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Scholarships for students with disabilities

ZDNet

In 2017, the National Disability Institute completed a financial survey. It showed that students with disabilities take out fewer loans than nondisabled individuals. However, 36% of respondents with student loan debt did not complete their degree. As someone living with a disability, you have other payment options -- like scholarships. Scholarships for students with disabilities can help you avoid some student loan debt.


CITP Seminar: Amy Winecoff - Today's Machine Learning Needs Yesterday's Social Science - Center for Information Technology Policy

#artificialintelligence

Click here to join the seminar. Research on machine learning (ML) algorithms, as well as on their ethical impacts, has focused largely on mathematical or computational questions. However, for algorithmic systems to be useful, reliable, and safe for human users, ML research must also wrangle with how users' psychology and social context affect how they interact with algorithms. This talk will address how novel research on how people interact with ML systems can benefit from decades-old ideas in social science. The first part of the talk will address how well-worn ideas from psychology and behavioral research methods can inform how ML researchers develop and evaluate algorithmic systems.


Best online marketing degrees 2022: Top picks

ZDNet

A marketing degree trains students to effectively and efficiently use advertisements, promotions, and media platforms to reach the public. With more platforms than ever on which to market and advertise products, marketing pros are in demand. Marketing involves appealing to people to sell products, services, and ideas. As a marketer, you'll use research and data methods and strategies to communicate and engage with target audiences. Here are our top picks for online marketing degrees in 2022.


A technique for making quantum computing more resilient to noise, which boosts performance

#artificialintelligence

Quantum computing continues to advance at a rapid pace, but one challenge that holds the field back is mitigating the noise that plagues quantum machines. This leads to much higher error rates compared to classical computers. This noise is often caused by imperfect control signals, interference from the environment, and unwanted interactions between qubits, which are the building blocks of a quantum computer. Performing computations on a quantum computer involves a "quantum circuit," which is a series of operations called quantum gates. These quantum gates, which are mapped to the individual qubits, change the quantum states of certain qubits, which then perform the calculations to solve a problem.


Bing SEO: Website Optimization Guide & Free SEO Tools

#artificialintelligence

Bing, previously known as Microsoft Live Search, is a search engine with over 40% market share in the US. Bing's SEO guide is aimed at helping small business owners to better optimize their websites for traffic and leads. If you're already using Google's SEO solutions (Analytics, Search Console, etc.) then Bing's guide will give you an edge over your competitors by showing you what additional steps to take and tools to use in order to increase your website traffic. The free SEO analysis tool is an extension for Chrome that automatically analyzes any page that it loads and tells you how well optimized the page is for Bing. Based on its results, you can then use this Bing's SEO guide to optimize your website further.


Less is Less: When Are Snippets Insufficient for Human vs Machine Relevance Estimation?

arXiv.org Artificial Intelligence

Traditional information retrieval (IR) ranking models process the full text of documents. Newer models based on Transformers, however, would incur a high computational cost when processing long texts, so typically use only snippets from the document instead. The model's input based on a document's URL, title, and snippet (UTS) is akin to the summaries that appear on a search engine results page (SERP) to help searchers decide which result to click. This raises questions about when such summaries are sufficient for relevance estimation by the ranking model or the human assessor, and whether humans and machines benefit from the document's full text in similar ways. To answer these questions, we study human and neural model based relevance assessments on 12k query-documents sampled from Bing's search logs. We compare changes in the relevance assessments when only the document summaries and when the full text is also exposed to assessors, studying a range of query and document properties, e.g., query type, snippet length. Our findings show that the full text is beneficial for humans and a BERT model for similar query and document types, e.g., tail, long queries. A closer look, however, reveals that humans and machines respond to the additional input in very different ways. Adding the full text can also hurt the ranker's performance, e.g., for navigational queries.


Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval

arXiv.org Artificial Intelligence

Ad-hoc search calls for the selection of appropriate answers from a massive-scale corpus. Nowadays, the embedding-based retrieval (EBR) becomes a promising solution, where deep learning based document representation and ANN search techniques are allied to handle this task. However, a major challenge is that the ANN index can be too large to fit into memory, given the considerable size of answer corpus. In this work, we tackle this problem with Bi-Granular Document Representation, where the lightweight sparse embeddings are indexed and standby in memory for coarse-grained candidate search, and the heavyweight dense embeddings are hosted in disk for fine-grained post verification. For the best of retrieval accuracy, a Progressive Optimization framework is designed. The sparse embeddings are learned ahead for high-quality search of candidates. Conditioned on the candidate distribution induced by the sparse embeddings, the dense embeddings are continuously learned to optimize the discrimination of ground-truth from the shortlisted candidates. Besides, two techniques: the contrastive quantization and the locality-centric sampling are introduced for the learning of sparse and dense embeddings, which substantially contribute to their performances. Thanks to the above features, our method effectively handles massive-scale EBR with strong advantages in accuracy: with up to +4.3% recall gain on million-scale corpus, and up to +17.5% recall gain on billion-scale corpus. Besides, Our method is applied to a major sponsored search platform with substantial gains on revenue (+1.95%), Recall (+1.01%) and CTR (+0.49%).


Retrieving Black-box Optimal Images from External Databases

arXiv.org Artificial Intelligence

Suppose we have a black-box function (e.g., deep neural network) that takes an image as input and outputs a value that indicates preference. How can we retrieve optimal images with respect to this function from an external database on the Internet? Standard retrieval problems in the literature (e.g., item recommendations) assume that an algorithm has full access to the set of items. In other words, such algorithms are designed for service providers. In this paper, we consider the retrieval problem under different assumptions. Specifically, we consider how users with limited access to an image database can retrieve images using their own black-box functions. This formulation enables a flexible and finer-grained image search defined by each user. We assume the user can access the database through a search query with tight API limits. Therefore, a user needs to efficiently retrieve optimal images in terms of the number of queries. We propose an efficient retrieval algorithm Tiara for this problem. In the experiments, we confirm that our proposed method performs better than several baselines under various settings.


What is Search Engine Optimization (SEO)? - Discover How to Make

#artificialintelligence

An entrepreneur or freelancer has two main strategies to tap into when marketing online. Search Engine Optimization (SEO), which attempts to rank your website on search engines "organically", and Search Engine Marketing (SEM), which ranks your website in search results in exchange for money. Both strategies can be used to build a business successfully--but which one is right for you? A great way to market your business is to use search engines to help your customers find you online. You will need a sales-focused website (e.g., one aimed at creating contact rather than one aimed at assuring customers that you are who you say you are) if you use this strategy; otherwise, your efforts will likely be wasted. You have two ways to use search engines to help people find your website; search engine optimization (SEO) and search engine marketing (SEM).


The Top 10 Search Engines Today

#artificialintelligence

In SEO, the focus is so often on Google. 'How do I rank higher in the Google SERPs?', or'How can I get more rich snippets on Google?' Of course, Google is one of the most popular search engines, but it's certainly not the only one. Different search engines have different audience demographics and different pros and cons, so when you're optimizing your website, you don't want to miss out on a significant share of a certain market. In this article, you will find a complete list of all top internet search engines, their pros and cons, and whether Google really is the most popular. We made a list of the top ten search engines widely used today.