ESF thus beckons the valuation of ecosystem services (VES) as a means to signalling nature's contribution to the (re)production of value (Barbier et al., 2009; Villa et al., 2009; Fisher et al., 2010; Gómez-Baggethun et al., 2016); for value is the central category of modern capitalist societies, and the valorisation of value -- i.e., economic growth sublimated into economic development -- their driving force (see, e.g., Mankiw (2016) and Holden et al. (2017)). VES is, in this sense, inscribed in an interpretive approach to modern capitalist praxis, not only invoking assumptions that are instrumentally validated in a retroactive manner, but also taking for granted precisely those historical and material conditions which VES is meant to interpret and, in doing so, reproduce. Overlooking the historical basis of ESF and VES has important practical consequences. When VES practitioners elicit value, a moment or specific field of the social praxis embodied in the valorisation of value is inaugurated, allowing value to mediate other social constructs built around the idea of nature. Since the patterns of actions that make up the capitalist social praxis are presupposed within this new ambit, value takes on a transhistorical quality that justifies its allencompassing and unreflective usage (see, e.g., Badura et al. (2016) and Gómez-Baggethun and Martín-López (2015)).

Businesses should start small and fail fast with machine learning (ML) projects to get the best ROI. Some of the most common use cases for small- to medium-sized businesses (SMBs) include fraud detection, sales optimization, marketing, and document analysis. But the benefits of implementing ML goes even further. This ebook, based on the latest ZDNet / TechRepublic special feature, helps small and medium-sized businesses build a technology stack that promotes innovation and enables growth. Steve Tycast, director of data and analytics at AIM Consulting, said ML efforts focused on operational analytics can reduce costs, drive efficiencies, and increase speed to market.

Businesses should start small and fail fast with machine learning (ML) projects to get the best ROI. Some of the most common use cases for small- to medium-sized businesses (SMBs) include fraud detection, sales optimization, marketing, and document analysis. But the benefits of implementing ML goes even further. This ebook, based on the latest ZDNet / TechRepublic special feature, helps small and medium-sized businesses build a technology stack that promotes innovation and enables growth. Steve Tycast, director of data and analytics at AIM Consulting, said ML efforts focused on operational analytics can reduce costs, drive efficiencies, and increase speed to market.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Get all the latest news on coronavirus and more delivered daily to your inbox. China has amassed a vast collection of information about its people in recent years as the Chinese Communist Party continues to deploy its surveillance apparatus to exercise control over its 1.4 billion inhabitants at the expense of privacy. In recent years, China has spent billions to purchase the latest technology like facial recognition, artificial intelligence and other digital technologies to add to its network of monitoring systems.

Tax policy is surely a complex beast, and depending on your political leanings, you probably have some strong feelings about how it should be implemented. Salesforce AI researchers are trying to build a model to bring artificial intelligence to bear on what will undoubtedly always be a highly political process. Richard Socher, who heads up AI research at Salesforce, says the company is researching all kinds of solutions related to AI and business, and how it could improve the Salesforce product family; however, he also looks at how his team could use AI to solve a set of broader social issues beyond what it can do for the product line. Socher says when you look at the biggest issues of our time, one of the largest is economic inequality, and how we could use policy to solve that. To that end, the company created a model it calls an AI economist that could look at various economic variables, a broad set of economic models and using the power of AI begin to demonstrate how various policies affect economic inequality versus productivity.

However, according to the USPTO's ruling, inventions can only be submitted (and depending on how philosophical you want to get, conceived) by a "natural person," as reflected in the language of patent law and also in previous federal court rulings. Speaking of philosophy, the ruling quotes a Federal Circuit court decision from 1994 that expounds on the nature of invention in a way that's certain to send your brain down the maze of reflexive self-awareness. "Conception is the touchstone of inventorship, the completion of the mental part of invention. It is the formation in the mind of the inventor, of a definite and permanent idea of the complete and operative invention ... [Conception] is a mental act ..." Patents that list DABUS as the inventor have also been denied in Europe and the UK for similar reasons related to personhood. The European Patent Office also raised the issue of who, exactly, would enforce the rights granted to an inventor under such a circumstance. Thaler, the mind behind DABUS, is a physicist and founder of Imagination Engines, a company that researches and develops artificial neural networks.

The nation's top inventors and businesses rely heavily on the United States Patent and Trademark Office to issue patents for inventions and register trademarks for product and intellectual property identification. The agency has come to embrace increasingly more emerging and advanced technologies in recent years to meet its mission, and it is now also enduring a large-scale modernization. USPTO Chief Information Officer Jamie Holcombe is working to help make the agency's systems "better, cheaper and faster." Holcombe joined the agency at the request of Under Secretary of Commerce for Intellectual Property and Director Andrei Iancu, who immediately articulated his aims to "propel the USPTO into the next decade." "He wanted to ensure that all the up-to-date commercial tools were available to the USPTO examiners so that we could conduct our business in the most advanced way possible," Holcombe told Nextgov in the latest episode of Critical Update.

Salesforce today announced the AI Economist, a research environment designed to elucidate how economic design might be improved with techniques from the field of AI and machine learning. The goal is to help economists, governments, and others design tax policies that optimize not only productivity and conservation, but that promote widespread, whole-country social equality. Studies have shown that income inequality gaps can negatively impact economic growth, economic opportunity, and even health. For example, over-taxation can discourage people from working, leading to lower productivity. But it's difficult to experiment with tax policies in the real world, at least in part because economic theory relies on stylized assumptions that are tough to validate, like people's sensitivity to taxes. The AI Economist, then, learns the best tax policies from simulations in which citizens and a government adapt and learn.

Artificial intelligence (AI) technology that uses algorithms to assist in decision-making offers tremendous opportunity to make predictions and evaluate "big data." The Federal Trade Commission (FTC), on April 8, 2020, provided reminders in its Tips and Advice blog post, Using Artificial Intelligence and Algorithms. This is not the first time the FTC has focused on data analytics. In 2016, it issued a "Big Data" Report. AI technology may appear objective and unbiased, but the FTC warns of the potential for unfair or discriminatory outcomes or the perpetuation of existing socioeconomic disparities.

These are just a few examples of how AI is increasingly at the core of CIOs' strategies, with a key focus on customer experience, bridging the efficiency of your employers with the expectations of your customers. If you are interested in finding out "why AI?" and "why now?", this topic is covered in depth in the new Capgemini point of view, AI: re-humanizing digital customer experience. It looks at the value human-like AI brings to the business and it discusses how it can enable employees and enterprise systems to better predict and address customer needs for a personalized experience. Still, AI comes with a set of potential risks, such as loss of control in self-driving cars or smart house tools, the misuse of confidential information, and the industrialization of bias through machine learning models. Instead of whitewashing the implications, I'm happy to highlight how Capgemini's point of view offers actions to ensure your customer-facing approach to AI promotes honesty, trust, and integrity – the top three emotions that drive loyalty according to our 2017 survey – Loyalty Deciphered.