Monitoring, understanding, and optimizing the energy consumption of Machine Learning (ML) are various reasons why it is necessary to evaluate the energy usage of ML. However, there exists no universal tool that can answer this question for all use cases, and there may even be disagreement on how to evaluate energy consumption for a specific use case. Tools and methods are based on different approaches, each with their own advantages and drawbacks, and they need to be mapped out and explained in order to select the most suitable one for a given situation. We address this challenge through two approaches. First, we conduct a systematic literature review of all tools and methods that permit to evaluate the energy consumption of ML (both at training and at inference), irrespective of whether they were originally designed for machine learning or general software. Second, we develop and use an experimental protocol to compare a selection of these tools and methods. The comparison is both qualitative and quantitative on a range of ML tasks of different nature (vision, language) and computational complexity. The systematic literature review serves as a comprehensive guide for understanding the array of tools and methods used in evaluating energy consumption of ML, for various use cases going from basic energy monitoring to consumption optimization. Two open-source repositories are provided for further exploration. The first one contains tools that can be used to replicate this work or extend the current review. The second repository houses the experimental protocol, allowing users to augment the protocol with new ML computing tasks and additional energy evaluation tools.

Dennard scaling is gone, Amdahl's Law is reaching its limit, and Moore's Law is becoming difficult and expensive to follow, particularly as power and performance benefits diminish. And while none of that has reduced opportunities for much faster, lower-power chips, it has significantly shifted the dynamics for their design and manufacturing. Rather than just different process nodes and half nodes, companies developing chips -- traditional chip companies, automotive OEMs, fabless and non-fabless IDMs, and large systems companies -- are now wrestling with more options and more unique challenges as they seek optimal solutions for their specific applications. And they are all demanding more from an EDA ecosystem, which is racing to keep up with these changes, including various types of advanced packaging, chiplets, and a demand for integrated and customized hardware and software. "While heterogeneous integration predates the end of Dennard scaling or flattening of Moore's Law by several years, silicon designers and system architects are embracing this paradigm now to retain their pursuit of PPA goals -- without empirical law and its derivatives," said Saugat Sen, vice president of R&D at Cadence. "While there are many architectural and design challenges in this era, addressing thermal concerns rise to the top. Efficiency in design and implementation has been intricately linked to closed-loop integration with multi-physics analyses for awhile. More-than-Moore has created a compelling case for the implementation-analyses microcosm to transcend across the fabrics of system design, from silicon to package, and even beyond, and more so in the systems companies that are at the bleeding edge of design innovation."

As the range of potential uses for Artificial Intelligence (AI), in particular machine learning (ML), has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory of AI ethics principles and the practical design of AI systems. In previous work, we analysed whether it is possible to close this gap between the'what' and the'how' of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice.

We've all come to terms with the fact that artificial intelligence (AI) is transforming how businesses operate and how much it can help a business in the long term. Over the past few years, this understanding has driven a spike in companies experimenting and evaluating AI technologies and who are now using it specifically in production deployments. Of course, when organisations adopt new technologies such as AI and machine learning (ML), they gradually start to consider how new areas could be affected by technology. This can range across multiple sectors, including production and logistics, manufacturing, IT and customer service. Once the use of AI and ML techniques becomes ingrained in how businesses function and in the different ways in which they can be used, organisations will be able to gain new knowledge which will help them to adapt to evolving needs.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real life questions, to choose and apply appropriate methods to answer those questions, and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, prediction with machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by over 360 practice questions and 120 data exercises.

We've all come to terms with the fact that artificial intelligence (AI) is transforming how businesses operate and how much it can help a business in the long term. Over the past few years, this understanding has driven a spike in companies experimenting and evaluating AI technologies and who are now using it specifically in production deployments. Of course, when organizations adopt new technologies such as AI and machine learning (ML), they gradually start to consider how new areas could be affected by the technology. This can range across multiple sectors, including production and logistics, manufacturing, IT and customer service. Once the use of AI and ML techniques becomes ingrained in how businesses function and in the different ways in which they can be used, organizations will be able to gain new knowledge which will help them to adapt to evolving needs.

In just the last few years, most companies that were evaluating or experimenting with AI are now using it in production deployments. When organisations adopt analytic technologies like AI and machine learning (ML), it naturally prompts them to start asking questions that challenge them to think differently about what they know about their business across departments, from manufacturing, production and logistics, to sales, customer service and IT. An organisation's use of AI and ML tools and techniques – and the various contexts in which it uses them – will change as they gain new knowledge. O'Reilly's learning platform is a treasure trove of information about the trends, topics, and issues tech and business leaders need to know to do their jobs and keep their businesses running. We recently analysed the platform's user usage to take a closer look at the most popular and most-searched topics in AI and ML.

In this special guest feature, Rachel Roumeliotis, Vice President of Content Strategy at O'Reilly Media, provides a deep dive into what topics and terms are on the rise in the data science industry, and also touches on important technology trends and shifts in learning these technologies. Rachel leads an editorial team that covers a wide variety of programming topics, ranging from data and AI, to open source in the enterprise, to emerging programming languages. She has been working in technical publishing for 14 years, acquiring content in many areas, including software development, UX, computer security and AI. There's no doubt that artificial intelligence continues to be swiftly adopted by companies worldwide. In just the last few years, most companies that were evaluating or experimenting with AI are now using it in production deployments.

Publishing video games online has been gaining in popularity for a number of years, but with the advent of social networks and the use of in-game data analysis recently, its potential profitability has skyrocketed. The power of video game analytics is immensely beneficial if done well; it can provide a lot of information with a high level of relevancy. Game Data Analysis – Tools and Methods is a practical, hands-on guide that provides you with a large overview of the choices available performing video game data analysis. From the technical aspect of the field to its implications in terms of game design, you will be able to choose the right tools for your needs. This book looks at the most useful key performance indicators used in video games and then highlights the strengths and weaknesses of different solutions that are available in order to collect your data.

Artificial intelligence-led services, among others, are already permeating our lives, with many more business use cases being analyzed and new technologies developed. As rapid advances begin to change industries, markets and the competitive landscape, how can a healthcare organization explore whether AI--and its branches of machine learning and deep learning--makes sense for implementation? There's a lot of buzz, there are plenty of gray areas. Many executives are under the impression they'll have to invest in AI to stay competitive, but they don't yet know how AI would fit into their organization's business model. At the same time, there's a plethora of companies, both established (Google, Microsoft, Amazon) and entrepreneurial (H2O.ai,