Ontologies have been around in artificial intelligence (AI) research for the last 40 years.[1] Just as trends come and go, ontologies too have had their ups and downs. Introduced in the 80s, ontologies became popular in the mid-90s. After machine learning (ML) came on the scene in 2000, the widespread opinion was that in the future every task performed with a computer (by means of AI and ML) could be solved with a smart algorithm. A lot of companies invested heavily in these algorithms hoping to have the next breakthrough in AI.

With the rapid development of AI and ML, especially after the emergence of the convolutional neural networks (CNN), the core technology – deep learning (DL) – has been growing rampantly in terms of parameter size and computing complexity. Today some of the most complex models have reached a scale of billions of parameters. [2]

Still, concerns have been raised regarding the current mainstream DL. Let us take supervised learning in image recognition as an example: the images used to train the AI models need to be manually identified in terms of the position and contour of the target objects, for the models to be able to find implicit pattern features between the data after comparing different labeling results. If we recall how we learn things as infants, us human beings can easily identify and classify different objects without needing this kind of instruction. [2]

DL methods have made tremendous progress and are now able to extract knowledge from the training data. However, this knowledge is not explicitly explainable, because the so-called “black box” training cannot reveal the complex relations hidden within the models. When facing new problems, the current DL models are unable to apply their acquired knowledge to solve new challenges in an effective way. [2]

There is another big concern regarding big data and the privacy issue related to it. What is more, current DL methods are based on big data which is not applicable to industries that generate small amounts of data such as certain fields in medicine and human resources. This case requires AI systems to have the ability to reason and judge, which can only be successful in specific domain areas. [3]

Many powerful ontologies already exist for specific domains, examples include the Financial Industry Business Ontology (FIBO) as well as numerous ontologies for healthcare, geography or occupations. It is widely believed that knowledge integration and DL are the important ideas for further amplifying the effectiveness of DL. For this reason, ontologies have made it back into the spotlight, along with many equivalents such as knowledge graphs or knowledge representations.

At JANZZ.technology, we started to build our ontology – JANZZon! – in 2008, before the tech giant Google invented and popularized the term “knowledge graph”. JANZZ.technology has been building its ontology using domain experts with various backgrounds (e.g. intellectual property law, fluid dynamics, car repair, open-heart surgery, or educational and vocational systems).

Today, JANZZon! is the largest multilingual encyclopedic knowledge representation in the field of occupation data. The main focus lies on jobs, job classifications, hard and soft skills, training/qualifications, etc. The number of stored nodes and relations comes to more than 350 million!

Integrated with both data-driven and expert consultation taxonomies, JANZZon! covers ESCO, O*Net, ISCO-08, GB/T 6556-2015, DISCO II and the UK skills taxonomy from Nesta just to name a few. Currently, 9 languages (German, English, French, Italian, Spanish, Portuguese, Dutch, Arabic and Norwegian) fully cover occupation, skills, specializations, function, education, etc., and we are working on achieving the same level in a total of 40 languages in 2020.

Being the backbone of our job and skills matching technology, JANZZon! represents the knowledge at the deepest level where all the entities have been encoded and vectorized in the semantic space. Therefore, when searching and matching, our technology can truly understand a concept and its semantic meaning and thus guarantee meaningful results.

If you are wondering how ontologies can help you empower your data in the fields of human resources and labor markets and aid you in realizing smart applications? Please write to sales@janzz.technology

 

[1] ODSC. 2018. Where Ontologies End and Knowledge Graphs Begin. URL: https://medium.com/predict/where-ontologies-end-and-knowledge-graphs-begin-6fe0cdede1ed [2019.11.20]

[2] Li Jun. 2019. Shen Du Xue Xi: Xin Shi Dai De Lian Jin Shu. URL : https://www.ftchinese.com/story/001084827?page=1&archive [2019.11.20]

[3] Cai Fangfang. 2019. Qin Hua Zi Ran Yu Yan Chu Li Ke Xue Jia Sun Maosong: Shen Du Xue Xi Peng Pi Zhi Hou, Wo Men Hai Neng Zuo Shen Me? URL: https://www.infoq.cn/article/OvhfhpPChTLpsMgrf43N [2019.11.20]

Over the past decade, thanks to the availability of large datasets and more advanced computing power, machine learning (ML), especially deep learning systems, have experienced a significant improvement. However, the dramatic success of ML has forced us to tolerate the process of Artificial Intelligence (AI) applications. Due to their increasingly more autonomous systems, current machines are unable to inform their users about their actions.

Nowadays, most AI technologies are made by private companies that make sure to keep their data processing a secret. Furthermore, many companies employ complex neural networks in AI technologies that cannot provide an explanation on how they come up with certain results.

