Russia's savage, imperialistic and childish war on Ukraine has been said by democracies to be a battle between democracies and autocracies, the free world and the the unfree. And it is the opening of the battle to come between two very different sets of values. The other, more subtle, nefarious, insidious and perhaps deadlier in some ways, war is that of Artificial Intelligence. The abuse of AI has the capability to destroy human agency, take away any sense of free will, devastate human rights, divide societies and turn people under its thumb into automatons to serve the elites of corrupt, autocratic and dictatorial countries. To see how autocracies will use AI to subjugate and destroy any sense of human agency in their populations, we only have to look at how they've done so with social media.

Niki Parmar and Ashish Vaswani co-authored a seminal paper that set the groundwork for ChatGPT and other generative AI models. ChatGPT has taken the world by storm, but seeds of the groundbreaking technology were sown at the USC Viterbi School of Engineering. The seminal paper "Attention Is All You Need," which laid the foundation for ChatGPT and other generative AI systems, was co-authored by Ashish Vaswani, a PhD computer science graduate ('14) and Niki Parmar, a master's in computer science graduate ('15). The landmark paper was presented at the 2017 Conference on Neural Information Processing Systems (NeurIPS), one of the top conferences in AI and machine learning. In the paper, the researchers introduced the transformer architecture, a powerful type of neural network that has become widely used for natural language processing tasks, from text classification to language modeling.

Due to the numerous advantages of machine learning (ML) algorithms, many applications now incorporate them. However, many studies in the field of image classification have shown that MLs can be fooled by a variety of adversarial attacks. These attacks take advantage of ML algorithms' inherent vulnerability. This raises many questions in the cybersecurity field, where a growing number of researchers are recently investigating the feasibility of such attacks against machine learning-based security systems, such as intrusion detection systems. The majority of this research demonstrates that it is possible to fool a model using features extracted from a raw data source, but it does not take into account the real implementation of such attacks, i.e., the reverse transformation from theory to practice. The real implementation of these adversarial attacks would be influenced by various constraints that would make their execution more difficult. As a result, the purpose of this study was to investigate the actual feasibility of adversarial attacks, specifically evasion attacks, against network-based intrusion detection systems (NIDS), demonstrating that it is entirely possible to fool these ML-based IDSs using our proposed adversarial algorithm while assuming as many constraints as possible in a black-box setting. In addition, since it is critical to design defense mechanisms to protect ML-based IDSs against such attacks, a defensive scheme is presented. Realistic botnet traffic traces are used to assess this work. Our goal is to create adversarial botnet traffic that can avoid detection while still performing all of its intended malicious functionality.

In conceptual modeling (CM), humans apply abstraction to represent excerpts of reality for means of understanding and communication, and processing by machines. Artificial Intelligence (AI) is applied to vast amounts of data to automatically identify patterns or classify entities. While CM produces comprehensible and explicit knowledge representations, the outcome of AI algorithms often lacks these qualities while being able to extract knowledge from large and unstructured representations. Recently, a trend toward intertwining CM and AI emerged. This systematic mapping study shows how this interdisciplinary research field is structured, which mutual benefits are gained by the intertwining, and future research directions.

Maximum 2-satisfiability (MAX-2-SAT) is a type of combinatorial decision problem that is known to be NP-hard. In this paper, we compare LightSolver's quantum-inspired algorithm to a leading deep-learning solver for the MAX-2-SAT problem. Experiments on benchmark data sets show that LightSolver achieves significantly smaller time-to-optimal-solution compared to a state-of-the-art deep-learning algorithm, where the gain in performance tends to increase with the problem size.

We consider the problem of computing reachable sets directly from noisy data without a given system model. Several reachability algorithms are presented for different types of systems generating the data. First, an algorithm for computing over-approximated reachable sets based on matrix zonotopes is proposed for linear systems. Constrained matrix zonotopes are introduced to provide less conservative reachable sets at the cost of increased computational expenses and utilized to incorporate prior knowledge about the unknown system model. Then we extend the approach to polynomial systems and, under the assumption of Lipschitz continuity, to nonlinear systems. Theoretical guarantees are given for these algorithms in that they give a proper over-approximate reachable set containing the true reachable set. Multiple numerical examples and real experiments show the applicability of the introduced algorithms, and comparisons are made between algorithms.

The video above represents one of the first times that a pangolin, one of the world's most critically endangered species, was detected in real time using artificial intelligence. A U.K.-based nonprofit called Conservation AI made this possible with the help of NVIDIA technology. Such use of AI can help track even the rarest, most reclusive of species in real time, enabling conservationists to protect them from threats, such as poachers and fires, before it's too late to intervene. The organization was founded four years ago by researchers at Liverpool John Moores University -- Paul Fergus, Carl Chalmers, Serge Wich and Steven Longmore. In the past year and a half, Conservation AI has deployed 70 AI-powered cameras across the world.

Standard Bank Group is a leading Africa-focused financial services group, and an innovative player on the global stage, that offers a variety of career-enhancing opportunities – plus the chance to work alongside some of the sector's most talented, motivated professionals. Our clients range from individuals, to businesses of all sizes, high net worth families and large multinational corporates and institutions. Bringing true, meaningful value to our clients and the communities we serve and creating a real sense of purpose for you. To work with business stakeholders to identify and deliver on new AI initiatives. To apply deep domain expertise to shape/influence the AI-thinking in the organisation through thought leadership; enabling the successful adoption and acceleration of AI and ML across Standard Bank Group (SBG), ensuring the needs of stakeholders are correctly understood and addressed.

Computers powered by human brain cells may sound like science fiction, but a team of researchers in the United States believes such machines, part of a new field called "organoid intelligence," could shape the future -- and now they have a plan to get there. Organoids are lab-grown tissues that resemble organs. These three-dimensional structures, usually derived from stem cells, have been used in labs for nearly two decades, where scientists have been able to avoid harmful human or animal testing by experimenting on the stand-ins for kidneys, lungs and other organs. Brain organoids don't actually resemble tiny versions of the human brain, but the pen dot-size cell cultures contain neurons that are capable of brainlike functions, forming a multitude of connections. Scientists call the phenomenon "intelligence in a dish."

Google is using AI to help people facing disease and natural disasters, and to provide new opportunities for underserved communities. The potential of AI to solve big problems is increasing all the time. In the past few years, AI and transformational innovations have become more important in confronting some of society’s biggest challenges. Today, AI is helping countries and communities facing disease and natural disasters, and providing new opportunities for historically underserved groups. Here are seven ways AI is already making the world a better place: 1. Forecasting floods and helping people stay safe through early warning systems Last year…