Accurate skill extraction from job descriptions is crucial in the hiring process but remains challenging. Named Entity Recognition (NER) is a common approach used to address this issue. With the demonstrated success of large language models (LLMs) in various NLP tasks, including NER, we propose fine-tuning a specialized Skill-LLM and a light weight model to improve the precision and quality of skill extraction. In our study, we evaluated the fine-tuned Skill-LLM and the light weight model using a benchmark dataset and compared its performance against state-of-the-art (SOTA) methods. Our results show that this approach outperforms existing SOTA techniques.

Artificial Intelligence (AI) has been making waves across various industries, and the field of software development is no exception. As AI continues to advance, it is crucial for developers to understand the implications of these changes and adapt to the new landscape. In this post, we will explore the impact of AI on software development, the evolving job market, and the skills developers need to succeed in this ever-changing environment. The advent of AI has introduced a new level of automation within the software development lifecycle, streamlining various tasks and improving efficiency. In this section, we will delve into the ways AI is automating key aspects of the development process, from code generation to bug detection and optimization.

Over the course of her nine-year tenure at LinkedIn, Ya Xu has held technology roles with increasing responsibility. Today, she heads the data function for the online professional networking platform. Ya Xu has been a driving force in transforming LinkedIn into a data-first company since she first joined the organization in 2013. As head of data, she leads a global team of about 1,000 data scientists and AI engineers whose work is at the core of delivering economic opportunities to LinkedIn's members and customers. Xu's emphasis on responsible AI and data science ensures that LinkedIn's AI systems put people first and enables the company to empower its members, better serve its customers, and benefit society. In addition to her work at LinkedIn, Xu has coauthored the book Trustworthy Online Controlled Experiments (Cambridge University Press, 2020), has been named to Fortune's 40 under 40 in tech, and was nominated for VentureBeat's Women in AI Awards. She has delivered countless speeches, including a commencement speech to Stanford's class of 2019 in mathematics, statistics, and mathematical and computational science. Previously, Xu worked at Microsoft and earned a Ph.D. in statistics from Stanford University. Ya joins hosts Sam Ransbotham and Shervin Khodabandeh in this episode of the Me, Myself, and AI podcast, where she discusses AI's essential role in helping LinkedIn create the best "matches" -- content creators with content consumers, job seekers with employers, and buyers with sellers -- within its three key marketplaces. Ya also describes how the company has fostered a data-first culture from the top down, and how its vast amount of economic activity data is helping governments and policy makers worldwide.

The Pentagon did not adequately document work on its flagship artificial intelligence effort according to a government watchdog report, increasing the risks of lapses in the future. The Department of Defense's inspector general evaluated whether the government monitored contacts in accordance with federal laws and policy for Project Maven, which aimed to accelerate the integration of big data and machine learning. It is frequently held up as the poster child for how DoD is using AI. Army Contracting Command and the Army Research Laboratory partnered with the Pentagon's Algorithmic Warfare Cross-Functional Team to support AI development and award four contracts and a cooperative agreement for Project Maven. ECS Federal scored three of the contracts, with Morse Corporation securing the fourth and Carnegie Mellon University receiving a cooperative agreement.

"We need to be an AI-enabled company." Replace the "AI" with any technology from history and this comment becomes a common refrain across businesses lured by the promises of new technology and fueled by FOMO (a fear of missing out). As enterprise strategists and former CXOs who have lived through many "technology is the solution, now what was the problem?" conversations, we talk extensively about this issue. To paraphrase Roy Amara, we overestimate the impact of a new technology early on. When it falls short of our expectations, our disappointment means we are less willing to adopt it when it is truly ready.

About the Role If you're looking to be part of a dynamic, highly-analytical team who enjoys improving the Gojek experience to delight and engage more users, look no further! As an Operations Analyst (Data Operations) for the Supply Operations team, you'll take the wheel in designing and building scalable analytics solutions through data pipelines and visualize datasets that empower the Singapore business with actionable analytics. Working closely with Product, Growth, Marketplace, Service Excellence, and Business Intelligence teams, you'll dive deep into local market insights to provide direction and analytical solutions that are timely, accurate, and actionable to drive growth and contribute to the continuous improvement of data pipelines within the business intelligence domain. Employing your stellar analytical skills and contextual business acumen, your efforts will improve the service reliability and experience for our driver-partners, and ensure that we provide both our ...

Consumers today are more AI-savvy than you think. Customers use their best interaction experience in one domain as a baseline expectation in others. This means that, when it comes to AI, every single business is in competition with the global giants, including Amazon and Netflix. AI is no longer a nice-to-have feature; it's a must-have -- and poor AI has become a real threat to businesses. When the algorithm fails to live up to people's expectations of the user experience and doesn't deliver the service its users want, the people using the system end up feeling annoyed, frustrated, and tired.

Putting this idea into practice is anything but simple. Even two of the same pieces of equipment (i.e. two of the same model of drill in a manufacturing operation) can have unique patterns of data – and thus require unique calibration and diagnosis. Sensors to detect heat may not work well in colder seasons, or may fall off or lose their sensitivity when exposed to extreme conditions. Determining precisely what data should be used as the diagnostic data stream for a specific piece of equipment is also not easy, and may require expensive and time-consuming iteration. Even the most well-funded firms in industrial AI are still pivoting in order to find the right applications for machine learning in order to deliver consistent results for clients.

This article was a request from one of our Catalyst Advisory Program members. The Catalyst Advisory Program is an application-only business growth coaching program for AI consultants and AI service providers. The program helps AI consulting and services leaders win more deals and deliver more client value. Members receive one-to-one advisory, group coaching, and proprietary Catalyst AI best-practice frameworks. Learn more or apply at: emerj.com/catalyst.

In a single day, the US's reconnaissance aircraft and satellites collect more raw data than the entire defence workforce could analyse in their aggregate lifetimes. Security officials are trying to find needles in ever-expanding digital haystacks. As a recent RUSI paper recognises, this information overload is also'perhaps the greatest technical challenge facing the UK's national security community'. Over the last year as a Kennedy Scholar at Harvard University, I explored how defence and intelligence organisations are using artificial intelligence (AI) to respond to that overload. Beyond the technology itself, I wanted to find out how the US has created the foundations for successfully deploying AI: a skilled workforce, data management, computational power, cloud platforms, technical foundations of security and trust, and a prudent policy framework.