While microservices are revolutionizing cloud computing by offering unparalleled scalability and independent deployment, their decentralized nature poses significant security and management challenges that can threaten system stability. We propose a framework based on MAPE-K, which leverages agentic AI, for autonomous anomaly detection and remediation to address the daunting task of highly distributed system management. Our framework offers practical, industry-ready solutions for maintaining robust and secure microservices. Practitioners and researchers can customize the framework to enhance system stability, reduce downtime, and monitor broader system quality attributes such as system performance level, resilience, security, and anomaly management, among others.

This review is focused on the data-driven approaches applied in different applications of Acoustic-to-Articulatory Inversion (AAI) of speech. This review paper considered the relevant works published in the last ten years (2011-2021). The selection criteria includes (a) type of AAI - Speaker Dependent and Speaker Independent AAI, (b) objectives of the work - Articulatory approximation, Articulatory Feature space selection and Automatic Speech Recognition (ASR), explore the correlation between acoustic and articulatory features, and framework for Computer-assisted language training, (c) Corpus - Simultaneously recorded speech (wav) and medical imaging models such as ElectroMagnetic Articulography (EMA), Electropalatography (EPG), Laryngography, Electroglottography (EGG), X-ray Cineradiography, Ultrasound, and real-time Magnetic Resonance Imaging (rtMRI), (d) Methods or models - recent works are considered, and therefore all the works are based on machine learning, (e) Evaluation - as AAI is a non-linear regression problem, the performance evaluation is mostly done by Correlation Coefficient (CC), Root Mean Square Error (RMSE), and also considered Mean Square Error (MSE), and Mean Format Error (MFE). The practical application of the AAI model can provide a better and user-friendly interpretable image feedback system of articulatory positions, especially tongue movement. Such trajectory feedback system can be used to provide phonetic, language, and speech therapy for pathological subjects.

Probabilistic circuits (PCs) offer a promising avenue to perform embedded reasoning under uncertainty. They support efficient and exact computation of various probabilistic inference tasks by design. Hence, hardware-efficient computation of PCs is highly interesting for edge computing applications. As computations in PCs are based on arithmetic with probability values, they are typically performed in the log domain to avoid underflow. Unfortunately, performing the log operation on hardware is costly. Hence, prior work has focused on computations in the linear domain, resulting in high resolution and energy requirements. This work proposes the first dedicated approximate computing framework for PCs that allows for low-resolution logarithm computations. We leverage Addition As Int, resulting in linear PC computation with simple hardware elements. Further, we provide a theoretical approximation error analysis and present an error compensation mechanism. Empirically, our method obtains up to 357x and 649x energy reduction on custom hardware for evidence and MAP queries respectively with little or no computational error.

It may sound counter-intuitive, but Mouse on Mars co-founder Jan St. Werner thinks artificial intelligence can help us expand what it means to be a human being. If the idea of increased enmeshment with technology strikes you as invasive and deeply unsettling on a gut level, you're certainly not alone. Even the band's latest collaborator, science fiction scholar Louis Chude-Sokei, freely admits that he can't forecast how AI will develop as we move forward. On AAI, the new Mouse on Mars album, the veteran electronic outfit (which also includes fellow co-founder Andi Toma and longtime drummer/percussionist Dodo NKishi) embraces that uncertainty with an inquisitive, even playful approach to the concept of artificial intelligence. Working alongside Chude-Sokei and a group of software programmers, the band captured a machine-learning process whereby an AI system becomes increasingly aware and, in a sense, sentient as the album progresses.

There is an increasing interest in the entwining of the field of antitrust with the field of Artificial Intelligence (AI), frequently referred to jointly as Antitrust and AI (AAI) in the research literature. This study focuses on the synergies entangling antitrust and AI, doing so to extend the literature by proffering the primary ways that these two fields intersect, consisting of: (1) the application of antitrust to AI, and (2) the application of AI to antitrust. To date, most of the existing research on this intermixing has concentrated on the former, namely the application of antitrust to AI, entailing how the marketplace will be altered by the advent of AI and the potential for adverse antitrust behaviors arising accordingly. Opting to explore more deeply the other side of this coin, this research closely examines the application of AI to antitrust and establishes an antitrust vigilance lifecycle to which AI is predicted to be substantively infused for purposes of enabling and bolstering antitrust detection, enforcement, and post-enforcement monitoring. Furthermore, a gradual and incremental injection of AI into antitrust vigilance is anticipated to occur as significant advances emerge amidst the Levels of Autonomy (LoA) for AI Legal Reasoning (AILR).

Hotel solutions provider HRS has introduced rate projection technology powered by augmented artificial intelligence (AAI). The company claims AAI is "superior" to standard artificial intelligence because it runs multiple data models instead of a single routine. Its own models leverage prediction algorithms used by financial institutions, social networks and large e-commerce marketplaces. These models are then combined and validated for accuracy before automation reviews all results and selects the model or output with the least degree of error. HRS says its AAI models are based on machine learning that identifies complex patterns from continuous hotel rate data feeds.

Artificial Intelligence (AI), one of the most thrilling and transformative opportunities of our time, is the topic de jour lately with every intersection of intellectual discourse and business discussions sounding in on the potential perks, risks and dangers. Africa's tech ecosystem, one of the most exciting in the world right now, has a growing community of African start-ups that are keen on developing solutions for African problems using this emerging technology. The most desired business outcomes from AAI are: to improve/develop new products/ services; to achieve cost efficiencies, streamline business operations; and accelerate decision making. Enterprises that have enabled AI have reported increased operational efficiency, making faster, more informed decisions and innovating new products and services. To date, strong AI has not yet come into existence, it's still hypothetical hence it exists in the dreams of research scientists and imagination of science fiction writers.

In the past, Artificial Intelligence (AI) was constantly present in our lives - but only in the entertainment media. Most of us were shaped by TV series like The Jetsons (Rosie), Futurama (Bender) or Star Trek – The Next Generation (Data) or influenced by films such as The Day the Earth Stood Still (Gort), Forbidden Planet (Robby the Robot), 2001: A Space Odyssey (HAL 9000), WarGames (WOPR), The Terminator (Skynet/Terminator), Aliens (Bishop), A.I. (David), I, Robot (Sonny) - not to forget The Matrix or R2-D2 and C-3PO from Star Wars. And the book readers among us might know Colossus by D. F. Jones and the so called robot series by the inventor of the Three Laws of Robotics, Isaac Asimov. Obviously, people have always been fascinated about this theme. Without any doubt, all these books, films and TV series have left their footprints about AI in our minds. Most likely many, if not all, scientists working on AI have received their enthusiasm from it.

Allow me if you will to begin with a short piece of speculative fiction. Artificial intelligence is a dumb idea. First is the AI found in self-driving cars, Siri, and probably every VC pitch for the next few years, which could also be called "computer programs that use statistics and lots of data." This represents a marginal step forward in humans' capacity to channel our creative output through a machine that returns a somehow preferable version of it, now irrevocably owned by whoever owns the machine. We will need to regulate the fuck out of this AI, just as we have industry and consumer products, because such things kill, displace and disenfranchise people in the absence of politically determined limits on their operation.

Artificial Intelligence evokes a whole gamut of reactions. The cinematic world has been taking unrestrained creative liberty for ages. Such ambiguities that hound artificial intelligence (AI) clearly stem from an inherent lack of understanding of its root concepts. Interestingly, in one form or the other, the human race is already surrounded with AI. The era of Artificial Intelligence has begun. The truest form of AI is referred to as Strong AI or True AI, which is the stage when machines can behave as skillfully and flexibly as humans.