Tag: AI

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Learning by using

Is learning by using different from learning by doing? In an economic model to test the employment/unemployment impact of AI in the USA, Wang & Wong (2025) suggest an important impact of employees’ productivity due to learning by using AI. In terms of the traditional language of economics the employees who use AI in their work shall have comparative advantage to those who don’t.
In a model of job search in the economy there is the additional possibility, similarly to robots previously, that certain tasks maybe influenced by the, more or less, plausible threat of an employer to replace the employee by training an AI system to perform the tasks. The credibility and acceptability of such threats are likely to impact wage claims and unemployment risks. All these effects do not happen instantaneously, but evolve over time with varying speed. Hence, calculations of effects have high error margins. The resulting model yields oscillations of “labor productivity, wages and unemployment with multiple steady states in the long run”.
Learning by using seems to be a good description of what occurs at the micro level (the employee) and at the macro level of an economic sector or the economy as a whole. Society may guide the use cases of AI just as much as the business case to use AI, for example in the creative industries as infringements of copyrights may occur on a massive scale. However, learning by using is not free of risks to society at large. Just like allowing people to use automotive vehicles has lead and still leads to thousands of deaths annually, learning by using produces external costs. Overall, this is another case for a benefit/cost analysis for businesses, the economy and society.

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AI 2nd round effects

The most popular topic currently is AI.
Most writers, assisted by some form of AI, will deal with the 1st round effects of AI. These consist in the immediate consequence of the use of AI in office work, medical and military applications, music and all producing or creative industries. As an economist you take the input – output matrix of the economy (OECD countries) and take AI as an additional dimension of this I/O matrix, for example. The result is an AI-augmented model of the economy. This 3-dimensional cubic view of the economy asks to reflect on the potential short-term and medium-term impact of AI.
Let’s take the example of translation and editing services. AI will in the short-term or the 1st round effects make it easier to offer mechanical translations with fast turnaround. Most likely, this will lead to less translators needed for routine translations of longer texts, which would otherwise be a very costly endeavour. The 2nd round effects, however, will make the expert knowledge of translators of texts, where every word counts, more necessary in order to provide the best version of a translation targeted on specific audiences.
In the legal domain, for example, the precision of words is primordial and errors can be very costly. Hence, the 2nd round effects of AI in this field will increase the demand for high quality translation services more than before the use of AI. The important shift consists in these 2nd round effects of AI, which give a push to multilingual societies as just one medium-term outcome.
Please use AI to read (listen) to this paragraph in your native language or even dialect using your favourite AI-tool.

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Hallucinations serious

There serious hallucinations by AI and there are funny hallucinations by AI. Do we want our various AI models, from time to time, to crack a serious or funny joke? Well, that’s a bit the spice of life. However, not knowing when the machine is joking and when it is serious, this is more likely to seriously disturb most of us. This reminds us of our school days were teachers were not amused some pupils not taking them seriously in their efforts to transmit information. Now we know that a good atmosphere is conducive for better learning progress. AI as teaching and learning assistance could well work best in a “fearless“ classroom. Repeating a lesson several times and at your own learning rhythm will help independent of the seriousness of your teacher. Self-directed learning with a little help by AI might do the trick for many to advance how and when they feel ready for it. Hallucinations rates are a standard test for AI models. They range from 1% to 25% of queries.  This is not in itself a problem. It has become tough to find out about the 1% -2% models because you no longer expect them to give wrong information. These are the 1-2 out of a hundred of cases where we are confronted with serious hallucinations, seriously.
(Image: extract from „cum Polaroids“ from Eva & Adele, Hamburger Bahnhof, Berlin 2024-5-22)

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Home extension

Most people think of home extension as some sort of extension of the roof, an additional room or the transformation of a garage into an additional room. However, the digital home requires a home extension of a different kind. In order for all rooms to be included into the digital home a range extender of your wireless might be necessary. Yes, this even includes the bathrooms, because otherwise you can no longer sing along your favourite tune under the shower if you are used to the streaming of the musical or orchestral accompaniment. Additionally, the immediate surroundings of a home with or without garden might make it necessary for your robot to mow properly or your digital letter box to send you the mail for the long awaited love letters while you out of home.
Being out of range in your home, is almost equal to not being home at all. Of course, you don’t have to automatically send an out of home message to all your contacts when you are too far away from your digital home for your digital device, but the comfort of a range extender may avoid the new “digital inequality” between adolescents in your home. Room choices are made according to wireless access points and signal strength rather than the room with the best view. Lots of new issues arise we did not even think we would have 10 years ago. Of course, we follow the suggestions of an AI chatbot that recommends the best location for us after we entered images and descriptions of the consistency of each wall into the system. Just a practical advice, install extensions out of reach of any toddler, because a sudden interruption of the connection will create very unpleasant surprises.

