Hasit Trivedi, CTO Digital Technologies & Global Head -- AI, Tech Mahindra explains how Artificial Intelligence will reshape the future, what technologies Tech Mahindra is using right now, and more. We are on the verge of a watershed moment in our species' history, one in which a product of our own ingenuity has the potential to change everything. Some see it as humanity's salvation, while others see it as our undoing. The age of artificial intelligence has arrived (AI). While AI is still a long way from the sentient machines depicted in science fiction, developing algorithms that can learn, understand language, and mimic some aspects of the human mind has resulted in tremendous progress.

Machine learning (ML) is one of the most profitable sectors of software development right now. That's because of how useful machine learning techniques are in the rapidly growing field of data science. Data science, a field of applied mathematics and statistics, gleans useful information by the analysis and modeling of large amounts of data. Machine learning involves developing computer systems that learn and adapt using algorithms and statistical models. Applying ML techniques to data science makes it possible to advance from insights to actionable predictions.

Isolation of spacecraft microvibrations is essential for the successful deployment of instruments relying on high-precision pointing. Hexapod platforms represent a promising solution, but the difficulties associated with attaining desirable 3D dynamics within acceptable mass and complexity budgets have led to a minimal practical adoption. This paper addresses the influence of strut boundary conditions (BCs) on system-level mechanical disturbance suppression. Inherent limitations of the traditional all-rotational joint configuration are highlighted and shown to originate in link mass and rotational inertia. A pin-slider BC alternative is proposed and analytically proven to alleviate them in both 2D and 3D. The advantages of the new BC hold for arbitrary parallel manipulators and are demonstrated for several hexapod geometries through numerical tests. A configuration with favourable performance is suggested. Finally, a novel planar joint that allows the physical realisation of the proposed BC is described and validated. Consequently, this work enables the development of platforms for microvibration attenuation that do not require active control.

Decision making algorithms, in practice, are often trained on data that exhibits a variety of biases. Decision-makers often aim to take decisions based on some ground-truth target that is assumed or expected to be unbiased, i.e., equally distributed across socially salient groups. In many practical settings, the ground-truth cannot be directly observed, and instead, we have to rely on a biased proxy measure of the ground-truth, i.e., biased labels, in the data. In addition, data is often selectively labeled, i.e., even the biased labels are only observed for a small fraction of the data that received a positive decision. To overcome label and selection biases, recent work proposes to learn stochastic, exploring decision policies via i) online training of new policies at each time-step and ii) enforcing fairness as a constraint on performance. However, the existing approach uses only labeled data, disregarding a large amount of unlabeled data, and thereby suffers from high instability and variance in the learned decision policies at different times. In this paper, we propose a novel method based on a variational autoencoder for practical fair decision-making. Our method learns an unbiased data representation leveraging both labeled and unlabeled data and uses the representations to learn a policy in an online process. Using synthetic data, we empirically validate that our method converges to the optimal (fair) policy according to the ground-truth with low variance. In real-world experiments, we further show that our training approach not only offers a more stable learning process but also yields policies with higher fairness as well as utility than previous approaches.

Bingxin Gu and Mingyuan Meng contributed equally to this work. Abstract Objective: Deep Learning-based Radiomics (DLR) has achieved great success in medical image analysis and has been considered as a replacement to conventional radiomics that relies on handcrafted features. In this study, we aimed to explore the capability of DLR for the prediction of 5-year Progression-Free Survival (PFS) in advanced Nasopharyngeal Carcinoma (NPC) using pretreatment PET/CT images. Methods: A total of 257 patients (170/87 patients in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled. We developed an end-to-end multi-modality DLR model, in which a 3D convolutional neural network was optimized to extract deep features from pretreatment PET/CT images and predict the probability of 5-year PFS. TNM stage, as a high-level clinical feature, could be integrated into our DLR model to further improve the prognostic performance. For a comparison between conventional radiomics and DLR, 1456 handcrafted features were extracted, and optimal conventional radiomics methods were selected from 54 cross-combinations of 6 feature selection methods and 9 classification methods. In addition, risk group stratification was performed with clinical signature, conventional radiomics signature, and DLR signature. Results: Our multi-modality DLR model using both PET and CT achieved higher prognostic performance (AUC = 0.842 0.034 and 0.823 0.012 for the internal and external cohorts) than the optimal conventional radiomics method (AUC = 0.796 0.033 and 0.782 0.012). Furthermore, the multi-modality DLR model outperformed single-modality DLR models using only PET (AUC = 0.818 0.029 and 0.796 0.009) or only CT (AUC = 0.657 0.055 and 0.645 0.021). For risk group stratification, the conventional radiomics signature and DLR signature enabled significant difference between the high-and low-risk patient groups in the both internal and external cohorts (P < 0.001), while the clinical signature failed in the external cohort (P = 0.177).

