Great Engineers, Terrible Philosophers A conversation on the rapid evolution of AI technology, the nature of intelligence, and the importance of the European project

Sam Altman has claimed that by the end of this year, OpenAI will be capable of "truly astonishing cognitive tasks." But what exactly does "cognition" mean in the context of artificial intelligence? As the sophistication of such technologies, our dependence on them, and the rhetoric used to sell them escalates, it's vital we grapple with this question. Earlier this year, Luciano Floridi, founding director of the Digital Ethics Center at Yale University, joined Alexandros Schismenos, post-doctoral researcher on the philosophy of artificial intelligence at the Aristotle University of Thessaloniki, and Giannis Perperidis, adjunct lecturer on the philosophy of AI at the Ionian University in Corfu, for an online conversation about the present and future of AI technology-and why solving a puzzle is still not the same as understanding it

A case control study examining the feasibility of using eye tracking perimetry to differentiate patients with glaucoma from healthy controls

Abstract To explore the feasibility of using Saccadic Vector Optokinetic Perimetry (SVOP) to differentiate glaucomatous and healthy eyes. A prospective case–control study was performed using a convenience sample recruited from a single university glaucoma clinic and a group of healthy controls. SVOP and standard automated perimetry (SAP) was performed with testing order randomised. The reference standard was a diagnosis of glaucoma based a comprehensive ophthalmic examination and abnormality on standard automated perimetry (SAP). The index test was SVOP. 31 patients with glaucoma and 24 healthy subjects were included. Mean SAP mean deviation (MD) in those with glaucoma was − 8.7 ± 7.4 dB, with mean SAP and SVOP sensitivities of 23.3 ± 0.9 dB and 22.1 ± 4.3 dB respectively. Participants with glaucoma were significantly older. On average, SAP sensitivity was 1.2 ± 1.4 dB higher than SVOP (95% limits of agreement = − 1.6 to 4.0 dB). SVOP sensitivity had good ability to differentiate healthy and glaucomatous eyes with a 95% CI for area under the curve (AUC) of 0.84 to 0.96, similar to the performance of SAP sensitivity (95% CI 0.86 to 0.97, P = 0.60). For 80% specificity, SVOP had a 95% CI sensitivity of 75.7% to 94.8% compared to 77.8% to 96.0% for SAP. SVOP took considerably longer to perform (514 ± 54 s compared to 267 ± 76 s for SAP). Eye tracking perimetry may be useful for detection of glaucoma but further studies are needed to evaluate SVOP within its intended sphere of use, using an appropriate design and independent reference standard

Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets

PMID: 20426000International audienceWe propose a new methodology to analyze the anatomical variability of a set of longitudinal data (population scanned at several ages). This method accounts not only for the usual 3D anatomical variability (geometry of structures), but also for possible changes in the dynamics of evolution of the structures. It does not require that subjects are scanned the same number of times or at the same ages. First a regression model infers a continuous evolution of shapes from a set of observations of the same subject. Second, spatiotemporal registrations deform jointly (1) the geometry of the evolving structure via 3D deformations and (2) the dynamics of evolution via time change functions. Third, we infer from a population a prototype scenario of evolution and its 4D variability. Our method is used to analyze the morphological evolution of 2D profiles of hominids skulls and to analyze brain growth from amygdala of autistics, developmental delay and control children

Atlas construction and image analysis using statistical cardiac models

International audienceThis paper presents a brief overview of current trends in the construction of population and multi-modal heart atlases in our group and their application to atlas-based cardiac image analysis. The technical challenges around the construction of these atlases are organized around two main axes: groupwise image registration of anatomical, motion and fiber images and construction of statistical shape models. Application-wise, this paper focuses on the extraction of atlas-based biomarkers for the detection of local shape or motion abnormalities, addressing several cardiac applications where the extracted information is used to study and grade different pathologies. The paper is concluded with a discussion about the role of statistical atlases in the integration of multiple information sources and the potential this can bring to in-silico simulations