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testing testing

Before the installation of the new AI chatbots or other agentic AI, they need profound testing. Wise statistics are quoted with the conviction: it is all about testing, testing, testing. Any systems that build on statistical reasoning (LLMs or machine learning) will behave erratically on what is known as an area with stronger impacts of, for example, statistical outliers. On both ends of the “normal distribution” of events or reasoning the statistical models and algorithms used in AI will produce “spurious” errors or have larger error margins on such topics a bit off the 95% of usual cases.
This means, testing, testing and testing again for the programmers of such AI systems before the release to the public or enterprise specific solutions. The tendency to keep costs of testing phases low  compared to developing costs bears obvious risks to the “precautionary principle” applied in the European Union. Testing is most important to check the WEIRD bias of the most basic AI systems. In this sense AI development has become a sociological exercise as they have to deal with “selection bias” of many kinds that could have very expensive legal consequences.
(Image: Extract from Bassano, Jacopo: Abduction of Europa by Zeus, Odessa Museum treasures at exhibition in Berlin Gemäldegalerie 2025-5).

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AI Workday

Our workdays have seen considerable changes throughout the last few days. The home office boom has allowed employees to work for extended hours from home. The there is an abundant literature on the effects of home office work on well-being or the work-life balance. Productivity gains could be reaped by employers and a better work-life balance was a lasting advantage for employees.
The increased use of AI specific to some occupations has introduced a new form of added  productivity for some occupations or professions, AI as complementarity, whereas other occupations suffered a higher risk of being substituted by AI applications.
Based on time diary data, the study by Wei Jiang et al. (2025) reports that users of AI have longer work time and reduced leisure time. Competitive labor markets increase the pressure to put in even higher hours of work. Nerds, just like workaholics, are likely to be drawn into excessive hours of work with increased health risks. Enterprises and consumers appear to be gaining more than the employees, who are at a higher risk of loosing out on their work-life balance over time.

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AI-assisted

We used to create and use an artificial word to shorten explications in conversations. It is easy to understand for everyone nowadays to describe an online search as “I googled something” without further concerns about the sources used in answering a question. Apple just announced that its search engine Safari was used less than expected recently. The reason might be that more and more people search online by using AI tools or Apps that directly provide answers to their queries. ChatGTP and Perplexity are 2 AI-programs that might replace the other search engines we have gotten used to over the last decades. Particularly “how to …” queries offer reasonable instructions. In Perplexity you get an answer after you specified a little bit your query and, therefore, the answer is more likely to be right to the point offering text, images and sources. In ChatGPT you enter into a dialogue to further specify your query if unsatisfied with the first response. The judgement of an authoritative response is up to you if you want to follow up. The assistance of an AI-system cares less about the source but about the speed of response that it can provide. For sensitive questions or queries with less obvious or clear answers more interactions with the tools is necessary. The pricing and our willingness or ability to pay in cash rather than through the selling of our data for advertising will determine whether we shall “chat-it” or “plex-it” in future.

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Reinforcement Learning

Education scientist have embraced the change of focus form a front loaded teaching format to a focus on the learning process of pupils or students. Computer scientists have adopted this strategy in shifting from just knowledge data bases and predictions of likely next words in a sentence or paragraph to learning models. Deep Seek has surprised most large language models by its successful strategy to focus on learning and reasoning. So-called reinforcement learning is key to the programming of next generation AI models. Reasoning in most cases builds on multiple step sequences in answering a more complex question. The model then returns the answer and the steps (reasoning) applied. There is a debate whether summaries or translations of texts would need the reasoning function of AI models. Most of the time reasoning might not be necessary or even counterproductive, if the translation would try to correct an obviously faulty reasoning in a text.
Maybe, imagine also that an ordinary LLM would translate a text containing fake news. A correction loop which involves a cross-checking with reliable external sources like any encyclopedia or wikipedia would complicate the answering procedure of any text. However, this is a bit like, how the process of reinforcement learning with human feedback (RLHF) works. Reinforcement learning applies a form of accuracy reward, which guides the learning or answering process with checks against mathematical or programming accuracy. Just think of basic logic to be respected in the answer.
Similarly, a formal accuracy control checks against mathematical models and ensures the answer is returning a text with a normal sentence structure or numbering of reasoning steps, an intro and concluding phrase, like we were all asked to do in school or universities. The amount of corrections from humans is reduced quite a lot and the computing resources are also only a fraction of the previous LLMs, which are retrieving answers from enormous databases or gigantic data factories consuming lots of energy in the processing of requests. Remember the movie on Kasparov, the world chess champion, who got beaten by a computer from IBM that did not only have a huge stock of previous games and tournaments, but could make judgments on positions and promising strategies to pursue. Don’t be surprised if a DeepSeek answer is superior to what our own mind and reasoning is capable of. Reinforcement learning is a learning tool, which we also may apply, if we deem it appropriate or just as one way of coming to an answer. (useful reference: Sebastian Raschka, Building a LLM from scratch, Manning). 

