Category: AI

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Chatbot Me We

In order to dig deeper into the functioning of AI, I deemed it expedient to construct, for example, a simple chatbot on a limited knowledge base from my own writings on AI (link to reader in previous blog entry here).
A toolbox from Google offers powerful assistance in such an endeavour. The outcome uses only my input text and no other sources. It is dynamic in the sense that it interprets questions and searches within the text file provided only. The answers are edited with a LLM (large language model) and provide flawless English texts. You can try it here using catchat as magic formula and Google account so far.
With a bit of programming knowledge (htlm, python, Java) and related learning sites it is feasible to come up with a “static” chatbot hosted at a free of charge provider as well. For learning purposes this step by step building and coding of a chatbot is helpful. The outcome is rather limited or requires a lot of time to increase the scope of Q & A interactions and to move from a static (predefined Q & As) to dynamic ones.
Full control of answers, excluding any hallucinations and high-speed replies, come at a cost. Take a look here. It is a very basic version so far, just to get the idea of it. full web address:
https://schoemannchatbot.eu.pythonanywhere.com/

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Chatbot Me

Chatbots are helpful to allow queries to larger data sets like the blog entries here. So here is a try of a Chatbot to query all entries on AI using ChatGPT to create a Chatbot that uses and references it source from www.schoemann.org/tag/ai and the AI reader in pdf-format.
Please send me an email if the hallucinations of this Chatbot 1.0 on AI from a social science perspective are giving strange results. I’ll get back to you. Please use at your own risk as I cannot guarantee for all answers. The usual disclaimer applies here.

ChatGPT proposed the following set of Questions and Answers on the blog for an entry into the chat: Example Q&A with the chatbot

Q: What are the social science concerns with AI?
A: Bias in results, job shifts, democracy risks, privacy, and new inequalities.

Q: What does the text say about reinforcement learning?
A: It’s seen as the next step for AI: focusing on learning and reasoning, not just predicting text. It also uses fewer resources.

Q: How are robots described in the document?
A: Robots are mostly assistants. They can follow people or carry small items, but more complex tasks need sensors and AI training.

Q: What about biased results?
A: Studies can be misleading if control groups are flawed. AI faces the same challenge — social scientists warn: “handle with care”.

Q: What is Schoemann’s blog view on AI?
A: He links AI to energy use, fairness, and its role in the “all-electric society” — stressing efficiency and responsibility.

More on the chatbot (in testing phase) and the Link to the coding help received from ChatGPT on this mini-test-project :
https://chatgpt.com/share/68c1d160-0cc0-8003-bf04-991b9e7c3b24

 

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AI Podcasting Me

Content producers have lots of tools at their disposal to get their content across to very different audiences. For some time the traditional media of newspapers, radio and TV were the prime outlets for content distribution. Social media have changed this to many more senders of content than before.
In the 21st century, AI allows to automate media productions. In a trial run I just used Google’s NetbookLM to generate 3 podcasts based on my own writings on AI over more than a year by now. The result is available and using artificial voices it is possible to broadcast yourself without revealing your own personal voice. I am not done with the evaluation of the outcome(s) yet, but the first impression is an interesting other form to spread content.
More tests are necessary to check for hallucinations as well.
Here are the links to my virtual podcasts:
AI, intimacy and insecurity

AI, Society and the Human Spirit

AI and the Human Mosaic: Navigating Our Interconnected Future

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Video Doku by AI

Based on my own blog on this webpage “schoemann.org” Google NotebookLM creates a video of about 7 minutes. Using Microsoft Clipchamp automatic subtitles with a slightly different storyline are produced based on the video data. In the end, the blog entries are re-modelled into something like a lecture on “AI in a wider social context” (see and play below). No voice layover so far, read by yourselves. A podcast format is another option.
It feels like walking across landscapes in my own mind. Content creators of today or the past never imagined the impact they might have through the powerful tools of AI. The only caveat, jokes I incorporated into the texts cannot really be handled by AI tools unless they are explicitly designated as such. These AI tools take me much more seriously as I do myself. This is serious.