If this kind of system, for example, wrongly affects customers’ traveling, this might not be of great consequence. However, what if it falsely impacts autonomous vehicles, medical diagnoses, policy-making or someone’s job? In this case it would be hard to blindly agree with a system’s decision-making process.

At the beginning of this year, the Organization for Economic Cooperation and Development (OECD) put forward its principles on AI with the purpose of promoting innovation and trustworthiness. One of the five complementary value-based principles for the responsible stewardship of a trustworthy AI is that “there should be transparency and responsible disclosure around AI systems to ensure that people understand AI-based outcomes and can challenge them.” [1]

Explainable AI (XAI) has recently emerged in the field of ML as a means to address the issue of “black box” decisions in AI systems. As mentioned above, most of the current algorithms used for ML cannot be understood by humans in terms of how and why a decision is made. It is hence hard to diagnose these decisions for errors and biases. This is especially the case for most of the popular algorithms in deep learning neural network approaches. [2]

Consequently, numerous regulatory parties, including the OECD, have urged companies for more XAI. The General Data Protection Regulation, which took effect in Europe, provided the people in the European Union with a “right to a human review” of any algorithmic decision. In the United States, insurance laws force companies to elaborate on their decisions such as why they reject the coverage of a certain group of people or charge only a few with a higher premium. [3]

There are two main problems associated with XAI. Firstly, it is challenging to correctly define the concept of XAI. Furthermore, users should be aware of what the limitations of their knowledge are. If companies had no choice but to provide detailed explanations for everything, intellectual property as a unique selling proposition (USP) would disappear. [4]

The second problematic factor is assessing the trade-off between performance and explainability. Do we need to standardize certain tasks and regulate industries to force them to search for transparently integrated AI solutions? Even if that means putting a very high burden on the potential of those industries.

At JANZZ.technology, we try our best to explain to our users how we match candidates and positions. Our unique matching software excludes secondary parameters such as gender, age, or nationality and only compares skills, education/training, specializations, experiences, etc. It only uses aspects that truly matter in order to find the perfect candidates.

Instead of giving one matching score, our unique matching system breaks down all criteria such as functions, skills, languages and availability. This allows users to have a better understanding of the results and sets the foundation for reskilling and upskilling the workforce to be analyzed. Do you want to know more about the ways in which JANZZ.technology applies explainable AI solutions? Please write now to  sales@janzz.technology

 

 

[1] OECD. 2019. OECD Principles on AI. URL :https://www.oecd.org/going-digital/ai/principles/ [2019.9.17].

[2] Ron Schmelzer. 2019. Understanding Explainable AI. URL: https://www.forbes.com/sites/cognitiveworld/2019/07/23/understanding-explainable-ai/#6b4882fa7c9e[2019.9.17].

[3] Jeremy Kahn. 2018. Artificial Intelligence Has Some Explaining to Do. URL: https://www.bloomberg.com/news/articles/2018-12-12/artificial-intelligence-has-some-explaining-to-do[2019.9.17].

[4] Rudina Seseri. 2018. The problem with ‘explainable AI’. URL: https://techcrunch.com/2018/06/14/the-problem-with-explainable-ai/[2019.9.17].

 

 

 

 

According to the latest Nahtstellenbarometers – Education Decisions after Compulsory Schooling, published by Innovation SBFI and State Secretariat for Education, Research and Innovation (SERI), the most desired professions for vocational training in Switzerland are commercial employees, followed by healthcare assistants and information technologists. This means that none of the professions in handicraft are among the top 5. Compared to boys, there are 15% less girls aspiring to follow the vocational training, and not one single girl reports to be interested in choosing an apprenticeship as electrician, as a car mechanic or as a polytechnical technician. [1]

There is currently a deficit of 42,778 craftspeople in Switzerland. This figure was published by a job search website that also pointed out that craft jobs form one of the most advertised job category in years: currently, there is a total number of 198,097 vacancies advertised. Companies in construction, and building and dwelling services are struggling to hire skilled workers. Stefan Danev, Managing Director of an electrical and safety engineering company in Winterthur, ZH, says that “finding personnel to fill our vacancies in the long term is not easy under the current market situation”. [2]

 Growing interest in the academic route

What causes this shortage of craftspeople in Switzerland? The increasing popularity of the academic route is mentioned as a contributing factor for this development. Between the ages of 12 and 14, Swiss adolescents decide whether to attend a pre-university baccalaureate school or search for an apprenticeship at a company (this is known as the dual-track system).

Thanks to its long tradition, the dual-track system was well received by parents, by adolescents and by society at large. It has largely contributed to a very low youth unemployment rate and high levels of workers’ competency and of quality in all kinds of skilled trades in Switzerland.

