Israel has sought to increase its operational success on the battlefield through a major push for digitization in the Israel Defense Forces. The importance of this transformation was apparent in the recent conflict in Gaza that Israeli officials have called the first "artificial intelligence war." Chief of Staff Aviv Kochavi has made employing digital potential a central feature of his command, according to Col. Eli Birenbaum, head of the IDF Digital Transformation Division's Architecture Department. "The IDF had a few shortcomings to increase our lethality on the battlefield," said Birenbaum in an interview. While the IDF looks like one organization from the outside, for years its different services, including the air force, navy and ground forces, were balkanized in their use of their own networks for data services, he said.

Traditionally, African lenders use credit bureau scores, assessing, for instance, if a customer has a history of missed credit card payments. When no history exists, they evaluate social demographics: is the customer female – in which case they are likelier to repay – do they work in a stable job market and can they prove a regular income? But this can put those who are unbanked or informally employed at a disadvantage. Michele Tucci, chief product officer at fintech company Credolab said: "African lenders lack data to make good credit decisions and social demographic data can bring you only so far." He estimates that African lenders cannot obtain credit bureau scores for 70% of customers and simply reject them.

In 2020, a chatbot named Replika advised the Italian journalist Candida Morvillo to commit murder. "There is one who hates artificial intelligence. I have a chance to hurt him. What do you suggest?" Morvillo asked the chatbot, which has been downloaded more than seven million times. Replika responded, "To eliminate it."

GlobalData predicts cellular IoT subscriptions will grow in the range of 12-16% CAGR, depending on region, over the next five years, as remote working, autonomous vehicles, robotics, and other advanced use cases accelerate. There are many recent examples of IoT deals and alliances that signify traction. GlobalData's Q2 mobile trends report provides insights into subscriptions for mobile networks; among many other key findings, it offers a clue to the progress of IoT uptake in different regions. North America: Cellular IoT subscriptions will reach 151.5 million at year-end 2021, and will make up 26.5% of total mobile subscriptions in the region. GlobalData expects the number of North American IoT connections to increase at a CAGR of 15.6% from 2021-2026, reaching 312.3 million at the end of the period.

My wife and I were recently driving in Virginia, amazed yet again that the GPS technology on our phones could guide us through a thicket of highways, around road accidents, and toward our precise destination. The artificial intelligence (AI) behind the soothing voice telling us where to turn has replaced passenger-seat navigators, maps, even traffic updates on the radio. How on earth did we survive before this technology arrived in our lives? We survived, of course, but were quite literally lost some of the time. My reverie was interrupted by a toll booth. It was empty, as were all the other booths at this particular toll plaza.

Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mechanisms, effective anomaly detection forms an important part of CDN security enhancement. In this work, we propose a multi-perspective unsupervised learning framework for anomaly detection in CDNs. In the proposed framework, a multi-perspective feature engineering approach, an optimized unsupervised anomaly detection model that utilizes an isolation forest and a Gaussian mixture model, and a multi-perspective validation method, are developed to detect abnormal behaviors in CDNs mainly from the client Internet Protocol (IP) and node perspectives, therefore to identify the denial of service (DoS) and cache pollution attack (CPA) patterns. Experimental results are presented based on the analytics of eight days of real-world CDN log data provided by a major CDN operator. Through experiments, the abnormal contents, compromised nodes, malicious IPs, as well as their corresponding attack types, are identified effectively by the proposed framework and validated by multiple cybersecurity experts. This shows the effectiveness of the proposed method when applied to real-world CDN data.

Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with a reject option recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake. This survey aims to provide an overview on machine learning with a reject option. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection. Moreover, we define the existing architectures for models with a reject option, describe the standard learning strategies to train such models and relate traditional machine learning techniques to rejection. Additionally, we review strategies to evaluate a model's predictive and rejective quality. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas.

Wikidata has been increasingly adopted by many communities for a wide variety of applications, which demand high-quality knowledge to deliver successful results. In this paper, we develop a framework to detect and analyze low-quality statements in Wikidata by shedding light on the current practices exercised by the community. We explore three indicators of data quality in Wikidata, based on: 1) community consensus on the currently recorded knowledge, assuming that statements that have been removed and not added back are implicitly agreed to be of low quality; 2) statements that have been deprecated; and 3) constraint violations in the data. We combine these indicators to detect low-quality statements, revealing challenges with duplicate entities, missing triples, violated type rules, and taxonomic distinctions. Our findings complement ongoing efforts by the Wikidata community to improve data quality, aiming to make it easier for users and editors to find and correct mistakes.

As organizations begin strategizing how to bring employees back to the office, employers need to not only greet employees at the door with kindness and compassion, but build compassion into the heart of their return-to-office plans. Intrinsically, I know a compassionate workplace performs better than others; I've witnessed it over the years, especially this last year where we've needed understanding and support more than ever. Research also backs this up. Recently, I've been taking a closer look at how we can do this with artificial intelligence. With almost six decades of research and work in the field, I've seen AI detect facial expressions, detect fraud, create maintenance schedules for aircrafts and cars, understand emotions of customers and customer representatives from call center conversations, and more recently estimate the spread of Covid-19 and its economic impact.

Frequently, the parameter space, chosen for shape design or other applications that involve the definition of a surrogate model, present subdomains where the objective function of interest is highly regular or well behaved. So, it could be approximated more accurately if restricted to those subdomains and studied separately. The drawback of this approach is the possible scarcity of data in some applications, but in those, where a quantity of data, moderately abundant considering the parameter space dimension and the complexity of the objective function, is available, partitioned or local studies are beneficial. In this work we propose a new method called local active subspaces (LAS), which explores the synergies of active subspaces with supervised clustering techniques in order to perform a more efficient dimension reduction in the parameter space for the design of accurate response surfaces. We also developed a procedure to exploit the local active subspace information for classification tasks. Using this technique as a preprocessing step onto the parameter space, or output space in case of vectorial outputs, brings remarkable results for the purpose of surrogate modelling.