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Mind Map Me

AI tools are great to assist learners in the task to get more structure into larger documents or books. It is up to the teachers or lecturers to use the tools themselves to pre-structure content they want other persons to learn. Mind maps are useful to summarise larger content and offer a tree-like structure to a text moving from the general to more specific content and then into details by at the same time not loosing sight of the overall structure of the content. Basics can be provided by Google’s NotebookLM and you may rework this basic structure yourself linking the mind map to the detailed content. Learning may start with a comprehensive mind map at the beginning to move on to details. Alternative versions of a mind map are equally feasible to come up with new combinations of subjects. This can be done using the tags of the blog entries in addition to the categories and fast search keywords.
It is a fascinating way to mind map yourself based on longer texts written by yourself. This clarifies a bit what potential readers or learners are likely to retain from reading your document(s) or blog entries.
Its worth trying, as others will most likely use these tools from now onwards anyway, whether we like it or not.
Image: Google NotebookLM Mind Map of my AI blog entries 2025-9-9.

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Own AI related entries

The digital drafting of blog posts facilitates the publishing of volumes based on a selection of entries. Pdf-files, or EPUB formats are quite common. The choice of a flipbook is more suitable for larger screens of tablets or notebooks. It also serves as a rapid stock taking of entries and missing topics to cover a broader field. In some fields AI is not yet popular, for example in agentic home applications, whereas in other fields text, translation or image production AI has grown into a threat to professional fields. Even computer languages are a major field of application with substantial negative employment effects.
Hence, enjoy the latest comments on AI towards the end of my latest Flipbook.
Just click here.

ChatGPT provides a summary of my AI-reader as follows:

From the uploaded text AI from a Social Science Perspective (Dr. Klaus Schoemann), social science concerns with AI focus on potentials and limitations of how AI interacts with society:

  • Labour and employment: AI impacts jobs, but adjustment processes are slow. For example, there’s a shortage of engineers and care workers, while robots are still limited to simple assistant tasks. This raises concerns about who benefits and who is left behind.
  • Bias and fairness: The book highlights issues of biased results in research and decision-making. When AI systems rely on incomplete or selective samples, results can be misleading — a challenge also familiar to social scientists.
  • Democracy and accountability: Journalists and scientists are framed as part of a “checks and balances” system. AI adds complexity here, as the fast spread of information without accountability can weaken democratic processes.
  • Human–machine relations: Topics like intimacy, housing, and memory are re-thought in the context of AI and digitalization, raising questions about privacy, social norms, and cultural heritage.
  • War and security: AI-driven technologies like drones change the nature of warfare, raising ethical and political questions.

On his blog, Schoemann also stresses the energy and resource costs of large AI models and how reinforcement learning offers a more efficient alternative. He warns that AI should be understood not just technically, but as part of an “all-electric society” where social sciences help assess risks, inequalities, and long-term consequences.

👉 In short: AI raises social science concerns about equity, bias, democracy, labour, cultural shifts, and global risks.

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Marc Aurel AI

In the 21st century it is possible to chat with Marcus Aurelius. Part of the exhibition at the Simeonstift is a chatbot you may freely consult and questions with or about Marc Aurel. Based on your questions the animated screen image of Marc Aurel will reply based on his own writings like the Meditations and (probably) other secondary literature on Marc Aurel. Questions about feminism or slavery are answered based on the original texts. Some of these answers  appeared rather modern like the basic equality of all including women or slaves. The Meditations are an idealistic vision of mankind in the stoic tradition. In practice such ideals have proven very ambitious for the many and growing temptations in the day-to-day lives of ordinary people including their political, religious, business and military leaders. The AI is confronted with the issue to give answers to ethical questions which refer to the time of the author, but not all can apply to today’s ethical standards and basic human rights. Reading the original source, therefore, remains the preferred choice. 

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AI in Central Banks

Yes of course, Central Banks will use AI, and some do so already (Kazinnik and Brynjolfsson, 2025). Beyond the standard application of AI by its employees, there are many potentials to use AI to analyse and publish data at a faster rate or in order to detect financial crimes. Similarly, data collection based on webpage harvesting might yield new indicators of inflation, expenditure for environmental risks (heat waves, flooding etc.) earlier and in addition to the normal set of indicators. Hence, Central banks might be better and faster in forecasting inflationary tendencies using more AI tools in their daily routines. Of course, it is difficult to predict a disruptive tariffs policy of a major economic player in the world economy, but the calculation of more, even hallucinatory scenarios become more feasible. It is feasible to weigh overall risks of different scenarios to the economy.
(Image: Celtic coins, Museum of the Belgian National Bank)