(Image, ChatGPT, 2 humanoid robots are thinking and discussing how to repair a notebook which is sitting on a workbench).

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Hands-on AI

The use of AI in translation and to streamline texts and preparation of communication has become a common experience. The applications in medical fields are less well known. Scans and checking of skin cancer could be a game changer for many who live far away from the next medical doctor. AI assisted brain surgery is another issue, but a very specialized application. The interface of AI and robotics might be another game changer as such applications where you train the robot with for example weight lifting and transportation charges can contribute to alleviate human skeletons. Handheld devices can guide the robot and data from sensors will complement the learning of the tasks through assistance from AI. Applications are manifold and we have not even seen the most promising ones. The application potential in warfare are particularly troublesome as humans do not necessarily enter into the concern of AI-assisted weaponry. In the social sphere trust is a crucial behavioral and ethical concern. These issues AI can only learn from us. Any attempts to do without human input and control is doomed to fail. We are not indispensable yet, we are simply turning more and more into responsible, supervisory roles. 

(Image: Acatech exhibition with hands-on AI applications, Berlin in collaboration with IQZ and DTM, German museum of technology.)

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Blue Sky

In the period of romanticism the associations with a blue sky were very different from today. Getting out into the sun was a kind of privilege for the “leisure class”, of people who could afford to enjoy time outside for boating, walking or other pleasures. In our technology-driven 21st century the associations with Blue Sky are more like a technically enhanced view through for example “Windows” at the news and opinion platform or “Bluesky”.
Several scientific websites that report data on blue skies and air quality more generally across the world report indicators like ozone values O3, sun intensity, micro particles 2.5µm and 10µm, Nitrogen Dioxid NO2 to name the mostly quoted indicators. Hence, just enjoying the blue sky outside isn’t the same as it was before. People working outside in the sun or at times of a blue sky but with high air pollution levels are incurring severe medium-term health risks. The Ozon layers at very high altitudes protect our skin and eyes against high UV-radiation, but O3 on the ground is tough for eyes and lungs.
Technology has come with many blessings, but the negative effects on a global scale become also more evident. Getting used to a particular lifestyle, which produces lots of emissions of aggressive fine particles will make it more difficult to just simply enjoy a blue sky.
In consideration of all these background data with regional variations, we surely need an AI-system which we can ask for advice, whether we should go outside and enjoy the blue sky with or without respiratory mask.
Romanticism has led us all the way to Californication and dangerous enshittification of the air that surrounds us. Youth and the next generations will have very different associations with Blue Sky than we have the chance to, at least, have had.

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Mindmap Me

Tools like artificial intelligence allow all sorts of transformations and depictions. The photo editing tools are widespread and particularly popular among the young users. My own transformative exercises, latest with www.bairbe.me, have yielded interesting insights, well worth an intergenerational playmate. For the guys there is the www.yobrick.com version for brick gamers.
The App “Canvas” allowed me to delve deeper into my own mind by giving instructions of how to create an image of the structure of the blog entries on this webpage. Of course, it is not (yet) a real AI-generated content map, but it is only a matter of time until such tools will exist. After all, this would be just an arranged and rearranged list of contents using the hyperlink structure of the texts as well.
For the time being, I derive my own structure of the blog entries by topics, categories and tags including the hyperlinks or internal referrals. Interlinkages are mostly stated explicitly. However, there are many implicit links, which are obvious to some, but not others. AI-systems could use occurrences of words,  synonyms and antonyms . Colors in addition to bubble sizes and (in)direct lines may complement such mindmaps. This can help to reveal another, additional layer to connections between categories or tags. The Ai-generated image shown below was created with the APP Canvas as a first approximation and AI-augmented test version in form of abstract images).
Next steps on the way to understand human intelligence and, maybe, augment it with a next generation AI-system would use a colored-3D version of such a mindmap and use the chronological evolution of the blog posts in a kind of evolutionary animation. This should allow us to go beyond the usual psychological classification of fluid and crystallized intelligence. We might come to grips what it means to be “in a Paris state of mind” or when hallucinations become overwhelming.

