In countries like the US, artificial intelligence is already being used at a large scale to evaluate student essays, saving educational institutes money and time. According to reports, at least 21 states in America have deployed some type of automated scoring, from middle school to college level. Students are being graded on their essays using such AI systems designed by different vendors for highly important tests like the Graduate Record Examinations (GRE). While educators in the US say they are not going back to using human teachers for essay grading, it has received major backlash from parents particularly those from state school systems. But, it's not all great when it comes to automated grading.

Artificial intelligence is coming for America's high-paid professions as it creates winners and losers across the labor market like never before. White-collar jobs and better-educated occupations along with production workers are among the most susceptible to AI's spread into the economy, according to a Brookings Institution report Wednesday that draws on a new analysis of patent data by Stanford University graduate student Michael Webb. "Webb's modeling suggests that just as the impacts of robotics and software tend to be sizable and negative on exposed middle- and low-skill occupations, so AI's inroads are projected to negatively impact higher-skill occupations," researchers Mark Muro, Jacob Whiton and Robert Maxim wrote, noting that their analysis shows potential impacts can be both positive and negative. Workers with graduate or professional degrees will be almost four times as exposed to AI as workers with just a high school degree, the report showed. The researchers also concluded that AI appears most likely to affect men, prime-age and white and Asian American workers.

Europe is focussed on making robots that work for the benefit of society. This requires empowering future roboticists and users of all ages and backgrounds. In its 9th edition, the European Robotics Week (#ERW2019) is expected to host more than 1000 events across Europe. Over the years, and over 5,000 events, the organisers have learned a thing or two about reaching the public, and ultimately making the robots people want. For many, robots are only seen in the media or science fiction.

Data visualization: In this section, you will learn how to create simple plots like scatter plot histogram bar, etc. Data manipulation: You will learn in detail about data manipulation. GUI Programming: This section is a combination of life instructor-led training and self-paced learning. Developing web Maps and representing information using plots: In this section, you will understand how to design Python applications. Computer vision using open CV and visualization using bokeh: You will also learn designing Python application in the section.

According to a recent survey, 82 per cent of HR leaders believe their roles will be completely different in a decade's time. Big things are happening, with Artificial Intelligence (AI) taking a starring role. More than a third of the 500 companies we recently polled said they had adopted some form of AI in the past year, and almost half of the HR leaders we surveyed said that machine learning – a form of AI – will improve their HR function. AI is already being put to work in key areas such as recruitment, onboarding and employee development. For talent teams, these technologies are helping to free up resources, make better decisions, and crucially, deliver the type of experience that encourages top talent to stick around.

Everybody wants to do machine learning these days. Machine learning, data science, artificial intelligence, deep learning, neural network -- these have become some of the most used phrases in the tech space today. I'm not saying it's particularly bad, but it definitely gets scary for somebody who doesn't really know what all this means but wants to get into the rat race. When you think about it, from a software developer's point of view, these are just different types of software or applications you work on, but with more math involved. I know I'm oversimplifying what data science is, but for somebody who doesn't have a mathematics or statistics background, it is very difficult to understand the jargon initially.

Jerry has extensive experience in machine learning, storage systems, online service, distributed systems, virtualization, and OS kernel. He has worked on high performance and large-scale systems at companies such as: Lyft, Box, Twitter, Zynga, and Microsoft. He has also authored the open-source project Lib Crunch and is a three-time Microsoft Gold Star Award winner. Jerry completed his master's degree in computer science at Shanghai University. His most recent startup is Datatron.

Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches. Using non-/semi-parametric models to estimate the value function, they learn rapidly, retrieving cached values from similar past states. In realistic scenarios, with limited resources and noisy data, maintaining meaningful representations in memory is essential to speed up the learning and avoid catastrophic forgetting. Unfortunately, EC methods have a large space and time complexity. We investigate different solutions to these problems based on prioritising and ranking stored states, as well as online clustering techniques. We also propose a new dynamic online k-means algorithm that is both computationally-efficient and yields significantly better performance at smaller memory sizes; we validate this approach on classic reinforcement learning environments and Atari games.

A BSTRACT Consider an imitation learning problem that the imitator and the expert have different dynamics models. Most of the current imitation learning methods fail because they focus on imitating actions. We propose a novel state alignment based imitation learning method to train the imitator to follow the state sequences in expert demonstrations as much as possible. The state alignment comes from both local and global perspectives and we combine them into a reinforcement learning framework by a regularized policy update objective. We show the superiority of our method on standard imitation learning settings and imitation learning settings where the expert and imitator have different dynamics models. 1 I NTRODUCTION Learning from demonstrations (imitation learning, abbr. Imitation learning methods can be generally divided into two categories: behavior cloning (BC) and inverse reinforcement learning (IRL). Behavior cloning (Ross et al., 2011b) formulates a supervised learning problem to learn a policy that maps states to actions using demonstration trajectories. Inverse reinforcement learning (Russell, 1998; Ng et al., 2000) tries to find a proper reward function that can induce the given demonstration trajectories. GAIL (Ho & Ermon, 2016) and its variants (Fu et al.; Qureshi et al., 2018; Xiao et al., 2019) are the recently proposed IRL-based methods, which uses a GAN-based reward to align the distribution of state-action pairs between the expert and the imitator.

We introduce the safe linear stochastic bandit framework-- a generalization of linear stochastic bandits--where, in each stage, the learner is required to select an arm with an expected reward that is no less than a predetermined (safe) threshold with high probability. We assume that the learner initially has knowledge of an arm that is known to be safe, but not necessarily optimal. Leveraging on this assumption, we introduce a learning algorithm that systematically combines known safe arms with exploratory arms to safely expand the set of safe arms over time, while facilitating safe greedy exploitation in subsequent stages. In addition to ensuring the satisfaction of the safety constraint at every stage of play, the proposed algorithm is shown to exhibit an expected regret that is no more than O ( T log( T)) after T stages of play. 1 Introduction We investigate the role of safety in constraining the design of learning algorithms within the classical framework of linear stochastic bandits (Dani, Hayes, and Kakade 2008; Rusmevichientong and Tsitsiklis 2010; Abbasi-Y adkori, P al, and Szepesv ari 2011). Specifically, we introduce a family of safe linear stochastic bandit problems where--in addition to the typical goal of designing learning algorithms that minimize regret--we impose a constraint requiring that an algorithm's stagewise expected reward remains above a predetermined safety threshold with high probability at every stage of play. In the proposed framework, we assume that a "safe" baseline arm is initially known, and consider a class of safety thresholds that are defined as fixed cutbacks on the expected reward of the known baseline arm. Accordingly, an algorithm that is deemed to be safe cannot induce stage-wise rewards that dip below the baseline reward by more than a fixed amount. Critically, the assumption of a known baseline arm--and the limited capacity for exploration implied by the class of safety thresholds considered--can be leveraged on to initially guide the exploration of allowable arms by playing combinations of the baseline arm and exploratory arms in a manner that expands the set of safe arms over time, while simultaneously preserving safety at every stage of play. There are a variety of real-world applications that might benefit from the design of stagewise-safe online learning algorithms (Khezeli and Bitar 2017; Li et al. 2019; Sui et al. 2015). Most prominently, clinical trials have long been used as a motivating application for the multi-armed bandit (Berry and Pearson 1985) and linear bandit (Dani, Hayes, and Kakade 2008) frameworks.