Machine learning has enabled significant benefits in diverse fields, but, with a few exceptions, has had limited impact on computer architecture. Recent work, however, has explored broader applicability for design, optimization, and simulation. Notably, machine learning based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This paper reviews machine learning applied system-wide to simulation and run-time optimization, and in many individual components, including memory systems, branch predictors, networks-on-chip, and GPUs. The paper further analyzes current practice to highlight useful design strategies and identify areas for future work, based on optimized implementation strategies, opportune extensions to existing work, and ambitious long term possibilities. Taken together, these strategies and techniques present a promising future for increasingly automated architectural design.

Active inference is a first (Bayesian) principles account of how autonomous agents might operate in dynamic, non-stationary environments. The optimization of congruent formulations of the free energy functional (variational and expected), in active inference, enables agents to make inferences about the environment and select optimal behaviors. The agent achieves this by evaluating (sensory) evidence in relation to its internal generative model that entails beliefs about future (hidden) states and sequence of actions that it can choose. In contrast to analogous frameworks $-$ by operating in a pure belief-based setting (free energy functional of beliefs about states) $-$ active inference agents can carry out epistemic exploration and naturally account for uncertainty about their environment. Through this review, we disambiguate these properties, by providing a condensed overview of the theory underpinning active inference. A T-maze simulation is used to demonstrate how these behaviors emerge naturally, as the agent makes inferences about the observed outcomes and optimizes its generative model (via belief updating). Additionally, the discrete state-space and time formulation presented provides an accessible guide on how to derive the (variational and expected) free energy equations and belief updating rules. We conclude by noting that this formalism can be applied in other engineering applications; e.g., robotic arm movement, playing Atari games, etc., if appropriate underlying probability distributions (i.e. generative model) can be formulated.

There has been an emerging trend in non-Euclidean dimension reduction of aiming to recover a low dimensional structure, namely a manifold, underlying the high dimensional data. Recovering the manifold requires the noise to be of certain concentration. Existing methods address this problem by constructing an output manifold based on the tangent space estimation at each sample point. Although theoretical convergence for these methods is guaranteed, either the samples are noiseless or the noise is bounded. However, if the noise is unbounded, which is a common scenario, the tangent space estimation of the noisy samples will be blurred, thereby breaking the manifold fitting. In this paper, we introduce a new manifold-fitting method, by which the output manifold is constructed by directly estimating the tangent spaces at the projected points on the underlying manifold, rather than at the sample points, to decrease the error caused by the noise. Our new method provides theoretical convergence, in terms of the upper bound on the Hausdorff distance between the output and underlying manifold and the lower bound on the reach of the output manifold, when the noise is unbounded. Numerical simulations are provided to validate our theoretical findings and demonstrate the advantages of our method over other relevant methods. Finally, our method is applied to real data examples.

In recent years, mobile devices have gained increasingly development with stronger computation capability and larger storage. Some of the computation-intensive machine learning and deep learning tasks can now be run on mobile devices. To take advantage of the resources available on mobile devices and preserve users' privacy, the idea of mobile distributed machine learning is proposed. It uses local hardware resources and local data to solve machine learning sub-problems on mobile devices, and only uploads computation results instead of original data to contribute to the optimization of the global model. This architecture can not only relieve computation and storage burden on servers, but also protect the users' sensitive information.

This paper presents an initial design concept and specification of a civilian Unmanned Aerial Vehicle (UAV) management simulation system that focuses on explainability for the human-in-the-loop control of semi-autonomous UAVs. The goal of the system is to facilitate the operator intervention in critical scenarios (e.g. avoid safety issues or financial risks). Explainability is supported via user-friendly abstractions on Belief-Desire-Intention agents. To evaluate the effectiveness of the system, a human-computer interaction study is proposed.

This "Artificial Intelligence in Marketing Market" research report provides a comprehensive overview of the markets between 2019-2024 and offers an …

Game Theory, quite unlike its name, is a serious affair to deal with when it comes to the configuration and planning of an AI model. In essence, while linear machine learning deals largely with single-dimensional elements in their very nature, the true power of AI is actually unleashed with game theory application, and it's various facets. To understand game theory power in AI, however, it is essential to understand the basics of what actually constitutes game theory and its applications. So here's the promised primer on what game theory actually comprises. In its textbook definition, "Game Theory is the study of strategic interaction".

In today's so-called smart era when everything is getting virtual, the implementation of Artificial Intelligence in e-Commerce is a remarkable movement towards progress. By adopting the AI technology, e-commerce businesses are creating a boom in the market. Artificial Intelligence is somewhat a challenge to the creative power of a human being. It aims to achieve something which at times becomes tedious for human employees. With the right coding, it learns faster and better than compared to humans. The absence of emotional issues and health-related restrictions allow it to think logically and perform better. It has the power to detect any fraudulent activity which might be overlooked by humans. For a strong understanding of the behavior of human users and to provide the customers with a satisfactory experience, the e-commerce companies are reportedly adopting the new AI technology. Artificial Intelligence in eCommerce has now emerged as an individual business solution and is dominating the market. Now the question lies in the part that how are the business companies improvising this unique and innovative technology? Many a time, the sales team fails to keep track of the leading products and services in the market and hence could not impress potential buyers who might have an interest in the item.

The professional world has changed a lot in the last decades. Now, many workers are remote or freelance contractors that get hired and paid on a per-project basis. Thanks to the nomadic nature of the digital workforce, the technology that these employees use is entirely different from the last generation's -- and the evolution of this technology does not seem to be slowing down any time soon. Below, 15 members of Forbes Technology Council explore some of the cutting-edge technology trends that already are or will soon be transforming the workplace, and how companies can adapt to make the most of these changes. Embracing a "remote-first" culture at workplaces will be a key factor in companies' success in the coming decade.

AI could improve operational consistencies and enhance equipment utilization, Navis survey shows. Global container terminals are expected to embrace automated decision making powered by artificial intelligence (AI) as they pursue ways to improve operational consistencies and enhance equipment utilization, a new survey shows. The findings indicate that container terminals, regardless of their AI maturity, are increasingly aware of the possibilities of automated decision-making, according to supply chain technology provider Navis LLC. The Oakland, California-based firm said its TechValidate customer survey included responses from nearly 60 Navis customers, representing a cross-section of container terminals around the world using various degrees of automation. In addition to the 86% who cited operational consistency and equipment utilization as the most important benefits of automated decision-making, port operators also named other goals.