Everyone loves a good conspiracy theory, the more absurd the better, especially when that conspiracy theory might actually have a kernel of truth in it. The "dead internet theory" that's been making the rounds on social media of late is a perfect example of this sort of thing. So what is this "dead internet theory" anyway? Well, according to this conspiracy theory, at some point in the not too distant past artificial intelligence decided to swap out the "real" internet with a "fake" version that it runs pretty much all by itself. The reason for it having legs, of course, has a lot to do with the massive proliferation of bots that have infested social media platforms like Twitter.

Research project'Technology Foresight on Biometrics' from EU border security agency Frontex, which studies the impact of emerging biometric technologies on the facilitation of border crossing at the EU external borders, has completed two new steps. Frontex announced the tender for the project in September 2021 with a contract value of EUR 500,000 (US$590,000). The project, led by Steinbeis 2i together with three subcontracted partners (4CF, ERREQUADRO and WAT) has been examining how to maximize the future benefits of biometrics in border management while minimizing its risks and ensuring full compliance with the existing legal, ethical and technological constraints. The research team created a taxonomy of biometric technologies and carried out a Delphi survey to gather information on key technologies. Looking at the taxonomy of technologies early on provides foresight into areas which need to be addressed, according to the report, and establishes a common and systematic understanding of the technological field.

We consider the problem of correctly identifying the mode of a discrete distribution $\mathcal{P}$ with sufficiently high probability by observing a sequence of i.i.d. samples drawn according to $\mathcal{P}$. This problem reduces to the estimation of a single parameter when $\mathcal{P}$ has a support set of size $K = 2$. Noting the efficiency of prior-posterior-ratio (PPR) martingale confidence sequences for handling this special case, we propose a generalisation to mode estimation, in which $\mathcal{P}$ may take $K \geq 2$ values. We observe that the "one-versus-one" principle yields a more efficient generalisation than the "one-versus-rest" alternative. Our resulting stopping rule, denoted PPR-ME, is optimal in its sample complexity up to a logarithmic factor. Moreover, PPR-ME empirically outperforms several other competing approaches for mode estimation. We demonstrate the gains offered by PPR-ME in two practical applications: (1) sample-based forecasting of the winner in indirect election systems, and (2) efficient verification of smart contracts in permissionless blockchains.

Abstract--As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing model degradation and attack detection failure. This is because traditional data analytics models are static models that cannot adapt to data distribution changes. In this paper, we propose a Performance Weighted Probability Averaging Ensemble (PWPAE) framework for drift adaptive IoT anomaly detection through IoT data stream analytics. Experiments on two public datasets show the effectiveness of our proposed PWPAE method compared against state-of-the-art methods. With the rapid development of the Internet of Things traffic data often changes unpredictably over time, known (IoT), IoT devices have provided numerous new capabilities as concept drift [15] [16].

We present ReasonBert, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts. Unlike existing pre-training methods that only harvest learning signals from local contexts of naturally occurring texts, we propose a generalized notion of distant supervision to automatically connect multiple pieces of text and tables to create pre-training examples that require long-range reasoning. Different types of reasoning are simulated, including intersecting multiple pieces of evidence, bridging from one piece of evidence to another, and detecting unanswerable cases. We conduct a comprehensive evaluation on a variety of extractive question answering datasets ranging from single-hop to multi-hop and from text-only to table-only to hybrid that require various reasoning capabilities and show that ReasonBert achieves remarkable improvement over an array of strong baselines. Few-shot experiments further demonstrate that our pre-training method substantially improves sample efficiency.

Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. We begin by collecting a new dataset of news articles with questions as titles and pairing them with summaries of varying length. This dataset is used to learn a QA pair generation model producing summaries as answers that balance brevity with sufficiency jointly with their corresponding questions. We then reinforce the QA pair generation process with a differentiable reward function to mitigate exposure bias, a common problem in natural language generation. Both automatic metrics and human evaluation demonstrate these QA pairs successfully capture the central gists of the articles and achieve high answer accuracy.

Driving is an intuitive task that requires skills, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users, including wild animals. These requirements are particularly important when approaching intersections, overtaking, giving way, merging, turning and while adhering to the vast body of road rules. Modern motor vehicles now include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. The UK Department of Transport's response to the Safe Use of Automated Lane Keeping System consultation proposes that these systems are tested for compliance with relevant traffic rules. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer's in-depth knowledge of traffic legislation as well. These skills are required to ensure the systems are able to safely perform their tasks while being observant of the law. This paper presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. The approach (de)constructs road rules in legal terminology and specifies them in structured English logic that is expressed as Boolean logic for automation and Lawmaps for visualisation. We demonstrate an example using these tools leading to the construction and validation of a Bayesian Network model. We strongly believe these tools to be approachable by programmers and the general public, and capable of use in developing Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.

In September last year, Google's cloud unit looked into using artificial intelligence to help a financial firm decide whom to lend money to. It turned down the client's idea after weeks of internal discussions, deeming the project too ethically dicey because the AI technology could perpetuate biases like those around race and gender. Since early last year, Google has also blocked new AI features analyzing emotions, fearing cultural insensitivity, while Microsoft restricted software mimicking voices and IBM rejected a client request for an advanced facial-recognition system. All these technologies were curbed by panels of executives or other leaders, according to interviews with AI ethics chiefs at the three U.S. technology giants. Reported here for the first time, their vetoes and the deliberations that led to them reflect a nascent industry-wide drive to balance the pursuit of lucrative AI systems with a greater consideration of social responsibility.

First came the statements from reproductive organizations. Then came the tech companies. The day after the Supreme Court decided not to block a law in Texas banning most abortions after six weeks, Dallas-based Match Group, which owns Tinder, OkCupid, and Hinge, sent a memo to its employees. "The company generally does not take political stands unless it is relevant to our business," CEO Shar Dubey wrote. "But in this instance, I personally, as a woman in Texas, could not keep silent."

AI is neither artificial, nor is it intelligent. AI cannot recognize things without extensive human training. AI exhibits completely different logic from humans in terms of recognizing, understanding and classifying objects or scenes. The label implies that AI is analogous to human intelligence. AI often lacks any semblance of common sense, can be easily fooled or corrupted and can fail in unexpected and unpredictable ways.