Sense is a bright-orange box that sits in your electrical breaker box and gives in-depth insight into your home's entire power usage. The whole system is quite clever and--thankfully--free of any monthly charges. But it learns very slowly, and that's likely to frustrate you. Sense ($299 at Amazon) works by electromagnetically listening to the power flowing along the two hot wires that run from your electric meter to your breakers. By measuring the current flow a million times each second, Sense can observe changes in load with precise detail and, based on a machine-learning database, attempt to identify the footprint of different devices from the noise they generate. This means it can tell you exactly how much energy different appliances in your house use, and it does all of this without requiring sensors or smart plugs on each device.

Lawrence Livermore National Laboratory (LLNL) has installed a state-of-the-art artificial intelligence (AI) accelerator from SambaNova Systems, the National Nuclear Security Administration (NNSA) announced today, allowing researchers to more effectively combine AI and machine learning (ML) with complex scientific workloads. LLNL has begun integrating the new AI hardware, SambaNova Systems DataScale, into the NNSA's Corona supercomputing cluster, an 11-plus petaFLOP machine that Lab scientists are using to conduct fusion energy research for stockpile stewardship applications, find therapeutics for COVID-19 and perform other unclassified basic science work. Lab researchers said the upgrade will allow them to run scientific simulations on the Corona system while offloading AI calculations from those simulations to the SambaNova DataScale system, improving overall speed, performance and productivity. "This integration enables low-latency communication between the two devices allowing them to operate in tandem with greater overall efficiency," said LLNL computer scientist Ian Karlin, who heads the SambaNova project. "In addition, scientific simulations running on Corona will feed data as they run into the SambaNova DataScale system to train new machine learning models based on their results."

We propose a novel framework and algorithm for hierarchical planning based on the principle of delegation. This framework, the Markov Intent Process, features a collection of skills which are each specialised to perform a single task well. Skills are aware of their intended effects and are able to analyse planning goals to delegate planning to the best-suited skill. This principle dynamically creates a hierarchy of plans, in which each skill plans for sub-goals for which it is specialised. The proposed planning method features on-demand execution---skill policies are only evaluated when needed. Plans are only generated at the highest level, then expanded and optimised when the latest state information is available. The high-level plan retains the initial planning intent and previously computed skills, effectively reducing the computation needed to adapt to environmental changes. We show this planning approach is experimentally very competitive to classic planning and reinforcement learning techniques on a variety of domains, both in terms of solution length and planning time.

Bayesian optimization forms a set of powerful tools that allows efficient black-box optimization and has been applied in a large variety of fields. In this thesis we first seek to advance Bayesian optimization by using estimated derivative observations. Later, we seek to tackle down the issues in Bayesian optimization when a large number of derivative observations and/or function observations are present. We start to describe our motivations in Chapter 1. We then give a broad review of Bayesian optimization in Chapter 2, where we start by covering the history of Bayesian optimization and its components.

In many reinforcement learning (RL) problems, it takes some time until a taken action by the agent reaches its maximum effect on the environment and consequently the agent receives the reward corresponding to that action by a delay called action-effect delay. Such delays reduce the performance of the learning algorithm and increase the computational costs, as the reinforcement learning agent values the immediate rewards more than the future reward that is more related to the taken action. This paper addresses this issue by introducing an applicable enhanced Q-learning method in which at the beginning of the learning phase, the agent takes a single action and builds a function that reflects the environments response to that action, called the reflexive $\gamma$ - function. During the training phase, the agent utilizes the created reflexive $\gamma$- function to update the Q-values. We have applied the developed method to a structural control problem in which the goal of the agent is to reduce the vibrations of a building subjected to earthquake excitations with a specified delay. Seismic control problems are considered as a complex task in structural engineering because of the stochastic and unpredictable nature of earthquakes and the complex behavior of the structure. Three scenarios are presented to study the effects of zero, medium, and long action-effect delays and the performance of the Enhanced method is compared to the standard Q-learning method. Both RL methods use neural network to learn to estimate the state-action value function that is used to control the structure. The results show that the enhanced method significantly outperforms the performance of the original method in all cases, and also improves the stability of the algorithm in dealing with action-effect delays.

