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Top 6 Data Science Use Cases that are Changing the World - DataFlair

#artificialintelligence

Earlier we saw many data science applications. Today we will see the diverse data science use cases. We will take examples of social media, e-commerce, transportation, and healthcare to demonstrate some of the important data science use cases in contemporary industries. Stay updated with latest technology trends Join DataFlair on Telegram!! Data Science has brought another industrial revolution to the world. Every industry in this world requires data.


The 5 Hottest Technologies In Banking For 2021

#artificialintelligence

In the movie All The President's Men, Woodward and Bernstein meet their informant in a parking garage who tells them: "Follow the money." If you want to know which technologies are hot in banking, you should do the same. The truly "hot" technologies in banking are the ones that financial institutions invest in--not necessarily the ones the pundits talk about. At the end of the past seven years, Cornerstone Advisors has surveyed financial institutions to find out where their technology dollars will go in the coming year. In Cornerstone's What's Going On in Banking 2021 study, the top five technologies for 2021 are: 1) Digital account opening; 2) Application programming interfaces (APIs); 3) Video collaboration; 4) P2P payments; and 5) Cloud computing.


The Future of Fake News

#artificialintelligence

Is Bitcoin the revolution against unequal economic systems, or a scam and money laundry mechanism? Will artificial intelligence (AI) improve and boost humankind, or terminate our species? These questions present incompatible scenarios, but you will find supporters for all of them. They cannot be all right, so who's wrong then? Ideas spread because they are attractive, whether they are good or bad, right or wrong.


[N] Interview with Justin Harris, one of the best experts on Decentralizing of AI

#artificialintelligence

Hi, we just started our new series of chats with ML practitioners. However, learning about the experience gained by researchers, engineers and entrepreneurs doing real machine learning work can result in a great source of knowledge and inspiration. Please meet Justin Harris, the Senior Software Developer at Microsoft Research who recently published the paper Decentralized & Collaborative AI on Blockchain. Justin is currently using his experience in machine learning and crowdsourcing to implement a framework for ML in smart contracts in order to collect quality data and provide models that are free to use. We asked him why decentralization is important for the future of AI.


A Survey on Data Pricing: from Economics to Data Science

arXiv.org Artificial Intelligence

How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work.


The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability

arXiv.org Artificial Intelligence

High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining. These representations have different degrees of interpretability, with efficient distributed representations coming at the cost of the loss of feature to dimension mapping. This implies that there is obfuscation in the way concepts are captured in these embedding spaces. Its effects are seen in many representations and tasks, one particularly problematic one being in language representations where the societal biases, learned from underlying data, are captured and occluded in unknown dimensions and subspaces. As a result, invalid associations (such as different races and their association with a polar notion of good versus bad) are made and propagated by the representations, leading to unfair outcomes in different tasks where they are used. This work addresses some of these problems pertaining to the transparency and interpretability of such representations. A primary focus is the detection, quantification, and mitigation of socially biased associations in language representation.


The Future of Fake News - KDnuggets

#artificialintelligence

Is Bitcoin the revolution against unequal economic systems, or a scam and money laundry mechanism? Will artificial intelligence (AI) improve and boost humankind, or terminate our species? These questions present incompatible scenarios, but you will find supporters for all of them. They cannot be all right, so who's wrong then? Ideas spread because they are attractive, whether they are good or bad, right or wrong.


The Future of Fake News

#artificialintelligence

Is Bitcoin the revolution against unequal economic systems, or a scam and money laundry mechanism? Will artificial intelligence (AI) improve and boost humankind, or terminate our species? These questions present incompatible scenarios, but you will find supporters for all of them. They cannot be all right, so who's wrong then? Ideas spread because they are attractive, whether they are good or bad, right or wrong.


The Future of AI Part 1

#artificialintelligence

It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".


Ranking for Individual and Group Fairness Simultaneously

arXiv.org Machine Learning

Search and recommendation systems, such as search engines, recruiting tools, online marketplaces, news, and social media, output ranked lists of content, products, and sometimes, people. Credit ratings, standardized tests, risk assessments output only a score, but are also used implicitly for ranking. Bias in such ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. In this paper, we study a trade-off between individual fairness and group fairness in ranking. We define individual fairness based on how close the predicted rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking. We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous individual and group fairness guarantees comparable to the lower bound we prove. Our algorithm can be used to both pre-process training data as well as post-process the output of existing ranking algorithms. Our experimental results show that our algorithm performs better than the state-of-the-art fair learning to rank and fair post-processing baselines.