Artificial intelligence (AI) and machine learning (ML) solutions, considered emerging technologies just a few years ago, are now within reach of more businesses, and they offer faster insights, greater efficiency, and enhanced customer experiences. The following industry thought leaders offer their predictions for how AI and ML will impact businesses across the range of vertical markets in 2023. "While active work is going on using AI for intelligent automation, an interesting trend in AI and ML is collaborative learning, that is, enabling AI to augment human intelligence. Through this collaborative intelligence, humans enable AI to train and learn the tacit knowledge that otherwise cannot be learned solely from the data, while AI enhances the human ability to make fast, informed, and smart decisions. This will not just level-up automation but will also enable more creative AI-human collaborations." "AI will yield tremendous breakthroughs in treating medical conditions in the next few years.

Abstract--Athlete performance measurement in sports videos requires modeling long sequences since the entire spatio-temporal progression contributes dominantly to the performance. It is crucial to comprehend local discriminative spatial dependencies and global semantics for accurate evaluation. However, existing benchmark datasets mainly incorporate sports where the performance lasts only a few seconds. Consequently, state-ofthe-art sports quality assessment methods specifically focus on spatial structure. Although they achieve high performance in short-term sports, they are unable to model prolonged video sequences and fail to achieve similar performance in long-term sports. To facilitate such analysis, we introduce a new dataset, coined AGF-Olympics, that incorporates artistic gymnastic floor routines. AFG-Olympics provides highly challenging scenarios with extensive background, viewpoint, and scale variations over an extended sample duration of up to 2 minutes. In addition, we propose a discriminative attention module to map the dense feature space into a sparse representation by disentangling complex associations. Extensive experiments indicate that our proposed module provides an effective way to embed long-range spatial and temporal correlation semantics. AQA conceptual workflow: discriminative non-local attention focuses on latent spatio-temporal association.

Before we get to the books you've chosen, can you give us a brief overview of what AI is? Artificial intelligence (AI) is the ability of computers and other machines to perform tasks that would normally require human intelligence, such as understanding language, recognizing images, making decisions, and solving problems. There are many different types of AI, including narrow or weak AI, which is designed to perform a specific task, and general or strong AI, which is designed to be capable of performing any intellectual task that a human can. One of the main goals of AI research is to create systems that can perform tasks without explicit instructions. To achieve this, AI systems are often designed to learn from data, using techniques from machine learning. Machine learning is a type of AI that allows systems to improve their performance on a particular task over time by learning from data, rather than being explicitly programmed. AI has the potential to revolutionize many industries and change the way we live and work. Some possible applications of AI include autonomous vehicles, medical diagnosis, language translation, and financial trading.

We study a scenario where an aircraft has multiple heterogeneous sensors collecting measurements to track a target vehicle of unknown location. The measurements are sampled along the flight path and our goals to optimize sensor placement to minimize estimation error. We select as a metric the Fisher Information Matrix (FIM), as "minimizing" the inverse of the FIM is required to achieve small estimation error. We propose to generate the optimal path from the Hamilton-Jacobi (HJ) partial differential equation (PDE) as it is the necessary and sufficient condition for optimality. A traditional method of lines (MOL) approach, based on a spatial grid, lends itself well to the highly non-linear and non-convex structure of the problem induced by the FIM matrix. However, the sensor placement problem results in a state space dimension that renders a naive MOL approach intractable. We present a new hybrid approach, whereby we decompose the state space into two parts: a smaller subspace that still uses a grid and takes advantage of the robustness to non-linearities and non-convexities, and the remaining state space that can by found efficiently from a system of ODEs, avoiding formation of a spatial grid.

