Textkernel’s labour market intelligence tool expands into Ireland
Textkernel is proud to announce the expansion of its renowned labour market intelligence tool, Jobfeed, to Ireland. With its mix of old and new sectors and growing tech industries, the Irish labour market offers both opportunities and challenges. Jobfeed offers indispensable insights into the Irish labour market that will empower staffing agencies and commercial organisations to make data-driven talent and recruitment decisions.
In a landscape as diverse and dynamic as Ireland, Jobfeed equips organisations to understand and analyse the labour market to navigate changes, unlock opportunities, and connect people and jobs better.
Exploring the Irish Labour Market with Jobfeed
The Irish job market is as diverse as it is dynamic, reflecting a blend of longstanding industries and emerging sectors. Let’s explore the key trends and insights that Jobfeed has uncovered.
Support Worker Popularity: In both the UK and Ireland, Support Workers are the most sought-after profession. Notably, Ireland offers a lucrative edge, with Support Workers earning almost more than 20% more, with an average salary of €35,000 annually.
Dublin’s Job Density: Dublin stands as Ireland’s employment hub, contributing a substantial 38% of all job offerings. This urban-centric trend echoes global patterns, exemplified in cities like Auckland, New Zealand.
Language Dynamics: While English, German, and French are dominant in job listings, there’s an increasing demand for Irish Gaelic, reflecting Ireland’s deep-seated cultural heritage.
Stay Ahead of Labour Market Dynamics with Textkernel’s Labour Market Intelligence:
- Unparalleled Access: Benefit from comprehensive, in-depth labour market data via an intuitive interface, a custom dataset, or our versatile API integration.
- Trend Identification: Always stay updated with the freshest market trends and gather invaluable job market information.
- Competitive Edge: Gain enhanced market visibility, ensuring a consistent lead over competitors and real-time trend identification.
- Optimal Integration: Seamlessly infuse Jobfeed into your operations, be it through direct interface access or our API, for a streamlined experience.
The Perfect Solution for Staffing Agencies
Jobfeed equips staffing agencies, both local and international, with real-time, deduplicated, and searchable data on the latest jobs in the market. Monitor the ads placed by potential or existing customers and competitors, and glean crucial insights into ongoing market trends.
The Perfect Solution for HR Teams
Jobfeed Ireland ensures effortless navigation through the Irish labour market for HR teams. With real-time data and insights, HR professionals can make informed decisions, refine recruitment strategies, and consistently remain a step ahead in the rapidly evolving talent acquisition space.
Why Opt for Jobfeed Ireland?
- Enhanced Market Insights: Jobfeed’s expansion into Ireland offers users a wider look at the European job market, highlighting Ireland’s important role.
- Global Reach and Lead Generation: Beyond local nuances, Jobfeed Ireland bridges you to a global talent pool, ensuring your organisation stays ahead in a changing market.
- Responsive Market Strategy: Our venture into Ireland resonates with the evolving market need for understanding this labour market. Esteemed industry leaders like Manpower, Hays, Stepstone, and McKinsey have already entered.
Jobfeed Ireland stands as more than an analytical tool — it’s your strategic ally in charting the vibrant Irish labour market. As we embark on this exciting journey, we invite you to discover all the great possibilities and benefits that Jobfeed Ireland has to offer.
Elevate your recruitment strategy with Jobfeed Ireland today.
There has never been so much data to inform, innovate and optimize talent acquisition strategies. Data is the untapped goldmine in HR and staffing organizations, housing countless potential candidates. But understanding and extracting value from this data is something many organizations are still struggling with. Organizations need to bridge the gap between data to connect people and jobs better.
So how should employers be thinking about data, implementing data-driven talent acquisition strategies and planning for a future of data-based decision-making?