Screenshot
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Hallucinations

In the 21st century hallucinations have become a daily experience. The origins of the word can be followed back at least to the Latin verb “alucinor”, best translated with “to hallucinate”. As a verb to can conjugate it, meaning that I can do it, you can do it, s/he can do it, and we may do it in groups. Roman emperors did it, American presidents do it and, of course, AI does it. Hence, it is a great subject to study.
In “Nature” 2025 we find ways to limit hallucinations of AI systems. The strategy consists mainly in repeated queries of the same type, but from different angles. It is a bit like cubism applied to informatics. On “github.com” we can follow the rankings of AI-models using LLMs based on the “hallucination-leaderboard” developed by Vectara. On “huggingface.com” you can test the Hughes Hallucination Evaluation Model. For example it is possible to run a test of your own small text documents (just like any blog entry on this webpage) and what the AI systems will do them in an attempt to summarize your ideas. According to the “hallucination-leaderboard” we are confronted with a 1.3%-4% hallucination rate of the top 25 LLMs as AI-systems. In text based systems the quantity of “errors” is a first indicator only. The seriousness of the omission, addition of wrong information or an erroneous judgment will be left to the reader or analyst to uncover.
There is now a lot to do to test various AI-systems on their “trustworthiness” in summarizing my own work. My very own daily hallucinations have become a large data base as a test case for the capacity of LLMs to make sense of them.
Based on the series of passed blog entries I shall test the capacity of AI to predict the n+1 blog entry. It would be great to know today what I am going to write about tomorrow etc. Thanks to AI I shall have (finally) a sort of intellectual life after death (not sure whether I should want this). Enough of hallucinations and on hallucinations for now, back to serious readings or fictionalized science. (Image: extract from Delphine Diallo, Kush, 2024 at Hangar Gallery Brussels).

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Virtual author

« La Mort de l’auteur « . In a radical sense Roland Barthes was the first to proclaim the death of author as the sole master or mastermind of a text or speech. In fact there are many more on whose direct or indirect contributions a text is based on. However, biographical accounts of an author can only enlighten some (minor) aspects of the influences on the author and the final version of a text, (l’écriture), and the reader (lecteur). In « Le degre zero » the analysis of the different styles of Flaubert and Proust are extolled ( p. 131-139). Flaubert is characterized as the author with infinite corrections on the same texts and sources. It appears today as an endless loop of an algorithm where the stopping rule is not properly defined or implemented. Much in line with the « tabula gratulatoria » of Barthes (p. 279 of Fragments…, see image below) some AI systems return fake versions of a bibliography some readers will rely on. 

In the 21st century l’écriture has become almost inexistant without the technical support of machines, but most of all artificial intelligence. The author is dead, long live the virtual author. The assistance of spelling, grammar and style editing from software programs has widened the spectrum of coauthoring. Editors take more influence through pushing marketing potentials of authors and their writings. Based on previous manuscripts and publications it is possible to produce hallucinations of an author whereby only the author might be able to identify the virtual authorship. At best AI generates first drafts, but similar to the linguist of Barthes, AI is likely to become another brother or sister of l’ecrivain (p. 139). 

The thrust of Barthes is to highlight that there is more to a text than just the version at hand. In fact there are texts in a text or multiple versions or layers of a text. « L’enjeu de l’analyse structurale n’est pas la vérité du texte mais son pluriel » (1972, par ou commencer p.148). In conclusion, almost 50 years after the death of the author we currently witness the miraculous rebirth of the virtual author as the original deus ex machina which we always feared. Don’t worry it’s just another sibling of the original authors.

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Game Tech

Gaming has moved digital and online for a long time. Networked gaming and following the best gamers online on video-platforms like twitch has captured a lot of attention from younger generations. With a real trend of gamification for industry and even public services, the digital gaming sector is moving from backstage to front end of companies and services. Public administration as a game. Enjoy the use of a public service through a game-like experience. Walk around in the metaverse world and get your admin work done. I would love to have such an experience. There are so many applications for gamification that the bottleneck is in the human resources to program all these applications. Coding the digital and virtual worlds to get real stuff done is just around the corner. The SCCON 2024 in Berlin showed these technologies next to each other. 2025 we might see integrated prototypes. I have a digital dream, others call it a vision for public services in the 21st century.

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Language Tech

Inclusive societies can build on many tools including AI to lower language barriers. It is not only a question of translation, but many other forms of language come to mind. Sign language or easy language are necessary to facilitate broader access to public services. Reading out texts on webpages or Braille translation for the blind to interact through keyboards are additional forms that are available in digital communication as well. The audio description of videos and images is well advanced (reverse engineered through AI) and allows people with limited vision to fully participate in society. Audio messaging and transcription are used by almost everyone by now. Public services will open up to these channels of communication as well. The technology around languages is much more than just translation and AI-assisted learning of languages (talkpal for example). The new lingua franca is language technology, because it enables us to speak many languages at the same time even dialects or lost languages and in many voices. (Image: Extract of Josef Scharl, the newspaper reader, 1935, Neue Nationalgalerie Berlin)

