CPO Rising's Technology Round-Up returns today with an assortment of supply management technology news and updates from the past month to share with our community. If you are a sourcing, procurement, or spend management solution provider and you are continually innovating the way that procurement and supply chain leaders and practitioners drive value, we'd love to hear from you. Please drop us a note at editor at cporising dot com. Late last month, at the WeAreDevelopers Congress in Vienna, JAGGAER unveiled its neural network-based Automatic Spend Classification tool. It leverages elements of artificial intelligence (AI), namely natural language processing (NLP) and neural networks, to analyze text descriptions, product names, and product numbers within invoices and other documents.

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Many of us are familiar with Google Translate, translation applications for travellers' smartphones and the instruction manuals of various devices and products. Professional translators also make use of machines. Training a computer to translate between two specific languages takes millions of sentences or billions of words worth of text. Maarit Koponen, a postdoctoral researcher at the University of Helsinki, is investigating which errors made by machines lead to misunderstandings and how those mistakes could be identified. The learning algorithms behind machine translation are called artificial intelligence, but machines are not intelligent in the way humans or the super AIs of science-fiction films are.

The text has been redacted and altered by the BMBF in addition to DW's normal editorial guidelines. As such, the text does not entirely reflect the audio of the interview as recorded on December 5, 2019. DW: We're in Berlin at an "Artificial Intelligence Camp" organized by the Gesellschaft für Informatik and the German Federal Ministry of Education and Research, where you head the department for "Research for Digitalization and Innovation." Artificial intelligence is in your remit. And all the people here are experts in the field.

This is what makes them dangerous.' Previously, I talked about how AI could change the human-machine relationship. Who should be making the decisions in an autonomous car? Should humans always be able to overrule robot decisions? What if you only have a split second to react?

What would a theory of artificial intelligence look like, and how might it be achieved? When designing a new engine or airplane wing, engineers can apply theories that have withstood years of scientific scrutiny, such as the Laws of Thermodynamics or Newton's Laws of Motion. To what theories --if any --can artificial intelligence (AI) researchers and technology pioneers turn when designing neural networks or algorithms? We asked experts from the fields of computer science, theoretical physics, and philosophy for their insights. The Encyclopedia Britannia defines a scientific theory as a "systematic ideational structure of broad scope, conceived by the human imagination, that encompasses a family of empirical (experiential) laws regarding regularities existing in objects and events, both observed and posited."

The advent of general adversarial networks (GANs) has led to increased popularity and adoption of artificial intelligence in the art world. It has been quite a few years since researchers have been trying to infuse the artistic skills into AI and there have been many interesting developments since then. Artists such as Mario Klingemann, Anna Ridler and many others have been at the forefront of this new-age GAN-powered art. Not only is AI creating breathtaking artwork but it is also being sold at auctions for hefty amounts. For instance, Canadian-Mexican artist Rafael Lozano-Hemmer has already made around $600,000 for an AI artwork.

China's rapidly growing tech economy is now facing some serious questions about the trade-offs involved in the widespread adoption of emerging technologies such as AI. In fact, China's Ministry of Science and Technology is now leading the debate over the relative benefits and drawbacks of artificial intelligence, with at least some recognition that certain AI applications – such as facial recognition technology – might have some very negative implications for personal privacy. At the same time, other regulatory authorities within China – including the Cyberspace Administration of China – are now taking a closer look at how popular consumer technologies (including mobile apps) might also be going too far when it comes to collecting, using and sharing user data. For now, the most high-profile emerging technology within China is artificial intelligence (AI), which is being embraced much more quickly and widely than in the West. For example, Chinese law enforcement authorities are using AI-powered facial recognition technologies to crack down on crime and terrorism, while urban planners and other policymakers are embracing AI as a way to come up with more efficient healthcare, education and transportation solutions.

Construction is one of the most challenging sectors for artificial intelligence and robotics. When it comes to manual skills and holistic thinking, no advanced technology can hold a candle to human labor. So far, we have seen a robot putting up a wall, the various uses of augmented and virtual reality (AR/VR) and AI systems performing repetitive tasks in the construction industry that people no longer want to do. While the concepts of work and tasks for people have changed with Industry 4.0, by 2050, it is estimated that two-thirds of the world's population will live in cities, which means new cities, more housing and more roads. To meet this demand, the construction sector needs to build around 13,000 homes every day worldwide.

Genpact's study, Commercial Banking: The Customer Experience Imperative, surveyed 500 senior commercial banking executives on the industry's changing landscape. The research reveals that while many banks understand the growing importance of CX and AI, questions remain regarding their ability to use AI for greater value and mine data effectively to embrace digital transformation and enhance service. The good news is most banks are optimistic about the future. Two-thirds of respondents say they expect to use AI across their organization in the next 12 to 18 months, compared with fewer than one in five today. Yet, some executives surveyed seem hesitant about whether AI will deliver on its promises to drive new personalized services to improve customer experience.