As electronic entry procedures usher arriving passengers more quickly through immigration control at airports around the world, one of the casualties of progress is the time-honored passport stamp. While many travelers welcome the improved efficiency, those who regard passport stamps as souvenirs of their travels are going to miss the memories that an immigration stamp can trigger of the far-off destinations they have visited. "There is a trend to eliminate the passport stamp to shorten processing times, especially in advanced countries," a Japanese airport official said. With air travel growing, airports are looking for ways to prevent congestion, and ditching the tradition is one of the solutions. In Japan, this has seen the use of biometric identification, including facial recognition, emerge as a way to replace passport stamping to track people entering and leaving the country.

I also remember looking at what technology did exist – the cars, televisions and phones surrounding us – and thinking, "Why can't Thailand create these such things for itself?" Those two questions inspired me to study telecommunications engineering. I completed an undergraduate degree from King Mongkut's Institute of Technology Lad Krabang. Then, I jumped into the AI world by pursuing a masters degree at Germany's Ulm university followed by a doctorate at Caen University, in France, majoring in computer visions and data mining. My first job was linked to the future of self-driven cars.

Today my guest on Singularity 1 on 1 is Ben Goertzel. During our 40 min conversation we cover a wide range of topics such as: Ben's original interest in time travel and science fiction; his decision to start working on artificial general intelligence and his views on the potential time-line thereof together with his evaluation of the software vs hardware requirements for building it; scientific funding for AI research in both the USA and China; the technological singularity and our chances of surviving it. As attested by his short bio below, Ben is a brilliant AI scientist with an incredibly wide spectrum of interests and talents. Even more, I can't help it but mention that he is also a truly nice guy because due to some technical difficulties he was willing to patiently reschedule our interview twice before we finally managed to get it right. Given the number of other things that Ben is juggling within his busy schedule, it would have been only natural for him to cancel.

At the Bristol Robotics Laboratory in southwest England, dozens of tiny metal machines were poised to dance over a shiny white surface in "swarming" experiments to track how they organize themselves and work together. The scene at the European Union-funded facility this week mirrored what the continent is now trying to do to compete with the U.S. and China in artificial intelligence, or AI. The European Commission said it wants to create a network of hundreds of so-called Digital Innovation Hubs like the one in Bristol. The aim is to help Europe's companies and scientists work together to boost research and the take-up of new technologies among the thousands of small and medium-sized companies that form the backbone of the economy. "The idea is that all these nodes will connect and communicate with each other, maximizing the impact of the technologies and expertise they develop," said Farid Dailami, an associate professor who runs the prototype hub in Bristol, which connects researchers and funding with companies that need robotics.

What do you do when you're a 14-year-old who wants a computer but doesn't have one? Most of us would beg and plead with parents, the more industrious among us may attempt to save up money, but Kriti Sharma did what seemed obvious to her: she devoured tech tomes and built herself one. The 30-year-old is essentially a problem solver. Problem No 1 on her list right now is sexism in tech. Frustrated by the reinforcement of sexist ideas by bots, she created Pegg, a gender-neutral personal finance assistant.

The graph represents a network of 5,541 Twitter users whose tweets in the requested range contained "#iot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 27 April 2018 at 14:30 UTC. The requested start date was Friday, 27 April 2018 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 17-hour, 9-minute period from Thursday, 26 April 2018 at 06:50 UTC to Friday, 27 April 2018 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The great transformation of media first began with Gutenberg's invention of the printing press in 1439. This made the written word accessible to a much larger of people than ever before. Printing gave way to books, newspapers and journals of various kinds. This was virtually the first new media invented in over 5000 years of recorded human history. Then came to great inventions in the 19th century-Photography and wireless that gave birth to newer media like cinema and Broadcasting.

Motivation: Ontologies are widely used in biology for data annotation, integration, and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotations commonly used in ontologies include class labels, descriptions, or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally unexploited by ontology-based analysis methods such as semantic similarity measures. Results: We propose a novel method, OPA2Vec, to generate vector representations of biological entities in ontologies by combining formal ontology axioms and annotation axioms from the ontology meta-data. We apply a Word2Vec model that has been pre-trained on PubMed abstracts to produce feature vectors from our collected data. We validate our method in two different ways: first, we use the obtained vector representations of proteins as a similarity measure to predict protein-protein interaction (PPI) on two different datasets. Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations. These two experiments are just an illustration of the possible applications of our method. OPA2Vec can be used to produce vector representations of any biomedical entity given any type of biomedical ontology. Availability: https://github.com/bio-ontology-research-group/opa2vec Contact: robert.hoehndorf@kaust.edu.sa and xin.gao@kaust.edu.sa.

We present a novel method for training deep neural network amenable to inference in low-precision arithmetic with quantized weights and activations. The training is performed in full precision with random noise injection emulating quantization noise. In order to circumvent the need to simulate realistic quantization noise distributions, the weight and the activation distributions are uniformized by a non-linear transformation, and uniform noise is injected. An inverse transformation is then applied. This procedure emulates a non-uniform k-quantile quantizer at inference time, which is shown to achieve state-of-the-art results for training low-precision networks on CIFAR-10 and ImageNet-1K datasets. In particular, we observe no degradation in accuracy for MobileNet and ResNet-18 on ImageNet with as low as 2-bit quantization of the activations and minimal degradation for as little as 4 bits for the weights.

Dempster-Shafer evidence theory has been widely used in various fields of applications, because of the flexibility and effectiveness in modeling uncertainties without prior information. Besides, it has been proven that the quantum theory has powerful capabilities of solving the decision making problems, especially for modelling human decision and cognition. However, the classical Dempster-Shafer evidence theory modelled by real numbers cannot be integrated directly with the quantum theory modelled by complex numbers. So, how can we establish a bridge of communications between the classical Dempster-Shafer evidence theory and the quantum theory? To answer this question, a generalized Dempster-Shafer evidence theory is proposed in this paper. The main contribution in this study is that, unlike the existing evidence theory, a mass function in the generalized Dempster-Shafer evidence theory is modelled by a complex number, called as a complex mass function. In addition, compared with the classical Dempster's combination rule, the condition in terms of the conflict coefficient between two evidences K < 1 is released in the generalized Dempster's combination rule so that it is more general and applicable than the classical Dempster's combination rule. When the complex mass function is degenerated from complex numbers to real numbers, the generalized Dempster's combination rule degenerates to the classical evidence theory under the condition that the conflict coefficient between the evidences K is less than 1. Numerical examples are illustrated to show the efficiency of the generalized Dempster-Shafer evidence theory. Finally, an application of an evidential quantum dynamical model is implemented by integrating the generalized Dempster-Shafer evidence theory with the quantum dynamical model. From the experimental results, it validates the feasibility and effectiveness of the proposed method.