Quizlet, a global learning platform with artificial intelligence (AI) powered study tools, has raised $30 million in a Series C funding round led by growth equity firm General Atlantic. The proceedings from the funding round are expected to help in driving Quizlet's continued product innovation with a focus on data science and machine learning capabilities. Apart from that, the capital will also be used by the learning platform towards its strategic expansion opportunities that are in line with its objective to help people practice and master whatever they wish to learn. Quizlet is said to enable learners to create, share, and consume high-quality user-generated content. The learning platform is said to serve a diverse userbase across geographies and stages of education.

What technologies are responsible for the recent success of modern data-driven artificial intelligence? There are a variety of industry trends driving data-driven A.I forward, including The Internet of Things (IoT), Big Data, virtual reality, data science, and more. However, there are three key technologies at the core of all modern AI In order to understand the recent success of AI and its future, it's critical that you have at least a basic understanding of the following three technologies. Machine learning is a subfield of AI based on statistics. It involves machines learning how to solve a problem without being explicitly programmed to do so.

According to research by IDC, global spending on cognitive and AI systems will reach $57.6 billion by 2021! Artificial Intelligence has surrounded us with the most innovative tech inventions, and almost every critical sector or industry relies on AI to accomplish a specific task that is difficult for humans to achieve. In this run, AI is also driving the market for education, and helping to automate the process to increase profitability for educators and students as well. Today, users are expecting more customized content and unbeatable browsing experience. It has forced web development companies to think out-of-the-box rather than getting glued to old and conventional methods.

Across industries, companies are applying artificial intelligence to their businesses, with mixed results. "What separates the AI projects that succeed from the ones that don't often has to do with the business strategies organizations follow when applying AI," writes Wharton professor of operations, information and decisions Kartik Hosanagar in this opinion piece. Hosanagar is faculty director of Wharton AI for Business, a new Analytics at Wharton initiative that will support students through research, curriculum, and experiential learning to investigate AI applications. He also designed and instructs Wharton Online's Artificial Intelligence for Business course. While many people perceive artificial intelligence to be the technology of the future, AI is already here.

As an important technique for modeling the knowledge states of learners, the traditional knowledge tracing (KT) models have been widely used to support intelligent tutoring systems and MOOC platforms. Driven by the fast advancements of deep learning techniques, deep neural network has been recently adopted to design new KT models for achieving better prediction performance. However, the lack of interpretability of these models has painfully impeded their practical applications, as their outputs and working mechanisms suffer from the intransparent decision process and complex inner structures. We thus propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model by backpropagating the relevance from the model's output layer to its input layer. The experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions, and partially validate the computed relevance scores from both question level and concept level. We believe it can be a solid step towards fully interpreting the DLKT models and promote their practical applications in the education domain.

Machine reading comprehension (MRC) aims to teach machines to read and comprehend human languages, which is a long-standing goal of natural language processing (NLP). With the burst of deep neural networks and the evolution of contextualized language models (CLMs), the research of MRC has experienced two significant breakthroughs. MRC and CLM, as a phenomenon, have a great impact on the NLP community. In this survey, we provide a comprehensive and comparative review on MRC covering overall research topics about 1) the origin and development of MRC and CLM, with a particular focus on the role of CLMs; 2) the impact of MRC and CLM to the NLP community; 3) the definition, datasets, and evaluation of MRC; 4) general MRC architecture and technical methods in the view of two-stage Encoder-Decoder solving architecture from the insights of the cognitive process of humans; 5) previous highlights, emerging topics, and our empirical analysis, among which we especially focus on what works in different periods of MRC researches. We propose a full-view categorization and new taxonomies on these topics. The primary views we have arrived at are that 1) MRC boosts the progress from language processing to understanding; 2) the rapid improvement of MRC systems greatly benefits from the development of CLMs; 3) the theme of MRC is gradually moving from shallow text matching to cognitive reasoning.

Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate behavior given either some demonstrations or a reward to guide its exploration with a reinforcement learning algorithm. Reinforcement learning algorithms rely on the definition of state and action spaces that define reachable behaviors. Their adaptation capability critically depends on the representations of these spaces: small and discrete spaces result in fast learning while large and continuous spaces are challenging and either require a long training period or prevent the robot from converging to an appropriate behavior. Beside the operational cycle of policy execution and the learning cycle, which works at a slower time scale to acquire new policies, we introduce the redescription cycle, a third cycle working at an even slower time scale to generate or adapt the required representations to the robot, its environment and the task. We introduce the challenges raised by this cycle and we present DREAM (Deferred Restructuring of Experience in Autonomous Machines), a developmental cognitive architecture to bootstrap this redescription process stage by stage, build new state representations with appropriate motivations, and transfer the acquired knowledge across domains or tasks or even across robots. We describe results obtained so far with this approach and end up with a discussion of the questions it raises in Neuroscience.

The popular show'Black Mirror' may be on hold due to the pandemic spreading across the globe, but fans of the dystopian world can create a part of the sci-fi series on their own. A Stanford student built a robot dog similar to that used in the episode titled'Metalhead' that hunts and kills humans in an apocalyptic setting. The miniature version, called the Stanford Pupper, was developed using a 3D printer, a PlayStation controller and other common pieces - and the team has shared all the details for the public to use. It has 12 degrees of freedom, meaning it can goes backwards, forwards, side-to-side and also features a'sneaky mode' that mimics the movement of a real canine creeping on the floor. A Stanford student built a robot dog similar to that used in the episode titled'Metalhead' that hunts and kills humans in an apocalyptic setting.

This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2). The reason I made this course is because there is a huge gap for many students between machine learning "theory" and writing actual code. As I've always said: "If you can't implement it, then you don't understand it". Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer. This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.

So you want to learn the Mathematics for Machine Learning? Well, for Machine Learning or Deep Learning and AI, a thorough mathematical understanding is not an option. I know the options out there; prerequisites and the skills you need to become successful in Machine Learning and AI. If you want to learn Machine Learning, these classes will help you to master the mathematical foundation required for writing programs and algorithms for Machine Learning, Deep Learning and AI. My goal in this piece is to help you find the resources to gain good intuition and get you the hands-on experience you need with coding neural nets, stochastic gradient descent, and principal component analysis.