In this episode, they discuss:
- The current state of talent markets
- Magnified skill shortages
- Extracting value from data
- What are the most important data sets employers have access to
- Skills taxonomies and internal mobility
- Up-skilling and succession planning
- Data & APIs
- The dangers of not using data to its full potential
- The role of AI
- Example use cases and outcomes
- What does the future look like, and how should we plan for it
Don’t miss out on the knowledge-packed episode!
Listen to the full episode here or on your favorite podcast network.
Textkernel is a global leader in AI-powered recruitment solutions, delivering multilingual parsing, semantic search and match, and labor market intelligence solutions to over 2,500 corporate and staffing organizations worldwide.
Our innovative technologies help companies better connect people and jobs.
Want to see how you can connect people and jobs better? Request a demo with our team.
This podcast was originally featured on The Recruiting Future Podcast.
AI has always been our method of choice in our mission to accelerate staffing, recruitment and HR processes. In fact, we have pioneered AI solutions for the recruitment domain over 20 years ago, and have been monitoring and applying developments in AI ever since. And that’s not just because it’s an exciting technology: AI actually makes our customers more effective! Whether it’s about automating data entry from CVs or vacancies, shortlisting candidates for jobs, or enabling market analytics, AI-driven software can hugely improve process efficiency. And over time we’ve learned that embracing new developments in AI is key to making sure that the quality of these systems gets ever better.
With all the media frenzy these days, many people would be surprised to learn that AI has been around since the invention of computers. What has changed over the years is the AI algorithms used to make computers intelligent.
The early days
The very AI algorithms of the 1980s consisted of a set of hard-coded assumptions and rules made by domain experts. Think of rules like “if a CV contains a 10-digit number, then it must be a phone number”, or “whatever follows the phrase “Name:” is someone’s name”. It turns out that language is way too complex to be captured with rules (phone numbers can be written with dashes in between digits, the phrase “Name:” can occur in phrases like “School Name:”). Rule-based AI systems tend to grow into a large stack of exceptions on top of exceptions: error-prone and difficult to maintain. Practical applications of such systems were out of reach.
Statistical machine learning
In the late 1990s statistical machine learning came to the rescue. Instead of writing rules manually, statistical algorithms (e.g. Hidden Markov Models in the early 2000s) can infer rules and patterns from annotated data. Those rules are generally better than those found by human engineers: they strike the right balance between being specific and generalizable, and use patterns in the data that humans wouldn’t have seen. Employing machine learning models in combination with various rich data sources, Textkernel achieved best-in-breed accuracy levels on the problems it set out to solve.
Introducing Deep Learning
But early machine learning models still had their limits: they were not able to digest a lot of context and still relied heavily on human expertise (of which signals/features are relevant for specific problems). To understand what a given word means, they would basically only consider the words in their direct neighborhood. A good understanding of a CV or job ad, however, requires understanding the context of the entire paragraph or even the full document.
This is why we invested in upgrading our models to a special kind of machine learning technology: Deep Learning. These somewhat more complex neural networks allowed for a much more contextualized form of document understanding. In addition, they could figure out by themselves which textual features are relevant to solve a given task. Deep Learning took academia by storm in the 2010s and in 2017 it was mature enough to be applied to business problems. Once we applied to parsing, it led to another substantial boost to our accuracy levels.
Recently we’ve been closely monitoring one of the most disruptive developments in language technology so far: Large Language Models (the technology behind ChatGPT) and their impressive ability to perform well on just about any language task and to encode knowledge of the world.
What are LLMs and why do they work so well?
Language models are AI systems with a surprisingly simple objective: “simulate” language. Given a sequence of words, their task is to predict the next most likely word. For example, “bank” or “ATM” are the most likely words that would follow the sequence “I withdrew some money from the …”. Language models have been around for about 30 years. In the past few years, people have been building language models using increasingly bigger neural networks with a special attention mechanism (transformers) and using more and more language data (see table below). It turns out that these Large Language Models (LLMs) start exhibiting abilities that even surprised their creators:
- Performing language tasks: in order to “simulate” language, they become very good at language tasks. They can generate high quality text, summarize text, rewrite text in specific styles, etc.
