The deep supervised and reinforcement learning paradigms (among others) have the potential to endow interactive multimodal social robots with the ability of acquiring skills autonomously. But it is still not very clear yet how they can be best deployed in real world applications. As a step in this direction, we propose a deep learning-based approach for efficiently training a humanoid robot to play multimodal games---and use the game of `Noughts & Crosses' with two variants as a case study. Its minimum requirements for learning to perceive and interact are based on a few hundred example images, a few example multimodal dialogues and physical demonstrations of robot manipulation, and automatic simulations. In addition, we propose novel algorithms for robust visual game tracking and for competitive policy learning with high winning rates, which substantially outperform DQN-based baselines. While an automatic evaluation shows evidence that the proposed approach can be easily extended to new games with competitive robot behaviours, a human evaluation with 130 humans playing with the Pepper robot confirms that highly accurate visual perception is required for successful game play.

Esri Inc., Redlands, CA 92 373 Summary Abstract: Artificial Intelligence ( AI) ha s received tremendous attention from academia, industry, and the general public in recent years. The integration of geography and AI, or GeoAI, provides novel approaches for addressing a variety of problems in the natural environment and our human society . This entry briefly reviews the recent development of AI with a focus on machine learning and deep learning approaches . We discuss the integration of AI with geography and particularly geographic information science, and present a number of GeoAI applicatio ns and p ossible future directions. Definitions 1. Artificial Intelligence: The study and design of machines or computational methods that can perform tasks that normally require human intelligence. Description/body 1. AI and Geography Artificial Intelligence (AI) has received tremendous attention in recent years from academia, industry, and the general public. Despite its recent popularity, the field was born back in 1956 at a workshop at Dartmouth College (McCarthy 1956) .

Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. We address such problems using clustered actions instead of infinite actions, and a simple but promising reward function based on human-likeness scores derived from human-human dialogue data. We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text---without any manual annotations. Experimental results using different splits of training data report the following. First, that our agents learn reasonable policies in the environments they get familiarised with, but their performance drops substantially when they are exposed to a test set of unseen dialogues. Second, that the choice of sentence embedding size between 100 and 300 dimensions is not significantly different on test data. Third, that our proposed human-likeness rewards are reasonable for training chatbots as long as they use lengthy dialogue histories of >=10 sentences.

Neural networks were difficult to train. Currently, we have a lot of tricks to achieve faster training and to solve the troubles that arise during model training. On the following, let's explore one of those tricks: batch normalization. When training a model, if the scale of two features (x1 1,2…, x2 100, 200 …) are quite different, the weight adjust in the small-scale features would be small. On the graph above, we observe that w1 just is adjusted slowly compared to w2 during the training.

The unprecedented growth of mobile devices, applications and services pose have placed utmost demands on mobile and wireless networking infrastructure. Rapid research and development of 5G systems, have found ways to support mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Moreover inference from heterogeneous mobile data from distributed devices experience challenges due to computational and battery power limitations. As a result models employed in the edge-based scenario are constrained to light-weight to achieve a trade-off between model complexity and accuracy. Also model compression, pruning, and quantization are largely in place.

Generative adversarial networks, or GANs for short, are an effective deep learning approach for developing generative models. Unlike other deep learning neural network models that are trained with a loss function until convergence, a GAN generator model is trained using a second model called a discriminator that learns to classify images as real or generated. Both the generator and discriminator model are trained together to maintain an equilibrium. As such, there is no objective loss function used to train the GAN generator models and no way to objectively assess the progress of the training and the relative or absolute quality of the model from loss alone. Instead, a suite of qualitative and quantitative techniques have been developed to assess the performance of a GAN model based on the quality and diversity of the generated synthetic images.

Data scientists used the opportunity to learn advanced trends in artificial intelligence, machine learning and deep learning methods in genomic prediction models. A deeper understanding of advanced trends in artificial intelligence (AI), machine learning (ML) and deep learning methods in genomic prediction models is critical to the success of smallholder agriculture. AI and ML algorithms are now being used to reduce risks in agriculture while also making it possible to forecast pest and disease outbreaks and alert farmers in advance. The annual collaborative workshop for Bioinformatics & Biometrics Community of Practices (CoP) under Excellence in Breeding (EiB) Platform Module 5, held in July in Montpellier, France, discussed the untapped potential of deep learning methods to make a significant impact on farming. With the theme: "Artificial Intelligence & Machine Learning with Genomic Selection Use Cases", the workshop served as a platform for data scientists across CGIAR institutions to explore using advanced agricultural research ML algorithms for genomics including prediction of plant phenotype, image identification, disease identification, and annotation of DNA sequences.

Axios Sign up for a daily newsletter defining what matters in business and markets Email A reality check for AI hubris Illustration: Sarah Grillo/Axios For the better part of a decade, artificial intelligence has been propelled by a rocket fuel in seemingly endless supply. Deep learning, a method that allows machines to identify hidden patterns in data, has powered commercial applications like autonomous vehicles and voice assistants, and it's potentially worth trillions of dollars a year. The other side: The rosy portrait of unstoppable progress belies a fear among some AI luminaries that things are not on the right path. In a new sort of resource curse, they say that deep learning has sucked energy away from other strains of inquiry without which AI may never approach even a child's intellectual capabilities. The big picture: For the past 5 years, Elon Musk and others have warned of a future disaster resulting from unchecked superintelligent AI. But today, much of the field is caught in a rather more elementary tug-of-war over which avenue will imbue AI even with the capacity for basic understanding.

Can you interpret a deep neural network? Building a complex and dense machine learning model has the potential of reaching our desired accuracy, but does it make sense? Can you open up the black-box model and explain how it arrived at the final result? These are critical questions we need to answer as data scientists. A wide variety of businesses are relying on machine learning to drive their strategy and spruce up their bottomline. Building a model that we can explain to our clients and stakeholders is key.