However, a growing number of parents hold the perception that the vocational system has a lower quality and that students who enter vocational schools are less capable or have lower aspirations compared to students with four-year university degrees. This is especially the case among parents with migration background. International companies, too, add pressure by not recognizing graduates from vocational training as equal to graduates with a bachelor’s degree. This, in turn, forces many young people to hesitate about pursuing vocational training.

Recently, there has been an increase in adolescents choosing the baccalaureate track, especially in the French and Italian parts of Switzerland. This poses a direct challenge for some of the professions in skilled trades. SERI noted in a statement that “across Switzerland, the baccalaureate has gained more interest and the desire for a general education is stronger than last year” [3].

Misconceptions in skilled trades

When looking at the media, one could think that the only prosperous professions in the future are computer-science-related, and that most skilled-trade jobs will be replaced by automation and robots. But under certain circumstances, machines do not yet have enough dexterity and fine motor skills to compete with human hands. For example, to grind bearings at an accuracy within a 100^th of millimeter requires years of practice and only experienced skilled craftspeople can do this. Moreover, in addition to experience, it also requires intuition that machines will never have.

One tends to hold the misconception of skilled trades as providing little chance of climbing up the career ladder, and, hence, of offering a small salary. But the reality is that there are many top executives in big companies, as well as well-known government figures in Switzerland who have worked their way up from apprenticeship. Highly qualified craftspeople are particularly in demand today and their salary is likely to increase in the near future.

Moreover, the job provides the opportunity to become an entrepreneur. Unfortunately, there are many people in the skilled trades business who intend to pass on their very successful and lucrative business but cannot find a willing successor. Apart from the fact that few young people are trained in such business, it is also reported in various surveys that Generation Y/Millennials (born between 1980-1995) are seeking more security and flexibility in work and prefer having a good, stable salary in an international company, instead of taking on the responsibility of entrepreneurship.

Are young people getting too lazy?

In addition to Switzerland, many other countries like Germany, Belgium, Austria and the United States are facing similar shortages of craftspeople. It is an undeniable fact that the nature of many skilled trades jobs is hard, and that there is a general trend among parents who were in those professions to push their children towards more comfortable jobs. For example, in an apprenticeship at a bakery, one has to start the job at 2am, a tough job that many are unwilling to take up nowadays.

One hardly hears young people saying that their dream job is to become a boilermaker, a metal worker or a welder, because few of them have the opportunity to be introduced to such professions at an early age. With no understanding of or even misconceptions about such jobs, more and more young people are likely to be discouraged from starting an apprenticeship in such professions.

Christoph Thomann, president of the Central Board of Vocational Education and Training Switzerland, believes that the skilled trades are outshined by an increased focus on computers and robots among young people [1]. However, digitalization should not only be regarded as a barrier from choosing vocational training. In fact, vocational training should embrace digitalization and promote an image of the digitalized craftsperson, a modern and less traditionally masculine image that attracts more young people, even young girls, to the field.

A collaborative effort

As in the other economic sectors, digitalization has also made its way into skilled trades and is having a profound impact by increasing the productivity and reducing business costs. For example, by using data analysis, roofers can now measure a house with a 3D scanner to order the exact number of roof tiles needed.

Building information modeling (BIM) helps to realize the integration of building information, which ranges from the design, construction and operation of the building to the end of the whole life cycle of the project. A lot of different information is integrated into a three-dimensional model information database for the stakeholders. There are of course many more examples, including virtual reality, drones, 3D mapping and 3D printing.

Despite great efforts in raising awareness of the importance of the professions in skilled trades communities, reality is disappointing. Matthias Engel from the Swiss Building Association appeals to the federal government in Bern by saying: “It is important that politicians do not send the wrong signals. It is important that the government not only promotes secondary and university education, but also vocational training”.

Indeed, a greater effort to actively promote vocational training, diminishing the gap between young men and women and encouraging more young women to take apprenticeships in certain professions ought to be made by the government. Furthermore, it needs to be considered to bring in young people from other countries than Switzerland, if necessary.

What can AI and big data do for contractors, headhunting firms specializing in skilled trades jobs and government agencies planning to promote vocational training?  How can you use digital platforms to hire the people with the skills you need? Write now to sales@janzz.technology and let JANZZ.technology help you with our intelligent data.

