'Fear and Loathing In the Age of QE … AI' is a presentation given at Mining Investment London earlier this week. Stephen Flood, CEO of GoldCore presentation (28 minutes) was well received at the conference which is a strategic mining and investment conference for leaders in the mining and investment sectors, bringing together attendees from 20 countries. 'Fear and Loathing In the Age of QE … AI' can be watched on Youtube here Why Silver Bullion Is Set To Soar – GoldCore Interview Gold Bullion Stored In Singapore Is Safest – Marc Faber Russia Seen More Likely to Sell Dollar Rather Than Gold Talking Gold with CNN's Richard Quest Gold holds near one-week low as dollar firms (Reuters.com) Goldman Says the Bitcoin Haters Just Don't Get It (Bloomberg.com) Goldman Warns That Market Valuations Are at Their Highest Since 1900 (Bloomberg.com)

In digital marketing and SEO there is no one-size-fits-all strategy or a formula for a guaranteed success. To make the best decisions for your company, it's invaluable to follow the trends and listen to established voices of the industry. Further we offer a short interview with one of the SEO influencers that has some useful insights to share. Maria knows 18 languages which has made her a multilingual SEO, PPC and social media marketing expert. She has managed software projects for well-known IT companies and banks, as well as cooperated with governments and police authorities.

Facebook has revealed more about how it is now using artificial intelligence to remove terror related content that appears on its platforms. The tech giant has come under pressure from the UK government on the matter, with Prime Minister Theresa May going as far as accusing Facebook and others of providing a "safe space" for terrorists. The social network said in an update on its efforts today that it was "hopeful AI will become a more important tool in the arsenal of protection and safety on the internet and on Facebook" and demonstrated the success of its early efforts. It said that 99 per cent of content related to ISIS and Al Qaeda that's removed is identified using AI before it is flagged by humans. However, it noted that AI could not be a silver bullet and human efforts were still required.

Universities in China and elsewhere in Asia are belatedly joining global alliances to promote ethical practices in artificial intelligence or AI, which were previously being studied in university research centres in a fragmented way. Countries like South Korea, Japan, China and Singapore are making huge investments in AI research and development, including the AI interface with robotics and are in some areas rapidly narrowing the gap with the United States. But crucially there are still no international guidelines and standards in place for ethical research, design and use of AI and automated systems. China's universities in particular are turning out a large number of researchers specialising in AI. Whereas in the past they would head for Silicon Valley in the US, many are now opting to stay in the country to work for home-grown technology giants such as Alibaba, Tencent and Baidu – companies which gather and use huge amounts of consumer data with few legal limits.

While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only compound when trying to generalize generative models to incorporate human input. We introduce Correlation Explanation (CorEx), an alternative approach to topic modeling that does not assume an underlying generative model, and instead learns maximally informative topics through an information-theoretic framework. This framework naturally generalizes to hierarchical and semi-supervised extensions with no additional modeling assumptions. In particular, word-level domain knowledge can be flexibly incorporated within CorEx through anchor words, allowing topic separability and representation to be promoted with minimal human intervention. Across a variety of datasets, metrics, and experiments, we demonstrate that CorEx produces topics that are comparable in quality to those produced by unsupervised and semi-supervised variants of LDA.

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.

Deep neural networks--a form of artificial intelligence--have demonstrated mastery of tasks once thought uniquely human. Their triumphs have ranged from identifying animals in images, to recognizing human speech, to winning complex strategy games, among other successes. Now, researchers are eager to apply this computational technique--commonly referred to as deep learning--to some of science's most persistent mysteries. But because scientific data often looks much different from the data used for animal photos and speech, developing the right artificial neural network can feel like an impossible guessing game for nonexperts. To expand the benefits of deep learning for science, researchers need new tools to build high-performing neural networks that don't require specialized knowledge.

The Islamic extremist group, the deadliest in Africa, has been targeted this year by nearly 30 U.S. military drone strikes after the Trump administration approved expanded operations against it and declared the southern part of the Horn of Africa nation a zone of active hostilities. The U.S. now has more than 500 military personnel in Somalia.

And yet our world increasingly resembles a fictional one, accelerating towards a dystopian reality that few would have predicted just a few years ago. Despite science's inexorable march of progress - from the discovery of new cancer drugs to the development of quantum computation - extremist political movements and the wanton spread of falsehoods frustrate its dissemination. This opposition to scientific culture has real consequences: diseases once eradicated re-emerge as anti-vaccination beliefs spread; cataclysmic hurricanes batter entire cities as climate-change denial prevents global solutions; democratic elections are undermined by shadowy adversaries using digital technology. In March this year, the scientific community, beleaguered by the anti-science sentiment stoked by conservative populism, took to the streets, marching for science across cities around the world. But as science becomes politicised, should scientists become political?

A new platform from cybersecurity research firm ERPScan uses machine and deep learning to cover all aspects of SAP security – predictive, preventive, detective and responsive capabilities – in a single solution. The platform, released in Las Vegas last week, introduces three features important for SAP security: threats and anomaly detection with a user-defined interface and functionality; the integration of all SAP security areas (including platform security, code security and segregation of duties); and support for SAP cybersecurity requirements from Gartner's PPDR (Predict, Prevent, Detect, Respond and Monitor) framework. ERPScan founder and chief technology officer, Alexander Polyakov, said the solution is a real breakthrough for the company. "We spent the last two years developing a solution that would be able to not only cover all areas of SAP cybersecurity, but also be intuitive by adding machine learning and adaptive interfaces. Our secret team of data scientists and machine learning experts battled with the experienced Research team and taught the system to detect advanced attacks and anomalous user behaviour. Now we are ready to present the new generation of SAP cybersecurity products, and it's so exciting," said Polyakov.