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Justice Tech

The digital or hybrid courtroom has become more the rule than the exception in Germany. Video conference equipment reduces costs and can speed judicial processes. Even the production of transcripts from the proceedings and circulation of documents and certificates, enhanced by AI will change the speed to exercise justice. Digital tools and technology has found its way into the courtroom and younger lawyers and judges as well as the accused or defendants will value the simplified procedures. Until this is the standard in all legal domains we shall have to wait a bit. In Germany 2026 is the deadline to install the adequate equipment and tech companies and consulting firms like Arktis are well prepared to support this overdue process. In terms of an economic theory of the judicial process a judgement that is delivered years later has to apply a discount rate of at least equal to annual inflation. For moral issues another discount rte might apply. Excessive delays of judgments may cause additional suffering on the side of victims. Justice Tech, therefore, has a role to play in the practical and theoretical debate about „doing justice“. (Image SCCON Berlin 2024-10)

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AI Nobel

Artificial Intelligence has made it into the ranks of Nobel prizes in 2024. As AI is been talked about everywhere by now the Nobel Committee has deemed it expedient to award Hopfield and Hinton the Nobel Prize not in informatics, this does not exist (yet), but in physics. Neural networks focus on the links between bits of information rather than just the sheer number of data points mimics the functioning of our brains. The most remarkable statement by Hinton is probably the one of the also dangerous potential of this invention. He has already issued a disclaimer on the potential of AI in warfare or other ways to impinge on our human idea of freedoms. The discovery of the applications, good and bad, of these AI-based systems has just begun. The comparison with Nobel‘s original discovery and mass production of explosives from which the prize draws its name has hardly been more adequate recently. (Image stockholm City Library)

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Artists Robots

We know that the scientific and artistic dealings with robots have a long tradition. Whereas art of impressionism took up the challenge to paint the world outside the studio and embellished technological achievements like bridges and trains post hoc, modern extensions of science fiction to the world of robotics has extrapolated from the present. Artists became forerunners of technical evolution and thereby contributed to the acceptance of artificial intelligence to broader audiences. In 2018 The “Grand Palais” in Paris hosted an exhibition on “Artists & Robots” (Pdf booklet). Jérôme Neuters contributed an essay to the catalog of the exhibition on “L’imagination artificielle” which identified a additional role for artists in combination with AI. Some of the early adopters of the new possibilities of robots assisting artists, Nicolas Schoeffer is quoted to state: “l’artiste ne crée plus une oeuvre, il crée la création”. Like an invention of painting techniques or light or perspective in painting, robots allow a new way of representation of emotions or space. (Image Manfred Mohr, 1974 video Cubic Limit, Artists & Robots p.92-93)

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Broken Promises

In a library catalogue, the entry of « broken promises «  returns more than 3000 times that the title has been used. « Promises kept » is almost as popular. A rapid inspection of titles reveals that the former titles suggest more factual analyses, whereas the latter is frequently used in the form of an imperative in combination with “should be kept”. The book by Fritz Bartel “The Triumph of Boken Promises …” (2022) demonstrates the importance of the concept of broken promises in the social sciences. The rivalry between socialism, capitalism and the rise of neoliberalism is strongly influenced by the way they handle the breaking of promises made to their respective societies. The promises of increasing wealth and wellbeing have been part of all political regimes. To keep these promises is a completely different story. Especially since the first and second oil crises and many other kinds of crises, it has become much harder to keep these promises. Working hours, retirement ages or minimum wages are all at risk to no longer live up to the promises made in earlier periods. This has put welfare states under pressure that millions of voters perceive politics as a “game” of broken promises. Socialist political regimes like Russia are ready to use physical violence to shut up people that remind leaders of these broken promises. In democracies the ballot box is often used to sanction governments that do not live up to expectations of previous promises. A lot is about public infrastructure which is failing people. Migration, education, social and labor reforms are on top of the political agenda if it comes down to broken promises. The elections of the European Parliament gave many a chance to express their discontent about various broken promises. Maybe democracy is better in providing forms of letting off steam early and protracted protests rather than the Russian way to suppress any critical analysis, let alone opposition movements. Just like the move from industrial production to services as production models, with AI we are likely to see similar problems and probably also broken promises. The challenge is huge and promises should be made with an eye of what promises could be kept.

Public swimming pool closed for reconstruction 2024
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AI Ghost Writer

Yes, with AI we have entered a new phase of the impact of IT. Beyond the general applications like ChatGPT there is a rapidly expanding market of AI applications with more specialized functions or capabilities. In the realm of scientific writing AI-Writer is an interesting example of the AI assisted production of scientific tests. After the specification of the topic you will receive several options to specify the content of the short paper you want to produce with AI-Writer. You may choose the headline, keywords, subtopics and the logical order of these subtopics depending on your audience. Alternatively, you leave all those decisions to the application and restrain yourself to fix the amount of words you would like the paper to have.
AI-Writer is a powerful ghost writer for much work even of advanced scientists. The quality of the paper needs to be checked by yourself, but the explicit list of references, from which AI-Writer derives its restatements of the content, is just next to it. Your ghost writer AI is likely to replace a number of persons that were previously involved to just produce literature reviews or large parts of textbooks sold to millions of students.
A much lesser known feature of such tools is the way it makes plagiarism much more transparent for the scientific communities and the public at large. These programs demonstrate the techniques of combining knowledge and the citation imperatives in a transparent, almost pedagogical way. This latter function will speed up scientific work like dissertation drafting, since the reading up and documentation of previous literature in a field is a time consuming early stage of academic degrees.
Email composition, rewording, plot generator or social media posts are additional nice-to-have features of the new AI-assistants. A lot of work that has been outsourced, for example, to lawyers, consultants or other technical professions, might equally be challenged. Ghost writers have been around for centuries. With AI for everybody, they will also be involved everywhere.
(Image screen shot of working with AI-Writer 2024-6)