When the artificial intelligence platform ChatGPT was released in late November, I was one of many educators who jumped on it, introducing it in the seminar I teach at Yale on the media and democracy. With its ability to communicate in plain-English prose, it was undeniably fun for the students to play with, composing everything from silly poems to job application letters. But it was also deeply troubling. When I prompted it to spread misinformation, it generated a news article falsely asserting the "U.S. Electoral Commission" had found "rampant voter fraud" in the 2020 election. It was also alarmingly quick to complete the term paper assignment that my students had been working on for weeks.

A new kind of lawyer is coming to court -- one that's powered by artificial intelligence. Next month, a "robot" lawyer, which tells defendants what to say via bluetooth, plans to fight two speeding tickets in court, according to USA Today. This marks the first time AI will be used in court, Joshua Browder, CEO of DoNotPay, the startup behind the project, told the outlet. Although the company isn't making any of the details, including the identities of the defendants, public, they told USA Today that one person will argue their case in person while another will do so over Zoom. DoNotPay bills itself as "the home of the world's first robot lawyer," and says its mission is to "level the playing field and make legal information and self-help accessible to everyone," per its website.

Of the hundreds of recognised dog breeds, the Belgian malinois has been named as the world's most intelligent by a new scientific study. Belgian malinois, often used as police dogs, achieved 35 points out of 39 in a series of cognitive tasks and behavioural tasks as part of the study. Naturally, the new study, conducted at the the University of Helsinki in Finland, has begged the question: which dog breed is the dumbest? According to Professor Stanley Coren, a canine expert at the University of British Columbia in Canada, the Afghan Hound is the least intelligent breed of dog when it comes to understanding and obeying commands. The following were ranked lowest by Professor Stanley Coren in his book'The Intelligence of Dogs'.

I decided to take ChatGPT3 to a writing class. I mean a real writing class. One that takes you seriously and tries to get a story out of your soul. One that works hard to help you when you fail to express yourself and helps you find your way. ChatGPT3 is an impressive piece of technology.

In this thesis a comprehensive verification framework is proposed to contend with some important issues in composability verification and a verification process is suggested to verify composability of different kinds of systems models, such as reactive, real-time and probabilistic systems. With an assumption that all these systems are concurrent in nature in which different composed components interact with each other simultaneously, the requirements for the extensive techniques for the structural and behavioral analysis becomes increasingly challenging. The proposed verification framework provides methods, techniques and tool support for verifying composability at its different levels. These levels are defined as foundations of consistent model composability. Each level is discussed in detail and an approach is presented to verify composability at that level. In particular we focus on the Dynamic-Semantic Composability level due to its significance in the overall composability correctness and also due to the level of difficulty it poses in the process. In order to verify composability at this level we investigate the application of three different approaches namely (i) Petri Nets based Algebraic Analysis (ii) Colored Petri Nets (CPN) based State-space Analysis and (iii) Communicating Sequential Processes based Model Checking. All three approaches attack the problem of verifying dynamic-semantic composability in different ways however they all share the same aim i.e., to confirm the correctness of a composed model with respect to its requirement specifications.

Federated learning (FL) is a powerful distributed machine learning framework where a server aggregates models trained by different clients without accessing their private data. Hierarchical FL, with a client-edge-cloud aggregation hierarchy, can effectively leverage both the cloud server's access to many clients' data and the edge servers' closeness to the clients to achieve a high communication efficiency. Neural network quantization can further reduce the communication overhead during model uploading. To fully exploit the advantages of hierarchical FL, an accurate convergence analysis with respect to the key system parameters is needed. Unfortunately, existing analysis is loose and does not consider model quantization. In this paper, we derive a tighter convergence bound for hierarchical FL with quantization. The convergence result leads to practical guidelines for important design problems such as the client-edge aggregation and edge-client association strategies. Based on the obtained analytical results, we optimize the two aggregation intervals and show that the client-edge aggregation interval should slowly decay while the edge-cloud aggregation interval needs to adapt to the ratio of the client-edge and edge-cloud propagation delay. Simulation results shall verify the design guidelines and demonstrate the effectiveness of the proposed aggregation strategy.