'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)

Mobility was a keyword at the world's largest robot exhibition, held in Tokyo. The T-HR3 - Toyota's latest robot - is not only good at Tai Chi but can mirror complex human movements in real time. Today it is being controlled by a human via a VR set: a 16 point manoeuvring system allows the pilot to control the robot remotely. Modules on the device directly communicate the pilot's actions to the robot, resulting in smooth and synchronised movement between robot and pilot. The humanoid robot is 1.5 metres tall, weighs 50 kg and is made up of 29 body parts.

Prowler.io CEO Vishal Chatrath and I are talking about artificial intelligence in a room called Maria. The meeting room, which forms part of Prowler's bright and buzzing Hills Road offices, doesn't have consciousness as far as I'm aware, but is named in honour of one of the main characters in Metropolis, a 1927 film which depicts an early vision of the rise of the machines. In it, Maria sees her likeness transferred to a robot, which leads an uprising to destroy the titular Metropolis. It's one of several nods to sci-fi at Prowler HQ – the firm's boardroom is called Skynet – but the company, which is being tipped by many as the Next Big Thing to emerge from the Cambridge cluster, has its sights set firmly on using AI to solve real-world problems. Vishal explains that these problems could be, well, just about anything.

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.

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose LatPlan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), and a pair of images representing the initial and the goal states (planning inputs), LatPlan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. The contribution of this paper is twofold: (1) State Autoencoder, which finds a propositional state representation of the environment using a Variational Autoencoder. It generates a discrete latent vector from the images, based on which a PDDL model can be constructed and then solved by an off-the-shelf planner. (2) Action Autoencoder / Discriminator, a neural architecture which jointly finds the action symbols and the implicit action models (preconditions/effects), and provides a successor function for the implicit graph search. We evaluate LatPlan using image-based versions of 3 planning domains: 8-puzzle, Towers of Hanoi and LightsOut.

In online social networks people often express attitudes towards others, which forms massive sentiment links among users. Predicting the sign of sentiment links is a fundamental task in many areas such as personal advertising and public opinion analysis. Previous works mainly focus on textual sentiment classification, however, text information can only disclose the "tip of the iceberg" about users' true opinions, of which the most are unobserved but implied by other sources of information such as social relation and users' profile. To address this problem, in this paper we investigate how to predict possibly existing sentiment links in the presence of heterogeneous information. First, due to the lack of explicit sentiment links in mainstream social networks, we establish a labeled heterogeneous sentiment dataset which consists of users' sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method. Then we propose a novel and flexible end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework to extract users' latent representations from heterogeneous networks and predict the sign of unobserved sentiment links. SHINE utilizes multiple deep autoencoders to map each user into a low-dimension feature space while preserving the network structure. We demonstrate the superiority of SHINE over state-of-the-art baselines on link prediction and node recommendation in two real-world datasets. The experimental results also prove the efficacy of SHINE in cold start scenario.

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.

SGD (Stochastic Gradient Descent) is a popular algorithm for large scale optimization problems due to its low iterative cost. However, SGD can not achieve linear convergence rate as FGD (Full Gradient Descent) because of the inherent gradient variance. To attack the problem, mini-batch SGD was proposed to get a trade-off in terms of convergence rate and iteration cost. In this paper, a general CVI (Convergence-Variance Inequality) equation is presented to state formally the interaction of convergence rate and gradient variance. Then a novel algorithm named SSAG (Stochastic Stratified Average Gradient) is introduced to reduce gradient variance based on two techniques, stratified sampling and averaging over iterations that is a key idea in SAG (Stochastic Average Gradient). Furthermore, SSAG can achieve linear convergence rate of $\mathcal {O}((1-\frac{\mu}{8CL})^k)$ at smaller storage and iterative costs, where $C\geq 2$ is the category number of training data. This convergence rate depends mainly on the variance between classes, but not on the variance within the classes. In the case of $C\ll N$ ($N$ is the training data size), SSAG's convergence rate is much better than SAG's convergence rate of $\mathcal {O}((1-\frac{\mu}{8NL})^k)$. Our experimental results show SSAG outperforms SAG and many other algorithms.

We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specifically, we replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel, thereby combining symbolic reasoning with learning subsymbolic vector representations. By using gradient descent, the resulting neural network can be trained to infer facts from a given incomplete knowledge base. It learns to (i) place representations of similar symbols in close proximity in a vector space, (ii) make use of such similarities to prove queries, (iii) induce logical rules, and (iv) use provided and induced logical rules for multi-hop reasoning. We demonstrate that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules.

The rhopoema nomadica, or nomadic jellyfish, is native to the Indian Ocean but in the eighties, they started turning up in the eastern Mediterranean, presumably through the Suez Canal. Now this jellyfish forms massive plumes, kilometers wide, along the coast of the eastern Mediterranean and Israel. For the first time, recently, a huge plume formed off Egypt's coast, and off Turkey and Lebanon. When it blooms intensely, it can get sucked into the watering systems that power plants use to cool machinery. Jellyfish are gooey, like a sink stopper, and clog the intake systems, so they have to shut down power plants until they can clear the bloom away.