A bill that U.S. Sen. Cory Gardner has co-sponsored to develop and guide the use of artificial intelligence in the federal government passed out of committee on Wednesday. "Our bill will bring agencies, industry, and others to the table to discuss government adoption of artificial intelligence and emerging technologies," Gardner said in a statement. The AI in Government Act defines artificial intelligence as any type of computer programming that would enable the computer to carry out tasks of the sort that "would require intelligence if performed by a human." The bill would create an AI Center of Excellence within the General Services Administration, which would coordinate AI use in the public interest and house the government's technical expertise. The center's responsibility would also include analyzing the ethical and civil liberties implications of artificial intelligence, helping state and local governments as needed.

ISLAMABAD (Web Desk) Aqsa Kausar, an electrical engineering graduate from NUST (National University of Science and Technology), has become the first female Google Developer Expert in Machine language from Pakistan. According to local news agency, she has risen to acclaim at such a young age through her contributions to the field of Machine Learning. Besides, there are other various awards to her credit for holding workshops in events like Google DevFest 2018 and Google Cloud Next Extended 2019. Moreover, not too long ago, she also participated in Google's Machine Learning Train-The-Trainer session which was held in Singapore. Aqsa currently working as an AI developer with a software organization named Red Buffer.

These are two examples of how NASA hopes to use artificial intelligence. As far-fetched as the concept sounds, the agency is already using AI in missions on both Earth and Mars. And there are other missions in the works that could see AI exploring icy moons in search of life. This bot-friendly future stands counter to some of the fuss in the press this past week, after Facebook shut down an experiment because two artificially intelligent bots began communicating in a shorthand language instead of English. Many in the media portrayed the bots as coming up with their own language.

Combining data from varied sources has considerable potential for knowledge discovery: collaborating data parties can mine data in an expanded feature space, allowing them to explore a larger range of scientific questions. However, data sharing among different parties is highly restricted by legal conditions, ethical concerns, and / or data volume. Fueled by these concerns, the fields of cryptography and distributed learning have made great progress towards privacy-preserving and distributed data mining. However, practical implementations have been hampered by the limited scope or computational complexity of these methods. In this paper, we greatly extend the range of analyses available for vertically partitioned data, i.e., data collected by separate parties with different features on the same subjects. To this end, we present a novel approach for privacy-preserving generalized linear models, a fundamental and powerful framework underlying many prediction and classification procedures. We base our method on a distributed block coordinate descent algorithm to obtain parameter estimates, and we develop an extension to compute accurate standard errors without additional communication cost. We critically evaluate the information transfer for semi-honest collaborators and show that our protocol is secure against data reconstruction. Through both simulated and real-world examples we illustrate the functionality of our proposed algorithm. Without leaking information, our method performs as well on vertically partitioned data as existing methods on combined data -- all within mere minutes of computation time. We conclude that our method is a viable approach for vertically partitioned data analysis with a wide range of real-world applications.

We introduce a framework for studying how distributional assumptions on the process by which data is partitioned into a training and test set can be leveraged to provide accurate estimation or learning algorithms, even for worst-case datasets. We consider a setting of $n$ datapoints, $x_1,\ldots,x_n$, together with a specified distribution, $P$, over partitions of these datapoints into a training set, test set, and irrelevant set. An algorithm takes as input a description of $P$ (or sample access), the indices of the test and training sets, and the datapoints in the training set, and returns a model or estimate that will be evaluated on the datapoints in the test set. We evaluate an algorithm in terms of its worst-case expected performance: the expected performance over potential test/training sets, for worst-case datapoints, $x_1,\ldots,x_n.$ This framework is a departure from more typical distributional assumptions on the datapoints (e.g. that data is drawn independently, or according to an exchangeable process), and can model a number of natural data collection processes, including processes with dependencies such as "snowball sampling" and "chain sampling", and settings where test and training sets satisfy chronological constraints (e.g. the test instances were observed after the training instances). Within this framework, we consider the setting where datapoints are bounded real numbers, and the goal is to estimate the mean of the test set. We give an efficient algorithm that returns a weighted combination of the training set---whose weights depend on the distribution, $P$, and on the training and test set indices---and show that the worst-case expected error achieved by this algorithm is at most a multiplicative $\pi/2$ factor worse than the optimal of such algorithms. The algorithm, and its proof, leverage a surprising connection to the Grothendieck problem.

