Imagine you're the president of a European country. You're slated to take in 50,000 refugees from the Middle East this year. Most of them are very religious, while most of your population is very secular. You want to integrate the newcomers seamlessly, minimizing the risk of economic malaise or violence, but you have limited resources. One of your advisers tells you to invest in the refugees' education; another says providing jobs is the key; yet another insists the most important thing is giving the youth opportunities to socialize with local kids.

On this week's episode of my podcast, I Have to Ask, I spoke to Annie Lowrey, a contributing editor at the Atlantic and the author of the new book Give People Money: How a Universal Basic Income Would End Poverty, Revolutionize Work, and Remake the World. It's about universal basic income--the idea that the government would give all its citizens checks every month. Versions of this proposal have caught on with people on the left as well as tech leaders in Silicon Valley and even some hardcore libertarians. Lowrey has written for many years now about economics, but Give People Money is both a reported work--she travels to Kenya, South Korea, and India to view their economic experiments--and a policy brief on what she believes can help alleviate some of the social and political discontent that has arisen from economic change and dislocation. Below is an edited excerpt from the show. In it, we discuss the benefits and drawbacks of UBI, whether or not we should be skeptical that so many Silicon Valley titans have embraced the idea, and how to make the safety net less vulnerable to political attacks.

It seems everyone is talking about artificial intelligence and machine learning, especially within the fraud prevention sphere. But despite all the buzz, it's not always clear how these intelligent elements actually help curb fraud rates. First things first: though they are often used interchangeably, artificial intelligence (AI) and machine learning (ML) are not the same thing. AI refers to machines that are able to carry out tasks in a human way, while machine learning is a component of AI that involves giving a machine access to large amounts of data and allowing it to learn for itself and solve problems based on that data and patterns the machine recognizes. The concepts of Artificial Intelligence and machine learning have been around since the 1950s. However, only recently have they become a reality for businesses due to advanced developments in the field and newfound affordability.

Two Japanese national research institutes have succeeded in using artificial intelligence to identify early stage stomach cancer with a high accuracy rate. The breakthrough may help extend the lives of patients in Japan, where stomach cancer is one of the leading causes of death. According to the National Cancer Center, 45,531 people died of stomach cancer in 2016. According to Riken and the National Cancer Center, it took AI only 0.004 seconds to judge whether an endoscopic image showed early stage cancer or normal stomach tissue. AI correctly detected cancer in 80 percent of cancer images, while the accuracy rate was 95 percent for normal tissue.

This work proposes an adaptive trace lasso regularized L1-norm based graph cut method for dimensionality reduction of Hyperspectral images, called as `Trace Lasso-L1 Graph Cut' (TL-L1GC). The underlying idea of this method is to generate the optimal projection matrix by considering both the sparsity as well as the correlation of the data samples. The conventional L2-norm used in the objective function is sensitive to noise and outliers. Therefore, in this work L1-norm is utilized as a robust alternative to L2-norm. Besides, for further improvement of the results, we use a penalty function of trace lasso with the L1GC method. It adaptively balances the L2-norm and L1-norm simultaneously by considering the data correlation along with the sparsity. We obtain the optimal projection matrix by maximizing the ratio of between-class dispersion to within-class dispersion using L1-norm with trace lasso as the penalty. Furthermore, an iterative procedure for this TL-L1GC method is proposed to solve the optimization function. The effectiveness of this proposed method is evaluated on two benchmark HSI datasets.

Speaking at the recent SAPICS Conference in Cape Town, Dull revealed how AI in the form of IBM Watson is being used to aid decision making in public health supply chains in developing countries and improve patients' access to life-saving medicines. She contends that the big challenges in these markets are using data to make better decisions and influencing skills. "Today's AI technology offers the solution, allowing us to leverage cognitive capabilities to create a transparent, intelligent and predictive supply chain. We asked ourselves what could be achieved if we could get IBM Watson to place chatbots on the platforms available to health workers in African supply chains, so that people can improve their learning." An AI powered chatbot can deliver personalised learning on mobile devices to enhance the supply chain skills of the health workers that staff most African healthcare supply chains.

Industrial Revolution (4IR) (Xing and Marwala, 2017), many countries (Shah et al., 2015; Ding and Li, 2015) are setting out an overarching goal of building/securing an "innovation-driven" economy. As innovation emphasizes the implementation of ideas, creativity is typically regarded as the first stage of innovation in which generating ideas becomes the dominant focus (Tang and Werner, 2017; Amabile, 1996; Mumford and Gustafson, 1988; Rank et al., 2004; West, 2002). In other words, if creativity is absent, innovation could be just luck. Though creativity can be generally understood as the capability of producing original and novel work or knowledge, the universal definition of creativity remains rather controversial, mainly due to its complex nature (Tang and Werner, 2017; Hernández-Romero, 2017). But putting it informally, by famous innovator Steve Jobs in 1995, we can think creativity like this way (Sanchez-Burks et al., 2015): "Creative people [are] able to connect experiences they've had and synthesize new things."

The graph represents a network of 2,727 Twitter users whose tweets in the requested range contained "#iot OR "internet of things"", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 20 July 2018 at 20:39 UTC. The requested start date was Thursday, 19 July 2018 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 13-day, 0-hour, 11-minute period from Thursday, 05 July 2018 at 07:32 UTC to Wednesday, 18 July 2018 at 07:43 UTC.

The graph represents a network of 2,727 Twitter users whose tweets in the requested range contained "#iot OR "internet of things"", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 20 July 2018 at 20:39 UTC. The requested start date was Thursday, 19 July 2018 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 13-day, 0-hour, 11-minute period from Thursday, 05 July 2018 at 07:32 UTC to Wednesday, 18 July 2018 at 07:43 UTC.

Last month, two parliamentary reports on the involvement of the British intelligence services in torture and rendition were released last week. What has been hypothesized by several journalists is now confirmed: that British functionaries--to include soldiers, civil servants and intelligence officers with MI5 and MI6--knew about and participated in a vast array of human rights abuses committed during the capture and interrogation of terrorism suspects. The Guardian's Peter Beaumont writes with great contempt for what has transpired since 2001, "[A]s it is now quite clear, it was all a bloody lie. The answers given to journalists at the Observer over the years, as well as colleagues at The Guardian and those at other news organisations, as they investigated these allegations, were rotten with untruth and evasion." Governments' lying to their citizens about covert wars is hardly new, nor is the pervasive use of kidnapping of terrorism suspects by the CIA to include its many "black sites."