On Tuesday, Google said it will begin serving up help-wanted job descriptions that its search engine collects across the Internet with help from artificial intelligence. Typing in the query "jobs near me," or "jobs as a chief" will return a swath of information. The data will enable users to filter the jobs by industry and location, when they were posted, and employer. The tool aggregates data from sites like LinkedIn, Monster, WayUp, CareerBuilder, and Glassdoor will include employer ratings from former and current and provide the distance for a typical commute to the job locations. It's a departure from the way that Google has aggregated and served information in its search engine in the past.

Google is trying to turn its search engine into an employment engine. Job hunters will be able to go to Google and see help-wanted listings that its search engine collects across the internet. The results will aim to streamline such listings by eliminating duplicate jobs posted on different sites. Google will also show employer ratings from current and former workers, as well as typical commute times to job locations. Google is trying to turn its search engine into an employment engine.

Looking for a new job is getting easier. Google today launched a new jobs search feature right on its search result pages that lets you search for jobs across virtually all of the major online job boards like LinkedIn, Monster, WayUp, DirectEmployers, CareerBuilder and Facebook and others. Google will also include job listings its finds on a company's homepage. The idea here is to give job seekers an easy way to see which jobs are available without having to go to multiple sites only to find duplicate postings and lots of irrelevant jobs. With this new feature, is now available in English on desktop and mobile, all you have to type in is a query like "jobs near me," "writing jobs" or something along those lines and the search result page will show you the new job search widget that lets you see a broad range of jobs.

Google is trying to turn its search engine into an employment engine. Beginning Tuesday, job hunters will be able to go to Google and see help-wanted listings that its search engine collects across the internet. The results will aim to streamline such listings by eliminating duplicate jobs posted on different sites. Google will also show employer ratings from current and former workers, as well as typical commute times to job locations. This detailed jobs information is a departure from the way Google's main search engine has traditionally shown only bare-bones links to various help-wanted sites.

This image provided by Google shows examples of help-wanted listings displayed on a smartphone. Google is trying to turn its search engine into an employment engine. Beginning Tuesday, June 20, 2017, job hunters will be able to go to Google and see help-wanted listings that its search engine is collecting across the internet. Google will also show employer ratings from current and former workers, as well as typical commute times to where a job is located. It's a departure from the the bare-bones links to various help-wanted sites that Google has traditionally shown.

I've never written one personally but I was having a discussion with the author of a great piece here on Search Engine Journal on AI and its impact on search and the question came up: Between machine learning and the limited space available for organic search, is it on its death spiral? Between machine learning, the limited space available for organic search, and the growth of both voice search and personal assistants, is it on its death spiral? To paint the picture of where this is going, let's look at just some of the changes over the past little while: I'm sure you can see the trend: Google is crafting the results layout in a way that minimizes the impact of organic results on commercially intent searchers. Not coincidentally, Google announced their personal assistant being released on all phones running Android 6.0 and above, taking us beyond running simple queries on our phone and onto more complicated communications and interactions with other systems -- all in a conversational manner.

The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.

This article is contributed by Atiq Rehman. It was initially intended for SEO people, though many will find it useful. For each one we've included a simple SEO based example of it in use. We've also included notes on it's uses for day-to-day SEO work and a link or two to useful more technical articles. There is also an appendix.

It was not long ago that Artificial Intelligence (AI) was only in the realm of science fiction. Today, it has become a reality and is only growing more prominent in many different industries every day. This includes the internet as AI in search engine technology has been around for a few years. The algorithms used to rank pages have been affected considerably by AI already and that trend will continue into the foreseeable future. Currently, Google's RankBrain, an AI process used help set search engine rankings, is having a major impact which is only expected to expand.

Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications. Planning the computational steps of query processing — Query Planning — is central to address these challenges. In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries. We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach. First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner. Our experiments on benchmark knowledge graphs including dbpedia, yago, and freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy.