An ecologist and an astronomer collaborated to count the endangered animals in South Africa, and drones were being used to perform this task. Drones equipped with infrared cameras combined with computer-vision and machine-learning techniques helped in identifying the animals. Serge Wich, the ecologist and Steven Longmore, the astronomer both from the Liverpool John Moores University in England came up with this idea and contributed to the conservation and fight against poaching. The drones equipped with thermal cameras can spot the heat signature of animals and the software recognizes the animals based on their shape. The team latest findings were presented by Claire Burke, an astrophysicist at the University.

At the end of each summer for the last 14 years, the small Welsh town of Porthcawl has been invaded. Every year its 16,000 population is swamped by up to 35,000 Elvis fans. Many people attending the yearly festival look the same: they slick back their hair, throw on oversized sunglasses and don white flares. At 2017's Elvis festival, impersonators were faced with something different. Police were trialling automated facial recognition technology to track down criminals.

The UK and Europe must continue to work closely together on artificial intelligence research regardless of Brexit, according to experts, as the race for AI leadership pushes nations to form strategic alliances. Dame Wendy Hall, Regius professor of computer science at the University of Southampton, said that the strength of UK research in the field meant that the European Union could ill afford not to strike a deal on collaboration with UK universities. She argued that a common goal of developing ethical practices in both research into and the application of AI technologies would give the UK and Europe a "high ground" and position them well to compete with the US and China. "There's a whole hype-wave around AI right now and every government in the world wants to get the best of it," said Dame Wendy, who was commissioned by the government to conduct a review of the UK's policies on AI last year. "[Anyone] not in a position to seize those opportunities and develop [their] own AI sector becomes dependent on the US or China. So it's very important that we build on our fantastic 50-year legacy of British AI research."

Oslo, Norway - Latest release of Puzzel cloud contact centre solution provides new bot options including its own Chat bot, multi-channel capabilities and introduces enhanced measures to protect customer data Puzzel has announced new functionality in the latest release of its cloud-based contact centre solution, designed to extend the system's multi-channel capabilities and help organisations to meet important changes in EU data protection legislation. Users are now able to integrate third party or Puzzel's own Chat bots directly into their core contact centre solution to improve first contacts with customers and save valuable live agent time. Furthermore, Puzzel has made several adjustments to its platform in preparation for the advent of General Data Protection Regulation (GDPR) in May this year. Christian Thorsrud, Product Manager at Puzzel commented, "Chat bots and GDPR are hot topics in the contact centre world today. On the one hand, innovations based on Artificial Intelligence such as Chat bots are creating new opportunities to expand and improve customer interactions and Puzzel's latest release is designed to make them a reality. On the other hand, the imminent arrival of GDPR is putting pressure on contact centres to review how they collect and store their own and third party data. The latest version of our cloud-based software brings renewed assurance that contact centres can rely on Puzzel to provide them with a secure and auditable framework to help meet critical new legislative requirements."

Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true data sample distributions. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Moreover, it has not been well studied as to how cost-sensitive learning could improve DBN performance on imbalanced data problems. This paper proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for imbalanced classification. ECS-DBN uses adaptive differential evolution to optimize the misclassification costs based on training data, that presents an effective approach to incorporating the evaluation measure (i.e. G-mean) into the objective function. We first optimize the misclassification costs, then apply them to deep belief network. Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge. The experiments have shown that the proposed approach consistently outperforms the state-of-the-art on both benchmark datasets and real-world dataset for fault diagnosis in tool condition monitoring.

We introduce an algorithmic method for population anomaly detection based on gaussianization through an adversarial autoencoder. This method is applicable to detection of `soft' anomalies in arbitrarily distributed highly-dimensional data. A soft, or population, anomaly is characterized by a shift in the distribution of the data set, where certain elements appear with higher probability than anticipated. Such anomalies must be detected by considering a sufficiently large sample set rather than a single sample. Applications include, but not limited to, payment fraud trends, data exfiltration, disease clusters and epidemics, and social unrests. We evaluate the method on several domains and obtain both quantitative results and qualitative insights.

The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist labels are available for a given audio segment. Therefore, in this work, we propose to apply the transfer learning framework, learning artist-related information which will be used at inference time for genre classification. We consider different types of artist-related information, expressed through artist group factors, which will allow for more efficient learning and stronger robustness to potential label noise. Furthermore, we investigate how to achieve the highest validation accuracy on the given FMA dataset, by experimenting with various kinds of transfer methods, including single-task transfer, multi-task transfer and finally multi-task learning.

SoundHound, a voice-enabled artificial intelligence (AI) and conversational intelligence technologies company, announced news on Thursday (May 3) that it has raised $100 million in new funding from a group of strategic investors. In a press release, the company said investors in the round of funding include Tencent Holdings, Daimler, Hyundai Motor Company, Midea Group and Orange. Those companies join existing investors Samsung, NVIDIA, KT Corporation, HTC, NAVER, LINE, Nomura, Sompo Japan Nipponkoa and Recruit. SoundHound plans to use the funding to drive adoption and distribution of Houndify, its voice AI platform in the automotive, Internet of Things, consumer products and enterprise apps and services markets. The funding will also be used to open new offices in China, France and Germany.

That's what an experimental app is offering to do for conservationists seeking to identify and track primates in the wild. It could even help wildlife crime investigators recognise individuals that have been killed or trafficked. While some researchers in the field are able to identify individual primates in the small populations they are studying, recognising them quickly in other contexts is very difficult, says Serge Wich at Liverpool John Moores University, who was not involved in the work. "We put camera traps out or …

Amsterdam Data Science, in which CWI participates as a partner, recently announced that 3 PhD projects will shortly commence, fully funded by Science Foundation Ireland (an award of around 0.5M€). The PhD students will spend their studentships between Insight Centre for Data Analytics (Ireland) and up to 1 year at an Amsterdam Data Science partner. This marks a further step in this already strong collaboration and an excellent opportunity for PhD students to benefit from the joint expertise. The vacancies will be advertised very soon. The awarded project in which CWI will be involved is called "Smart Partnerships" to develop STEM competencies that transform lives to live and thrive in a complex connected global society.