Ride-hailing major Ola on Monday announced to expand its Artificial Intelligence (AI)-based safety feature called'Guardian' across several cities in the country. 'Guardian' uses real-time data from rides to automatically detect irregular trip activity, including prolonged stops and unexpected route deviations. After running a pilot project, the feature is now live in 16 Indian cities as well as in Perth, Australia and Ola aims to take'Guardian' to more cities in the coming quarter. "We are focused on developing innovations that place customer safety at the heart of platform experience. Guardian brings together the precision of artificial intelligence with the assurance of human intervention, enabling a uniform and safe mobility experience across the markets we operate in," Arun Srinivas, Chief Sales and Marketing Officer, Ola, said in a statement.

Abstract--Space-time adaptive processing (ST AP) algorithms with coprime arrays can provide good clutter suppression po - tential with low cost in airborne radar systems as compared with their uniform linear arrays counterparts. However, th e performance of these algorithms is limited by the training samples support in practical applications. T o address this issue, a robust two-stage reduced-dimension (RD) sparsity-aware S T AP algorithm is proposed in this work. In the first stage, an RD virtual snapshot is constructed using all spatial channels but only m adjacent Doppler channels around the target Doppler frequency to reduce the slow-time dimension of the signal. In the second stage, an RD sparse measurement modeling is formulated based on the constructed RD virtual snapshot, wh ere the sparsity of clutter and the prior knowledge of the clutte r ridge are exploited to formulate an RD overcomplete diction ary. Moreover, an orthogonal matching pursuit (OMP)-like metho d is proposed to recover the clutter subspace. In order to set the stopping parameter of the OMP-like method, a robust clutter rank estimation approach is developed. Compared wi th recently developed sparsity-aware ST AP algorithms, the si ze of the proposed sparse representation dictionary is much smal ler, resulting in low complexity. Simulation results show that t he proposed algorithm is robust to prior knowledge errors and can provide good clutter suppression performance in low sam ple support. Index T erms--Robust space-time adaptive processing, coprime arrays, prior knowledge, reduced-dimension, sparsity-aw are.

Signalized intersections are managed by controllers that assign right of way (green, yellow, and red lights) to non-conflicting directions. Optimizing the actuation policy of such controllers is expected to alleviate traffic congestion and its adverse impact. Given such a safety-critical domain, the affiliated actuation policy is required to be interpretable in a way that can be understood and regulated by a human. This paper presents and analyzes several on-line optimization techniques for tuning interpretable control functions. Although these techniques are defined in a general way, this paper assumes a specific class of interpretable control functions (polynomial functions) for analysis purposes. We show that such an interpretable policy function can be as effective as a deep neural network for approximating an optimized signal actuation policy. We present empirical evidence that supports the use of value-based reinforcement learning for on-line training of the control function. Specifically, we present and study three variants of the Deep Q-learning algorithm that allow the training of an interpretable policy function. Our Deep Regulatable Hardmax Q-learning variant is shown to be particularly effective in optimizing our interpretable actuation policy, resulting in up to 19.4% reduced vehicles delay compared to commonly deployed actuated signal controllers.

Automatic differentiation frameworks are optimized for exactly one thing: computing the average mini-batch gradient. Y et, other quantities such as the variance of the mini-batch gradients or many approximations to the Hessian can, in theory, be computed efficiently, and at the same time as the gradient. While these quantities are of great interest to researchers and practitioners, current deep-learning software does not support their automatic calculation. Manually implementing them is burdensome, inefficient if done na ıvely, and the resulting code is rarely shared. This hampers progress in deep learning, and unnecessarily narrows research to focus on gradient descent and its variants; it also complicates replication studies and comparisons between newly developed methods that require those quantities, to the point of impossibility. Its capabilities are illustrated by benchmark reports for computing additional quantities on deep neural networks, and an example application by testing several recent curvature approximations for optimization. The success of deep learning and the applications it fuels can be traced to the popularization of automatic differentiation frameworks. However, this specialization also has its shortcomings: it assumes the user only wants to compute gradients or, more precisely, the average of gradients across a mini-batch of examples. Other quantities can also be computed with automatic differentiation at a comparable cost or minimal overhead to the gradient backpropagation pass; for example, approximate second-order information or the variance of gradients within the batch. These quantities are valuable to understand the geometry of deep neural networks, for the identification of free parameters, and to push the development of more efficient optimization algorithms. But researchers who want to investigate their use face a chicken-and-egg problem: automatic differentiation tools required to go beyond standard gradient methods are not available, but there is no incentive for their implementation in existing deep-learning software as long as no large portion of the users need it. Second-order methods for deep learning have been continuously investigated for decades (e.g., Becker & Le Cun, 1989; Amari, 1998; Bordes et al., 2009; Martens & Grosse, 2015).