- Encoding knowledge of the world: language can not be simulated well without world knowledge (e.g. you can not write good quality text about Obama unless you know he was a president of the USA). LLMs magically capture and represent that knowledge just by reading lots of text.
- Some cognitive skills: LLMs try to simulate text that was manually created by people by applying various cognitive skills: inference, deduction, simple reasoning, etc. LLMs seem to develop – or at least mimic – such skills in order to be good at simulating text. It is hypothesized that the size of the neural network and attention mechanism is key for this. In addition, since their training data also includes computer programs, their documentation and the text around them, LLMs are surprisingly good at generating code too. In fact, LLMs can even learn new skills.
Key ingredients in LLMs
It turns out that for LLMs the saying is true: “If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck”. In other words, in the process of simulating human language, these systems have become very good at mimicking the very skills and knowledge that produced that language.
LLMs in recruitment: potential and limitations
The HR media are flooded with suggestions on how ChatGPT and similar tools can be applied to streamline workflows. Ideas range from automated content generation (vacancies, interview questions, marketing content) to improved candidate screening and automated communication. Some of these will be more fruitful than others, but one thing is for sure: recruitment and HR are among the many industries that will be shaken up, if not revolutionized, by this new generation of AI technology.
Apart from giving rise to innovative products, it’s also clear that LLMs will help existing AI-based tools reach higher accuracy and improve their user experience. That’s also true for our software: just like we’ve seen that previous AI developments brought significant quality improvements, LLMs will most certainly benefit the quality of our software for document understanding, candidate sourcing and matching, data enrichment, and analytics. In the next parts of this blog series we will share how we’re using the technology at the moment, and what’s to come.
Not so fast?
Having pursued AI-driven innovation for over two decades, at Textkernel we are well aware that technological breakthroughs are not merely reasons for excitement. And we’re not the first to note that the use of technologies like ChatGPT come with risks and limitations. There are technical limitations concerning scalability and cost. For example, building LLMs is a very complex and very expensive process. It is estimated that it cost OpenAI 4 million dollars to train their GPT-3. Keep in mind that ChatGPT is based on an even newer version, GPT-3.5. At least for the near future, it is envisioned that companies will use LLMs from a small number of providers rather than build an in-house LLM. Running LLMs is also costly which in turn affects the cost of services built on top of them.
Lastly, and not to be underestimated, there are valid concerns about data privacy, transparency and bias. These concerns should be taken very seriously, and the various upcoming forms of AI legislation, such as the EU AI Act and the NY AEDT Law, will help ensure these concerns are treated seriously.
Stay tuned to the next parts of this blog series to hear more about how LLMs relate to AI legislation and how we envision combining compliance with cutting-edge innovation.
You can’t have missed it: Artificial Intelligence tools like ChatGPT are taking the world by storm. They are making waves in HR and recruitment media with suggestions on how they can streamline workflows. From generating content (such as job vacancies, interview questions, and marketing materials) to AI-aided candidate screening and communication, ideas are plentiful. While some of these will be more fruitful than others, it is certain that recruitment and HR are among the many industries that will be impacted, if not revolutionized, by this new generation of AI technology.
In this blog, we explain what this technology is about and how it relates to previous generations of AI. In subsequent blogs, we will delve deeper into the limitations you should be aware of and evaluate the pros and cons of applications in recruitment technology.
A brief history of AI disruptions
The adoption of AI algorithms in recruitment and HR processes has accelerated over the past few decades. Early rule-based AI systems of the 80s had limitations due to their error-prone and difficult-to-maintain nature, which affected their quality.
The introduction of statistical machine learning technology started to change this, as it could help automate language tasks with substantial accuracy (e.g. data entry from CVs). However, these systems were still limited in their ability to digest context and required more human engineering than desired.