The paper investigates the second-order blameworthiness or duty to warn modality "one coalition knew how another coalition could have prevented an outcome". The main technical result is a sound and complete logical system that describes the interplay between the distributed knowledge and the duty to warn modalities.

The Pearson's $\chi^2$ test and residual deviance test are two classical goodness-of-fit tests for binary regression models such as logistic regression. These two tests cannot be applied when we have one or more continuous covariates in the data, a quite common situation in practice. In that case, the most widely used approach is the Hosmer-Lemeshow test, which partitions the covariate space into groups according to quantiles of the fitted probabilities from all the observations. However, its grouping scheme is not flexible enough to explore how to adversarially partition the data space in order to enhance the power. In this work, we propose a new methodology, named binary regression adaptive grouping goodness-of-fit test (BAGofT), to address the above concern. It is a two-stage solution where the first stage adaptively selects candidate partitions using "training" data, and the second stage performs $\chi^2$ tests with necessary corrections based on "test" data. A proper data splitting ensures that the test has desirable size and power properties. From our experimental results, BAGofT performs much better than Hosmer-Lemeshow test in many situations.

The manual design of analog circuits is a tedious task of parameter tuning that requires hours of work by human experts. In this work, we make a significant step towards a fully automatic design method that is based on deep learning. The method selects the components and their configuration, as well as their numerical parameters. By contrast, the current literature methods are limited to the parameter fitting part only. A two-stage network is used, which first generates a chain of circuit components and then predicts their parameters. A hypernetwork scheme is used in which a weight generating network, which is conditioned on the circuit's power spectrum, produces the parameters of a primal RNN network that places the components. A differential simulator is used for refining the numerical values of the components. We show that our model provides an efficient design solution, and is superior to alternative solutions.

We study the problem of collaborative machine learning markets where multiple parties can achieve improved performance on their machine learning tasks by combining their training data. We discuss desired properties for these machine learning markets in terms of fair revenue distribution and potential threats, including data replication. We then instantiate a collaborative market for cases where parties share a common machine learning task and where parties' tasks are different. Our marketplace incentivizes parties to submit high quality training and true validation data. To this end, we introduce a novel payment division function that is robust-to- replication and customized output models that perform well only on requested machine learning tasks. In experiments, we validate the assumptions underlying our theoretical analysis and show that these are approximately satisfied for commonly used machine learning models.

We consider a dynamic pricing problem for repeated contextual second-price auctions with strategic buyers whose goals are to maximize their long-term time discounted utility. The seller has very limited information about buyers' overall demand curves, which depends on $d$-dimensional context vectors characterizing auctioned items, and a non-parametric market noise distribution that captures buyers' idiosyncratic tastes. The noise distribution and the relationship between the context vectors and buyers' demand curves are both unknown to the seller. We focus on designing the seller's learning policy to set contextual reserve prices where the seller's goal is to minimize his regret for revenue. We first propose a pricing policy when buyers are truthful and show that it achieves a $T$-period regret bound of $\tilde{\mathcal{O}}(\sqrt{dT})$ against a clairvoyant policy that has full information of the buyers' demand. Next, under the setting where buyers bid strategically to maximize their long-term discounted utility, we develop a variant of our first policy that is robust to strategic (corrupted) bids. This policy incorporates randomized "isolation" periods, during which a buyer is randomly chosen to solely participate in the auction. We show that this design allows the seller to control the number of periods in which buyers significantly corrupt their bids. Because of this nice property, our robust policy enjoys a $T$-period regret of $\tilde{\mathcal{O}}(\sqrt{dT})$, matching that under the truthful setting up to a constant factor that depends on the utility discount factor.