Rapid advances in technology, connectivity and telecommunications are conspiring to make Africa's large, rapidly growing population a valuable asset for the automation revolution. It is imperative that Africa quickly develop agency in data and artificial intelligence and it will be lucrative for investors who support them by financing Africa's telecom and data backbone. Africa must urgently develop cogent digital strategy. This at first seems fanciful, or even superfluous, given the continent's relative lack of more basic development. Indeed, there are myriad other challenges to which most would assign primacy.

The supply chain is an ecosystem that affects businesses around the world, and the COVID-19 pandemic has thrown a monkey wrench into this previously undisturbed process. With region-specific restrictions, limited supply of certain goods, and a constantly changing consumer mindset, almost all businesses are playing catch up in addressing the needs of every consumer. Add to that the oil price war and the result is near chaos for both consumers and businesses. It may be a gamble to implement new supply chain systems in these circumstances, but it's a bet that could pay dividends not just now but in the long term. Artificial intelligence (AI) and data analytics tools can provide the much-needed push companies need to keep their businesses afloat--and maybe even thrive--despite the global crisis.

Large, expensive, computing-intensive research initiatives have historically promoted high-performance computing (HPC) in the wealthiest countries, most notably in the U.S., Europe, Japan, and China. The exponential impact of the Internet and of artificial intelligence (AI) has pushed HPC to a new level, affecting economies and societies worldwide. In Latin America, this was no different. Nevertheless, the use of HPC in science affected the countries in the region in a heterogeneous way. Since the first edition in 1993 of the TOP500 list of most powerful supercomputing systems in the world, only Mexico and Brazil (with 18 appearances each) made the list with research-oriented supercomputers.

We use the term imaging sciences to refer to the overarching spectrum of scientific and technological contexts which involve images in digital format including, among others, image and video processing, scientific visualization, computer graphics, animations in games and simulators, remote sensing imagery, and also the wide set of associated application areas that have become ubiquitous during the last decade in science, art, human-computer interaction, entertainment, social networks, and many others. As an area that combines mathematics, engineering, and computer science, this discipline arose in a few universities in Argentina mostly in the form of elective classes and small research projects in electrical engineering or computer science departments. Only in the mid-2000s did some initiatives aiming to generate joint activities and to provide identity and visibility to the discipline start to appear. In this short paper, we present a brief history of the three laboratories with the most relevant research and development (R&D) activities in the discipline in Argentina, namely the Imaging Sciences Laboratory of the Universidad Nacional del Sur, the PLADEMA Institute at the Universidad Nacional del Centro de la Provincia de Buenos Aires, and the Image Processing Laboratory at the Universidad Nacional de Mar del Plata. The Imaging Sciences Laboratorya of the Electrical and Computer Engineering Department of the Universidad Nacional del Sur Bahía Blanca began its activities in the 1990s as a pioneer in Argentina and Latin America in research and teaching in computer graphics, and in visualization.

The Millennium Institute for Foundational Research on Dataa (IMFD) started its operations in June 2018, funded by the Millennium Science Initiative of the Chilean National Agency of Research and Development.b IMFD is a joint initiative led by Universidad de Chile and Universidad Católica de Chile, with the participation of five other Chilean universities: Universidad de Concepción, Universidad de Talca, Universidad Técnica Federico Santa María, Universidad Diego Portales, and Universidad Adolfo Ibáñez. IMFD aims to be a reference center in Latin America related to state-of-the-art research on the foundational problems with data, as well as its applications to tackling diverse issues ranging from scientific challenges to complex social problems. As tasks of this kind are interdisciplinary by nature, IMFD gathers a large number of researchers in several areas that include traditional computer science areas such as data management, Web science, algorithms and data structures, privacy and verification, information retrieval, data mining, machine learning, and knowledge representation, as well as some areas from other fields, including statistics, political science, and communication studies. IMFD currently hosts 36 researchers, seven postdoctoral fellows, and more than 100 students.