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.

Join the Search Science team at eBay! Do you have what it takes to improve a world-class real-time search engine that serves millions of queries a day? Do you thrive on developing data mining techniques to pull insight out of large data sets. We are passionate about building the best search platform for the world--s largest online marketplace and are looking for top-notch software Engineering and Data Science leaders. The eBay marketplace allows users to search through a repository of a billion items, and unlike a traditional search engine, 20% of these expire (are sold) each day. This creates a unique and interesting set of challenges in the areas of data mining, machine learning and engineering that you won--t find anywhere else. Join the Search Science team at eBay! Do you have what it takes to improve a world-class real-time search engine that serves millions of queries a day?

In analytics, we retrieve information from various data sources; it can be structured or unstructured. The biggest challenge here is to retrieve information from unstructured data mainly texts. Here machine learning comes into the picture to overcome this challenge. Different algorithms have been designed in different platforms but here we will discuss one technique that can be applied in python. The process can be explained better by an example.

At Pubcon 2016 in Las Vegas, I had the opportunity to speak with Eric Enge, CEO and Founder of Stone Temple Consulting, about machine learning and its impact on SEO. If hearing the phrase "machine learning" has you worried, because it's about to become one more thing to think about in SEO, please watch the video below. Eric Enge explains how machine learning will impact SEO and digital marketing. You can also listen to the interview in podcast form here. Please visit SEJ's YouTube page for more video interviews.

With so much time spent online, it can be near impossible to remember where you might have seen that interesting nugget of information. One Seattle-based software firm is trying to battle the digital memory haze by taking snap shots of your computer and tracking everything you have ever looked at on your machine. Called Atlas Recall, its makers say the software is a one-stop search platform that'gives you a photographic memory for your digital life'. A Seattle-based software firm is trying to battle the digital memory haze by tracking everything you have ever looked at on your machine. The makers of Atlas Recall say it is a one-stop search platform that'gives you a photographic memory for your digital life' Atlas Recall runs in the background on a users to device to monitor everything they have looked at.