There has been a lot of buzz surrounding the adoption of artificial intelligence. According to a recent report from McKinsey 57 percent of companies are now using AI in at least one function. But how much is hype and how much is built on a sound commercial base? We spoke to Mike Loukides, VP of emerging tech content at O'Reilly Media and author of O'Reilly Media's widely-cited AI Adoption in the Enterprise report, to discuss the current state of AI and what lies ahead. BN: Are we moving beyond the adoption of AI because it's new and cool to having a serious business case?

Google has published two books about Site Reliability Engineering (SRE) principles, best practices, and practical applications.1,2 In the heat of the moment when handling a production incident, however, a team's actual response and debugging approaches often differ from ideal best practices. This article covers the outcomes of research performed in 2019 on how engineers at Google debug production issues, including the types of tools, high-level strategies, and low-level tasks that engineers use in varying combinations to debug effectively. It examines the research approach used to capture data, summarizing the common engineering journeys for production investigations and sharing examples of how experts debug complex distributed systems. Finally, the article extends the Google specifics of this research to provide some practical strategies that you can apply in your organization. As this study began, its focus was on developing an empirical understanding of the debugging process, with the overarching goal of creating optimal product solutions that met the needs of Google engineers. We wanted to capture the data that engineers need when debugging, when they need it, the communication process among the teams involved, and the types of mitigations that are successful.

Adopters of AI in the enterprise are focusing on specific production workloads centered around supervised and deep learning while the number of organizations using AI in production or evaluating the technology jumped to 85 percent of companies polled in an annual survey. Another indication of maturing enterprise AI initiatives is a heavier emphasis on data governance, according to an AI adoption survey released Wednesday (March 18) by O'Reilly Media. More than 26 percent of respondents said they are instituting formal governance processes as concerns about privacy and "trustworthy" AI grow. Nearly 35 percent of those surveyed said they expect to launch AI data governance efforts over the next three years, O'Reilly reported. "AI adoption is proceeding apace," wrote report authors Roger Magoulas and Steve Swoyer.

Interactive learning is the fastest way to explore a new technology. Because you're not just reading about it--you're also manipulating it in real time to discover how it works. In the past, companies had to set up complex environments and datasets for their teams to get their heads around cloud-based infrastructure and orchestration. O'Reilly interactive learning is powered by our recent acquisition of Katacoda. Try it yourself with unlimited access for 10 days--free.

The world of data can be chaos! New technologies, tools, products and the ever-changing industry dynamics--there sure is a lot to keep up with. So, what do you do to cut through the noise? Well… one way is to follow the greatest in the world of data science and simply hang on to their every word. We created a list of people who are followed by the humans of data around the world, share their experiences and insights regularly on social media and are well connected to the community.

Artficial intelligence (AI) can be used in the frontline of customer service, helping customers get instant answers, fast outcomes and consistency. The infuriatingly long wait for a phone call with a live agent, just to receive an over-simplistic answer, is now being replaced with chatbot technology, powered by AI, for an immediate solution. "Chatbots are capable of sourcing data at a much faster speed than an individual working behind the scenes," explains Bernd Gross, chief technology officer at Software AG. "This not only speeds up the time taken to deliver a service, but it also frees up employees' time to focus on more value-added tasks." This frontline revolution is in its early stages, but there are a number of industries that have already embraced self-service customer experience.

What methodologies (such as Agile) do they use to develop ML? Do they build their ML models using internal teams, external consultants, or cloud APIs? How long have they deployed ML in production? How do they evaluate success with machine learning? If you're curious (we were), check out our free ebook, The State of Machine Learning Adoption in the Enterprise. GET THE FREE EBOOK Ben Lorica Chief Data Scientist P.S.

- This paper contains analysis of main modern approaches to dynamic code generation, in particular creation of new classes during program execution. The main attention was paid to universal exploiters of homogeneous classes of objects, which were proposed as a part of such knowledge-representation model as object-oriented dynamic networks, as the tools for creation of new classes of objects in program runtime. As the result, algorithms for implementation of such universal exploiters of classes of objects as union, intersection, difference and symmetric difference were developed. These algorithms can be used in knowledge-based intelligent systems, which are based on object-oriented dynamic networks, and they can be adapted for some object-oriented programming languages with powerful metaprogramming opportunities. INTRODUCTION As the result of intensive development of programming languages and technologies during a few last decades, many new programming techniques, tools, technologies and directions within the area have aroused. One of the important and attractive directions within the modern programming is metaprogramming, the main ideas of which is an ability of programs to analyze, modify and generate codes of other programs, including their own.

If you want to carve out a career in machine learning then knowing where to start can be daunting. Not only is the technology built on college-level math, jobs in the field typically ask for a Master's degree in a related technical field. Yet if you're willing to work at it, it's never been easier to learn about machine learning, and getting started doesn't even require much mathematical knowledge. Here's five tips for breaking into the field from senior data scientists and machine-learning engineers, speaking to TechRepublic at the AI Conference presented by O'Reilly and Intel AI. If you plan to start tweaking the machine-learning models used then you'll need need a reasonably deep knowledge of math, spanning linear algebra, calculus and statistics.

While the use of machine learning (ML) in production started near the turn of the century, it's taken roughly 20 years for the practice to become mainstream throughout industry. With this report, you'll learn how more than 11,000 data specialists responded to a recent O'Reilly survey about their organization's approach--or intended approach--to machine learning. Data scientists, machine learning engineers, and deep learning engineers throughout the world answered detailed questions about their organization's level of ML adoption. About half of the respondents work for enterprises in the early stages of exploring ML, while the rest have moderate or extensive experience deploying ML models to production.