There's no question about it: LAI is the new hotness in personal computers. An intriguing new report claims Microsoft will push the pedal to the metal even harder with a revolutionary new version of Windows 11 (or 12?) in 2024, which is designed to make AI helpful at deeply practical levels. Microsoft just put AI front-and-center with Windows 11's massive 2023 Update, which added the Windows Copilot AI assistant and awesome AI "Cocreator" features to Paint. Copilot is coming to Windows 10, too. And Intel, AMD, and Qualcomm have been busy integrating AI-boosting "NPUs" (neural processing units) to the PC chips destined to hit the streets next year, aiming to enhance tasks with local AI that runs on your computer's hardware rather than hitting up servers in the cloud.

Most weakly supervised named entity recognition (NER) models rely on domain-specific dictionaries provided by experts. This approach is infeasible in many domains where dictionaries do not exist. While a phrase retrieval model was used to construct pseudo-dictionaries with entities retrieved from Wikipedia automatically in a recent study, these dictionaries often have limited coverage because the retriever is likely to retrieve popular entities rather than rare ones. In this study, we present a novel framework, HighGEN, that generates NER datasets with high-coverage pseudo-dictionaries. Specifically, we create entity-rich dictionaries with a novel search method, called phrase embedding search, which encourages the retriever to search a space densely populated with various entities. In addition, we use a new verification process based on the embedding distance between candidate entity mentions and entity types to reduce the false-positive noise in weak labels generated by high-coverage dictionaries. We demonstrate that HighGEN outperforms the previous best model by an average F1 score of 4.7 across five NER benchmark datasets.

I start my day by asking ChatGPT questions about an upcoming trip I am planning, including where I am heading, with whom I am traveling, and the expected weather. ChatGPT provides an itinerary of where to visit, places to eat, and what to bring on my trip. Five minutes later, I visit several ecommerce websites to find and buy the recommended items. The search box barely fits two keywords, and after several tries, I give up and visit a second site and then a third. I run out of time and VPN into my client's network to begin my work.

In this special guest feature, Ryan Welsh, Co-founder and CEO of Kyndi, discusses how organizations are leveraging the latest natural language processing techniques to enable sophisticated natural language understanding. Ryan started Kyndi in 2014 with a vision of creating a world where AI would empower humans to do their most meaningful work. Under his leadership, Kyndi has created the natural language enablement category, offering a powerful Natural Language Enablement Platform and natural language-enabled solutions. Ryan received his B.A. in Anthropology from The Catholic University of America, his M.S. in Applied Math/Economics from Rutgers University, and an M.B.A. from the University of Notre Dame. According to Deloitte, as much as 80% of all information is hidden in unstructured, text-based data living in various systems inside and outside of the companies.

Traditional search engines use manual tagging or keywords queried against their index to provide results to a customer. This neglects what your customers think, how they behave and what they expect from their search experience. With the evolution of search experiences provided by personalization masters like Google, Amazon and Netflix, customers want the same personalized experience on every website they visit. Natural language search is essential to providing users with the relevant search they crave. It moves beyond keyword matching and programming tedious manual rules.

Business intelligence and analytics platform vendors are now providing augmented analytics capabilities that empower citizen data scientists. Specifically, they use natural language understanding capabilities to enable natural language searches and deliver the results using natural language generation. The result is a "conversation" between the user and the system. Augmented analytics tools also come with pre-built machine learning models to empower any user to do single click forecasts, identify trends and trend reversals, anomalies, outliers -- tasks that in the past required involvement from professional data scientists. In short, the opportunities are many, but many enterprises have a way to go before their businesses are truly "insight-driven."

Organizations have been using natural language processing (NLP) for text analytics to identify patterns in data such as social media sentiment and contract review, but NLP usage has been expanding. "The big change that's happened in the last five years is the amount of context and understanding that can be extracted or used when understanding documents," said Nigel Duffy, global artificial intelligence leader at EY. "Our ability to understand information from documents is much, much greater than it was a few years ago." BI and analytics vendors are adding NLP capabilities to their products such as natural language generation for data visualization narration and natural language understanding for natural language searches. In doing all of this, they're making data visualizations easier to understand and their products easier to use. For example, Tableau, Sisense, and Qlik have all partnered with Narrative Science to narrate data visualizations with text.

As human beings, we use our voices for conversation. When we interact with voice interfaces, therefore, our natural instinct is to apply the same rules that we would to a human conversation. We expect to be understood, but more than this, we expect the entity we're conversing with to remember the history of our conversation and understand the context of any following remarks. For some time, major search companies like Google and Bing have worked to teach their search engines to understand queries in natural language. Natural language search queries are queries that sound natural spoken aloud, such as, "How high is the Empire State building?" They often begin with question words ("When…?" "How…?" "Why…?"), contain stop words ("a", "the", "of", "for") and full sentences.

Natural language processing became another buzzword these years. But not everyone really understands what NLP is and how it can be used to improve efficiency of the process and impact your business in a positive way. Let's start with basics, natural language processing (NLP) is the ability of a computer program to understand human speech as it is spoken. It is a component of artificial intelligence (AI) – actually another big trend these years. In other words, Natural language processing is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human languages. It is a computer activity in which computers analyze, understand and generate natural language.

Retailers are starting to use artificial intelligence to power both customer engagement and service. Artificial intelligence (AI) is on the brink of going mainstream in retail because it shows real potential in helping traders give their customers better service. As more retailers are investing in developing their own approaches to AI, commerce platform providers are also adding more and more automated decision making and machine learning to their software. The time is right for businesses of any shape and size to look into the potential of this technology. Retailer Shop Direct is already using AI and machine learning to talk to its customers but has plans to push this further in the next 12 months. Last year, its Very.co.uk brand launched an automated'Very Assistant' within its mobile iOS app that answers shoppers' customer service questions through a conversational user interface (CUI).