The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers. In this paper, we introduce PRIMEQA: a one-stop and open-source QA repository with an aim to democratize QA re-search and facilitate easy replication of state-of-the-art (SOTA) QA methods. PRIMEQA supports core QA functionalities like retrieval and reading comprehension as well as auxiliary capabilities such as question generation.It has been designed as an end-to-end toolkit for various use cases: building front-end applications, replicating SOTA methods on pub-lic benchmarks, and expanding pre-existing methods. PRIMEQA is available at : https://github.com/primeqa.

Applications range from similarity search to complex NLP-driven data extractions to generate structured databases. Semantic workflows transform and find data driven by user intent. The following applications are powered by txtai.

The financial services industry is buried in paperwork, and the NLP use-cases in banking and insurance grow every year. For the last three years, we've closely followed the application space of AI-based search applications. These applications tend to be broad, and can hypothetically handle nearly any text-related data format, and could hypothetically be used to address nearly any document or data-related use workflow. Over the last 18 months we've interviewed and directly analyzed 15 AI-based search vendors selling into the retail banking sector, including startups like Expert.ai, Our goal was to gain more clarity on how these broad search applications are actually used in practice, and what actual business problems they are addressing.

Applications range from similarity search to complex NLP-driven data extractions to generate structured databases. The following applications are powered by txtai.

Users expect to see that friendly search box in their applications. They seem to really like it, because it's so simple to use. You don't need a user manual to figure out search. In fact, if your application doesn't have search, you'll be pelted with negative reviews. No wonder you see search in so many applications. It's very difficult to implement. We all know it's more than just simple text matching. Those of us with database backgrounds know that searching for "prefix*" is a lot easier than searching for "*suffix". And users want to do all sorts of weird searches like "*run*", which should match ran, or shrunken or brunt, or--you get the idea. Quick search results and performance are important, as is accuracy and ranking.

Retailers know that it is harder and more expensive to acquire new customers than to sell new things to existing customers. That's why they spend a lot on loyalty programs and Customer 360/Customer Journey programs. One of the best tools a retailer has for selling products to customers is recommendations. Recommendations are simply that, suggestions by the retailer on other things the customer may be interested in. In order to do this, a retailer needs to know the customer.

Artificial intelligence (AI) promises to deliver enterprises higher efficiency, increased accuracy and greater utilization of corporate information assets. But these promises can only come true if the AI is built on a solid information architecture. While many would like to believe that AI is combination of magic and pixie dust, for human-to-machine conversations to become reliable, a significant amount of foundational effort must take place. And this process begins with the basics of knowledge management. Developing a framework starts with the concept of a "domain model."

SAN FRANCISCO - SEPTEMBER 08: Google Vice President of Search Product and User Experience Marissa Mayer speaks during an announcement September 8, 2010 in San Francisco, California. Google announced the launch of Google Instant, a faster version of Google search that streams results live as you type your query. Google is consistently exploring all facets of virtual learning with the success of online applications, with a new artificial intelligence lab. Focusing on machines learning for the advancement of its products, Google Research Europe would be based in Switzerland. Following Google's recently launched Assistant, the company's European research team on artificial intelligence would be under the expertise of Emmanuel Mogenet plus up to a few hundred colleagues.

Google (NASDAQ:GOOG) (NASDAQ:GOOGL) CEO Sundar Pichai said at the Google I/O conference that the company has built a Tensor Processing Unit (TPU) as part of its AI (artificial intelligence) initiative. Google built an AI computer system for Chinese board game Go which beat a human player. The AI system, called AlphaGo, was powered by the TPU. Although AlphaGo's win is somewhat like IBM (NYSE:IBM) Watson's Jeopardy win in 2011, building a TPU could have deeper implications for developing tomorrow's AI systems. Tensor Processing Unit (TPU) is a custom ASIC for machine learning that fits in the same footprint of a hard drive, and was the secret sauce for AlphaGo in Korea.

Google (NASDAQ:GOOG) (NASDAQ:GOOGL) CEO Sundar Pichai said at Google I/O conference that the company has built a Tensor Processing Unit (TPU) as part of its AI (artificial intelligence) initiative. Google built an AI computer system for Chinese board game Go which beat a human player. The AI system, called AlphaGo, was powered by the TPU. Although AlphaGo's win is somewhat like IBM (NYSE:IBM) Watson's Jeopardy win in 2011, building a TPU could have deeper implications for developing tomorrow's AI systems. Tensor Processing Unit (TPU) is a custom ASIC for machine learning that fits in the same footprint of a hard drive, and was the secret sauce for AlphaGo in Korea.