Artificial intelligence will transform many companies and create completely new types of businesses. The cofounder of Coursera, AI Fund, and Landing.AI shares how businesses can benefit. Artificial intelligence (AI) is at the cutting edge of innovation. But how do companies find the expertise necessary to utilize it, and then take it to market? In this video, recorded at the Aspen Ideas Festival in June, Andrew Ng, cofounder of Coursera, AI Fund, and Landing.AI, discusses the difference between an AI-enabled business versus a true AI company, and how businesses can organize, hire, and make use of AI to add value.

There are tons of resources to help you learn the different aspects of R, and as a beginner this can be overwhelming. It's also a dynamic language and rapidly changing, so it's important to keep up with the latest tools and technologies. That's why R-bloggers and DataCamp have worked together to bring you a learning path for R. Each section points you to relevant resources and tools to get you started and keep you engaged to continue learning. Just like R, this learning path is a dynamic resource.

Long-term observation of changes in Activities of Daily Living (ADL) is important for assisting older people to stay active longer by preventing aging-associated diseases such as disuse syndrome. Previous studies have proposed a number of ways to detect the state of a person using a single type of sensor data. However, for recognizing more complicated state, properly integrating multiple sensor data is essential, but the technology remains a challenge. In addition, previous methods lack abilities to deal with misclassified data unknown at the training phase. In this paper, we propose an architecture for multimodal sensor-based ADL recognition which spontaneously acquires knowledge from data of unknown label type. Evaluation experiments are conducted to test the architecture's abilities to recognize ADL and construct data-driven reactive planning by integrating three types of dataflows, acquire new concepts, and expand existing concepts semi-autonomously and in real time. By adding extension plugins to Fluentd, we expended its functions and developed an extended model, Fluentd++. The results of the evaluation experiments indicate that the architecture is able to achieve the above required functions satisfactorily.

Automated essay scoring (AES) is a broadly used application of machine learning, with a long history of real-world use that impacts high-stakes decision-making for students. However, defensibility arguments in this space have typically been rooted in hand-crafted features and psychometrics research, which are a poor fit for recent advances in AI research and more formative classroom use of the technology. This paper proposes a framework for evaluating automated essay scoring models trained with more modern algorithms, used in a classroom setting; that framework is then applied to evaluate an existing product, Turnitin Revision Assistant.

We uncover a fairly general principle in online learning: If regret can be (approximately) expressed as a function of certain "sufficient statistics" for the data sequence, then there exists a special Burkholder function that 1) can be used algorithmically to achieve the regret bound and 2) only depends on these sufficient statistics, not the entire data sequence, so that the online strategy is only required to keep the sufficient statistics in memory. This characterization is achieved by bringing the full power of the Burkholder Method --- originally developed for certifying probabilistic martingale inequalities --- to bear on the online learning setting. To demonstrate the scope and effectiveness of the Burkholder method, we develop a novel online strategy for matrix prediction that attains a regret bound corresponding to the variance term in matrix concentration inequalities. We also present a linear-time/space prediction strategy for parameter free supervised learning with linear classes and general smooth norms.

On January 16, a new course launched on the online learning platform Coursera with an unassuming name: The Google IT Support Professional Certificate. It promised to prepare beginners for entry-level jobs in IT in eight to 12 months. That day, it attracted the largest-ever group of first-time Coursera users, almost half of them people without college degrees. More than 18,000 people have enrolled in the $49-a-month program so far, 160 of whom have completed it. "Even as we were building it, even as it was about to launch, I never anticipated the success of it," says Natalie Van Kleef Conley, Google's product lead for the program.

Training and education of the workforce is key to the digital transformation success of many businesses. One tool that has helped foster this is e-learning, especially, as Digital Journal has reported, e-learning is leading the way as businesses shift their training priorities to embrace a digital-first approach. There are many different forms of e-learning, involving a mix of different channels, content, use of video and so on. A shared objective is the importance of flexibility and a blended approach. Advantages to businesses include lower costs, since one training session can be delivered to many people.

Every year a large amount of data is generated which needs to be stored and analyzed. Apache Spark allows you to process such big data. The real power and value proposition of Apache Spark is its speed and platform to execute data science tasks. Spark's unique use case is that it combines ETL, batch analytic, real-time stream analysis, machine learning, graph processing, and visualizations to allow data scientists to tackle the complexities that come with raw unstructured data sets. Spark embraces this approach and has the vision to make the transition from working on a single machine to working on a cluster, something that makes data science tasks a lot more agile.

Python is gaining an ever larger footprint in the interesting world of Data Science. KDnuggets says that Python R is even over as leader in the AI and Machine Learning standings platforms. But what makes Python so special for data science? One of the reasons is that Python is a general-purpose programming language. This means that Python has no specific purpose, which makes it possible to use models directly in a broader context.

Matrix calculations, derivatives, eigenvalues, Set Theory, functions, vectors, linear transformations, etc. are extremely important to understand the theory behind statistical methods and programming. Therefore, before starting your next MOOC or Machine Learning book it's crucial to review all those concepts again. Most schools request students to be proficient at these methods in order to graduate, but the silver lining is that it won't require too much of your time to refresh or obtain this knowledge. There are plenty of resources to start, but what worked for me was The Manga Guide to Linear Algebra, which is very simple, graphic and provides a great foundation prior getting into more complex stuff. My suggestion is to schedule some weeks to review these concepts and to use the Feynman Technique to be able to explain in simple terms each of these topics. One of the issues people face today when trying to get into a field such as Data Science is Information Overload, a term used when talking in relation to the effect of having too many resources at the disposal.