7 ways to search without using Google

FOX News

A fired Google engineer who published a memo about diversity is threatening to sue the tech giant. Yes, it's the most powerful search engine ever created. Yes, it processes 40,000 searches per second. And yes, Google is the go-to search engine for the majority of us. There are many Google resources that most people don't know about, including Google's advanced search features that let you narrow searches by time, file type and website type.

Android TV's universal search feature finally works with Netflix


Android TV's helpful search feature now digs into the Netflix vault to find what you're looking for. Search is usually at the heart of Google-built products, so the inability to apply that to Netflix stood out, especially since similar search capabilities worked with Netflix on Apple TV and Roku devices. It just got easier on #AndroidTV, now with universal search. Google's screenshots indicate you'll see a card with details about the show and the ability to launch it inside of the Netflix app. It appears this could be a server-side switch, since there's no update at this time for the Netflix Android TV app.

Personalizing Image Search Results on Flickr

arXiv.org Artificial Intelligence

The social media site Flickr allows users to upload their photos, annotate them with tags, submit them to groups, and also to form social networks by adding other users as contacts. Flickr offers multiple ways of browsing or searching it. One option is tag search, which returns all images tagged with a specific keyword. If the keyword is ambiguous, e.g., ``beetle'' could mean an insect or a car, tag search results will include many images that are not relevant to the sense the user had in mind when executing the query. We claim that users express their photography interests through the metadata they add in the form of contacts and image annotations. We show how to exploit this metadata to personalize search results for the user, thereby improving search performance. First, we show that we can significantly improve search precision by filtering tag search results by user's contacts or a larger social network that includes those contact's contacts. Secondly, we describe a probabilistic model that takes advantage of tag information to discover latent topics contained in the search results. The users' interests can similarly be described by the tags they used for annotating their images. The latent topics found by the model are then used to personalize search results by finding images on topics that are of interest to the user.

Online Marketing with Artificial Intelligence


Have you thought about how artificial intelligence (i.e. I hope so, as the rise of artificial intelligence is undeniable in the digital marketing world. In just the past few years, artificial intelligence has already started to make us think about what can and should be delegated to automation controlled by AI. In this article I will explain how online marketing with artificial intelligence will allow you to drive better results through more quantified and automated strategies. More companies than ever -- from Google and Facebook to your aunt's crafting business -- are turning to AI to improve their business and marketing tactics and I hope to help you do so as well. A few years ago, everyone was talking about the rise of big data. Businesses would soon have more information about the industry, their customers, and even employee performance than ever before.

Around the Water Cooler: Shared Discussion Topics and Contact Closeness in Social Search

AAAI Conferences

Search engines are now augmenting search results with social annotations, i.e., endorsements from users’ social network contacts. However, there is currently a dearth of published research on the effects of these annotations on user choice. This work investigates two research questions associated with annotations: 1) do some contacts affect user choice more than others, and 2) are annotations relevant across various information needs. We conduct a controlled experiment with 355 participants, using hypothetical searches and annotations, and elicit users’ choices. We find that domain contacts are preferred to close contacts, and this preference persists across a variety of information needs. Further, these contacts need not be experts and might be identified easily from conversation data.