Meta learning has demonstrated tremendous success in few-shot learning with limited supervised data. In those settings, the meta model is usually overparameterized. While the conventional statistical learning theory suggests that overparameterized models tend to overfit, empirical evidence reveals that overparameterized meta learning methods still work well -- a phenomenon often called "benign overfitting." To understand this phenomenon, we focus on the meta learning settings with a challenging bilevel structure that we term the gradient-based meta learning, and analyze its generalization performance under an overparameterized meta linear regression model. While our analysis uses the relatively tractable linear models, our theory contributes to understanding the delicate interplay among data heterogeneity, model adaptation and benign overfitting in gradient-based meta learning tasks. We corroborate our theoretical claims through numerical simulations.

Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts that have shown how such explanations can be distorted (attacked) by minor input perturbations. While there have been many surveys that review explainability methods themselves, there has been no effort hitherto to assimilate the different methods and metrics proposed to study the robustness of explanations of DNN models. In this work, we present a comprehensive survey of methods that study, understand, attack, and defend explanations of DNN models. We also present a detailed review of different metrics used to evaluate explanation methods, as well as describe attributional attack and defense methods. We conclude with lessons and take-aways for the community towards ensuring robust explanations of DNN model predictions.

A key question in a number of court cases is whether a speaker on an audio recording is a particular known speaker, for example, whether a speaker on a recording of an intercepted telephone call is the defendant. In most English-speaking countries, expert testimony is only admissible in a court of law if it will potentially assist the judge or the jury to make a decision. If the judge or the jury's speaker identification were equally accurate or more accurate than a forensic scientist's forensic voice comparison, then the forensic-voice-comparison testimony would not be admissible. In a research paper published in the journal Forensic Science International, a multidisciplinary international team of researchers has reported the first set of results from a comprehensive study that compares the accuracy of speaker-identification by individual listeners (like judges or jury members) with the accuracy of a forensic-voice-comparison system that is based on state-of-the-art automatic-speaker-recognition technology, and that does so using recordings that reflect the conditions of an actual case. The questioned-speaker recording was of a telephone call with background office noise, and the known-speaker recording was of a police interview conducted in echoey room with background ventilation-system noise.

Alphabet's Wing division has teamed up with DoorDash to deliver some convenience and grocery items -- such as pantry staples, snacks and household essentials -- by drone. Customers can place an order through the "DoorDash Air" section of the DoorDash app and receive their items in as little as 15 minutes. When they check out, users will need to select a delivery spot for the drone to drop off their package. The DoorDash app will ask them to confirm that the drop zone is clear before the user completes the order. The pilot is live in Logan, Australia, where Wing has been testing its services for a few years.

Peach, the global market leader in video advertising workflow and delivery has announced support of Netflix's new ad-supported service Basic with Ads. To coincide with the launch of the service, Peach has launched new destinations enabling clients to deliver ads to Netflix across multiple territories including UK, Australia, Germany, France, Italy, Spain, Mexico, Brazil with more to follow. Peach provides a connected advertising workflow, enabling clients to get their ads delivered to Netflix straight from the edit suite, while ensuring the highest possible quality, formatting and accuracy. Doug Conely, Chief Product and Technology Officer at Peach, said: "This is a pivotal moment for TV advertising. As leaders in global creative ad delivery for over 25 years, we've seen ad spend in Connected TV grow rapidly in the UK* and the rest of the world, and we expect to see further acceleration of growth driven by ad-supported tiers such as Netflix. AI and ML News: An Investment Into Artificial Intelligence as Daktela Buys Coworkers.ai "Netflix's Basic with Ads will bring our clients new audiences in a premium environment, creating opportunities for more addressable and premium content.

If speed dating mixed with blind dating sounds like your idea of hell, look away now. Ten years since dating app Tinder first encouraged users to swipe through potential partners based largely on their looks, some singles are doing away with profile photos altogether. In the absence of Cilla and "our Graham", those looking for love are turning instead to a new cohort of "blind dating apps" in the hope of making more meaningful connections. "I'm already on Tinder, Badoo, Bumble, Hinge – all of them!" says Victoria Brown, a 26-year-old client success manager from Upminster, east London. "A blind dating app seemed like a good idea because usually you think: 'Oh, he's really good-looking' but then, when you start talking, the chat's not that good. Not seeing what someone looks like, at least at first, gives it a bit of a twist – although I was nervous about the reveal."

This paper revisits the work of Rauch et al. (1965) and develops a novel method for recursive maximum likelihood particle filtering for general state-space models. The new method is based on statistical analysis of incomplete observations of the systems. Score function and conditional observed information of the incomplete observations/data are introduced and their distributional properties are discussed. Some identities concerning the score function and information matrices of the incomplete data are derived. Maximum likelihood estimation of state-vector is presented in terms of the score function and observed information matrices. In particular, to deal with nonlinear state-space, a sequential Monte Carlo method is developed. It is given recursively by an EM-gradient-particle filtering which extends the work of Lange (1995) for state estimation. To derive covariance matrix of state-estimation errors, an explicit form of observed information matrix is proposed. It extends Louis (1982) general formula for the same matrix to state-vector estimation. Under (Neumann) boundary conditions of state transition probability distribution, the inverse of this matrix coincides with the Cramer-Rao lower bound on the covariance matrix of estimation errors of unbiased state-estimator. In the case of linear models, the method shows that the Kalman filter is a fully efficient state estimator whose covariance matrix of estimation error coincides with the Cramer-Rao lower bound. Some numerical examples are discussed to exemplify the main results.

Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval. To tackle the challenges, we demonstrate two tasks along withsolutions, COVID-19 literature retrieval, and question answering. COVID-19 literature retrieval task screens matching COVID-19 literature documents for textual user query, and COVID-19 question answering task predicts proper text fragments from text corpus as the answer of specific COVID-19 related questions. Based on transformer neural network, we provided solutions to implement the tasks on CORD-19 dataset, we display some examples to show the effectiveness of our proposed solutions.

Software has been developed for knowledge discovery, prediction and management for over 30 years. However, there are still unresolved pain points when using existing project development and artifact management methodologies. Historically, there has been a lack of applicable methodologies. Further, methodologies that have been applied, such as Agile, have several limitations including scientific unfalsifiability that reduce their applicability. Evident, a development methodology rooted in the philosophy of logical reasoning and EKB, a knowledge base topology, are proposed. Many pain points in data mining, machine learning and general knowledge management are alleviated conceptually. Evident can be extended potentially to accelerate philosophical exploration, science discovery, education as well as knowledge sharing & retention across the globe. EKB offers one solution of storing information as knowledge, a granular level above data. Related topics in computer history, software engineering, database, sensing hardware, philosophy, and project & organization & military managements are also discussed.

The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing. Recently, Zhu et al. (PLDI 2020) have formulated differentiable quantum programming with bounded loops, providing a framework for scalable gradient calculation by quantum means for training quantum variational applications. However, promising parameterized quantum applications, e.g., quantum walk and unitary implementation, cannot be trained in the existing framework due to the natural involvement of unbounded loops. To fill in the gap, we provide the first differentiable quantum programming framework with unbounded loops, including a newly designed differentiation rule, code transformation, and their correctness proof. Technically, we introduce a randomized estimator for derivatives to deal with the infinite sum in the differentiation of unbounded loops, whose applicability in classical and probabilistic programming is also discussed. We implement our framework with Python and Q#, and demonstrate a reasonable sample efficiency. Through extensive case studies, we showcase an exciting application of our framework in automatically identifying close-to-optimal parameters for several parameterized quantum applications.