 

[1] gfs.bern. 2019. Nahtstellenbarometer 2019. URL: https://cockpit.gfsbern.ch/de/cockpit/nahtstellenbarometer-2019/ [2019.07.25]

[2] Ulrich Rotzinger and Julia Fritsche. 2019. In der Schweiz fehlen 42’778 Handwerker. URL: https://www.blick.ch/news/wirtschaft/in-der-schweiz-fehlen-42778-handwerker-schreiner-sanitaere-und-elektroinstallateure-verzweifelt-gesucht-id15399544.html?fbclid=IwAR0TU2tUpeSmljN23gLwK5S09DOpvURnFdNqBNoR6nRSfLo_Z3ChojKrVYE [2019.07.25]

[3] Isobel Leybold-Johnson. 2019. What careers did Switzerland’s students choose this year? URL: https://www.swissinfo.ch/eng/continuing-education_what-careers-did-switzerland-s-students-choose-this-year-/45035674 [2019.07.25]

While low-skilled workers are going to suffer the most from the consequences of digital transformation long-term (with some exceptions), opportunities for middle-skilled jobs are shrinking the most, according to recent observations in OECD countries.

We used to talk about digitalization and automatization only as processes that will change our working environment in the future, for example through the replacement of humans by robots. Meanwhile, the situation has changed: many of us already feel the effects of digitization and automation. These effects are likely to be amplified even further in future.

Due to the energy revolution that digitalization induced, many companies in the energy business, blindsided by the speed of this revolution, are faced with overcapacity. In Switzerland, General Electric (GE) has just announced a major workforce reduction, which causes 450 employees in Baden and Birr to lose their job. In order to compete with international online providers Migros, one of Switzerland’s biggest retail companies, too, undergoes such transformation. In June, the Genossenschaft Migros Ostschweiz released the dismissal of 90 employees in Gossau. The Organization for Economic Cooperation and Development (OECD) predicts difficult times for Swiss employees: 700,000 jobs are associated with a “high risk of automation.” [1] Moreover, this is only a small, partial reflection of the whole, global labor market.

It is difficult to account for the full impact of digitalization since it bears both positive and negative effects for the job market. Statistical evidence however indicates that digitalization affects the distribution of work, income and wages. [2] With skills like problem-solving, creative thinking and complex communication that are complementary to digitization, high-skilled workers tend to benefit the most from digitalization. As a result, we can observe an increase in high-skilled jobs in most OECD countries. Likewise, the share of low-skilled jobs grew while the share of middle-skilled jobs decreased. [3]

Why are middle-skilled workers at greatest risk to be disadvantaged under digitalization? Martin Wörter, Professor of Innovation Economics at ETH Zurich explains that “repetitive activities in the office or in industrial production can be replaced more easily by computers or robots.” Federal employment statistics for Switzerland back up this statement. Within 20 years the number of office workers decreased by 150,000 while the number of craftsmen fell by 90,000. Conversely, the number of academic professions grew by 470,000. [1]

However, it is short-sighted for companies to simply lay off unqualified workers and to replace them with employees who fit the demanded skills profile. Since skill requirements are changing faster than ever, even if companies could replace their unqualified workers today, what about tomorrow? The only way to solve this problem is to enable reskilling of the existing workforce. Further training could largely reduce redundancies and benefit the company at large. As Bruno Staffelbach, Professor of Human Resource Management and President of the University of Lucerne, says: “Company-specific know-how will become even more important in the future. However, employees can only acquire these skills on the job in their company.” [1]

Many companies have realized this and adopted effective skills development programs.  However, as we have written before on this site, workforce reskilling requires an ecosystem approach that involves individuals, companies, industries, as well as governments. Based on calculations by the World Economic Forum, in the US 45% of workers at risk could be collectively reskilled through businesses working together. If combined with governmental efforts, this number could increase to even 77%. [4]

For almost a decade, JANZZ.technology has been watching and collaborating in many labor markets worldwide. We offer our know-how and the right data on skills and specializations to tackle general challenges in the job market. Our latest product, Labor Market Dashboard uses real time data in order to establish important labor market indexes such as the most required skills, the most searched for positions or the female/male ratio. Should we have caught your interest and should you wish to learn more about JANZZ.technology’s offers, please write now to sales@janzz.technology

 

 

[1] Albert Steck. 2019.Digitalisierung gefährdet Jobs von Mittelqualifizierten am stärksten. URL: https://nzzas.nzz.ch/wirtschaft/digitalisierung-gefaehrdet-jobs-von-mittelqualifizierten-am-staerksten-ld.1492570#swglogin [2019.07.02]

[2] OECD. 2015. OECD skills outlook 2015: youth, skills and employability, OECD Publishing, Paris, URL: https://doi.org/10.1787/9789264234178-en [2019.07.02]

[3] OECD. 2019. OECD skills outlook 2019: thriving in a digital world, OECD Publishing, Paris, URL: https://doi.org/10.1787/df80bc12-en [2019.07.02]

[4] Borge Brende. 2019. We need a reskilling revolution. Here’s how to make it happen. URL: https://www.weforum.org/agenda/2019/04/skills-jobs-investing-in-people-inclusive-growth/ [2019.07.02]