It's a gutsy move for Microsoft to challenge utterly dominant Google with its AI-boosted Bing search engine, but the results look promising to me. I tried the same queries on Google and the new Bing to see how well the latter search engine lives up to Microsoft's bold claims and if it matches the wow factor that came with the ChatGPT AI chatbot. Bing brings a breath of fresh air to online search. In eight of the 10 tests I describe here, I preferred Bing, thanks to its AI abilities. It isn't yet clear how the new competition will change our daily lives.

Microsoft's often-overlooked search engine, Bing, is mounting a comeback with ChatGPT, the suddenly ubiquitous chatbot capable of composing song lyrics, writing academic essays and answering all manner of questions. The new version of Bing was released to a limited group of users on Tuesday. The revamped product is part of Microsoft's $13 billion investment in OpenAI, the artificial intelligence lab behind ChatGPT that Microsoft is betting on to stay competitive with its big tech rivals like Google, Apple and Meta. But those companies are also racing to incorporate the new technology into their own software. A day before the unveiling of the new Bing, Google announced that it would soon release an experimental chatbot called Bard for its own search engine, which is much more widely used than Bing.

AI-driven decision-making can lead to discrimination against certain individuals or social groups based on protected characteristics/attributes such as race, gender, or age. The domain of fairness-aware machine learning focuses on methods and algorithms for understanding, mitigating, and accounting for bias in AI/ML models. Still, thus far, the vast majority of the proposed methods assess fairness based on a single protected attribute, e.g. only gender or race. In reality, though, human identities are multi-dimensional, and discrimination can occur based on more than one protected characteristic, leading to the so-called ``multi-dimensional discrimination'' or ``multi-dimensional fairness'' problem. While well-elaborated in legal literature, the multi-dimensionality of discrimination is less explored in the machine learning community. Recent approaches in this direction mainly follow the so-called intersectional fairness definition from the legal domain, whereas other notions like additive and sequential discrimination are less studied or not considered thus far. In this work, we overview the different definitions of multi-dimensional discrimination/fairness in the legal domain as well as how they have been transferred/ operationalized (if) in the fairness-aware machine learning domain. By juxtaposing these two domains, we draw the connections, identify the limitations, and point out open research directions.

Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving, because it allows creating statistically better perception models and signaling the model's certainty in its predictions to the decision method or a human supervisor. In this paper, we propose a Variational Neural Network-based version of a Voxel Pseudo Image Tracking (VPIT) method for 3D Single Object Tracking. The Variational Feature Generation Network of the proposed Variational VPIT computes features for target and search regions and the corresponding uncertainties, which are later combined using an uncertainty-aware cross-correlation module in one of two ways: by computing similarity between the corresponding uncertainties and adding it to the regular cross-correlation values, or by penalizing the uncertain feature channels to increase influence of the certain features. In experiments, we show that both methods improve tracking performance, while penalization of uncertain features provides the best uncertainty quality.

Despite superior performance in many situations, deep neural networks are often vulnerable to adversarial examples and distribution shifts, limiting model generalization ability in real-world applications. To alleviate these problems, recent approaches leverage distributional robustness optimization (DRO) to find the most challenging distribution, and then minimize loss function over this most challenging distribution. Regardless of achieving some improvements, these DRO approaches have some obvious limitations. First, they purely focus on local regularization to strengthen model robustness, missing a global regularization effect which is useful in many real-world applications (e.g., domain adaptation, domain generalization, and adversarial machine learning). Second, the loss functions in the existing DRO approaches operate in only the most challenging distribution, hence decouple with the original distribution, leading to a restrictive modeling capability. In this paper, we propose a novel regularization technique, following the veins of Wasserstein-based DRO framework. Specifically, we define a particular joint distribution and Wasserstein-based uncertainty, allowing us to couple the original and most challenging distributions for enhancing modeling capability and applying both local and global regularizations. Empirical studies on different learning problems demonstrate that our proposed approach significantly outperforms the existing regularization approaches in various domains: semi-supervised learning, domain adaptation, domain generalization, and adversarial machine learning.

ChatGPT has become a global obsession in recent weeks, with experts warning its eerily human replies will put white-collar jobs at risk in years to come. But questions are being asked about whether the $10billion artificial intelligence has a woke bias. This week, several observers noted that the chatbot spits out answers which seem to indicate a distinctly liberal viewpoint. Elon Musk described it as'concerning' when the program suggested it would prefer to detonate a nuclear weapon, killing millions, rather than use a racial slur. The chatbot also refused to write a poem praising former President Donald Trump but was happy to do so for Kamala Harris and Joe Biden. And the program also refuses to speak about the benefits of fossil fuels.

Two years ago, Microsoft president Brad Smith told a US congressional hearing that tech companies like his own had not been sufficiently paying media companies for the news content that helps fuel search engines like Bing and Google. "What we're talking about here is far bigger than us," he said, testifying alongside news executives. "Let's hope that, if a century from now people are not using iPhones or laptops or anything that we have today, journalism itself is still alive and well. Because our democracy depends on it." Smith said tech companies should do more and that Microsoft was committed to continuing "healthy revenue-sharing" with news publishers, including licensing articles for Microsoft news apps.

One of the primary goals in spectrum occupancy mapping is to create a system that is robust to assumptions about the number of sensors, occupancy threshold (in dBm), sensor noise, number of emitters and the propagation environment. We show that such a system may be designed with neural networks using a process of aggregation to allow a variable number of sensors during training and testing. This process transforms the variable number of measurements into approximate log-likelihood ratios (LLRs), which are fed as a fixed-resolution image into a neural network. The use of LLR's provides robustness to the effects of noise and occupancy threshold. In other words, a system may be trained for a nominal number of sensors, threshold and noise levels, and still operate well at various other levels without retraining. Our system operates without knowledge of the number of emitters and does not explicitly attempt to estimate their number or power. Receiver operating curves with realistic propagation environments using topographic maps with commercial network design tools show how performance of the neural network varies with the environment. The use of very low-resolution sensors in this system can still yield good performance. Manuscript received: February 14, 2023.

The majority of existing post-hoc explanation approaches for machine learning models produce independent, per-variable feature attribution scores, ignoring a critical inherent characteristics of homogeneously structured data, such as visual or text data: there exist latent inter-variable relationships between features. In response, we develop a novel model-agnostic and permutation-based feature attribution approach based on the relational analysis between input variables. As a result, we are able to gain a broader insight into the predictions and decisions of machine learning models. Experimental evaluations of our framework in comparison with state-of-the-art attribution techniques on various setups involving both image and text data modalities demonstrate the effectiveness and validity of our method.

In the era of big data, we are often facing the challenge of data heterogeneity and the lack of label information simultaneously. In the financial domain (e.g., fraud detection), the heterogeneous data may include not only numerical data (e.g., total debt and yearly income), but also text and images (e.g., financial statement and invoice images). At the same time, the label information (e.g., fraud transactions) may be missing for building predictive models. To address these challenges, many state-of-the-art multi-view clustering methods have been proposed and achieved outstanding performance. However, these methods typically do not take into consideration the fairness aspect and are likely to generate biased results using sensitive information such as race and gender. Therefore, in this paper, we propose a fairness-aware multi-view clustering method named FairMVC. It incorporates the group fairness constraint into the soft membership assignment for each cluster to ensure that the fraction of different groups in each cluster is approximately identical to the entire data set. Meanwhile, we adopt the idea of both contrastive learning and non-contrastive learning and propose novel regularizers to handle heterogeneous data in complex scenarios with missing data or noisy features. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of the proposed framework. We also derive insights regarding the relative performance of the proposed regularizers in various scenarios.