systems, which presents descriptive knowledge structured along the four meta-dimensions task characteristics, learning paradigm, human-AI interaction, and AI-human interaction. Moreover, I identified 16 sub-dimensions and a total of 50 categories for the proposed taxonomy. By following a taxonomy development methodology (Nickerson et al. 2013), I extracted interdisciplinary knowledge on human-in-the-loop approaches in ML and the interaction between human and AI. I extended those findings with an examination of seven empirical applications of hybrid intelligence systems.
In 1999, as Steven Spielberg was preparing to make the movie “Minority Report”, he assembled a team of 15 technology experts to help him depict the world as it would look in 2054, the year that the movie takes place. The result was an impressive and somewhat dystopic future scape where technology permeates our lives.It is too soon to say whether the vision of the future depicted in the movie will become reality, but 18 years after the film’s release, artificial intelligence (AI) and what is often called intelligent enterprise automation have had a profound impact in some areas.
More than 5 million entrepreneurs have used the Business Model and Value Proposition Canvas both of which are excellent tools to define core functional areas of a startup
In today's complex and uncertain world, something like those clearly defined industries no longer exists. That's why companies need to stop thinking in industries and rather move towards thinking about strategic fields (or “arenas” how Rita McGrath the professor of management at the Columbia Business School calls it). And this significantly changes how to think about competition.
Previous advances in research on artificial intelligence (AI) also raised issues related to security. A central goal of advanced autonomous systems is, therefore, to ensure that they are aligned with human values. Similar to our development approach, researchers at Open AI recently designed a hybrid avenue that integrates human preferences, debate and iterated amplification in the process of teaching machines and ensures that AI systems behave reliably on their intended goal. Our team highly appreciates the progress in this area and also their call on the social scientist for AI safety.
HIx- Hybrid Intelligence Xperience Design is how we define our approach to develop intelligent systems that combine human and artificial intelligence
Get in contact and stay up to date on decision optimization