QuillBot, a startup developing AI tools that intelligently rewrite text, today announced that it raised $4 million in financing. Fresh capital in hand, the cofounders hope to make QuillBot's platform a one-stop editing shop, with modules that will summarize information from articles and complete paragraphs by synthesizing sentences informed by intent. QuillBot also plans to establish an R&D lab to conduct and publish AI and machine learning work, with an emphasis on natural language processing (NLP). QuillBot is the brainchild of the University of Illinois (UIUC) dropout Anil Jason and 2017 alums Rohan Gupta and David Silin. Jason and Silin collaborated on a senior thesis project involving a system that, given an article, generated multiple-choice questions about the content of that article.

The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones. To address this problem, we represent the knowledge using a neural gas (NG) network, which can learn and preserve the topology of the feature manifold formed by different classes. On this basis, we propose the TOpology-Preserving knowledge InCrementer (TOPIC) framework. TOPIC mitigates the forgetting of the old classes by stabilizing NG's topology and improves the representation learning for few-shot new classes by growing and adapting NG to new training samples. Comprehensive experimental results demonstrate that our proposed method significantly outperforms other state-of-the-art class-incremental learning methods on CIFAR100, miniImageNet, and CUB200 datasets.

Deep reinforcement learning (RL) algorithms frequently require prohibitive interaction experience to ensure the quality of learned policies. The limitation is partly because the agent cannot learn much from the many low-quality trials in early learning phase, which results in low learning rate. Focusing on addressing this limitation, this paper makes a twofold contribution. First, we develop an algorithm, called Experience Grafting (EG), to enable RL agents to reorganize segments of the few high-quality trajectories from the experience pool to generate many synthetic trajectories while retaining the quality. Second, building on EG, we further develop an AutoEG agent that automatically learns to adjust the grafting-based learning strategy. Results collected from a set of six robotic control environments show that, in comparison to a standard deep RL algorithm (DDPG), AutoEG increases the speed of learning process by at least 30%.

Be it or name any business, small or large, AI has made its own space in the industry. AI has become conventional technology for most of the businesses and it will grow in the coming years. The reason behind the need and use of AI in the industry is because it allows improving the business processes, it helps in handling the data in a better way. Another trend under AI will be automated machine learning, which will help in better data management. To understand AI and bring out more changes in AI and Data Science, you must be an expert in it and for that, you have to be trained well!

This Earth Day, SAS and the International Institute for Applied Systems Analysis (IIASA) are implementing the next generation of crowd-driven artificial intelligence (AI) to help power AI algorithms designed to help us better understand our planet. IIASA is an independent, international research institute that is known for its expertise in systems analysis and providing policy solutions and baseline information on pressing concerns for humanity. IIASA is placing a spotlight on the issue of deforestation to learn about the application of AI to elucidate the interconnectivity of Earth's ecosystems. Today, our systems are changing rapidly, approaching levels that exceed existing human experiences, scientific understanding and traditional modeling capabilities, making it increasingly difficult for humans to intervene efficiently, effectively and timeously. "The urgency required to address these transformations requires the application of best of breed technology solutions. That's why we turned to SAS," said Albert van Jaarsveld, CEO at IIASA.

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Data Science. With every class, you will develop new skills and improve your understanding of this challenging yet lucrative field of Data Science. This course is fun and exciting, but at the same time, we dive deep into Machine Learning. Moreover, the course is packed with practical exercises that are based on real-life examples.

The applications of machine learning transcend boundaries and industries so why should we let tools and languages hold us back? Yes, Python is the language of choice in the industry right now but a lot of us come from a background where Python isn't taught! The computer science faculty in universities are still teaching programming in C – so that's what most of us end up learning first. I understand why you should learn Python – it's the primary language in the industry and it has all the libraries you need to get started with machine learning. But what if your university doesn't teach it?

The last decade has seen a vast increase both in the diversity of applications to which machine learning is applied, and to the import of those applications. Machine learning is no longer just the engine behind ad placements and spam filters; it is now used to filter loan applicants, deploy police officers, and inform bail and parole decisions, among other things. The result has been a major concern for the potential for data-driven methods to introduce and perpetuate discriminatory practices, and to otherwise be unfair. And this concern has not been without reason: a steady stream of empirical findings has shown that data-driven methods can unintentionally both encode existing human biases and introduce new ones.7,9,11,60 At the same time, the last two years have seen an unprecedented explosion in interest from the academic community in studying fairness and machine learning. "Fairness and transparency" transformed from a niche topic with a trickle of papers produced every year (at least since the work of Pedresh56 to a major subfield of machine learning, complete with a dedicated archival conference--ACM FAT*). But despite the volume and velocity of published work, our understanding of the fundamental questions related to fairness and machine learning remain in its infancy.

Women are underrepresented in the fields of science, technology, engineering, and mathematics (STEM) in most countries, including Germany and the U.S.29,32 This was demonstrated in several surveys investigating the proportion of women in the STEM fields for specific populations. Some of these studies, for example, investigated the number of enrolled students10,30 or the percentage of female professors at universities. Other studies analyzed the disparities in research funding.23 Nearly all these surveys selected a particular population of women in consideration of their university degree or their nationality.11,34 Like many other studies investigating the gender gap and its reasons in science, these surveys are usually based on data records from several kinds of registrations or enrollments, for example, the enrollment as student or doctoral student, the registration of finished doctoral theses or the membership as professor in a certain country.1,14,16,28 However, researchers at the postdoctoral level or industrial researchers are often not registered and unfortunately drop out of the surveys.

With the increased prevalence of U.S. states including computer science as a required subject in K-8 education (and as an elective in 9-12), in the next decade, nearly every child in the U.S. will be taking CS classes. The rapid integration of CS into the current education system has challenged states, districts, and teacher preparation programs to revamp their current efforts considerably. As this is a relatively new innovation and challenge, it provides us with a unique opportunity to consider our agenda: What is the goal of CS education? In the K--12 context, CS is often synonymous with coding--in fact, to many educators, CS is only coding. We suggest the goal of CS K--12 education should be for K--12 students to understand CS beyond simply learning to code.