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AI earnings effects

In the first few years of wider adoption of AI in an economy, there is the expectation that this might lead to substantial productivity gains for enterprises which use it as well as for employees who are early adopters of the relatively new technology. The study by the Stanford Digital Economy Lab by  Chen, Chandar and Brynjolfsson (2025) showed that so far there are no significant earnings effects for employees. Based on millions of recent payroll data from US companies productivity gains have not trickled through to the paycheck in terms of monthly salaries. Participation of staff in a company’s overall turnover or profit might change this as time evolves. For civil servants the adoption of AI might mean increases in cases dealt with as some tasks can be executes faster than before with the use of AI.
The evidence points to employment effects of AI rather than earnings effects so far. A hypothesis is yet unresolved: senior employees using AI might employ fewer junior workers at entry positions, if these “hallucinating” young professionals can be replaced by hallucinating AI. In science the hallucination has sometimes lead to disruptive new approaches and findings. It is a tough choice to pick the young entrants with high productivity potential and eventually high remuneration for this in terms of labor earnings.

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AI employment effects

The first robust empirical evidence about employment effects of AI in the USA has been published by the Stanford Digital Economy Lab by  Chen, Chandar and Brynjolfsson (2025). A previous paper by Wang and Wang (2025) highlighted the comparative advantage of persons who use AI in their work compared to others and the authors coined the term “learning by using technology”.  The prediction of the model was that there might be job losses of more than 20% in the long run and half of this already in the first 5 years of the introduction of the technology. The Stanford economists have estimated with real world data these effects in the USA and find quite surprisingly that the negative employment effects of AI have the strongest impact on young labor market entrants with few years of labor market experience. Middle-aged and more senior employees seem to benefit from “tacit knowledge” about the work, which is more difficult to replace with AI, at least for the time being of the early days of AI. This evidence is based on recent payroll data from the largest payroll processing firm “ADP” in the USA which has firms overrepresented from the manufacturing and services industries as reported in another paper  (Firm size maybe another source of bias).  However, the effect that youth 22-25 years of age suffered the most calls into question the common belief that older workers are more likely to suffer the consequences as during in the rise of the digital economy around the year 2000. (AI Image created with Canva)

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Bench the benchmarks

In the social sciences as well as in engineering it is common practice to use benchmarks as indicators of performance. Thereby, several countries or regions within a country are compared with respect to quantitative indicator. Let’s take employment ratios. A higher employment ratio, which includes many persons working few hours in part-time work, is different from a slightly lower employment ratio, but hardly any part-time employees.
The same rationale holds true for benchmarks of AI systems or the newer versions of agentic AI that are under construction in many fields. The paper by Yuxuan Zhu et al. (2025) proposes the ABC (agentic behavior checklist) for agentic AI developers. The reporting of benchmarks by such models should include (1) transparency and validity, (2) Mitigation efforts of limitations and (3) result interpretation using statistical significance measures and interpretation guidelines.
The aim of this research is to establish a good practice in establishing benchmarks in the field of agentic AI. The sets of criteria to test for is large and the focus of how the agentic AI treats, for example, statistical outliers much above or below the average i.e. (> 2 standard deviations from the average) assuming a normal distribution, is one case of application only.
We welcome the efforts to bench the benchmarks in the field of AI as is common practice in other sciences as well.

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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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Home Leaks

When did you last think about leaks in your home?
Usually we associate leaks with water leaks, or the heating system leaking somewhere. In the 21st century leaks at home are more importantly the leaks of your home security, especially your email, digital and cloud services which are at risk. You may test your favourite AI system to advice you on your risks for digital leaks, but we know little whether these systems are yet another dangerous port of entry into your home or privacy themselves.
There is a helpful tool to find, whether your email has been hacked or distributed widely already for potential thefts of your identity. Hence, better check this from time to time using for example the “leak checker“, just like checking whether you closed your door or the water tap before leaving for vacations. As we live more and more in “virtual homes” in addition to our physical homes, checking your digital identities should become a part of our personal hygiene routine. Let’s just take a shower from time to time and change passwords regularly.

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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 security

Digitalization has made it possible to step up home security at reasonable costs. The video surveillance of homes inside and the immediate surroundings are feasible through the use of connected cameras. The footage can reach sizable amounts of data, but intermittent recordings reduce or the AI-assisted detection of movements on a person’s property have become standard home security. Even for apartments the video enabled door bells and digital locks have improved the security level for those who are willing to invest in home security. A good neighborhood watch system is, of course, in most cases a superior solution. But neighbors change and social interaction is often reduced to minimal contact in most suburban regions. As with heating of homes, home security is also depending on what makes you feel comfortable at home. For some 20 degrees Celsius is enough and a solid mechanical lock is sufficient. Others have made very different experiences and want their digital devices directly linked to a professional security or police service. Like it or not, home security is part of the modern home just like many other digital devices or TV sets. 