These drawbacks were overcome by a special kind of machine learning technology called deep learning (see this blog and this one), which excels at learning complex patterns without the need to tell it what to learn. It turned out that these capacities could be further amplified by making deep learning models larger and feeding them with more data. Scaling the model and data size eventually gave rise to a family of deep learning models called Large Language Models (LLMs). These include OpenAI’s GPT-4 and its chat-oriented cousin ChatGPT, as well as Google’s LaMDA and other industry competitors.
*Want to know more about Textkernel’s journey through these different stages, and the impact each one of them had on recruitment technology? Come back to our website to see our upcoming blog.
LLMs are among the most disruptive developments in language technology. LLMs are AI systems that simulate language and try to predict the most likely word to follow a sequence of previous words. By using increasingly larger neural networks and more language data, LLMs have started to exhibit abilities that surprised even their creators, such as generating high-quality text, summarizing text, and rewriting text in specific styles. The possibilities of LLMs are exciting, but with great power comes responsibility.
Not so fast?
Having pursued AI-driven innovation for over two decades, at Textkernel we are well aware that technological breakthroughs are not merely reasons for excitement. And we’re not the first to note that the use of technologies like ChatGPT come with limitations and risks. There are technical limitations concerning scalability and cost. But more importantly, valid concerns exist about data privacy, transparency and bias. These concerns should be taken very seriously, and upcoming AI legislation, such as the EU AI Act and the NY AEDT Law, will help ensure they are addressed.
Stay tuned for the next part of this blog series to hear more about how LLMs relate to AI legislation, how Textkernel envisions combining compliance with cutting-edge innovation and how we are already putting LLMs in practice in a responsible manner.
*** Read more in the extended version ChatGPT and LLMs: the next chapter on Textkernel’s AI Journey
The value of recruitment automation starts with quality data and as the leading AI-powered recruitment technology provider our AI is the foundation of successful recruitment automation.
We’re releasing a new Jobfeed user interface designed to take your lead generation process to the next level.
Business development and lead generation are vital components of the recruitment industry, and in today’s fast-changing, highly competitive job market and increased need for personalized services, staying ahead requires access to the right tools, technologies and information. That’s why we’re excited to announce the upgrade to our Jobfeed Portal, a powerful tool that provides access to over 3 billion current and historical job postings, with a dedicated focus on lead generation.
The new and improved Jobfeed Portal is faster, more intuitive, and designed specifically to help you identify and win new clients.
With fewer clicks, automated processes, and quick access to essential features, you can streamline your workflow and accomplish more in less time.
Starting a search now takes just two clicks, and continuing from your last search takes just one click, allowing you to stay up-to-date with the latest market labor changes, trends and requirements with ease.
One of the key improvements in the Jobfeed Portal is its more intuitive search function. Following “what” and “where” logic, you can quickly find what you need.
Clear company overviews and “click to action” contact details make it easy to contact prospects directly from within the portal screen. The interface is intuitive, with a logical information layout, making it easy to navigate.
Improved lead generation functionality
In today’s highly competitive job market, having an edge in identifying and winning new clients is crucial, and the new and improved Jobfeed Portal offers just that.
Its user-friendly interface, comprehensive labor market data, and efficient lead generation capabilities make it an essential resource for staffing agencies and recruiters alike.
To help you get the most out of the new interface, we’ve created a video that explains all of the new features and improvements. Click here to watch the video now and see how the new Jobfeed portal can take your lead generation process to the next level.
With the upgraded Jobfeed Portal, you can stay ahead of the competition, identify new business opportunities, and tailor your offerings to the needs of your clients.
Partner with Textkernel’s Jobfeed, the labor market intelligence tool for professionals
Partnering with Jobfeed gives you access to best-in-class labor market data for organizational decision-making. Plus, with our new interface innovations, your lead generation process will reach unprecedented levels.
To find out how Jobfeed can provide you with a competitive advantage in an intensely competitive market, book a personalized demo now.