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AI Citation

In science we love citations. The whole issue about plagiarism is about use and abuse of citations. It is a core competence of scientists to properly cite the work of other persons who dealt with the same or similar topic. There are lots of conventions or ways of how to cite mostly defined by professional academic groups. How do we cite texts that originate from an AI-system? We shall have to establish ways of how to do this properly rather than to ignore the spreading practice of its use.
For the time being, we test AI-systems that provide references in addition to the text and even direct clickable links to the original work they use. The AI-toolbox is called “scite”. Your assistant by scite will draft a short note on a topic (for example: Minkowski space, see trial below) for you and provide the linked citations for follow-up. At the price of about 15 €/months it is affordable for students and young researchers. The texts generated will then, in many instances, acquire “intellectual property and publishing rights” by persons.
The ways to follow back on citations of AI-produced texts seems a trustworthy step ahead. The authors of millions of papers cannot claim more than the original ownership of the text. The academic mantra “publish or perish” has been turned into “publish and perish”. AI-enabled citations might alleviate the pain only a little bit. The profession of even university professors shifts as reviewer of texts from students to texts of machines.

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Forecasting floods

As floods as becoming more frequent and more severe forecasting of such events is crucial. The recent example in Bavaria (Germany) of the Danube river (2nd longest in Europe) has demonstrated the role of forecasting to spur adequate behavior of people living in areas at risk of flooding. With the weather forecast announcing lots of rain for a large area the forecasting of floods needs to follow closely these trends. It is not only a question of expectations, but an issue of adaptive expectations for people to adopt appropriate precautions. In retrospect the early forecasts turned out to be fairly accurate in terms of the peak of flooding to be expected in June 2024. The Bavarian “Hochwassernachrichtendienst” (no joke, one word) forecasted on the 2nd of June about 7.50 as the peak to be reached in 2 days in the city of Kelheim. This was beyond the usual 4 warning levels based on an escalation scale. The forecast was beyond the frequent flooding levels established in the last decades. People and emergency services would have to adapt their expectations accordingly. Renewing forecasts is essential to guide people and services in their efforts to deal with emergencies and repair damages as flood levels recede. Management of crises critically depends on forecasting even if they are obviously prone to error margins which should usually be reported as well just like in weather forecasts. Adaptive expectations are key in combination with forecasts to ensure survival.

Hochwassernachrichtendienst Bayern 2024-6
Kelheim on Danube
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AI Racing

AI has entered the racing of cars after we have been racing horses, dogs and camels for many decades. The fact behind all these races is the huge market for gambling. Anything you can bet on will do for juicy profits in that industry. The recent “Abu Dhabi Autonomous Racing League” is the latest addition to the racing craze. Moving online with 600000 spectators at its peak on video and gaming platforms the investment seems promising. The only problem, AI is not yet ready to really compete with the world of real drivers. The progress, however, is astonishing. Just one lap of 2 minutes on the circuit yields 15 Terrabyte of data from 50 sensors. These are closed circuits so no person can enter or animal can get in their way. The challenge to integrate more data and faster processing as well as algorithms for fast decision making is steep. Great learning opportunities for advances in robotics. The hype has not been able to live up to the expectations as no real racing took place yet. We have replaced the gladiators of the Roman empire with Formula 1 drivers. It is only fair to retire those drivers soon and let AI race cars against each other. It feels like a computer game on screen and it is as we shall most likely watch these races on a screen as well. Hence, what is the point. Watching youth on TWITCH play racing games will probably not change the viewing behavior of the masses. The programmers have nevertheless great learning opportunities and will find their way rapidly into the job market. The other challenges of ASPIRE seem more important for humanity like human rescue and food for the growing world population. In the meantime let the boys play around with cars and learn about potentials as well as failures of AI-programmers and dealing with both.

