A Japanese startup has developed software that can identify potential shoplifters on video surveillance footage. Japanese startup Vaak has developed artificial intelligence (AI) software that looks for potential shoplifters on video surveillance footage. Last year, Vaak helped catch a shoplifter at a convenience store in Yokohama, Japan. The startup had set up its software in the shop as a test case, and was able to identify previously undetected shoplifting activity. The ability to detect and analyze unusual human behavior also has other applications.

Artificial Intelligence (AI) and machine learning algorithms such as Deep Learning have become part of our daily lives. The technology is used in everything from translation services to improving medical diagnostics and banking to creating computer models in climate science. Our current machines have been very successful at solving hard application problems, displaying seemingly intelligent behavior. Based on the ever-increasing amount of data and more complicated computers available today, learning algorithms appear to reach human capabilities. But the question still remains - Users still don't know exactly how AI systems reach their conclusions. Researchers from TU Berlin, Fraunhofer Heinrich Hertz Institute HHI and Singapore University of Technology and Design (SUTD) wanted to know if AI systems were truly intelligent or did they make some lucky guesses.

Venture capital investment in Singaporean startups has been faring well in the last few years with a lot of investments flowing in AI, IoT, analytics, data science and cloud startups. As the country is aiming to be the smart nation in the globe, there are has been a tremendous increase in the number of startups in the tech space. This article covers 10 leading startups in the emerging tech space that have the biggest war chest in terms of funding over the last two years. Singapore's image recognition startup Trax Retail is the most heavily funded AI startup followed by ViSenze that received a fresh capital boost of $20 million. This list is in alphabetical order. This Singapore-based Fintech startup uses artificial intelligence to deliver conversational banking services where they use it to redefine their digital strategy for the future while bringing automation and ensuring insightful customer engagement.

Tokyo-based Drone Fund has invested in the Aerodyne Group, a Malaysia-based drone service provider with offices in 10 nations around the world. The amount of the investment was not disclosed. In addition to resources the deal will give Aerodyne access to the Drone Fund's stable of innovative drone-related entrepreneurs as well as its established technology partners, Aerodyne said in a statement. "This partnership with Aerodyne is important for the drone start-ups we have invested in to make further inroads into the growing Asian market and beyond. Aerodyne is truly a leading industry solution provider with DaaS (Drone as a Service) and SaaS (Software as a Service) solutions that leverage the power of AI (artificial intelligence)," said Soki Ohmae, a Drone Fund executive board member.

Artificial Intelligence is nowadays one of the most popular topics to discus upon. It only continues to significantly increase in its role in business and daily life in general. It is important to discuss that what role women are likely to play in the world of Artificial Intelligence, writes Nandita Koshal, Research Associate, International Institute for Higher Education Research and Capacity Building, O P Jindal Global University. AI and IoT technologies are increasingly being fashioned on women and their perceived roles. They play the role of our guide as GPS that takes us to our destination; as Cortana and Siri they become our personal assistants who aid in expediting our day to day commitments; and as Alexa and Google play they become our companions and friends who respect and fulfil our requests.

Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood. In particular, it is commonly believed that Gaussian encoder/decoder assumptions reduce the effectiveness of VAEs in generating realistic samples. In this regard, we rigorously analyze the VAE objective, differentiating situations where this belief is and is not actually true. We then leverage the corresponding insights to develop a simple VAE enhancement that requires no additional hyperparameters or sensitive tuning. Quantitatively, this proposal produces crisp samples and stable FID scores that are actually competitive with a variety of GAN models, all while retaining desirable attributes of the original VAE architecture. A shorter version of this work will appear in the ICLR 2019 conference proceedings (Dai and Wipf, 2019). The code for our model is available at https://github.com/daib13/

Generative Adversarial Networks (GANs) have become exceedingly popular in a wide range of data-driven research fields, due in part to their success in image generation. Their ability to generate new samples, often from only a small amount of input data, makes them an exciting research tool in areas with limited data resources. One less-explored application of GANs is the synthesis of speech and audio samples. Herein, we propose a set of extensions to the WaveGAN paradigm, a recently proposed approach for sound generation using GANs. The aim of these extensions - preprocessing, Audio-to-Audio generation, skip connections and progressive structures - is to improve the human likeness of synthetic speech samples. Scores from listening tests with 30 volunteers demonstrated a moderate improvement (Cohen's d coefficient of 0.65) in human likeness using the proposed extensions compared to the original WaveGAN approach.

We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets were arranged in a lifelong learning and evaluation scenario and CodaLab was used as the challenge platform. The challenge attracted more than 300 participants in its two month duration. This chapter describes the design of the challenge and summarizes its main results.

We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore, multi-relational link prediction and entity matching communities can benefit from this resource. We believe this data set has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs.We validate the utility ofMMKG in the sameAs link prediction task with an extensive set of experiments. These experiments show that the task at hand benefits from learning of multiple feature types.

Recent developments in multi-agent imitation learning have shown promising results for modeling the behavior of human drivers. However, it is challenging to capture emergent traffic behaviors that are observed in real-world datasets. Such behaviors arise due to the many local interactions between agents that are not commonly accounted for in imitation learning. This paper proposes Reward Augmented Imitation Learning (RAIL), which integrates reward augmentation into the multi-agent imitation learning framework and allows the designer to specify prior knowledge in a principled fashion. We prove that convergence guarantees for the imitation learning process are preserved under the application of reward augmentation. This method is validated in a driving scenario, where an entire traffic scene is controlled by driving policies learned using our proposed algorithm. Further, we demonstrate improved performance in comparison to traditional imitation learning algorithms both in terms of the local actions of a single agent and the behavior of emergent properties in complex, multi-agent settings.