Innovating with Artificial Intelligence: Capturing the Constructive Functional Capabilities of Deep Generative Learning

As an emerging species of artificial intelligence, deep generative learning models can generate an unprecedented variety of new outputs. Examples include the creation of music, text-to-image translation, or the imputation of missing data. Similar to other AI models that already evoke significant changes in society and economy, there is a need for structuring the constructive functional capabilities of DGL. To derive and discuss them, we conducted an extensive and structured literature review. Our results reveal a substantial scope of six constructive functional capabilities demonstrating that DGL is not exclusively used to generate unseen outputs. Our paper further guides companies in capturing and evaluating DGL’s potential for innovation. Besides, our paper fosters an understanding of DGL and provides a conceptual basis for further research

MACHINE LEARNING APPROACHES ALONG THE RADIOLOGY VALUE CHAIN – RETHINKING VALUE PROPOSITIONS

Radiology is experiencing an increased interest in machine learning with its ability to use a large amount of available data. However, it remains unclear how and to what extent machine learning will affect radiology businesses. Conducting a systematic literature review and expert interviews, we compile the opportunities and challenges of machine learning along the radiology value chain to discuss their implications for the radiology business. Machine learning can improve diagnostic quality by reducing human errors, accurately analysing large amounts of data, quantifying reports, and integrating data. Hence, it strengthens radiology businesses seeking product or service leadership. Machine learning fosters efficiency by automating accompanying activities such as generating study protocols or reports, avoiding duplicate work due to low image quality, and supporting radiologists. These efficiency improvements advance the operational excellence strategy. By providing personnel and proactive medical solutions beyond the radiology silo, machine learning supports a customer intimacy strategy. However, the opportunities face challenges that are technical (i.e., lack of data, weak labelling, and generalisation), legal (i.e., regulatory approval and privacy laws), and persuasive (i.e., radiologists’ resistance and patients’ distrust). Our findings shed light on the strategic positioning of radiology businesses, contributing to academic discourse and practical decision-making

How Can Organizations Design Purposeful Human-AI Interactions: A Practical Perspective From Existing Use Cases and Interviews

Artificial intelligence (AI) currently makes a tangible impact in many industries and humans’ daily lives. With humans interacting with AI agents more regularly, there is a need to examine human-AI interactions to design them purposefully. Thus, we draw on existing AI use cases and perceptions of human-AI interactions from 25 interviews with practitioners to elaborate on these interactions. From this practical lens on existing human-AI interactions, we introduce nine characteristic dimensions to describe human-AI interactions and distinguish five interaction types according to AI agents’ characteristics in the human-AI interaction. Besides, we provide initial design guidelines to stimulate both research and practice in creating purposeful designs for human-AI interactions

Local Equation of State and Velocity Distributions of a Driven Granular Gas

We present event-driven simulations of a granular gas of inelastic hard disks with incomplete normal restitution in two dimensions between vibrating walls (without gravity). We measure hydrodynamic quantities such as the stress tensor, density and temperature profiles, as well as velocity distributions. Relating the local pressure to the local temperature and local density, we construct a local constitutive equation. For strong inelasticities the local constitutive relation depends on global system parameters, like the volume fraction and the aspect ratio. For moderate inelasticities the constitutive relation is approximately independent of the system parameters and can hence be regarded as a local equation of state, even though the system is highly inhomogeneous with heterogeneous temperature and density profiles arising as a consequence of the energy injection. Concerning the local velocity distributions we find that they do not scale with the square root of the local granular temperature. Moreover the high-velocity tails are different for the distribution of the x- and the y-component of the velocity, and even depend on the position in the sample, the global volume fraction, and the coefficient of restitution.Comment: 14 pages, 14 figures of which Figs. 13a-f and Fig. 14 are archived as separate .gif files due to upload-size limitations. A version of the paper including all figures in better quality can be downloaded at http://www.theorie.physik.uni-goettingen.de/~herbst/download/LocEqSt.ps.gz (3.8 MB, ps.gz) or at http://www.theorie.physik.uni-goettingen.de/~herbst/download/LocEqSt.pdf (4.9 MB, pdf

Task delegation from AI to humans: A principal-agent perspective

Increasingly intelligent AI artifacts in human-AI systems perform tasks more autonomously as entities that guide human actions, even changing the direction of task delegation between humans and AI. It has been shown that human-AI systems achieve better results when the AI artifact takes the leading role and delegates tasks to a human rather than the other way around. This study presents phenomena, conflicts, and challenges that arise in this process, explored through the theoretical lens of principal-agent theory (PAT). The findings are derived from a systematic literature review and an exploratory interview study and are placed in the context of existing constructs of PAT. Furthermore, this article paper identifies new causes of tensions that arise specifically in AI-to-human delegation and calls for special mechanisms beyond the classical solutions of PAT. The paper thus contributes to the understanding of autonomous AI and its implications for human-AI delegation

Bonded straight and helical flagellar filaments form ultra-low-density glasses

We study how the three-dimensional shape of rigid filaments determines the microscopic dynamics and macroscopic rheology of entangled semi-dilute Brownian suspensions. To control the filament shape we use bacterial flagella, which are micron-long helices assembled from flagellin monomers. We compare the dynamics of straight rods, helical filaments, and shape diblock copolymers composed of seamlessly joined straight and helical segments. Caged by their neighbors, straight rods preferentially diffuse along their long axis, but exhibit significantly suppressed rotational diffusion. Entangled helical filaments escape their confining tube by corkscrewing through the dense obstacles created by other filaments. By comparison, the adjoining segments of the rod-helix shape-diblocks suppress both the translation and the corkscrewing dynamics, so that shape-diblocks become permanently jammed at exceedingly low densities. We also measure the rheological properties of semi-dilute suspensions and relate their mechanical properties to the microscopic dynamics of constituent filaments. In particular, rheology shows that an entangled suspension of shape rod-helix copolymers forms a low-density glass whose elastic modulus can be estimated by accounting for how shear deformations reduce the entropic degrees of freedom of constrained filaments. Our results demonstrate that the three-dimensional shape of rigid filaments can be used to design rheological properties of semi-dilute fibrous suspensions.Comment: 24 pages, 7 figure

On-chip interrogator based on Fourier Transform spectroscopy

In this paper, the design and the characterization of a novel interrogator based on integrated Fourier transform (FT) spectroscopy is presented. To the best of our knowledge, this is the first integrated FT spectrometer used for the interrogation of photonic sensors. It consists of a planar spatial heterodyne spectrometer, which is implemented using an array of Mach-Zehnder interferometers (MZIs) with different optical path differences. Each MZI employs a 3$\times$3 multi-mode interferometer, allowing the retrieval of the complex Fourier coefficients. We derive a system of non-linear equations whose solution, which is obtained numerically from Newton's method, gives the modulation of the sensor's resonances as a function of time. By taking one of the sensors as a reference, to which no external excitation is applied and its temperature is kept constant, about 92$\%$ of the thermal induced phase drift of the integrated MZIs has been compensated. The minimum modulation amplitude that is obtained experimentally is 400 fm, which is more than two orders of magnitude smaller than the FT spectrometer resolution.Comment: 15 pages, 6 figure