For further commentary beyond the report, see the accompanying blog post. For a table of the full list of countries and their numbers, or to submit information about the talent pool in your region, send a message using the contact form. The demand for AI experts has grown exponentially over the last few years. As companies increasingly adopt AI solutions for their businesses, the need for highly experienced, PhD-educated, and technically-adept talent shows no signs of stopping anytime soon. Although these data visualizations map the distribution of worldwide talent at the start of 2018, we want to acknowledge that this is a predominantly Western-centric model of AI expertise. We are submitting our work amidst similar, though much broader, reports such as Tencent's recent "2017 Global AI Talent White Paper," which focused primarily on China in comparison to the United States.

This post originally appeared in The Cipher Brief. When we think of arms races, we are naturally drawn to the physical: missiles, satellites, fighter jets, submarines and aircraft carriers. While those assets are important for conventional warfare, there is an emerging technological arms race in artificial intelligence (AI) that will have profound implications for the balance of power in the 21st Century. When it comes to AI, the arms race is a two-country competition, between the U.S. and China. Given the economic implications as well as the tactical consequences, the stakes are incredibly high for both countries.

Bottom Line: China's nationwide pursuit to become the world leader in artificial intelligence (AI) is an attempt to not only match U.S. economic power, but to bypass it geo-strategically. While Beijing's involvement is spurred by economic ambitions, it has made it clear that the development of AI will simultaneously be for military applications that could change the character of warfare and place the U.S. at a geopolitical disadvantage. Background: China has quickly spurred its innovation engines into action, seeking to leapfrog U.S. military and technological supremacy through advanced AI and machine learning. Their unique brand of capitalism and government control has enabled bottom-up innovation that is broadly guided by the hand of the Chinese Community Party. China's whole-of-nation approach means the U.S. has found itself in a race against a strategic competitor.

Lito Tayag, country managing director of Accenture PH, explains the importance of artificial intelligence (AI) technologies for businesses during a video conference with Cebu media at Robinsons Cybergate on Feb. 7, 2018. There is now an urgent need for businesses and organizations in Cebu to employ more intelligent technologies in their operations, an official in the industry said. Lito Tayag, country managing director of Accenture Philippines, said Cebu is lagging behind as the rest of the world embraces artificial intelligence (AI) technologies as well as proper human-machine collaboration in doing business. "Technology is a way to improve business. However, many are lagging behind. Our Cebu business counterparts have lower appreciation of this urgency compared to those in our study," he said in a video conference yesterday at the Accenture office at Robinsons Cybergate.

Digitisation and its futuristic approach is taking the global business landscape by storm and air cargo industry is not an exception. More than being physical, it is a mindset change. The winners will act now by forgoing resistance and embracing technology! Rewind just six or seven years and you will find the air cargo industry happily transporting cargo believing it has mastered the art, until a wave of technology roiled and disrupted the well set standards. In due course, the consumer base also became more aware and their rising expectations gave major goals to the industry to not only track and trace but also make the entire supply chain leaner.

Many efforts have been taken to train spiking neural networks (SNNs), but most of them still need improvements due to the discontinuous and non-differential characteristics of SNNs. While the mammalian brains solve these kinds of problems by integrating a series of biological plasticity learning rules. In this paper, we will focus on two biological plausible methodologies and try to solve these catastrophic training problems in SNNs. Firstly, the biological neural network will try to keep a balance between inputs and outputs on both the neuron and the network levels. Secondly, the biological synaptic weights will be passively updated by the changes of the membrane potentials of the neighbour-hood neurons, and the plasticity of synapses will not propagate back to other previous layers. With these biological inspirations, we propose Voltage-driven Plasticity-centric SNN (VPSNN), which includes four steps, namely: feed forward inference, unsupervised equilibrium state learning, supervised last layer learning and passively updating synaptic weights based on spike-timing dependent plasticity (STDP). Finally we get the accuracy of 98.52% on the hand-written digits classification task on MNIST. In addition, with the help of a visualization tool, we try to analyze the black box of SNN and get better understanding of what benefits have been acquired by the proposed method.

Matching pedestrians across multiple camera views has attracted lots of recent research attention due to its apparent importance in surveillance and security applications.While most existing works address this problem in a still-image setting, we consider the more informative and challenging video-based person re-identification problem, where a video of a pedestrian as seen in one camera needs to be matched to a gallery of videos captured by other non-overlapping cameras. We employ a convolutional network to extract the appearance and motion features from raw video sequences, and then feed them into a multi-rate recurrent network to exploit the temporal correlations, and more importantly, to take into account the fact that pedestrians, sometimes even the same pedestrian, move in different speeds across different camera views. The combined network is trained in an end-to-end fashion, and we further propose an initialization strategy via context reconstruction to largely improve the performance. We conduct extensive experiments on the iLIDS-VID and PRID-2011 datasets, and our experimental results confirm the effectiveness and the generalization ability of our model.

We argue that chemistry should be the next grand challenge for Artificial Intelligence. The AI research community and humanity would benefit tremendously from focusing AI research on chemistry on a regular basis, as a benchmark as well as a real-world application domain. To support our position, we review the importance of chemical compound discovery and synthesis planning and discuss the properties of search spaces in a chemistry problem. Knowledge acquired in domains such as two-player board games or single-player puzzles places the AI community in a good position to solve critical problems in the chemistry domain. Yet, we show that searching in chemistry problems poses significant additional challenges that will have to be addressed. Finally, we envision how several AI areas like Natural Language Processing, Machine Learning, planning and search, are relevant for chemistry.

Support Vector Machines (SVMs) are excellent candidate solutions to solving multi-class problems, and multi-class SVMs can be trained by several different methods. Different training methods commonly produce SVMs with different effectiveness, and no multi-class SVM training method always outperforms other multi-class SVM training methods on all problems. This raises difficulty for practitioners to choose the best training method for a given problem. In this work, we propose a Multi-class Method Selection (MMS) approach to help users select the most appropriate method among one-versus-one (OVO), one-versus-all (OVA) and structural SVMs (SSVMs) for a given problem. Our key idea is to select the training method based on the distribution of training data and the similarity between different classes. Using the distribution and class similarity, we estimate the unclassifiable rate of each multi-class SVM training method, and select the training method with the minimum unclassifiable rate. Our initial findings show: (i) SSVMs with linear kernel perform worse than OVO and OVA; (ii) MMS often produces SVM classifiers that can confidently classify unseen instances.

Video summarization aims to facilitate large-scale video browsing by producing short, concise summaries that are diverse and representative of original videos. In this paper, we formulate video summarization as a sequential decision-making process and develop a deep summarization network (DSN) to summarize videos. DSN predicts for each video frame a probability, which indicates how likely a frame is selected, and then takes actions based on the probability distributions to select frames, forming video summaries. To train our DSN, we propose an end-to-end, reinforcement learning-based framework, where we design a novel reward function that jointly accounts for diversity and representativeness of generated summaries and does not rely on labels or user interactions at all. During training, the reward function judges how diverse and representative the generated summaries are, while DSN strives for earning higher rewards by learning to produce more diverse and more representative summaries. Since labels are not required, our method can be fully unsupervised. Extensive experiments on two benchmark datasets show that our unsupervised method not only outperforms other state-of-the-art unsupervised methods, but also is comparable to or even superior than most of published supervised approaches.