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This extensive post covers NLP use cases, basic examples, Tokenization, Stop Words Removal, Stemming, Lemmatization, Topic Modeling, the future of NLP, and more. Everything we express (either verbally or in written) carries huge amounts of information. The topic we choose, our tone, our selection of words, everything adds some type of information that can be interpreted and value extracted from it. In theory, we can understand and even predict human behaviour using that information.

The Plato Research Dialogue System enables experts and non-experts alike to quickly build, train, and deploy conversational AI agents. Intelligent conversational agents have evolved significantly over the past few decades, from keyword-spotting interactive voice response (IVR) systems to the cross-platform intelligent personal assistants that are becoming an integral part of daily life. Along with this growth comes the need for intuitive, flexible, and comprehensive research and development platforms that can act as open testbeds to help evaluate new algorithms, quickly prototype, and reliably deploy conversational agents.

Text summarization is one of famous NLP application which had been researched a lot and still at its nascent stage compared to manual summarization. In simple terms, the objective is to condense unstructured text of an article into a summary automatically. There are two types of summarization techniques. For summarizing the reviews, we may want to pick highly positive and negative sentences as summary. Another approach could be picking only the sentences which contains some object/entity (Company name, person, dates, location etc).

Most text classification examples that you see on the Web or in books focus on demonstrating techniques. This will help you build a pseudo usable prototype. If you want to take your classifier to the next level and use it within a product or service workflow, then there are things you need to do from day one to make this a reality. I’ve seen classifiers failing miserably and being replaced with off the shelf solutions because they don’t work in practice.

While Kubernetes has many advantages, it also brings new security challenges. Kubernetes, the most popular container orchestration system and one of the fastest-growing projects in the history of open source, becomes a significant part of many companies’ compute stack. The flexibility and scalability of containers encourage many developers to move their workloads to Kubernetes. While Kubernetes has many advantages, it also brings new security challenges that should be considered. Therefore, it is crucial to understand the various security risks that exist in containerized environments, and specifically in Kubernetes.

On top of ventilators, face masks and health care workers, you can now add COBOL programmers to the list of what several states urgently need as they battle the coronavirus pandemic. In New Jersey, Gov. Phil Murphy has put out a call for volunteers who know how to code the decades-old computer programming language called COBOL because many of the state’s systems still run on older mainframes. In Kansas, Gov.

We recently finished a major networking project which let us control internal traffic in our platform. And we’ve been thinking about how we can add similar security to network traffic leaving our platform. At Monzo, the Security Team’s highest priority is to keep your money and data safe. And to achieve this, we’re always adding and refining security controls across our banking platform. Late last year, we wrapped up a major networking project which let us control internal traffic in our platform (read about it here).

We recently migrated the CAPTCHA provider we use from Google’s reCAPTCHA to a service provided by the independent hCaptcha. Since this change potentially impacts all Cloudflare customers, we wanted to walk through the rationale in more detail. We recently migrated the CAPTCHA provider we use from Google’s reCAPTCHA to a service provided by the independent hCaptcha. We’re excited about this change because it helps address a privacy concern inherent to relying on a Google service that we’ve had for some time and also gives us more flexibility to customize the CAPTCHAs we show.

Here is our annual list of technological advances that we believe will make a real difference in solving important problems. How do we pick? We avoid the one-off tricks, the overhyped new gadgets. Instead we look for those breakthroughs that will truly change how we live and work. Source: technologyreview.com

Cascading failures are failures that involve some kind of feedback mechanism. In distributed software systems they generally involve a feedback loop where some event causes either a reduction in capacity, an increase in latency, or a spike of errors. Laura Nolan explores them using public accounts of real production incidents. Source: infoq.com