Prior work introduced a gradient descent trained expert system that conceptually combines the learning capabilities of neural networks with the understandability and defensible logic of an expert system. This system was shown to be able to learn patterns from data and to perform decision-making at levels rivaling those reported by neural network systems. The principal limitation of the approach, though, was the necessity for the manual development of a rule-fact network (which is then trained using backpropagation). This paper proposes a technique for overcoming this significant limitation, as compared to neural networks. Specifically, this paper proposes the use of larger and denser-than-application need rule-fact networks which are trained, pruned, manually reviewed and then re-trained for use. Multiple types of networks are evaluated under multiple operating conditions and these results are presented and assessed. Based on these individual experimental condition assessments, the proposed technique is evaluated. The data presented shows that error rates as low as 3.9% (mean, 1.2% median) can be obtained, demonstrating the efficacy of this technique for many applications.

In this week's geeked-out edition of the Good News Roundup, Ukraine's jaw-dropping battlefield victories with HIMARS are documented using OSINT, South Africa implements AI technology to track dangerous locust swarms, biologists and naturalists overwhelmingly agree that gay sex is normal throughout the animal kingdom, and BirdNet proves reliable at crowdsourcing the task of identifying wild birds by their songs. In wholesome news for sci fi/space fantasy fans everywhere, Ukraine's president Zelensky continues attending technology trade shows through holograms in which he promises that Ukraine will defeat the Empire. Ukrainians are also using 3d imaging technology to preserve the cultural heritage of their country from looters and bombs, storing their data in a digital archive that will support restoration work when the invaders have been defeated. And in good news for new Ukrainian parents, the non-profit Embrace Global is making headlines for using innovative technology to provide incubators for babies in Ukraine at a tiny fraction of their usual cost. You can see their TED talk by entrepreneur Jane Chen here.

Dr. Reinhard Klette, Fellow of the Royal Society of New Zealand, is a Professor at the Auckland University of Technology (AUT). His numerous publications include the books "Computer Vision for Driver Assistance" (co-authored by Mahdi Rezaei), "Multi-target Tracking" (co-authored by Junli Tao), "Concise Computer Vision", "Euclidean Shortest Paths" (co-authored by Fajie Li), "Panoramic Imaging" (co-authored by Fay Huang and Karsten Scheibe), "Digital Geometry" (co-authored by the late Azriel Rosenfeld), "Computer Vision - Three-Dimensional Data from Images" (co-authored by Karsten Schluens and Andreas Koschan), "The Handbook of Image Processing Operators" (co-authored by the late Piero Zamperoni), and "Fast Algorithms and their Implementation on Specialized Parallel Computers" (co-authored by Jozef Miklosko, Marian Vajtersic, and Imre Vrto)

The Artificial Intelligence as a Service market research report provides a competitive edge to stakeholders by tracking the past and present industr

The largest prostate cancer biopsy dataset – involving over 95,000 images – has been created by researchers in Sweden to ensure AI can be trained to diagnose and grade prostate cancer for real world clinical applications. The researchers will call today, at the European Association of Urology annual congress (EAU22), for large-scale clinical trials of artificial intelligence (AI) algorithms and greater global coordination to ensure that AI enhanced diagnostics, prognostication, and treatment selection can help save lives. There is a shortage of pathologists around the world, both generalists and those specialised in urology. AI can help in detecting prostate cancer at an early stage, but because of the vast differences in the way clinics prepare samples, scan images and in the diverse patient populations they serve, many algorithms do not have universal application. The team, from Karolinska Institutet, worked with colleagues from Radboud University Medical Center in the Netherlands, University of Turku in Finland and Google Health in the US to run an AI competition involving nearly 1,300 developers from around the world.