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AI Disruption

Many scientists started to question the disruptive potential of AI in, for example, the military’s domain. The Journal of Strategic Studies featured 3 papers on AI and autonomous systems more generally. The major argument by Anthony King is the reliance of autonomous systems on other systems mainly human operators even in the background to get these systems off the ground and maybe back again. Not only logistic support but also satellite communication is needed to guide and protect the operations. In quoting Clausewitz, Anthony King stated that war is a “collision of two living forces”. Strategy and counter-strategy will co-evolve as will attack and defence.
Jackie G. Schneider and Julia Macdonald (2024) advocate the use of autonomous and unmanned systems for their cost effectiveness. Economic costs as well as political costs are lower for these new strategic weapons. Mass fire power from swarms of drones is much cheaper than nuclear warheads and the home electorate is assumed to be more willing to accept and support limited and more precisely targeted unmanned missions. The disruption potential of AI is huge but it is most likely an addition to the arsenals than replacing them. (Image 2 swarms of drones fly in the air above tanks, created by AI – copilot-designer 2024-4-29).

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Hannover Fair

The annual science fair at Hannover is a kind of a show of things to touch and of those things that come to the public market in the near future. Most of the annual hype is about potentials of production. Rationalization, using few resources or innovative solutions of digitization are high on the agenda. Create your digital twin, save energy, make production more safe or cyber secured.
Robotics is another reason to visit the fair. Some 7 years ago I had my Sputnik experience there. The robotics company KUKA had demonstrated live the that assembling a car from pre-manufactured components takes just 10 minutes for the robots. Shortly afterwards the whole company was bought by Chinese investors. Roughly 5 years later we are swamped by cars from China. It was not that difficult to predict this at that time. Okay, we need to focus on more value added production and take our workforces (not only) in Europe along on the way. Reclaiming well-paid, unionized jobs in manufacturing, as Joe Biden does, will not be an easy task. Robots and their programming is expensive, but skilled workers, too. Hence, the solution is likely to be robot-assisted manufacturing as a kind of hybrid solution for cost-effective production systems.
Following the proceedings of the 2024 fair we are astonished to realize that visiting the fair is still a rather “physical exercise” walking through the halls. After the Covid-19 shock we expected a lot more “online content”. Instead we keep referring to webpages and newletters rather than virtual visits and tours. The preparation of the visit in advance remains a laborious adventure. However, the in-person networking activities in the industry are largely advanced by ease of exchanging virtual business cards and the “FEMWORX” activities.
This year’s Sputnik moment at Hannover is probably most likely related to the pervasive applications of AI across all areas of the industry and along the whole supply chain. Repairing and recycling have become mainstream activities (www.festo.com). Robotics for learning purposes can also be found to get you started with automating boring household tasks (www.igus.eu).
Visiting Hannover in person still involves lengthy road travel or expensive public transport (DB with ICE). Autonomous driving and ride sharing solutions might be a worthwhile topic for next year’s fair. Last year I thought we would meet in the “metaverse fair” rather than in Hannover 2024. Be prepared for another Sputnik moment next year, maybe.
(Image: Consumer’s Rest by Stiletto, Frank Schreiner, 1983)

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AI Defence

For those following the development in robotics we have been astonished by the progress of, for example, rescue robots. After an earthquake such robots could enter a building that is about to collapse and search the rooms for survivors. A recent article in “Foreign Affairs” by Michèle A. Flournoy has started its thinking about the use of AI in the military with a similar 20 year old example. A small drone flying through a building and inspecting the dangers of entering for persons or soldiers. Since then technology has advanced and the use of AI for automatic detection of dangers and “neutralising” it, is no longer science fiction. The wars of today are a testing ground for AI enhanced military strategies. It is about time that social scientists get involved as well.
Warfare left to robots and AI is unlikely to respect human values unless we implement such thoughts right from the be beginning into the new technology. An advanced comprehension of what algorithms do and what data they are trained on are crucial elements to watch out for. According to Flourney, AI will assist in planning as well as logistics of the military. Additionally, AI will allow a “better understanding of what its potential adversaries might be thinking”. Checking through hours of surveillance videos is also likely to be taken over by AI as the time consuming nature of the task binds a lot of staff, that may be put to work on other tasks. Training of people and the armed forces become a crucial part of any AI strategy. The chances to develop a “responsible AI” are high in the free world that cherishes human rights and democratic values. Raising curiosity about AI and an awareness of the dangers are two sides of the same coin or bullet. Both need to grow together.
(Image created by Dall-E Copilot Prompt: “5 Robots disguised as soldiers with dash cams on helmet encircle a small house where another robot is hiding” on 2024-4-23)

