How many times has your website taken a while to load? How many times have you said, "Meh. Your Google page speed score and your core web vitals are more important than ever. Even if you're making sales right now, it's only a matter of time before your competition decides it's better to be the hare and not the tortoise. All of the great content, social media promotion, and keyword research in the world won't matter if your website is a slug on a rainy day.

We've all come across warnings when visiting suspicious websites. Your browser or search engine might even block you from entering, displaying a message that this site may harm your device. But what if the site you're trying to visit is not flagged as malicious? According to SiteLock's 2022 Security Report, 92% of infected websites are not blacklisted by search engines. This means that businesses and individuals are vulnerable to attack when they visit these sites.

From translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools, NLP is at the core of tools in our everyday life. NLP -- Natural Language Processing trying to make machines that can think and act like humans (Don't worry they won't be Human as humans are). It is used to understand human behavior by feeding it with syntax, language, accents, and many other forms of sensory data that human captures. Algorithms then convert this data, rather say transforms this data in the language that the machine understands, thus making the machine learn on a certain rule to perform actions and solve problems. So How Does NLP Work?

If you want to gain the skills necessary to query big data with modern distributed SQL engines, then this specialization is for you. The best part of this course is that it will teach you a newer breed of SQL engine: distributed query engines Hive and Impala. Hive and Impala are open-source SQL engines capable of querying enormous datasets. Another advantage of this specialization program is that this program provides excellent preparation for the Cloudera Certified Associate (CCA) Data Analyst certification exam. This Specialization program consists of 3 Courses.

The world's oceans are amazing carbon sponges, capturing a quarter of human-produced carbon dioxide when surface waters react with the greenhouse gas in the air or marine organisms gobble it up as they grow. Some research groups and start-ups want to help accelerate this natural process by adding certain minerals to the oceans that could help them lock up even more carbon and slow climate change. The idea has attracted a lot of excitement and investment. However, a number of recent studies suggest that some of these approaches may not be as effective as scientists had hoped. That's disappointing news, because the world may need to suck up an additional 10 billion tons of carbon annually by midcentury to limit warming to 2 C, according to a recent report.

To support MIT Technology Review's journalism, please consider becoming a subscriber. But critics are starting to push back, arguing that the approach is wrong-headed. Asking computers a question and getting an answer in natural language can hide complexity behind a veneer of authority that is not deserved. "We got too bogged down by what we could do; we haven't looked at what we should do," says Chirag Shah at the University of Washington, who works on search technologies. On March 14, Shah and his University of Washington colleague Emily M. Bender, who studies computational linguistics and ethical issues in natural-language processing, published a paper that criticizes what they see as a rush to embrace language models for tasks they are not designed to address.

Wouldn't it be handy to have a cheat-sheet for a search engine you use daily? That too for a search engine that has tons of goodness to make life easy and display instant results?! Yes, we too thought the same and here is a smart sheet that can actually help you to get better and desired search results easily and quickly. If you are a regular user of DuckDuckGo, scroll to get the cheat-sheet, for others, here is a quick briefing about the search engine. DuckDuckGo is a search engine just like Google, Yahoo, Bing and so on! But, there is a great difference and the difference is what we treasure – privacy!

While the natural language processing (NLP) field has been growing at an exponential rate for the last two years -- thanks to the development of transfer-based models -- their applications have been limited in scope for the job search field. LinkedIn, the leading company in job search and recruitment, is a good example. While I hold a Ph.D. in Material Science and a Master in Physics, I am receiving job recommendations such as Technical Program Manager at MongoDB and a Go Developer position at Toptal which are both web developing companies that are not relevant to my background. This feeling of irrelevancy is shared by many users and is a cause of big frustration. In general, however, traditional job search engines are based on simple keyword and/or semantic similarities that are usually not well suited to providing good job recommendations since they don't take into account the interlinks between entities.

Effects of Optical Character Recognition (OCR) quality on historical information retrieval have so far been studied in data-oriented scenarios regarding the effectiveness of retrieval results. Such studies have either focused on the effects of artificially degraded OCR quality (see, e.g., [1-2]) or utilized test collections containing texts based on authentic low quality OCR data (see, e.g., [3]). In this paper the effects of OCR quality are studied in a user-oriented information retrieval setting. Thirty-two users evaluated subjectively query results of six topics each (out of 30 topics) based on pre-formulated queries using a simulated work task setting. To the best of our knowledge our simulated work task experiment is the first one showing empirically that users' subjective relevance assessments of retrieved documents are affected by a change in the quality of optically read text. Users of historical newspaper collections have so far commented effects of OCR'ed data quality mainly in impressionistic ways, and controlled user environments for studying effects of OCR quality on users' relevance assessments of the retrieval results have so far been missing. To remedy this The National Library of Finland (NLF) set up an experimental query environment for the contents of one Finnish historical newspaper, Uusi Suometar 1869-1918, to be able to compare users' evaluation of search results of two different OCR qualities for digitized newspaper articles. The query interface was able to present the same underlying document for the user based on two alternatives: either based on the lower OCR quality, or based on the higher OCR quality, and the choice was randomized. The users did not know about quality differences in the article texts they evaluated. The main result of the study is that improved optical character recognition quality affects perceived usefulness of historical newspaper articles significantly. The mean average evaluation score for the improved OCR results was 7.94% higher than the mean average evaluation score of the old OCR results.

Introduction Computers are able to see, hear and learn. Welcome to the future.Dave Waters In this post, I want to talk about a computer vision use case, it's called Content Based Image Retrieval or CBIR in short. In simple words, retrieving images relevant to the user needs from image databases on the basis of low-level visual features. Image Search…