In our recent article on Indonesia's Big "Big Data" Problem, we looked at how technologies like blockchain and artificial intelligence (AI) are being used to uncover new sets of data that corporations can use to better understand the world's fourth biggest country by population. A consistent theme throughout the time we spent talking to local tech firms was that great potential was simply waiting to be unlocked, and that probably holds true at a smaller scale for many developed market corporations. For example, think about something like "data entry." The mere fact that you require a human to take data from FORM A and then manually input that data into FORM B means that your archaic business processes need changing. This is the equivalent of those backwards companies that ask you to fax them a form in order to request a change of address for your account.

A new report on global AI patents and publications has offered an interesting snapshot of the current boom--including the uneven way it is being commercialized. Run the numbers: The report (pdf) from the World Intellectual Property Organization shows that since the field of AI was established in the 1950s, 340,000 AI-related inventions have been patented and over 1.6 million scientific papers published. Unsurprisingly, the data shows that interest in AI has exploded in the past five years and that China and the US are dominant in the technology. IBM was the company that owned the most patents. Tunnel vision: The figures also show the disproportionate attention going to one application of AI.

It is the sign of the times to come, the impending fourth industrial revolution. AWS which is now almost about Machine Learning and hosts a variety of such services for every possible application, is being used by both public and private entities world over. Machine Learning is getting more and more pervasive, and the proof lies in the pudding and it is clear now, that pudding is selling like hot cake. This would be second re-invention of Amazon, which first launched AWS as primarily for cloud data services and is now a full-fledged automated cloud computing and machine learning integrated solution. The competitors, notably Microsoft would be surely watching closely.

The recent proposal of learned index structures opens up a new perspective on how traditional range indexes can be optimized. However, the current learned indexes assume the data distribution is relatively static and the access pattern is uniform, while real-world scenarios consist of skew query distribution and evolving data. In this paper, we demonstrate that the missing consideration of access patterns and dynamic data distribution notably hinders the applicability of learned indexes. To this end, we propose solutions for learned indexes for dynamic workloads (called Doraemon). To improve the latency for skew queries, Doraemon augments the training data with access frequencies. To address the slow model re-training when data distribution shifts, Doraemon caches the previously-trained models and incrementally fine-tunes them for similar access patterns and data distribution. Our preliminary result shows that, Doraemon improves the query latency by 45.1% and reduces the model re-training time to 1/20.

Projects consist of interconnected dimensions such as objective, time, resource and environment. Use of these dimensions in a controlled way and their effective scheduling brings the project success. Project scheduling process includes defining project activities, and estimation of time and resources to be used for the activities. At this point, the project resource-scheduling problems have begun to attract more attention after Program Evaluation and Review Technique (PERT) and Critical Path Method (CPM) are developed one after the other. However, complexity and difficulty of CPM and PERT processes led to the use of these techniques through artificial intelligence methods such as Genetic Algorithm (GA). In this study, an algorithm was proposed and developed, which determines critical path, critical activities and project completion duration by using GA, instead of CPM and PERT techniques used for network analysis within the scope of project management. The purpose of using GA was that these algorithms are an effective method for solution of complex optimization problems. Therefore, correct decisions can be made for implemented project activities by using obtained results. Thus, optimum results were obtained in a shorter time than the CPM and PERT techniques by using the model based on the dynamic algorithm. It is expected that this study will contribute to the performance field (time, speed, low error etc.) of other studies.

Machine Learning is an important sub-field of the Artificial Intelligence and it has been become a very critical task to train Machine Learning techniques via effective method or techniques. Recently, researchers try to use alternative techniques to improve ability of Machine Learning techniques. Moving from the explanations, objective of this study is to introduce a novel SVM-CoDOA (Cognitive Development Optimization Algorithm trained Support Vector Machines) system for general medical diagnosis. In detail, the system consists of a SVM, which is trained by CoDOA, a newly developed optimization algorithm. As it is known, use of optimization algorithms is an essential task to train and improve Machine Learning techniques. In this sense, the study has provided a medical diagnosis oriented problem scope in order to show effectiveness of the SVM-CoDOA hybrid formation.

We analyze a simple prefiltered variation of the least squares estimator for the problem of estimation with biased, semi-parametric noise, an error model studied more broadly in causal statistics and active learning. We prove an oracle inequality which demonstrates that this procedure provably mitigates the variance introduced by long-term dependencies. We then demonstrate that prefiltered least squares yields, to our knowledge, the first algorithm that provably estimates the parameters of partially-observed linear systems that attains rates which do not not incur a worst-case dependence on the rate at which these dependencies decay. The algorithm is provably consistent even for systems which satisfy the weaker marginal stability condition obeyed by many classical models based on Newtonian mechanics. In this context, our semi-parametric framework yields guarantees for both stochastic and worst-case noise.

Many randomized algorithms in machine learning can be analyzed as some kind of stochastic process. For example, MCMC algorithms intentionally inject carefully designed randomness in order to sample from a desired target distribution. There is a second category of randomized algorithms for which the for which the goal is optimization rather than sampling, and the randomness is viewed as a price to pay for computational tractability. For example, stochastic gradient methods for large scale optimization use noisy estimates of a gradient because they are cheap. While such algorithms are not designed with the goal of sampling from a target distribution, an algorithm of this kind has random outputs, and its behavior is determined by the distribution of its output. Results in this paper provide tools for analyzing the convergence of such algorithms as stochastic processes.

Incremental learning suffers from two challenging problems; forgetting of old knowledge and intransigence on learning new knowledge. Prediction by the model incrementally learned with a subset of the dataset are thus uncertain and the uncertainty accumulates through the tasks by knowledge transfer. To prevent overfitting to the uncertain knowledge, we propose to penalize confident fitting to the uncertain knowledge by the Maximum Entropy Regularizer (MER). Additionally, to reduce class imbalance and induce a self-paced curriculum on new classes, we exclude a few samples from the new classes in every mini-batch, which we call DropOut Sampling (DOS). We further rethink evaluation metrics for forgetting and intransigence in incremental learning by tracking each sample's confusion at the transition of a task since the existing metrics that compute the difference in accuracy are often misleading. We show that the proposed method, named 'MEDIC', outperforms the state-of-the-art incremental learning algorithms in accuracy, forgetting, and intransigence measured by both the existing and the proposed metrics by a large margin in extensive empirical validations on CIFAR100 and a popular subset of ImageNet dataset (TinyImageNet).

We provide a high-dimensional semi-supervised inference framework focused on the mean and variance of the response. Our data are comprised of an extensive set of observations regarding the covariate vectors and a much smaller set of labeled observations where we observe both the response as well as the covariates. We allow the size of the covariates to be much larger than the sample size and impose weak conditions on a statistical form of the data. We provide new estimators of the mean and variance of the response that extend some of the recent results presented in low-dimensional models. In particular, at times we will not necessitate consistent estimation of the functional form of the data. Together with estimation of the population mean and variance, we provide their asymptotic distribution and confidence intervals where we showcase gains in efficiency compared to the sample mean and variance. Our procedure, with minor modifications, is then presented to make important contributions regarding inference about average treatment effects. We also investigate the robustness of estimation and coverage and showcase widespread applicability and generality of the proposed method.