According to an article published Tuesday on Wired, Google's featured snippets on desktop will now be rendered completely through artificial intelligence. What are being called "sentence compression algorithms" just went live today in desktop search only. These sentence compression algorithms, with the help from deep neural networks, are capable of combing through large amounts of data and text to extract just the information you're looking for. So when you ask a question like "what is the best Christmas movie?", Favorite films aside, what you can notice here is that the featured snippet cut through the entire article to render a quick list of exactly what was being searched for.

Ask the Google search app "What is the fastest bird on Earth?," and it will tell you. "Peregrine falcon," the phone says. "According to YouTube, the peregrine falcon has a maximum recorded airspeed of 389 kilometers per hour." That's the right answer, but it doesn't come from some master database inside Google. When you ask the question, Google's search engine pinpoints a YouTube video describing the five fastest birds on the planet and then extracts just the information you're looking for.

With the growth of user-generated content, we observe the constant rise of the number of companies, such as search engines, content aggregators, etc., that operate with tremendous amounts of web content not being the services hosting it. Thus, aiming to locate the most important content and promote it to the users, they face the need of estimating the current and predicting the future content popularity. In this paper, we approach the problem of video popularity prediction not from the side of a video hosting service, as done in all previous studies, but from the side of an operating company, which provides a popular video search service that aggregates content from different video hosting websites. We investigate video popularity prediction based on features from three primary sources available for a typical operating company: first, the content hosting provider may deliver its data via its API, second, the operating company makes use of its own search and browsing logs, third, the company crawls information about embeds of a video and links to a video page from publicly available resources on the Web. We show that video popularity prediction based on the embed and link data coupled with the internal search and browsing data significantly improves video popularity prediction based only on the data provided by the video hosting and can even adequately replace the API data in the cases when it is partly or completely unavailable.

While discussing a concrete controversial topic, most humans will find it challenging to swiftly raise a diverse set of convincing and relevant arguments. In this paper we present a system that, given a point of view about a controversial topic, automatically generates arguments supporting and contesting it. This is achieved by breaking the task of automatic argument construction into a pipeline of successive modules, each is responsible for a specific tangible task such as documents retrieval, identifying building blocks of arguments within a document, and analyzing whether these building blocks support or contest the point of view. By providing an interface for humans to interact and intervene at different points in the pipeline, we present an interactive research tool which, for a given topic and a corpus of documents such as Wikipedia or newspaper archive, provides a more comprehensive view and deeper insights than can be obtained using standard search engines.

As I found myself unexpectedly snowed in this weekend, I decided to take on a weekend project for fun. While searching for something to catch my fancy, I ran across an old Slate article claiming that they found a foolproof strategy for finding Waldo in the classic "Where's Waldo?" book series. Now, I'm no Waldo-spotting expert, but even I could tell that the strategy they proposed there is far from perfect. That's when I decided what my weekend project would be: I was going to pull out every machine learning trick in my tool box to compute the optimal search strategy for finding Waldo. I was going to crush Slate's supposed foolproof strategy and carve a trail of defeated Waldo-searchers in my wake.

The brain is often said to be like a computer. Now it turns out that we store memories of famous people in a similar way to Google. Our hippocampi – two small, curved brain structures towards the sides of our head – are crucial for memory. Studies have found that people with damage in these areas can no longer make memories of new events. By studying people who had recording electrodes put into their hippocampi, Rodrigo Quian Quiroga at the University of Leicester, UK, previously found that some neurons in these areas fire only when we see particular celebrities or people we recognise.

A free AI-based scholarly search engine that aims to outdo Google Scholar is expanding its corpus of papers to cover some 10 million research articles in computer science and neuroscience, its creators announced on 11 November. Since its launch last year, it has been joined by several other AI-based academic search engines, most notably a relaunched effort from computing giant Microsoft. Semantic Scholar, from the non-profit Allen Institute for Artificial Intelligence (AI2) in Seattle, Washington, unveiled its new format at the Society for Neuroscience annual meeting in San Diego. Some scientists who were given an early view of the site are impressed. "This is a game changer," says Andrew Huberman, a neurobiologist at Stanford University, California.

Researchers, scientists and academics around the world publish roughly 2.5 million scientific papers each year, on top of a backlog of more than 50 million papers dating back to 1665. Plus, the rate at which researchers publish these academic papers keeps rising, a la Moore's Law. It's impossible for scientists to read every paper published in their fields, and searching for a specific study can be a daunting task. Enter: Paul Allen, Microsoft co-founder and leader of the non-profit Allen Institute for Artificial Intelligence. The Allen Institute's latest effort is Semantic Scholar, a scientific-paper search engine powered by machine learning and other artificial intelligence systems.

We will mainly be focusing on various sources of data that you might have to fetch or be given to build a search engine in the first place. So, if you are just an enthusiast or you have to build a professional search engine from scratch, you have come to the right place! A search engine differs from objective to objective but the core functionality remains the same – information retrieval. Here are some of the sources of data that you might be given or you want to build a search engine for. At the heart they are all quite the same but they have quite different approaches to solving the same problem.