Digital note-taking is gaining popularity, offering a durable, editable, and easily indexable way of storing notes in the vectorized form, known as digital ink. However, a substantial gap remains between this way of note-taking and traditional pen-and-paper note-taking, a practice still favored by a vast majority. Our work, InkSight, aims to bridge the gap by empowering physical note-takers to effortlessly convert their work (offline handwriting) to digital ink (online handwriting), a process we refer to as Derendering. Prior research on the topic has focused on the geometric properties of images, resulting in limited generalization beyond their training domains. Our approach combines reading and writing priors, allowing training a model in the absence of large amounts of paired samples, which are difficult to obtain. To our knowledge, this is the first work that effectively derenders handwritten text in arbitrary photos with diverse visual characteristics and backgrounds. Furthermore, it generalizes beyond its training domain into simple sketches. Our human evaluation reveals that 87% of the samples produced by our model on the challenging HierText dataset are considered as a valid tracing of the input image and 67% look like a pen trajectory traced by a human.

Understanding cluster-wide I/O patterns of large-scale HPC clusters is essential to minimize the occurrence and impact of I/O interference. Yet, most previous work in this area focused on monitoring and predicting task and node-level I/O burst events. This paper analyzes Darshan reports from three supercomputers to extract system-level read and write I/O rates in five minutes intervals. We observe significant (over 100x) fluctuations in read and write I/O rates in all three clusters. We then train machine learning models to estimate the occurrence of system-level I/O bursts 5 - 120 minutes ahead. Evaluation results show that we can predict I/O bursts with more than 90% accuracy (F-1 score) five minutes ahead and more than 87% accuracy two hours ahead. We also show that the ML models attain more than 70% accuracy when estimating the degree of the I/O burst. We believe that high-accuracy predictions of I/O bursts can be used in multiple ways, such as postponing delay-tolerant I/O operations (e.g., checkpointing), pausing nonessential applications (e.g., file system scrubbers), and devising I/O-aware job scheduling methods. To validate this claim, we simulated a burst-aware job scheduler that can postpone the start time of applications to avoid I/O bursts. We show that the burst-aware job scheduling can lead to an up to 5x decrease in application runtime.

Many of the online courses teach you to code but not the theory/way of thinking behind it why would we choose a while but not a for loop, why should we pass 2 parameters to a function but not only one? We provide a platform for thousands of people to expand the understanding of programming and computer science. Founded in 2013 our mission is to spread the love for programming. To achieve this, we're working hard on providing content that will help people build a solid foundation in those subjects. This course will help you to master the foundation and know-how to solve problems with Python code.

CQRS stands for Command Query Responsibility Segregation. It's a pattern that I first heard described by Greg Young. At its heart is the notion that you can use a different model to update information than the model you use to read information. For some situations, this separation can be valuable, but beware that for most systems CQRS adds risky complexity. The mainstream approach people use for interacting with an information system is to treat it as a CRUD datastore.

The traditional von Neumann architecture differentiates between a CPU (Central Processing Unit) and three levels of memory: registers -- very fast, but with storage capability limited to a few values; main memory (e.g. RAM)-- faster, with enough storage to accommodate for instructions and data to run a program, and external memory (e.g. Memory-Augmented Neural Networks (MANNs) are differentiable versions of the von Neumann architecture (more on this in the next section). The bulk of the neural network can be thought of as the CPU. Certain architectures like RNNs (Recurrent Neural Networks) have built-in memory that is analogous to the registers, storing short-term information.

An artificial intelligence project backed by SpaceX founder Elon Musk has been so successful its developers are not releasing it to the public for fear it will be misused. Research group Open AI developed a'large-scale unsupervised language model' that is able to generate news stories from a simple headline. But the group insists it will not be releasing details of the programme and instead has unveiled a much smaller version for research purposes. Its developers claim the technology is poised to rapidly advance in the coming years and the full specification and details of the project will only be released when the negative applications have been discussed by researchers. Elon Musk's AI research group Open AI announced in a paper yesterday that it has generated'a large-scale unsupervised language model' that can write news stories from little more than a headline.

When driving to the office, Marie, CIO of a large bank in France, often has this quote from Bill Gates on her mind: "We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next 10. Don't let yourself be lulled into inaction." This morning the thought is particularly persistent. Her company has recently embarked on a digital transformation program and she has been looking to recruit employees with different skill sets in areas such as artificial intelligence (AI), cybersecurity and the Internet of Things (IoT). Marie knows that the secret to digital is analog.

If you've ever dealt with an AI technology that has conversed with you, either via speech or text, then you have indirectly used AI that had natural language processing (NLP) capabilities. Language-capable AI is different than most other machine learning algorithms in that the data that they have to deal with is inherently messier. Instead of being given very structured data, usually in a row/column format, language-capable AIs must deal with messy, unstructured text, different styles of writing, typos, accents, etc., in order to make accurate predictions and take specific actions. This article will focus on those algorithms that work with the written language but the concepts transfer over quite well to AI that deals with speech. Here are four major components of designing AI to read and write.

Let's hear it for the written word. Learning to read can have profound effects on the wiring of the adult brain – even in regions that aren't usually associated with reading and writing. That's what Michael Skeide of the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, and his colleagues found when they taught a group of illiterate adults in rural India to read and write. Skeide and his colleagues wanted to study how culture changes the brain, so focused on reading and writing. These cultural inventions have appeared only recently in our evolutionary history, so we haven't had a chance to evolve specific genes for such skills.

About Rylan: Rylan Schaeffer is a recent graduate from UC Davis with a double Bachelor's in computer science engineering and statistics. He's currently looking for a job or an internship, so if you know of any software engineering, machine learning or data science opportunities, please contact him. He lives in Mountain View and spends his time coding, swimming and reading science fiction when not blogging about academic papers. I've found that the overwhelming majority of online information on artificial intelligence research falls into one of two categories: the first is aimed at explaining advances to lay audiences, and the second is aimed at explaining advances to other researchers. I haven't found a good resource for people with a technical background who are unfamiliar with the more advanced concepts and are looking for someone to fill them in.