In a feedforward neural network, we have X(input) and H(Hidden) and y(output). We can have as many hidden layers as we want but weights (W)for every hidden layer are and the weights for every neuron corresponding to the input are different. Above we have weights Wh0 and Wh1, which corresponds to two different layers, while Wh00, Wh01 and so on, represent different weights corresponding to different neuron and with respect to the input. The RNN cell contains a set of feed forward neural networks cause we have time steps. The RNN has sequential input, sequential output, multiple time-steps, and multiple hidden layers. Unlike FFNN, here we calculate hidden layer values not only from input values but also previous time step values and Weights ( W) at hidden layers are the same for time steps. Here is the complete picture for RNN and its Math.

To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional, relational, and hierarchical structure of the world, and learn to transfer it to the task at hand. Recent advances in representation learning for language make it possible to build models that acquire world knowledge from text corpora and integrate this knowledge into downstream decision making problems. We thus argue that the time is right to investigate a tight integration of natural language understanding into RL in particular. We survey the state of the field, including work on instruction following, text games, and learning from textual domain knowledge. Finally, we call for the development of new environments as well as further investigation into the potential uses of recent Natural Language Processing (NLP) techniques for such tasks.

Many circumstances of practical importance have performance or success metrics which exist implicitly---in the eye of the beholder, so to speak. Tuning aspects of such problems requires working without defined metrics and only considering pairwise comparisons or rankings. In this paper, we review an existing Bayesian optimization strategy for determining most-preferred outcomes, and identify an adaptation to allow it to handle ties. We then discuss some of the issues we have encountered when humans use this optimization strategy to optimize coloring a piece of abstract artwork. We hope that, by participating in this workshop, we can learn how other researchers encounter difficulties unique to working with humans in the loop.

A significant part of the literature in deontic logic revolves around the discussions of puzzles and paradoxes which show that certain logical systems are not acceptable--typically, this happens with deontic KD, i.e., Standard Deontic Logic (SDL)--or which suggest that obligations and permissions should enjoy some desirable properties. One well-known puzzle is the the so-called Free Choice Permission paradox, which was originated by the following remark by von Wright in [23, p. 21]: "On an ordinary understanding of the phrase'it is permitted that', the formula'P(p q)' seems to entail'Pp Pq'. If I say to somebody'you may work or relax' I normally mean that the person addressed has my permission to work and also my permission to relax. It is up to him to choose between the two alternatives." Usually, this intuition is formalised by the following schema: P(p q) (Pp Pq) (FCP) Many problems have been discussed in the literature around FCP: for a comprehensive overview, discussion, and some solutions, see [11, 14, 20]. Three basic difficulties can be identified, among the others [11, p. 43]: - Problem 1: Permission Explosion Problem - "That if anything is permissible, then everything is, and thus it would also be a theorem that nothing is obligatory," [20], for example "If you may order a soup, then it is not true that you ought to pay the bill" [6];

Which Way Next? is a Singularity University webcast series of monthly roundtable discussions on exponential technologies with leaders in industry, science and technology. This is a recording of our live premiere episode with Dr. Sebastian Thrun, PhD, research professor and executive director of the Artificial Intelligence Lab at Stanford University and Google Fellow on Tuesday, Dec. 6, 2011. On this episode, he joined SU Chairman & Co-Founder Dr. Peter Diamandis, MD in a discussion on artificial intelligence and the Google autonomous Car. Earlier this year, Fast Company honored Dr. Thrun with the title of "fifth most creative person in the world." About Singularity University: Singularity University is a benefit corporation headquartered at NASA's research campus in Silicon Valley.

This is not an entirely new problem. Computer programs have always had bugs. Over decades, software engineers have assembled an impressive toolkit of techniques, ranging from unit testing to formal verification. These methods work well on traditional software, but adapting these approaches to rigorously test machine learning models like neural networks is extremely challenging due to the scale and lack of structure in these models, which may contain hundreds of millions of parameters. This necessitates the need for developing novel approaches for ensuring that machine learning systems are robust at deployment.

Since 1950, when Alan Turing proposed what has since come to be called the Turing test, the ability of a machine to pass this test has established itself as the primary hallmark of general AI. To pass the test, a machine would have to be able to engage in dialogue in such a way that a human interrogator could not distinguish its behaviour from that of a human being. AI researchers have attempted to build machines that could meet this requirement, but they have so far failed. To pass the test, a machine would have to meet two conditions: (i) react appropriately to the variance in human dialogue and (ii) display a human-like personality and intentions. We argue, first, that it is for mathematical reasons impossible to program a machine which can master the enormously complex and constantly evolving pattern of variance which human dialogues contain. And second, that we do not know how to make machines that possess personality and intentions of the sort we find in humans. Since a Turing machine cannot master human dialogue behaviour, we conclude that a Turing machine also cannot possess what is called ``general'' Artificial Intelligence. We do, however, acknowledge the potential of Turing machines to master dialogue behaviour in highly restricted contexts, where what is called ``narrow'' AI can still be of considerable utility.

Learning agents that are not only capable of taking tests but are also innovating are becoming a hot topic in artificial intelligence (AI). One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for others. However, the existing evaluation platforms are either not compatible with multi-agent settings, or limited to a specific game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logic and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided games set based on a GUI-configurable social tree and five basic multi-agent reward schemes. Finally, we provide python implementations of five state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. As such, the research community can perform comparisons under a stable and uniform standard. Code for the games, building toolkit and baselines are released at https://github.com/YuhangSong/Arena-BuildingToolkit and https://github.com/YuhangSong/Arena-Baselines.

Qualitative and quantitative analysis of the market based on segmentation involving both economic as well as non-economic factors Provision of market value (USD Billion) data for each segment and sub-segment • Indicates the region and segment that is expected to witness the fastest growth as well as to dominate the market • Analysis by geography highlighting the consumption of the product/service in the region as well as indicating the factors that are affecting the market within each region • Competitive landscape which incorporates the market ranking of the major players, along with new service/product launches, partnerships, business expansions and acquisitions in the past five years of companies profiled • Extensive company profiles comprising of company overview, company insights, product benchmarking and SWOT analysis for the major market players • The current as well as future market outlook of the industry with respect to recent developments (which involve growth opportunities and drivers as well as challenges and restraints of both emerging as well as developed regions • Includes an in-depth analysis of the market of various perspectives through Porter's five forces analysis • Provides insight into the market through Value Chain • Market dynamics scenario, along with growth opportunities of the market in the years to come • 6-month post sales analyst support

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural network model for learning branch-and-bound variable selection policies, which leverages the natural variable-constraint bipartite graph representation of mixed-integer linear programs. We train our model via imitation learning from the strong branching expert rule, and demonstrate on a series of hard problems that our approach produces policies that improve upon state-of-the-art machine-learning methods for branching and generalize to instances significantly larger than seen during training. Moreover, we improve for the first time over expert-designed branching rules implemented in a state-of-the-art solver on large problems. Code for reproducing all the experiments can be found at https://github.com/ds4dm/learn2branch.