In December 2015, the revered French fashion house Louis Vuitton made a surprise announcement about the advertising campaign for its forthcoming spring-summer collection. The new range of clothes and accessories would be modelled on screen and in the pages of glossy magazines not by a famous actor or popstar but by a video game character: the pink-haired warrior Lightning from Final Fantasy XIII. Nicolas Ghesquière, the brand's creative director told the press he considered Lightning to be the "perfect avatar for a global heroic woman". The fictional character even carried out interviews to promote the partnership. It was not the first time a fashion brand had collaborated with a major video game. Previously, H&M, Moschino and Diesel had made digital clothes for The Sims.

New technologies can create new opportunities to engage with customers — but is it always worth it for companies to build out a presence on these platforms? When it comes to launching a voice assistant on Amazon Echo or Google Nest, recent research suggests the investment won’t necessarily pay off. The authors analyzed stock price data for nearly 100 companies before and after they released voice assistant features, and they found that while some firms experienced a positive bump in valuation after launching their voice assistant, others experienced no increase or even a notable decrease in market value. Specifically, firms that launched informational features experienced an average 1% increase in valuation, firms that launched object-control features experienced no change in stock price, and firms that launched transactional features actually experienced an average 1.2% decrease in market value. As such, the authors argue that companies should think carefully before investing in a voice assistant to ensure that the value added will be worth the substantial development costs.

A range of regulatory changes and new hires from the Biden administration signals a more proactive stance by the federal government towards artificial intelligence (AI) regulation, which brings the U.S. closer to that of the European Union (EU). These developments are promising, as is the inclusion of AI issues in the new EU-U.S. Trade and Technology Council (TTC). But there are other steps that these leading democracies can take to build alignment on curtailing AI harms. Since 2017, at least 60 countries have adopted some form of artificial intelligence policy, a torrent of activity that nearly matches the pace of modern AI adoption. The expansion of AI governance raises concerns about looming challenges for international cooperation.

Manufacturing plays a big role in today's society. With the impact of the recent pandemic, the industry has shifted to a digital transformation and the oncoming of the industrial revolution 4.0. With the application of AI, companies have gained operational efficiency and increased production quality while reducing risk and improving safety. If I say the word "manufacturing," would it capture your attention? What if I mention Apple and their "California Streaming" event from a couple days ago?

The majority of new products fail, despite the good intentions and aspirations of the entrepreneurs behind them. There are multiple reasons as to why, but without the right product research upfront, entrepreneurs risk investing thousands of dollars without any reliable data as to whether their idea is viable, manufacturable or in demand - all to achieve the necessary margins to run a profitable business. Many try it and never do it again because they lost money, had unreasonable expectations or got stuck somewhere in the process. Predictive analytics, better data around product engineers, and supply chain AI is helping entrepreneurs be more efficient, choose the right ideas and partner with the best resources. If entrepreneurs want to compete by bringing the right product to market at the right time and cost to be profitable, they need to become more efficient and have good data.

As we typically hear it referred to in industry today, a digital twin is a virtual representation of both the components and the real-time dynamics of a physical product throughout its life cycle. For example, an accurate digital twin of an aircraft engine not only matches all of the engine's initial qualities (weight, composition, structure), but also continues to match the engine's condition by accounting for every flight taken, every weather condition encountered, every maintenance or repair performed, type of fuel used, etc. This extensive data, gathered from IoT devices like smart sensors and cameras, should enable the extrapolation of the actual engine's conditions, making it easier to predict failure and schedule preventative maintenance. Because the data used to maintain the digital twin is unique--no two engines will take identical flights or encounter identical conditions--the digital twins of two different units of the same aircraft engine should evolve differently, dependent on the different conditions each engine is subjected to. In this article, we would like to extend the concept of digital twins beyond just the end product and into the process and resources used to create and maintain the product.

Digital innovation requires enterprises to learn how to understand, manage and change increasingly complicated processes. A new generation of process mining tools promises to make it easier to automatically interpret the digital exhaust of modern enterprises to help improve decision-making, drive innovation, and offer new products and services. "By understanding how processes really operate, companies can create operational fluidity to drive more efficient and productive operations that create better customer experiences," said Alexander Rinke, CEO and co-founder of Celonis, a process mining platform based in Germany. "Instead of simply identifying areas of friction, AI will further evolve process mining by allowing businesses to implement recommended changes with employees, enhancing productivity while also saving resources." The core idea of process mining lies in finding new ways to create and calibrate models of how things work with event logs.

Combining a digital twin with artificial intelligence (AI) can remove much of the guesswork and expense that comes with manufacturing a product. But what exactly is a digital twin, or virtual replica, and how does it streamline your production process in the real world? Dr. Norbert Gaus of Siemens Corporate Technology defines the concept of a digital twin as a "digital representation of a physical product in all its aspects." Digital twin technology can speed time to market, reduce costs, and allow a company to create a much broader portfolio of products. Dr. Gaus explains how AI-based simulations can take the place of creating multiple physical prototypes to achieve new designs. He describes how Siemens combines a digitized version of the physical product with artificial intelligence throughout the product life cycle. That product lifecycle includes design, components, manufacturing, operations, and service and maintenance. In this video, Dr. Gaus also discusses the challenges that Siemens has faced in the last ten years bringing digital twin automation to life.

Digital twin technology can speed time to market, reduce costs, and allow a company to create a much broader portfolio of products. Some leading companies are doing just that, through an approach called "digital twins." This essentially amounts to a "digital representation of a physical product in all its aspects," says Dr. Norbert Gaus of Siemens Corporate Technology. Gaus recently joined CXOTalk host Michael Krigsman to describe Siemens' adoption of digital twin technology. Siemens combines a digitized version of physical products with AI.

A digital twin is a digital replica of a living or non-living physical entity.[1] By bridging the physical and the virtual world, data is transmitted seamlessly allowing the virtual entity to exist simultaneously with the physical entity. Digital twin refers to a digital replica of physical assets (physical twin), processes, people, places, systems and devices that can be used for various purposes.[2] The digital representation provides both the elements and the dynamics of how an Internet of things device operates and lives throughout its life cycle.[3] Definitions of digital twin technology used in prior research emphasize two important characteristics. Firstly, each definition emphasizes the connection between the physical model and the corresponding virtual model or virtual counterpart[4]. Secondly, this connection is established by generating real time data using sensors. Digital twins integrate internet of things, artificial intelligence, machine learning and software analytics with spatial network graphs[5] to create living digital simulation models that update and change as their physical counterparts change.