Artificial Intelligence (AI) is fast becoming a buzzword in the technology industry. If you are following up technology news, you will get to read eye catching AI news almost every day. Large companies are realising that they will be at a loss if they remain behind in acquiring competence in AI. Prominent technology companies like Google, Facebook, Microsoft and IBM are investing billions of dollars in developing AI teams and technologies. Venture capitalists are investing in a number of start-ups promising to make AI products.

Amazon, DeepMind/Google, Facebook, IBM and Microsoft announced yesterday that they will create a non-profit organization that will work to advance public understanding of artificial intelligence technologies and formulate best practices on the challenges and opportunities within the field. Academics, non-profits and specialists in policy and ethics will be invited to join the Board of the organization, named the Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI). The objective of the Partnership on AI is to address opportunities and challenges with AI technologies to benefit people and society. Together, the organization's members will conduct research, recommend best practices and publish research under an open license in areas such as ethics, fairness and inclusivity; transparency, privacy and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability and robustness of the technology. It does not intend to lobby government or other policymaking bodies.

In my previous blog I have explained about linear regression. In today's post I will explain about logistic regression. Consider a scenario where we need to predict a medical condition of a patient (HBP),HAVE HIGH BP or NO HIGH BP, based on some observed symptoms – Age, weight, Issmoking, Systolic value, Diastolic value, RACE, etc.. In this scenario we have to build a model which takes the above mentioned symptoms as input values and HBP as response variable. Note that the response variable (HBP) is a value among a fixed set of classes, HAVE HIGH BP or NO HIGH BP.

This paper reports on the progress of a co-creative pretend play agent designed to interact with users by recognizing and responding to playful actions in a 2D virtual environment. In particular, we describe the design and evaluation of a classifier that recognizes 2D motion trajectories from the user’s actions. The performance of the classifier is evaluated using a publicly available dataset of labeled actions highly relevant to the domain of pretend play. We show that deep convolutional neural networks perform significantly better in recognizing these actions than previously employed methods. We also describe the plan for implementing a virtual play environment using the classifier in which the users and agent can collaboratively construct narratives during improvisational pretend play.

This paper examines the performance of a number of AI agents on the games included in the General Video Game Playing Competition. Through analyzing these results, the paper seeks to provide insight into the strengths and weaknesses of the current generation of video game playing algorithms. The paper also provides an analysis of the given games in terms of inherent features which define the different games. Finally, the game features are matched with AI agents, based on performance, in order to demonstrate a plausible case for algorithm portfolios as a general video game playing technique.

Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs -- extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout's uncertainty. Various network architectures and non-linearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predictive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout's uncertainty in deep reinforcement learning.

If classifiers are selected from a hypothesis class to form an ensemble, bounds on average error rate over the selected classifiers include a co mponent for selectivity, which grows as the fraction of hypothesis classifiers selected for the ensemble shrinks, and a component for variety, which grows with the size of the hypothesis class or in-sample data set. W e show that the component for se lectivity asymptotically dominates the component for variety, meaning tha t variety is essentially free.

Gaussian processes are powerful, yet analytically tractable models for supervised learning. A Gaussian process is characterized by a mean function and a covariance function (kernel), which are determined by a model selection criterion. The functions to be compared do not just differ in their parametrization but in their fundamental structure. It is often not clear which function structure to choose, for instance to decide between a squared exponential and a rational quadratic kernel. Based on the principle of approximation set coding, we develop a framework for model selection to rank kernels for Gaussian process regression. In our experiments approximation set coding shows promise to become a model selection criterion competitive with maximum evidence (also called marginal likelihood) and leave-one-out cross-validation.

This paper considers online convex optimization over a complicated constraint set, which typically consists of multiple functional constraints and a set constraint. The conventional projection based online projection algorithm (Zinkevich, 2003) can be difficult to implement due to the potentially high computation complexity of the projection operation. In this paper, we relax the functional constraints by allowing them to be violated at each round but still requiring them to be satisfied in the long term. This type of relaxed online convex optimization (with long term constraints) was first considered in Mahdavi et al. (2012). That prior work proposes an algorithm to achieve $O(\sqrt{T})$ regret and $O(T^{3/4})$ constraint violations for general problems and another algorithm to achieve an $O(T^{2/3})$ bound for both regret and constraint violations when the constraint set can be described by a finite number of linear constraints. A recent extension in Jenatton et al. (2016) can achieve $O(T^{\max\{\beta,1-\beta\}})$ regret and $O(T^{1-\beta/2})$ constraint violations where $\beta\in (0,1)$. The current paper proposes a new simple algorithm that yields improved performance in comparison to prior works. The new algorithm achieves an $O(\sqrt{T})$ regret bound with finite constraint violations.

Meanwhile, robotics (among other fields) can often only use a few dozen data points because acquiring them involves a process that is expensive or time-consuming. How can an algorithm learn with only a few data points? Watching a child learn reveals how well humans can learn: a child may only need a few examples of a concept to "learn it". By contrast, the impressive results achieved with modern machine learning (in particular, by deep learning) are made possible largely by the use of huge datasets. For instance, the ImageNet database used in image recognition contains about 1.2 million labelled examples; DeepMinds's AlphaGo used more than 38 million positions to train their algorithm to play Go; and the same company used more than 38 days of play to train a neural network to play Atari 2600 games, such as Space Invaders or Breakout.