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.
Machine learning
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.
Deep learning
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
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 long 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 have exciting news to share!
Textkernel acquires Joboti! We have compiled a list of questions that we anticipate our customers, partners and other stakeholders in Textkernel may have about this latest acquisition.
What is the news?
Textkernel is proud to announce that it has acquired Joboti B.V, an Amsterdam-based software company specializing in multi-channel communication solutions and automated candidate engagement workflows.
Who is Joboti?
Joboti is an Amsterdam-based start-up that specialzes in cutting-edge recruitment technology. Founded in 2016 by Luuk van Neerven and Stephan Kockelkoren, the company offers multi-channel communication solutions (such as Whatsapp and SMS) and candidate engagement workflow automation to provide a seamless recruitment experience.
Joboti’s technology offers a personalized job recommendation, guidance, and feedback to job seekers, resulting in a more user-friendly and intuitive experience throughout the application process. Their solutions are designed to increase the efficiency of HR professionals and recruiters and streamline the recruitment process..
Recognized for their innovative approach to recruitment, Joboti’s client base continues to grow worldwide. Their solutions provide valuable automation and streamlining of recruitment processes, resulting in a more efficient and effective recruitment experience.
With a mission to transform the recruitment industry by improving the experience for both job seekers and recruiters, Joboti is committed to providing innovative solutions.
Why is Textkernel acquiring Joboti?
Textkernel’s acquisition of Joboti allows for the extension of our AI-powered recruitment solutions by incorporating Joboti’s advanced capability to engage with multiple candidates simultaneously through multi-channel communication, including popular platforms like Whatsapp, and automate time-consuming steps in the recruitment process. With the combination of these technologies, clients can now benefit from an automated recruitment solution that empowers recruiters to focus on high-value tasks while simultaneously providing candidates with a top-notch experience. By merging automated candidate engagement with AI expertise, Textkernel is poised to remain at the forefront of AI-powered recruitment technology.
In what regions does Joboti operate?
With over 200 customers and with integrations into leading ATS and Candidate CRM applications Juboti is available across the Benelux, UK and North America today. As the software is able to support many languages, the acquisition will allow Joboti to benefit from the global presence of Textkernel and move faster into new countries.
What’s the added business value of combining Joboti and Textkernel?
The combined business allows us to achieve our growth potential faster and provide clients with even more advanced tools to automate the candidate engagement and recruitment process. Together we are able to combine our expertises to better service our customers’ growing recruitment automation needs.
How does the acquisition benefit Textkernel customers?
Textkernel Group and Joboti already share many customers and partners. These customers and partners will benefit from the joint development efforts we will do to integrate the solutions and get more value from the investments they have already made. By joining Textkernel, Joboti will be part of a larger organisation, which has been leading innovation for the past 22 years in the talent acquisition and talent management industry. This will benefit Joboti’s customers through more investment capabilities, faster development and higher business continuity guarantees. We believe the combination answers the needs of staffing agencies and corporates, which are looking for automated and integrated solutions to help them succeed in the tight labor market. Joboti’s customers can now enjoy a truly integrated automated solution that combines recruitment search and match with automated candidate engagement. Allowing AI powered technology to take care of low valued time consuming tasks while recruiters focus on engagement with candidates that are already interested and available.
How does this acquisition benefit Joboti customers?
Textkernel Group and Joboti already share many customers and partners. These customers and partners will benefit from the joint development efforts we will do to integrate the solutions and get more value from the investments they have already made. By joining Textkernel, Joboti will be part of a larger organisation, which has been leading innovation for the past 22 years in the talent acquisition and talent management industry. This will benefit Joboti’s customers through more investment capabilities, faster development and higher business continuity guarantees. We believe the combination answers the needs of staffing agencies and corporates, which are looking for automated and integrated solutions to help them succeed in the tight labor market. Joboti’s customers can now enjoy a truly integrated automated solution that combines recruitment search and match with automated candidate engagement. Allowing AI powered technology to take care of low valued time consuming tasks while recruiters focus on engagement with candidates that are already interested and available.
Are there any planned organisational changes?
No! Joboti will work as an operating unit within the Textkernel Group for the immediate future. Joboti customers will continue to be serviced by existing Joboti contacts and Textkernel will be adding highly qualified people to make sure both Joboti and Textkernel customers can expect the best possible service going forward.
Will the Joboti management team remain with Textkernel?
Yes! All Joboti employees, including those holding managerial positions and the founders, will remain with the company.
What about the security of your solutions?
Textkernel has reviewed Joboti’s security policies and have concluded that their security is at a good level. Textkernel will support Joboti in becoming ISO27001 certified and further improve their IT security standards.
How can I buy Joboti or Textkernel solutions?
Please reach out to your account manager who will be happy to assist you with purchasing Joboti or Textkernel Solutions.
Should you have any other questions, do not hesitate to reach out to your account manager, marketing, or support through the usual channels.
As a member of the Textkernel team, I am excited to announce our recent acquisition of Joboti. The Amsterdam-based company is dedicated to providing innovative candidate engagement technology, and with this acquisition, Textkernel’s global buy-and-build strategy takes another significant step forward, enabling us to offer even more value to our clients.
Gerard Mulder, CEO of Textkernel
This acquisition brings together two organizations with a shared vision of providing seamless, automated, and scalable solutions for recruiters and sourcing professionals. The combination of Textkernel’s cutting-edge parsing and matching technology and Joboti’s innovative candidate engagement technology creates a powerful platform for recruiters to find and engage with the right candidates with minimal effort.
Currently, recruiters are limited by a mainly manual process of finding the right candidates and then reaching out to them through social media, email, phone, or instant message to confirm their availability and interest. However, with the combination of our technologies, recruiters can quickly find relevant candidates through AI-powered match technology and engage with those who have the right skills and have indicated their interest, availability, updated skills, and even completed a vetting question.
Moreover, the integration of Joboti’s technology into our solutions will allow recruiters to automate communication workflows in the recruitment process, including job alerts, GDPR checks, pre-screenings, interview scheduling, and feedback messages. This will streamline the process of engaging with candidates, freeing recruiters to focus on higher value tasks such as starting meaningful engagements with available candidates.
Our customers can expect to see even more innovative features and products in the coming months and years. The combination of Joboti’s technology and our AI-powered recruitment solutions will provide advanced tools for engaging with candidates, improving the recruitment process, and shortening the time to hire. With the ability to keep candidate records up to date and engage only with available and interested candidates, recruiters can ensure a positive candidate experience.
We remain committed to innovation and making an impact on the recruitment industry. The acquisition of Joboti strengthens our position as a leader in AI-powered recruitment solutions, and we are excited to welcome the Joboti team to Textkernel. Together, we look forward to creating even more innovative recruitment solutions that will revolutionize the industry and help our clients achieve their recruitment goals with even greater efficiency and ease.
For more information and an FAQ about the acquisition, visit our website.
About Joboti
Joboti is an Amsterdam-based start-up that specializes in cutting-edge recruitment technology. Founded in 2016 by Luuk van Neerven and Stephan Kockelkoren, the company offers multi-channel communication solutions (such as Whatsapp and SMS) and candidate engagement workflow automation to provide a seamless recruitment experience.