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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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Humanoid services

In the shadow of AI enhanced chatbots, agentic AI and generative Ai, the developers make considerable progress in robotics. The humanoid versions like from Persona AI will surround us in months, or maybe a few years from today. Investors believe it is rather sooner than later. There are many use cases for humanoids that may take over dangerous, hazardous or unhealthy tasks from humans. But even simple tasks like carrying home most of our shopping could be done for us by humanoids that follow you around the shopping mall and home. This would be a kind of personal assistant. I even thought of my humanoid robot to walk my dog on some occasions on the usual trail. 

Welcoming visitors at the doorstep could be another function to delegate in offices or even in private homes, although as a sociologist I would recommend to carefully check the sorting algorithm(s) applied to avoid unpleasant situations. The administration of medication might be another option, if only we could trust that the correct dosage would be applied. 

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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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Robot assistant

Adjustment processes on the labour market take their time. This means that care workers are in short supply in most countries of the OECD. Engineers as well. There are so many robots, care robots I mean, still to develop that the shortage of engineers give little hope that we shall have affordable solutions in this area for the next few years. The issue is mainly about integrating and enhancing already existing solutions. If you are a mechanical engineer you can put together motorised mechanical pieces, small motorised electric devices and, for example, small infrared emitting and receiving devises.
All this is child’s play these days (see image below). The assembling of a small Robot assistant that follows movements or can escape from a small labyrinth makes such simple structures transparent for learners and users. There is nothing magical about it, just adding together small pieces and the electronic devices to steer the movements. The learning tool from KOSMOS has been on the market for 5 years. It is a helpful device to explain basics through hands-on experience. The limits of robotics equally become more evident. Our own health and safety is concerned with larger devices cohabiting with us. A robot assistant can take on easy tasks like to follow me through my living space in old age carrying a mobile phone, keys or an emergency device. More sophisticated tasks need more sensors and AI to train the most needed and best routines. For many years this needs our input and our control as well as supervision of such devices. Most robots will operate as assistants with us in the driving seat or the boss.
This is yet another element of the “all electric society“. We are moving towards the use of more electronics assisting us from year to year.

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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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Intimate housing

The housing of famous persons has always attracted a lasting fascination. The “esprit des lieux” or the experience of the surroundings of an artist, composer, author or otherwise famous person, where most of the work had taken place is of general interest. In today’s language we would describe this as the context of creation. Biographical information and references to specific objects central to the process of creation play a crucial role in understanding what is behind human creation as it is district from AI. There are objects ( compare MAD Paris) and there are the social networks of creators. Modern biographies include both, some even in graphic forms similar to ancestry diagrams. 

The design of intimate living spaces for private life or the adequate environment for creativity are becoming more clear with the consciousness of the importance of dedicated designs of furniture, accessories and even scent. Our brains are multimodal and working as well as processing lots of information even beyond our conscious realizations. This black box of creativity is rather unique to the human brain and AI will probably take decades to emulate such complex processing. The MAD exhibition reveals that intimacy is also related to housing design and interior design. It doesn’t seem to be a random process, but habituation is part of the continued creative process. 

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Augmented Cognition

With everybody talking about artificial intelligence we tend to overlook recent advances in augmented cognition. Just 2 examples of studies presenting new findings published in 2025 suffice to make that point. Automated versus manual driving have different effects on our brains. Automation makes us sleepy and inattentive with potentially dangerous consequences. Using EEG measures of neuromarkers the drop in attention shows up earlier than through measures of keeping one’s lane while driving. For jobs or duties that involve extended hours of attention the wearing of a mobile EEG-device could avoid lots of accidents. Drugs, alcohol or medication abuse would also show up before it is too late, I suppose (Scanion et al. 2025).
The 2nd study involved a robotic assistance to piano playing which achieved surprising training effects for learners of the music instrument. Objective measurement of speed and finger coordination might even allow overall accuracy to improve significantly. The technology around us and the one in direct contact with us, our behavior and performances shall have an impact on all of us sooner or later. We have to pose the question of how far we want to go along with these evolutions. Augmented cognition will not only be of interest for civil purposes, but military applications or dual use is evidently another option.

