As long as this world is moving ahead, the visualization of AI is turning out into a reality at quite a relentless pace. At present, the election candidates are all humans and there is absolutely no involvement of AI in any elections of the world. But, to turn the tables around, New Zealand is all set to launch their first AI politician-Sam in the upcoming elections of 2020. The Veteran politician, called Sam, was created by a 49-year old Entrepreneur- Nick Gerritsen in New Zealand. Under constant learning and with an intention to create something new in this ever running world, scientists developed the world's first AI politician that can answer to any persons queries, policies around housing, local issues education, migration and much more.

DERA ISMAIL KHAN, Pakistan – Pakistani intelligence officials say a suspected U.S. drone strike has hit a militant compound near the Afghan border, killing three militants. Two officials say the unmanned drone fired two missiles at the Ghazni compound of the militant Haqqani network's commander Abdur Rasheed early in the morning on Thursday. The network is affiliated with the Taliban. They said it's unclear if Rasheed was at the compound located on the Pesho Ghar mountain in the Kurram tribal region's Ghuzgari area. The officials spoke on condition of anonymity because they are not authorized to speak to the media.

The futurist and inventor believes that we have no more than "a five to 10 per cent chance" of successfully making artificial intelligence safe enough not to wipe out the human race. Musk seeks a proactive approach to what he sees as a potentially deadly worldwide AI crisis, which means that governments must become well-versed in the concepts before such understanding becomes a matter of life and death. "Normally the way regulations are set up is when a bunch of bad things happen, there's a public outcry, and after many years a regulatory agency is set up to regulate that industry," he said. That, in the past, has been bad but not something which represented a fundamental risk to the existence of civilization." And Musk is certain that approach will not work for artificially intelligent superweapons: "Once there is awareness, people will be extremely afraid, as they should be… By the time we are reactive in AI regulation, it'll be too late."

SAN FRANCISCO – Top executives at Uber Technologies Inc. used the encrypted chat app Wickr to hold secret conversations, current and former workers testified in court this week, setting up what could be the first major legal test of the issues raised by the use of encrypted apps inside companies. The revelations Tuesday and Wednesday about the extensive use of Wickr inside Uber upended the high-stakes legal showdown with Alphabet Inc.'s Waymo unit, which accuses the ride-hailing firm of stealing its self-driving-car secrets. There is nothing inherently unlawful about instructing employees to use disappearing messaging apps, said Timothy Heaphy, a lawyer at Hunton & Williams and a former U.S. attorney in Virginia. However, companies have an obligation to preserve records that may be reasonably seen as relevant to litigation or that fall under data retention rules set by industry regulators. In Uber's situation, chat logs that could help get to the bottom of the trade secrets case are now inaccessible.

With movie theaters full of films about rogue robots taking over the world, many fear the impact artificial intelligence (AI) might have on human life. In light of this year's Queen's Lecture at the Technical University (TU) Berlin, DW has caught up with AI and engineering expert Zoubin Ghahramani. We asked him about human and artificial intelligence, machine learning and what he thinks our future with AI might look like. DW: Professor Ghahramani, before we speak about artificial intelligence and machine learning, could you define human intelligence for us? Zoubin Ghahramani: When they hear the word'intelligence' people often think about the differences between individual humans, but actually the more interesting question is'how are we different from other animals, plants and computers?'

This summer, Intel has been collaborating with the NASA Frontier Development Lab (FDL), an AI R&D accelerator targeting knowledge gaps useful to the space program. The NASA FDL, hosted at the SETI Institute, was established to apply AI to five specific challenges in areas relevant to the space program: Planetary Defense (defending the Earth from potentially hazardous asteroids), Space Weather (better predicting solar activity) and Space Resources (locating and accessing the resources we'll need to go back to the moon and expand into the solar system). Earlier this summer, we introduced you to this collaboration, and we have exciting updates to share. The NASA FDL team successfully applied the Intel Nervana Deep Learning platform to automate the creation of lunar maps at our Moon's poles – a critical step in helping both identify potential landing sites and navigation in the shadowed regions of the moon. Here, permanent darkness and extremely low temperatures make for an ideal location for water ice (and other volatiles), but highly challenging conditions for future missions that would be impossible without detailed mapping.

We consider the problem of winner determination under Chamberlin--Courant's multiwinner voting rule with approval utilities. This problem is equivalent to the well-known NP-complete MaxCover problem and, so, the best polynomial-time approximation algorithm for it has approximation ratio 1 - 1/e. We show exponential-time/FPT approximation algorithms that, on one hand, achieve arbitrarily good approximation ratios and, on the other hand, have running times much better than known exact algorithms. We focus on the cases where the voters have to approve of at most/at least a given number of candidates.

AI developments on smart cities, if not critical, risk making a flawed urban model more efficient. Instead, we suggest that AI should challenge the mainstream techno-optimistic approach to solving urban problems by dialoguing with other academic fields, questioning the dominant urban paradigm, and creating transformative solutions. We claim that doing differently, rather than doing better, may be smarter for cities and the common good. This article is part of the special track on AI and Society.

We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm produces rule lists with optimal training performance, according to the regularized empirical risk, with a certificate of optimality. By leveraging algorithmic bounds, efficient data structures, and computational reuse, we achieve several orders of magnitude speedup in time and a massive reduction of memory consumption. We demonstrate that our approach produces optimal rule lists on practical problems in seconds. Our results indicate that it is possible to construct optimal sparse rule lists that are approximately as accurate as the COMPAS proprietary risk prediction tool on data from Broward County, Florida, but that are completely interpretable. This framework is a novel alternative to CART and other decision tree methods for interpretable modeling.

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.