Machines might scare policymakers from Brussels to Washington, but artificial intelligence could yield a significant developmental dividend in the developing world. In African markets, the technology behind Alexa and Siri can be harnessed to diagnose illness or address traffic gridlock. One of the most transformative applications of artificial intelligence (AI) is in financial technology, where global investment has risen 38% over the last year. Machine learning, whereby algorithms make predictions and improve based on large amounts of data, is often relegated to the realm technologists and the elite; but for the two billion unbanked adults worldwide, this technology could light a path out of poverty by helping traditional lenders approve loans using hundreds of non-traditional data points. AI has the capacity to add value at the individual, small business, and the large corporate level alike across Africa.

We consider the transfer of experience samples (i.e., tuples < s, a, s', r >) in reinforcement learning (RL), collected from a set of source tasks to improve the learning process in a given target task. Most of the related approaches focus on selecting the most relevant source samples for solving the target task, but then all the transferred samples are used without considering anymore the discrepancies between the task models. In this paper, we propose a model-based technique that automatically estimates the relevance (importance weight) of each source sample for solving the target task. In the proposed approach, all the samples are transferred and used by a batch RL algorithm to solve the target task, but their contribution to the learning process is proportional to their importance weight. By extending the results for importance weighting provided in supervised learning literature, we develop a finite-sample analysis of the proposed batch RL algorithm. Furthermore, we empirically compare the proposed algorithm to state-of-the-art approaches, showing that it achieves better learning performance and is very robust to negative transfer, even when some source tasks are significantly different from the target task.

Artificial Intelligence, or AI, is empowering people with physical disabilities, allowing them to take charge of their own lives but it's also having a surprising impact on people with neuro-diverse conditions like autism. It's easy to generalise about people on the autism spectrum; they like consistency, take things literally and like routine. They are built to provide consistency. They don't (yet) understand sarcasm and they like logic, a lot. But it's important to remember that although people on the autism spectrum will share certain difficulties, everyone's experience of the condition will be very different.

Booz Allen and NVIDIA are offering deep learning training. NVIDIA is working with Booz Allen Hamilton to rapidly build solutions that are needed in cyberdefense for both government and commercial customers. Now, certified Deep Learning Institute instructors from NVIDIA and Booz Allen are offering training to a variety of customers on how to build your own effective deep learning and data-driven solutions. 'Deep Learning Demystified,' hosted by Booz Allen and NVIDIA, will provide an introduction to deep learning, explore key fundamentals and opportunities, and how to best address current challenges. If you can't make our June 7th, 7:30AM - 11:00AM course, this course will also be offered over another two dates: "Together with NVIDIA, we've already seen through the Data Science Bowl how deep learning can speed cancer and heart disease diagnoses. Machine intelligence, powered by deep learning and other techniques, will help organizations -- public and private sector alike -- supercharge human ingenuity to uncover new revelations about the complex systems in which we live and work," said Dr. Josh Sullivan, senior vice president of data science at Booz Allen.

Inspired by the tremendous success of deep generative models on generating continuous data like image and audio, in the most recent year, few deep graph generative models have been proposed to generate discrete data such as graphs. They are typically unconditioned generative models which has no control on modes of the graphs being generated. Differently, in this paper, we are interested in a new problem named \emph{Deep Graph Translation}: given an input graph, we want to infer a target graph based on their underlying (both global and local) translation mapping. Graph translation could be highly desirable in many applications such as disaster management and rare event forecasting, where the rare and abnormal graph patterns (e.g., traffic congestions and terrorism events) will be inferred prior to their occurrence even without historical data on the abnormal patterns for this graph (e.g., a road network or human contact network). To achieve this, we propose a novel Graph-Translation-Generative Adversarial Networks (GT-GAN) which will generate a graph translator from input to target graphs. GT-GAN consists of a graph translator where we propose new graph convolution and deconvolution layers to learn the global and local translation mapping. A new conditional graph discriminator has also been proposed to classify target graphs by conditioning on input graphs. Extensive experiments on multiple synthetic and real-world datasets demonstrate the effectiveness and scalability of the proposed GT-GAN.

Implementing deep learning in enterprise settings requires a lot more than just downloading some open source algorithms, but with talent scarce, businesses are finding it takes creativity and an open-minded approach to achieve results. "Established industries are largely missing out on the benefits of AI," said Ryan Kottenstette, co-founder and CEO of Silicon Valley geospatial data company Cape Analytics LLC. "If you're not in the tech sector, you might be waiting a bit longer for the benefits of AI to be realized." In recent years, deep learning has taken huge strides. Algorithmic processes like neural networks, which historically lived more in the realm of mathematical theory, have moved into some enterprise use cases, like computer vision and process automation. But adoption has been uneven.

In recent years, sequence-to-sequence (seq2seq) models are used in a variety of tasks from machine translation, headline generation, text summarization, speech to text, to image caption generation. The underlying framework of all these models are usually a deep neural network which contains an encoder and decoder. The encoder processes the input data and a decoder receives the output of the encoder and generates the final output. Although simply using an encoder/decoder model would, most of the time, produce better result than traditional methods on the above-mentioned tasks, researchers proposed additional improvements over these sequence to sequence models, like using an attention-based model over the input, pointer-generation models, and self-attention models. However, all these seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently a completely fresh point of view emerged in solving these two problems in seq2seq models by using methods in Reinforcement Learning (RL). In these new researches, we try to look at the seq2seq problems from the RL point of view and we try to come up with a formulation that could combine the power of RL methods in decision-making and sequence to sequence models in remembering long memories. In this paper, we will summarize some of the most recent frameworks that combines concepts from RL world to the deep neural network area and explain how these two areas could benefit from each other in solving complex seq2seq tasks. In the end, we will provide insights on some of the problems of the current existing models and how we can improve them with better RL models. We also provide the source code for implementing most of the models that will be discussed in this paper on the complex task of abstractive text summarization.

Neural networks are one of the most investigated and widely used techniques in Machine Learning. In spite of their success, they still find limited application in safety- and security-related contexts, wherein assurance about networks' performances must be provided. In the recent past, automated reasoning techniques have been proposed by several researchers to close the gap between neural networks and applications requiring formal guarantees about their behavior. In this work, we propose a primer of such techniques and a comprehensive categorization of existing approaches for the automated verification of neural networks. A discussion about current limitations and directions for future investigation is provided to foster research on this topic at the crossroads of Machine Learning and Automated Reasoning.

There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a `good' explanation. There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations towards the explanation process. This paper argues that the field of explainable artificial intelligence should build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.

By Will Yang Technology in today's day and age has enabled a human to do things and accomplish far more than one could think of a few years back. Thanks to rapidly evolving and innovative technologies, personal lives have become more enriched. Meaningful collaborations between a human and machine/technology has in many ways provided a wealth of opportunities to us making our lives comfortable. One such technology buzzword in the industry today is Artificial Intelligence. Once a topic for science fiction, Artificial Intelligence technology is now being used by brands across industries and categories.