If human or natural intelligence is not enough to address whatever problems that ail modern societies, why not turn to Artificial Intelligence? The inexorable push for all things AI is now on. Several months ago, the National Institution for Transforming India (NITI) Aayog put out a proposal costing upwards of a billion dollars to create a national infrastructure for the promotion and adoption of AI techniques to resolve a variety of issues in the fields of agriculture, health, education, urbanisation and mobility. The presumed benefits of this proposal, if adopted, would be the addition of almost a trillion dollars to India's GDP and a 1.3% net increase in the annual growth rate by the year 2035. What would an India so transformed look like?

Artificial Intelligence is considered as the next new trend that is revolutionizing the modern world. It is making headlines on a daily basis in technology space with its recent innovations across different industries. This technology has become a driving force behind some of the renowned innovations such as speech recognition, chatbots, etc. So, this is the right time for people who are thinking of making a career into this wonderful technology. Artificial Intelligence is a computer system that has the ability to perform a task that basically requires human intelligence.

I started my programming journey recently, having used computers only before for work, gaming, and the one high-school Maya animation class. I upgraded from an old PC to a new Maingear custom built PC, good for gaming and work. I wanted to learn how to code, find work as a developer, and learn the best programming languages of 2019. I looked at bootcamps and online certifications, but the cost of these degrees, their length and commitment, and the fact that their curriculum is not always up-to-date, made me decide to go the self-taught way. I found a lot of media and tutorials offering to get me started, and trusting reviews for FreeCodeCamp.org,

--This study refers to a reverse question answering (reverse QA) procedure, in which machines proactively raise questions and humans supply answers. This procedure exists in many real human-machine interaction applications. A crucial problem in human-machine interaction is answer understanding. Existing solutions rely on mandatory option term selection to avoid automatic answer understanding. However, these solutions lead to unnatural human-computer interaction and harm user experience. T o this end, this study proposed a novel deep answer understanding network, called AntNet, for reverse QA. The network consists of three new modules, namely, skeleton extraction for questions, relevance-aware representation of answers, and multi-hop based fusion. As answer understanding for reverse QA has not been explored, a new data corpus is compiled in this study. Experimental results indicate that our proposed network is significantly better than existing methods and those modified from classical natural language processing (NLP) deep models. The effectiveness of the three new modules is also verified. UTOMA TIC question answering (QA) is a crucial component in many human-machine interaction systems, such as intelligent customer service, as it can provide a natural way for humans to acquire information [1]. Therefore, QA has received increasing attention in academic research and industry communities in recent years [2]. Questions are solely raised by humans, and answers are then returned by machines in the conventional QA scenario. How to select the best matched answer is the key problem in this setting [3].

It has already been established that digital traces can be used to predict various human attributes. In most cases, however, predictive models rely on features that are specific to a particular source of digital trace data. In contrast, short texts written by users $-$ tweets, posts, or comments $-$ are ubiquitous across multiple platforms. In this paper, we explore the predictive power of short texts with respect to the academic performance of their authors. We use data from a representative panel of Russian students that includes information about their educational outcomes and activity on a popular networking site, VK. We build a model to predict academic performance from users' posts on VK and then apply it to a different context. In particular, we show that the model could reproduce rankings of schools and universities from the posts of their students on social media. We also find that the same model could predict academic performance from tweets as well as from VK posts. The generalizability of a model trained on a relatively small data set could be explained by the use of continuous word representations trained on a much larger corpus of social media posts. This also allows for greater interpretability of model predictions.

Bipartite ranking is an important supervised learning problem; however, unlike regression or classification, it has a quadratic dependence on the number of samples. To circumvent the prohibitive sample cost, many recent work focus on stochastic gradient-based methods. In this paper we consider an alternative approach, which leverages the structure of the widely-adopted pairwise squared loss, to obtain a stochastic and low cost algorithm that does not require stochastic gradients or learning rates. Using a novel uniform risk bound---based on matrix and vector concentration inequalities---we show that the sample size required for competitive performance against the all-pairs batch algorithm does not have a quadratic dependence. Generalization bounds for both the batch and low cost stochastic algorithms are presented. Experimental results show significant speed gain against the batch algorithm, as well as competitive performance against state-of-the-art bipartite ranking algorithms on real datasets.

Abstraction reasoning is a long-standing challenge in artificial intelligence. Recent studies suggest that many of the deep architectures that have triumphed over other domains failed to work well in abstract reasoning. In this paper, we first illustrate that one of the main challenges in such a reasoning task is the presence of distracting features, which requires the learning algorithm to leverage counterevidence and to reject any of the false hypotheses in order to learn the true patterns. We later show that carefully designed learning trajectory over different categories of training data can effectively boost learning performance by mitigating the impacts of distracting features. Inspired by this fact, we propose feature robust abstract reasoning (FRAR) model, which consists of a reinforcement learning based teacher network to determine the sequence of training and a student network for predictions. Experimental results demonstrated strong improvements over baseline algorithms and we are able to beat the state-of-the-art models by 18.7% in the RAVEN dataset and 13.3% in the PGM dataset.

In this paper, we present a technique that improves the process of training an agent (using RL) for instruction following. We develop a training curriculum that uses a nominal number of expert demonstrations and trains the agent in a manner that draws parallels from one of the ways in which humans learn to perform complex tasks, i.e by starting from the goal and working backwards. We test our method on the BabyAI platform and show an improvement in sample efficiency for some of its tasks compared to a PPO (proximal policy optimization) baseline.

Texas A&M, established in 1876 as the first public university in Texas, is one of the nation's largest universities with more than 66,000 students and more than 440,000 living alumni residing in over 150 countries around the world. A tier-one university, Texas A&M holds the rare triple land-, sea- and space-grant designation. Research conducted at Texas A&M represented annual expenditures of more than $905.4 million in fiscal year 2017. Texas A&M's research creates new knowledge that provides basic, fundamental and applied contributions resulting, in many cases, in economic benefits to the state, nation and world.

The desire for data-science and machine learning (ML) skills will continue strongly into next year, according to developers surveyed by analyst firm SlashData. SlashData queried some 20,500 respondents from 167 countries, which means this is a pretty comprehensive survey from a global perspective. Responses were additionally weighted in order to "derive a representative distribution for platforms, segments, and types of IoT [projects]," according to the report accompanying the data. According to the survey, some 45 percent of developers want to either learn or improve their existing data science/machine learning skills. This outpaces the desire to learn UI design (33 percent of respondents), cloud native development such as containers (25 percent), project management (24 percent), and DevOps (23 percent).