We discuss a data market technique based on intrinsic (relevance and uniqueness) as well as extrinsic value (influenced by supply and demand) of data. For intrinsic value, we explain how to perform valuation of data in absolute terms (i.e just by itself), or relatively (i.e in comparison to multiple datasets) or in conditional terms (i.e valuating new data given currently existing data).

SAN FRANCISCO - San Francisco is on track to become the first U.S. city to ban the use of facial recognition by police and other city agencies, reflecting a growing backlash against a technology that's creeping into airports, motor vehicle departments, stores, stadiums and home security cameras. Government agencies around the U.S. have used the technology for more than a decade to scan databases for suspects and prevent identity fraud. But recent advances in artificial intelligence have created more sophisticated computer vision tools, making it easier for police to pinpoint a missing child or protester in a moving crowd or for retailers to analyze a shopper's facial expressions as they peruse store shelves. Efforts to restrict its use are getting pushback from law enforcement groups and the tech industry, though it's far from a united front. Microsoft, while opposed to an outright ban, has urged lawmakers to set limits on the technology, warning that leaving it unchecked could enable an oppressive dystopia reminiscent of George Orwell's novel "1984."

In this Oct. 31, 2018, file photo, a man, who declined to be identified, has his face painted to represent efforts to defeat facial recognition during a protest at Amazon headquarters over the company's facial recognition system, "Rekognition," in Seattle. San Francisco is on track to become the first U.S. city to ban the use of facial recognition by police and other city agencies. SAN FRANCISCO – San Francisco is on track to become the first U.S. city to ban the use of facial recognition by police and other city agencies, reflecting a growing backlash against a technology that's creeping into airports, motor vehicle departments, stores, stadiums and home security cameras. Government agencies around the U.S. have used the technology for more than a decade to scan databases for suspects and prevent identity fraud. But recent advances in artificial intelligence have created more sophisticated computer vision tools, making it easier for police to pinpoint a missing child or protester in a moving crowd or for retailers to analyze a shopper's facial expressions as they peruse store shelves.

No startup is easy to grow, and with more and more artificial intelligence (AI) companies being founded around the world, the reality is that they can't all lead the way. Some will struggle along for years, unable to find any real traction, while others will perish completely shortly after they're funded. As a founder or marketer, you need to be able to effectively communicate your value proposition. People respond to great products, so it's crucial to understand that product is the new marketing. Only the strongest, smartest, and most creative will survive and thrive.

As natural language uses a diverse and often vague way to express ideas, identifying a norm conflict and its causes While most social norms are informal, they are often in contracts is a challenging task. An ever larger number of formalized by companies in contracts to regulate contracts being currently generated necessitates a fast and reliable trades of goods and services. When poorly process to identify norm conflicts. However, since such written, contracts may contain normative conflicts contracts are written in natural language, traditional revision resulting from opposing deontic meanings or contradict methods involve contract makers reading the contract and specifications. As contracts tend to be identifying conflicting points between norms. Such a method long and contain many norms, manually identifying requires huge human-effort and may not guarantee a revision such conflicts requires human-effort, which is that eliminates all conflicts. In response, we provide three time-consuming and error-prone. Automating such contributions towards automatically identifying and classifying task benefits contract makers increasing productivity potential conflicts between norms in contracts.

Voice recordings captured by Amazon's Alexa can be deleted but the automatically produced transcriptions remain in the company's cloud, according to reports. After Alexa hears its'wake' word, the smart assistant starts listening and transcribing everything it hears. All the voice commands said to the virtual assistant can be deleted from the central system, but the company still has the the text logs, according to CNET. This data is kept on its cloud servers, with no option for users to delete it, but the company claims it is working on ways to make the data inaccessible. Amazon workers are listening to private and sometimes disturbing voice recordings to improve the voice-assistants understanding of human speech.

In February 2019 Polish government added an amendment to a banking law that gives a customer a right to receive an explanation in case of a negative credit decision. This means that a bank needs to be able to explain why the loan wasn't granted if the decision process was automatic. In October 2018 world headlines reported about Amazon AI recruiting tool that favored men. Amazon's model was trained on biased data that were skewed towards male candidates. It has built rules that penalized résumés that included the word "women's".

Thanks to the Big Data revolution and increasing computing capacities, Artificial Intelligence (AI) has made an impressive revival over the past few years and is now omnipresent in both research and industry. The creative sectors have always been early adopters of AI technologies and this continues to be the case. As a matter of fact, recent technological developments keep pushing the boundaries of intelligent systems in creative applications: the critically acclaimed movie "Sunspring", released in 2016, was entirely written by AI technology, and the first-ever Music Album, called "Hello World", produced using AI has been released this year. Simultaneously, the exploratory nature of the creative process is raising important technical challenges for AI such as the ability for AI-powered techniques to be accurate under limited data resources, as opposed to the conventional "Big Data" approach, or the ability to process, analyse and match data from multiple modalities (text, sound, images, etc.) at the same time. The purpose of this white paper is to understand future technological advances in AI and their growing impact on creative industries. This paper addresses the following questions: Where does AI operate in creative Industries? What is its operative role? How will AI transform creative industries in the next ten years? This white paper aims to provide a realistic perspective of the scope of AI actions in creative industries, proposes a vision of how this technology could contribute to research and development works in such context, and identifies research and development challenges.

We consider a stochastic inventory control problem under censored demands, lost sales, and positive lead times. This is a fundamental problem in inventory management, with significant literature establishing near-optimality of a simple class of policies called ``base-stock policies'' for the underlying Markov Decision Process (MDP), as well as convexity of long run average-cost under those policies. We consider the relatively less studied problem of designing a learning algorithm for this problem when the underlying demand distribution is unknown. The goal is to bound regret of the algorithm when compared to the best base-stock policy. We utilize the convexity properties and a newly derived bound on bias of base-stock policies to establish a connection to stochastic convex bandit optimization. Our main contribution is a learning algorithm with a regret bound of $\tilde{O}(L\sqrt{T}+D)$ for the inventory control problem. Here $L$ is the fixed and known lead time, and $D$ is an unknown parameter of the demand distribution described roughly as the number of time steps needed to generate enough demand for depleting one unit of inventory. Notably, even though the state space of the underlying MDP is continuous and $L$-dimensional, our regret bounds depend linearly on $L$. Our results significantly improve the previously best known regret bounds for this problem where the dependence on $L$ was exponential and many further assumptions on demand distribution were required. The techniques presented here may be of independent interest for other settings that involve large structured MDPs but with convex cost functions.

The data points in machine learning are often real human beings. There is legitimate concern that traditional machine learning algorithms that are blind to this fact may inadvertently exacerbate problems of bias and injustice in society [25]. Motivated by concerns ranging from the granting of bail in the legal system to the quality of recommender systems, researchers have devoted considerable effort to developing fair algorithms for the canonical supervised learning tasks of classification and regression [13, 28, 20, 27, 34, 11, 30, 35, 26, 18, 21]. We extend this work to a canonical problem in unsupervised learning: centroid clustering. In centroid clustering, we want to partition data into k clusters by choosing k "centers" and then matching points to one of the centers.