Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm. These studies share the common characteristic of updating only a fraction of the population at any given iteration of the algorithm. In this work we investigate a new, simpler partial update strategy, in which a random subset of solutions is selected at every iteration. The performance of the MOEA/D using this new resource allocation approach is compared experimentally against that of the standard MOEA/D-DE and the MOEA/D with relative improvement-based resource allocation. The results indicate that using the MOEA/D with this new partial update strategy results in improved HV and IGD values, and a much higher proportion of non-dominated solutions, particularly as the number of updated solutions at every iteration is reduced.

Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper, we make the case for explicitly stating interesting statements which are not true. Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking. We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences, we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental results show that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statements for 100K popular Wikidata entities.

Sunspring debuted at the SCI-FI LONDON film festival in 2016. Set in a dystopian world with mass unemployment, the movie attracted many fans, with one viewer describing it as amusing but strange. But the most notable aspect of the film involves its creation: an artificial-intelligence (AI) bot wrote Sunspring's screenplay. "Maybe machines will replace human storytellers, just like self-driving cars could take over the roads." A closer look at Sunspring might raise some doubts, however.

The problem of finding the sparsest vector (direction) in a low dimensional subspace can be considered as a homogeneous variant of the sparse recovery problem, which finds applications in robust subspace recovery, dictionary learning, sparse blind deconvolution, and many other problems in signal processing and machine learning. However, in contrast to the classical sparse recovery problem, the most natural formulation for finding the sparsest vector in a subspace is usually nonconvex. In this paper, we overview recent advances on global nonconvex optimization theory for solving this problem, ranging from geometric analysis of its optimization landscapes, to efficient optimization algorithms for solving the associated nonconvex optimization problem, to applications in machine intelligence, representation learning, and imaging sciences. Finally, we conclude this review by pointing out several interesting open problems for future research.

NAIROBI (Thomson Reuters Foundation) - Countries are rapidly developing "killer robots" - machines with artificial intelligence (AI) that independently kill - but are moving at a snail's pace on agreeing global rules over their use in future wars, warn technology and human rights experts. From drones and missiles to tanks and submarines, semi-autonomous weapons systems have been used for decades to eliminate targets in modern day warfare - but they all have human supervision. Nations such as the United States, Russia and Israel are now investing in developing lethal autonomous weapons systems (LAWS) which can identify, target, and kill a person all on their own - but to date there are no international laws governing their use. "Some kind of human control is necessary ... Only humans can make context-specific judgements of distinction, proportionality and precautions in combat," said Peter Maurer, President of the International Committee of the Red Cross (ICRC).

Finnish technology firm Reaktor and the University of Helsinki joined forces to educate people on AI for free. The institutions combined to develop an online course to teach the basics of AI to anyone interested in the technology. Reaktor and the University also challenged organizations to train their staff in AI, so far over 200 organisations have pledged to do so – including banks, telecoms, and healthcare organizations. Almost 90 000 students have signed up for the course since it began in May. While popular with Finns, the course is already seeing strong demand globally, attracting students from over 80 different countries.

In the 10 days leading up to Christmas this year, I searched on Instagram for three of my exes, an acquaintance I met on a trip to Cuba four years ago, an account dedicated to astrology memes, a past roommate, my own dog's account (@lucythetherapypup), my best friend's sweater-wearing poodle, a famous Pomeranian who lives in New York, a bird named Parfait I recently met at a San Francisco market, 10 contestants of the reality TV show Love Island, and the hashtag #wienerdog. I know all of this because Instagram told me. That's because this month, I submitted a data request under California's new privacy law to see just how much information the company has on me. What I got was a wide-ranging look at how my life has changed in the last 10 years since I first logged on to Instagram, and a window into what the company is willing to share about what it knows about me. Under the California Consumer Privacy Act, I have the right to demand companies disclose "any personal information" they collect about me and request a copy of that information.

Find here a listing of the latest industry news in genomics, genetics, precision medicine, and beyond. Updates are provided on a monthly basis. Sign-Up for our newsletter and never miss out on the latest news and updates. As 2019 came to an end, Veritas Genetics struggled to get funding due to concerns it had previously taken money from China. It was forced to cease US operations and is in talks with potential buyers. The GenomeAsia 100K Project announced its pilot phase with hopes to tackle the underrepresentation of non-Europeans in human genetic studies and enable genetic discoveries across Asia. Veritas Genetics, the start-up that can sequence a human genome for less than $600, ceases US operations and is in talks with potential buyers Veritas Genetics ceases US operations but will continue Veritas Europe and Latin America. It had trouble raising funding due to previous China investments and is looking to be acquired. Illumina loses DNA sequencing patents The European Patent ...

Users expect to see that friendly search box in their applications. They seem to really like it, because it's so simple to use. You don't need a user manual to figure out search. In fact, if your application doesn't have search, you'll be pelted with negative reviews. No wonder you see search in so many applications. It's very difficult to implement. We all know it's more than just simple text matching. Those of us with database backgrounds know that searching for "prefix*" is a lot easier than searching for "*suffix". And users want to do all sorts of weird searches like "*run*", which should match ran, or shrunken or brunt, or--you get the idea. Quick search results and performance are important, as is accuracy and ranking.

The discriminatory biases of the algorithms, the invasion of privacy, the risks of facial recognition and the regulation of human-machine relations are challenges that AI needs to face. However, the interests of governments and large companies often prevail over good practices. Artificial intelligence (AI) is no longer a science fiction thing, it is everywhere. Your bank uses it to know if it is going to give you a credit or not and the ads you see on your social networks come out of a classification carried out by an algorithm, which has microsegmented and'decided' if it shows you offers of wrinkle creams or high-end cars. Facial recognition systems, which use airports and security forces, are also based on this technology.