Spectrum coexistence is essential for next generation (NextG) systems to share the spectrum with incumbent (primary) users and meet the growing demand for bandwidth. One example is the 3.5 GHz Citizens Broadband Radio Service (CBRS) band, where the 5G and beyond communication systems need to sense the spectrum and then access the channel in an opportunistic manner when the incumbent user (e.g., radar) is not transmitting. To that end, a high-fidelity classifier based on a deep neural network is needed for low misdetection (to protect incumbent users) and low false alarm (to achieve high throughput for NextG). In a dynamic wireless environment, the classifier can only be used for a limited period of time, i.e., coherence time. A portion of this period is used for learning to collect sensing results and train a classifier, and the rest is used for transmissions. In spectrum sharing systems, there is a well-known tradeoff between the sensing time and the transmission time. While increasing the sensing time can increase the spectrum sensing accuracy, there is less time left for data transmissions. In this paper, we present a generative adversarial network (GAN) approach to generate synthetic sensing results to augment the training data for the deep learning classifier so that the sensing time can be reduced (and thus the transmission time can be increased) while keeping high accuracy of the classifier. We consider both additive white Gaussian noise (AWGN) and Rayleigh channels, and show that this GAN-based approach can significantly improve both the protection of the high-priority user and the throughput of the NextG user (more in Rayleigh channels than AWGN channels).

As a rather commercially successful author once wrote, "the night is dark and full of terrors, the day bright and beautiful and full of hope." It's fitting imagery for AI, which like all tech has its upsides and downsides. Art-generating models like Stable Diffusion, for instance, have led to incredible outpourings of creativity, powering apps and even entirely new business models. On the other hand, its open source nature lets bad actors to use it to create deepfakes at scale -- all while artists protest that it's profiting off of their work. Will regulation rein in the worst of what AI brings, or are the floodgates open?

Around 150 delegates from 50 participating countries took part in the recent plenary meeting of the ISO and IEC joint committee on artificial intelligence (ISO/IEC JTC 1/SC 42). At the meeting, delegates heard from the European Commission (EC) and approved a number of resolutions. The keynote speaker was Salvatore Scalzo, an EC Policy and Legal Officer in the field of AI. He works in the Directorate‑General for Communications Networks, Content and Technology, which develops and implements digital policies for Europe. Mr. Scalzo said that the EC was taking a strong interest in ISO/IEC AI standards as work continued on a future AI act.

Overview: Samsung Research America (SRA) plays a pivotal role in developing the next generation of discovery in software, user experience and services for future products that can enrich your life. Our mission is to research and develop new technologies by partnering with the best and brightest and creating a collaborative environment between industry and academia. Headquartered in Silicon Valley, with locations in many technology centers in North America, SRA is driven to build a culture of innovation that rapidly translates research and new ideas into the unexpected. Bixby is an intelligent personal assistant which is only available as a built-in service on Samsung flagship devices and wearables. Bixby uses state-of-art Speech Recognition & Natural Language Processing and Knowledge-Based AI to perform tasks on these devices using multimodal inputs and additional contextual information, including but not limited to making phone calls, sending text messages, setting up meetings, opening apps, setting alarms and timers, getting directions, answering general questions, providing information about restaurants and other businesses, etc.

At Tech Mahindra, we not only provide Agile and DevOps methodologies to our customers, we have adopted the same within the company as well. Our nimble processes are not mired in red tape, yet robust, flexible and result-oriented. We are Software Engineers, Technical Architects, Cloud and DevOps specialists. But the most important, we are dreamers, creators and challengers. Each day, we strive to make great come alive.

Data-centric AI is at the center of a fundamental shift in software engineering where machine learning becomes the new software, powered by big data and computing infrastructure. Here software engineering needs to be re-thought where data becomes a first-class citizen on par with code. One striking observation is that a significant portion of the machine learning process is spent on data preparation. Without good data, even the best machine learning algorithms cannot perform well. As a result, data-centric AI practices are now becoming mainstream. Unfortunately, many datasets in the real world are small, dirty, biased, and even poisoned. In this survey, we study the research landscape for data collection and data quality primarily for deep learning applications. Data collection is important because there is lesser need for feature engineering for recent deep learning approaches, but instead more need for large amounts of data. For data quality, we study data validation, cleaning, and integration techniques. Even if the data cannot be fully cleaned, we can still cope with imperfect data during model training using robust model training techniques. In addition, while bias and fairness have been less studied in traditional data management research, these issues become essential topics in modern machine learning applications. We thus study fairness measures and unfairness mitigation techniques that can be applied before, during, or after model training. We believe that the data management community is well poised to solve these problems.

At the core of insurance business lies classification between risky and non-risky insureds, actuarial fairness meaning that risky insureds should contribute more and pay a higher premium than non-risky or less-risky ones. Actuaries, therefore, use econometric or machine learning techniques to classify, but the distinction between a fair actuarial classification and "discrimination" is subtle. For this reason, there is a growing interest about fairness and discrimination in the actuarial community Lindholm, Richman, Tsanakas, and Wuthrich (2022). Presumably, non-sensitive characteristics can serve as substitutes or proxies for protected attributes. For example, the color and model of a car, combined with the driver's occupation, may lead to an undesirable gender bias in the prediction of car insurance prices. Surprisingly, we will show that debiasing the predictor alone may be insufficient to maintain adequate accuracy (1). Indeed, the traditional pricing model is currently built in a two-stage structure that considers many potentially biased components such as car or geographic risks. We will show that this traditional structure has significant limitations in achieving fairness. For this reason, we have developed a novel pricing model approach. Recently some approaches have Blier-Wong, Cossette, Lamontagne, and Marceau (2021); Wuthrich and Merz (2021) shown the value of autoencoders in pricing. In this paper, we will show that (2) this can be generalized to multiple pricing factors (geographic, car type), (3) it perfectly adapted for a fairness context (since it allows to debias the set of pricing components): We extend this main idea to a general framework in which a single whole pricing model is trained by generating the geographic and car pricing components needed to predict the pure premium while mitigating the unwanted bias according to the desired metric.

Causal mediation analysis can unpack the black box of causality and is therefore a powerful tool for disentangling causal pathways in biomedical and social sciences, and also for evaluating machine learning fairness. To reduce bias for estimating Natural Direct and Indirect Effects in mediation analysis, we propose a new method called DeepMed that uses deep neural networks (DNNs) to cross-fit the infinite-dimensional nuisance functions in the efficient influence functions. We obtain novel theoretical results that our DeepMed method (1) can achieve semiparametric efficiency bound without imposing sparsity constraints on the DNN architecture and (2) can adapt to certain low dimensional structures of the nuisance functions, significantly advancing the existing literature on DNN-based semiparametric causal inference. Extensive synthetic experiments are conducted to support our findings and also expose the gap between theory and practice. As a proof of concept, we apply DeepMed to analyze two real datasets on machine learning fairness and reach conclusions consistent with previous findings.

MB: I hope it's the opposite. I think in technology, we see over and over that products come out that skirt the edges of the law, but then someone comes by and finds a better way to do it. So, in the early 2000s, you had Napster, which everybody loved but was completely illegal. And today, we have things like Spotify and iTunes. And how did these systems arise?

Today I woke up to find that one of my articles had been "comment bombed" by somebody. The person left the same comment around 20 times. He wrote: "This guy replies to comments and then blocks the author so they can't respond!" Do you know what I did? I replied with, "Bye!" then I blocked and reported the writer. A moment after that, all twenty comments were gone from my article.