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AI Reader

In the middle of the hype around AI it is useful to take stock of the reflection and evolution of AI. In my own analyses and writings on AI it evident that a narrowing of focus has taken place. Whereas before 2022 the writing dealt more with digital technologies in general. The links to the literature on the social construction of technologies was obvious. Algorithms and AI was a part of the broader topic of society and technology.
This has changed. The public debate is focused on “everything AI now”. We look at technological developments largely through the lens of AI now. Hence, my focus of assessments of technology from a societal perspective follows this trend. In a collection of blog entries on AI we try to demonstrate the far reaching changes that have started to have an impact on us. In the last few months the all encompassing concern about AI’s effect on us needs full attention of social scientists, policy makers, companies and the public at large. We can no longer leave this topic to the software engineers alone. By the way, they themselves ask us to get involved and take the latest advances in AI more seriously.
As a “flipbook” the online reading is rather comfortable (Link to flipbook publisher MPL). The pdf or epub files of the blog entries allow to directly follow the links to sources in webpages or other publications (AI and Society 2p 2024-4-18). The cycles of analyses and comments have become faster. Traditional book writing suffers from time lags that risk to make pubications outdated rather quickly. Dynamic ebook writing might bridge the gap between time to reflect and speed to publish or inform the wider public. The first update as .pdf-file is available here: AI and Society(2).

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AI Travel

Playing around with AI it is nice to test take fun examples. Image you want to plan a vacation, then the use of AI is ready to suggest to you a couple of things to do. Of course, AI is eager to propose travelling services like transport or accommodation to you where it is likely to earn some commissions. So far, the use of the “Vacation Planer of Microsoft’s BING Copilot” is free of charge. In entering the time period and a region as well as some basic activities you’ll receive suggestions with quotes on the sources (webpages of public services from tourist offices mostly). It seems like trustworthy sources and the suggestions of D-Day activities in Normandy is a positive surprise to me. These are popular activities which attract huge international crowds every year.
Thinking further on the potentials it becomes evident that travel suggestions will be biased to those paying for ranking higher on the algorithms selection criteria, which are not disclosed. Entering into the chat with the AI you and AI can target more precisely locations and also hotels etc. You are disclosing more of your own preferences in the easy-going chat and probably next time you will be surprised to be recommended the same activities at another location again.
So far, I have bought travel guides or literature about locations to prepare vacations. This is likely to change. I complement my traditional search or planning with the “surprises” from AI for travelling. I rediscovered, for example, the public service of tourist offices and their publications ahead of the travel rather than the leaflets at the local tourist office. In order to plan ahead there is value in the augmented search and compilation capacities of AI. Drafting a letter in foreign languages is also no problem for AI. The evaluation of the usefulness of AI, however, can only be answered after the vacation. Outdated info or databases have a huge potential to spoil the fun parts as well.

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AI and languages

A big potential of AI is in the field of languages. Translations have been an expert domain and a pain for pupils at school. In professional settings translations are an expensive extra service for some or a good source of revenue. AI has shifted the translation game to a new level. In terms of speed of translating large amounts of written text AI is hard to beat. In terms of quality the battle of translaters against AI is still on. For chess players the battle against AI has been lost some years ago already. It remains an open question whether translators can still outperform AI or just adapt to using the technology themselves to improve both speed and quality of translations. The European Union with its many languages and commitment to cultural diversity can serve even more language communities with documents in their own language than before at marginally higher costs. A panel on the 9th day of translations at the „foire du livre de Bruxelles” 2024 expressed their reservations with regard to the use of AI in translation of political text or speech. Misunderstanding and misinterpretation will be the rule rather than the exception with potentially harmful consequences. Checking the correctness of translations is a permanent challenge for translators and can be very time consuming. There is room for an AI-assisted translation, but similar to other fields of application of AI, relying exclusively on AI bears high risks as well. We should not underestimate the creative part of translators to do full justice to a text or speech.

www.flb.be 2024 Translation
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AI and PS

AI like in ChatGPT is guided by so-called prompts. After the entry of “what is AI” the machine returns a definition of itself. If you continue the chat with ChatGPT and enter: “Is it useful for public services” (PS), you receive an opinion of AI on its own usefulness (of course positive) and some examples in which AI in the public services have a good potential to improve the state of affairs. The AI ChatGPT is advocating AI for the PS for mainly 4 reasons: (1) efficiency purposes; (2) personalisation of services; (3) citizen engagement; (4) citizen satisfaction. (See image below). The perspective of employees of the public services is not really part of the answer by ChatGPT. This is a more ambiguous part of the answer and would probably need more space and additional explicit prompts to solicit an explicit answer on the issue. With all the know issues of concern of AI like gender bias or biased data as input, the introduction of AI in public services has to be accompanied by a thorough monitoring process. The legal limits to applications of AI are more severe in public services as the production of official documents is subject to additional security concerns.
This does certainly not preclude the use of AI in PS, but it requires more ample and rigorous testing of AI-applications in the PS. Such testing frameworks are still in development even in informatics as the sources of bias a manifold and sometimes tricky to detect even for experts in the field. Prior training with specific data sets (for example of thousands of possible prompts) has to be performed or sets of images for testing adapted to avoid bias. The task is big, but step by step building and testing promise useful results. It remains a challenge to find the right balance between the risks and the potentials of AI in PS.