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Apocalypse Depicted

At the beginning of the 21st century we have a renewed interest in depictions of the Apocalypse. “L’Apocalisse di Giovanni” has been illustrated in various Christian monasteries and some traveled quite a bit. The version of the Apocalypse (Link) which was attributed to Cambrai is a copy of the older original version found in the City library of Trèves, which dates back to the year of about 825. A flemish version of the 16th century is to be found at the BNF in Paris. Throughout the centuries the “Apocalypsis”has been reworked. The version of Trèves has the “carolingish” depiction characteristized by a presentation of image on one page and text on another page. Nowadays we are used to futuristic images and have known even worse experiences of apocalyptic events (Nazi terror). Earth quakes or man-made climate change with heating of the planet, storms, draughts and floods testify for the renewed interest in the Apocalypse. Other man-made technological innovations like rockets. atomic bombs, chemical and biological weapons overshadow even the “natural” ones. The potential of AI to cause destruction is not fully assessed until today. Swarms of drones guided by AI might reach apocalyptic levels as well. We just remind ourselves of the bombing of walls of a water reservoir by Russia in Ukraine. The Apocalypse will remain the original example of a horror story and our strange fascination for such texts, images or movies is likely to stay. (Image: extract of anonymous 1st quarter of 9th century. Trier Stadtbibliothek, Schatzkammer. Wikipedia public domain).

Apocalypse
Screenshot
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Präludium Prélude

Learners of how to play the piano are likely to pass the composition of J. S. Bach entitled Präludium in German and Prélude in French. Maybe that influences the approach to the piece of music. The title sounds different in each language. It is interesting to follow the Präludium Prélude throughout the history of composition. The pianist Jan Lisiecki has just recorded and released a disc with Deutsche Grammophon following the historic evolution of this genre. The rather easy-going beginnings by Bach (see below for extract of first motive) have turned out to become rather complex preludes with subsequent composers. We may ask ourselves at this occasion: a prelude to what? Your own associations are welcome. In international politics we can observe strategic movements of countries and their military equipment as a prelude of war, but also of peace. Small aggressions may turn into wars. In philosophy Nietzsche praised the dawn (Morgenröte) and focused a lot on new beginnings. Composers might be the best forecasters of what is to come in the following years. Certainly the preludes have foreshadowed some of their later work. Nowadays, many LLMS in AI build on algorithms that predict what is the next word to write or task to do. Even in composition AI is moving ahead fast. Hence, we e are likely to study preludes much more for several reasons now.  

(Image: extract of first motive from J. S. Bach Präludium Nr 1)

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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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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.

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Text to Image

Long before everybody started to discuss Artificial Intelligence, which in many applications takes the form of transformation of a textual prompt into an Image, Photographers have had literature or quotations in their mind that shaped their images. This was a kind of poetic imagery not always easy to recognize. The exhibition in the „Institut de France“, Bibliothèques Mazarine (LINK), with photographs by Nicolas Fève (LINK) offfers a great insight into this way to conceive of an image and its realization through photography. Exposing the sources of inspiration as well as the photo is like adding textual citations to an image in a much more inspirational and transforming manner than AI is doing these days in 2025. 

Text to image is only one out of the many ways texts might guide imagination, but it is a powerful and gripping one. The history of literature is full of other forms like videos based on novels, comic strips to make classic texts in Latin more accessible. As we shall ask AI products like texts and images to cite their sources and honor authorship, photography as art and science might enhance the literary experience by adding citations to an image. This has the additional advantage that more people will follow up on the sources of inspiration.

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AI Images

The creation of images using any AI system is fast and easy. Many people have tested the systems and experimented with the more or less explicit prompting needed for LLMs to come up with several suggestions. Through the use of AI in the creation of images you are indirectly become your own curator of these creations as you choose among many suggestions of AI for the same prompt. The next step in the process of these artifacts it to assemble several ones and submit your selection of images to a gallery for an exhibition. If you have a coherent approach or a specifically interesting creative idea you might get selected in a competition to show your AI assisted images in a gallery with a reputation to  exhibit photography.  The Brussels Photo Festival (2025) presented the submissions to a call for AI images with a broad range of AI assisted imagery. The focus of this project was on „historical events and figures“. In situations where images are absent such a newly created imagery might be helpful in re-creating narratives about undocumented wars or conflicts. Speculative fiction about other historical options or „roads not taken “ have found their way into museums of history even. Decolonizing imagery is an interesting aspect to get a grip on another way to view historical evolutions. Projecting biological growth processes into the future with pervasive bio-engineering allows is to imagine potential future scenarios. As AI in biology, pharmacy and nutrition is only about to rake off, the AI artists play an interesting role of new avantgarde in the 21st century before we shall be submerged by AI images on all social media platforms. (Image taken at Hangar.art 2025)

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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).