Given a set of source data with pre-trained classification models, how can we fast and accurately select the most useful source data to improve the performance of a target task? We address the problem of measuring transferability for heterogeneous domains, where the source and the target data have different feature spaces and distributions. We propose Transmeter, a novel method to efficiently and accurately measure transferability of two datasets. Transmeter utilizes a pre-trained source classifier and a reconstruction loss to increase its efficiency and performance. Furthermore, Transmeter uses feature transformation layers, label-wise discriminators, and a mean distance loss to learn common representations for source and target domains. As a result, Transmeter and its variant give the most accurate performance in measuring transferability, while giving comparable running times compared to those of competitors.

We address two central notions of fairness in the literature of planning on nondeterministic fully observable domains. The first, which we call stochastic fairness, is classical, and assumes an environment which operates probabilistically using possibly unknown probabilities. The second, which is language-theoretic, assumes that if an action is taken from a given state infinitely often then all its possible outcomes should appear infinitely often (we call this state-action fairness). While the two notions coincide for standard reachability goals, they diverge for temporally extended goals. This important difference has been overlooked in the planning literature, and we argue has led to confusion in a number of published algorithms which use reductions that were stated for state-action fairness, for which they are incorrect, while being correct for stochastic fairness. We remedy this and provide an optimal sound and complete algorithm for solving state-action fair planning for LTL/LTLf goals, as well as a correct proof of the lower bound of the goal-complexity (our proof is general enough that it provides new proofs also for the no-fairness and stochastic-fairness cases). Overall, we show that stochastic fairness is better behaved than state-action fairness.

Minimum Routing Cost Clustered Tree Problem (CluMRCT) is applied in various fields in both theory and application. Because the CluMRCT is NP-Hard, the approximate approaches are suitable to find the solution for this problem. Recently, Multifactorial Evolutionary Algorithm (MFEA) has emerged as one of the most efficient approximation algorithms to deal with many different kinds of problems. Therefore, this paper studies to apply MFEA for solving CluMRCT problems. In the proposed MFEA, we focus on crossover and mutation operators which create a valid solution of CluMRCT problem in two levels: first level constructs spanning trees for graphs in clusters while the second level builds a spanning tree for connecting among clusters. To reduce the consuming resources, we will also introduce a new method of calculating the cost of CluMRCT solution. The proposed algorithm is experimented on numerous types of datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, partially on large instances

The fact is quite evident that the world is witnessing a lack of top-tier AI talents across various regions of the globe. The technology is touching new boundaries on a regular basis, new advents and innovations are endless, all that is required is efficient talent and AI educated candidates to harness its potentials. However, a number of countries, realizing the growth potential and scope for artificial intelligence, have incorporated the professional curriculum to enhance the skills and talent across their industries. To understand the literacy of professionals in AI, JF Gagne's survey "Global AI Talent Report 2019" delved deep into the publications from 21 leading scientific conferences in the field of AI and analyzed the profiles of the authors. Secondly, it analyzed the results of several targeted LinkedIn searches, which depicted how many individuals are self-reporting that they have doctorates as well as the requisite skills in different regions around the world. According to the report, the countries with the highest number of high-impact researchers are the United States, China, the United Kingdom, Australia, and Canada.

For years, the Denver public school system worked with Video Insight, a Houston-based video management software company that centralized the storage of video footage used across its campuses. So when Panasonic acquired Video Insight, school officials simply transferred the job of updating and expanding their security system to the Japanese electronics giant. That meant new digital HD cameras and access to more powerful analytics software, including Panasonic's facial recognition, a tool the public school system's safety department is now exploring. Denver, where some activists are pushing for a ban on government use of facial recognition, is not alone. Mass shootings have put school administrators across the country on edge, and they're understandably looking at anything that might prevent another tragedy. Safety concerns have led some schools to consider artificial intelligence-enabled tools, including facial recognition software; AI that can scan video feeds for signs of brandished weapons; even analytics tools that warn when there's been suspicious movement in a usually-empty hallway.

On our sidewalks, in our skies, in our every store… Over the next decade, robots will enter the mainstream of retail. As countless robots work behind the scenes to stock shelves, serve customers, and deliver products to our doorstep, the speed of retail will accelerate. These changes are already underway. In this blog, we'll elaborate on how robots are entering the retail ecosystem. On August 3rd, 2016, Domino's Pizza introduced the Domino's Robotic Unit, or "DRU" for short.