One belief underlying the power-hungry approach to machine learning advanced by OpenAI and Mistral AI is that an artificial intelligence model must review its entire dataset before spitting out new insights. Sepp Hochreiter, an early pioneer of the technology who runs an AI lab at Johannes Kepler University in Linz, Austria, has a different view, one that requires far less cash and computing power. He's interested in teaching AI models how to efficiently forget. Hochreiter holds a special place in the world of artificial intelligence, having scaled the technology's highest peaks long before most computer scientists. As a university student in Munich during the 1990s, he came up with the conceptual framework that underpinned the first generation of nimble AI models used by Alphabet, Apple and Amazon.

The ThEdu series pursues the smooth transition from an intuitive way of doing mathematics at secondary school to a more formal approach to the subject in STEM education, while favouring software support for this transition by exploiting the power of theorem-proving technologies. What follows is a brief description of how the present volume contributes to this enterprise. The 12th International Workshop on Theorem Proving Components for Educational Software(ThEdu'23), was a satellite event of the 29th international Conference on Automated Deduction (CADE 2023), July 1-4, 2023, Rome, Italy. ThEdu'23 was very successful, with one invited talk, by Yves Bertot (Inria, France), "The challenges of using Type Theory to teach Mathematics", and seven regular contributions. An open call for papers was then issued, to which eight contributions were submitted. Seven submissions have been accepted by our reviewers, who jointly produced at least three careful reports on each of the contributions. The resulting revised papers are collected in the present volume. We, the volume editors, hope that this collection of papers will further promote the development of theorem-proving based software, and that it will allow to improve the mutual understanding between computer scientists, mathematicians and stakeholders in education. PC Chairs:Julien Narboux (University of Strasbourg, France); Walther Neuper (JKU, Johannes Kepler University, Linz, Austria); Pedro Quaresma (University of Coimbra, Portugal)

Artificial muscles are a progressing technology that could one day enable robots to function like living organisms. Such muscles open up new possibilities for how robots can shape the world around us; from assistive wearable devices that can redefine our physical abilities at old age, to rescue robots that can navigate rubble in search of the missing. But just because artificial muscles can have a strong societal impact during use, doesn't mean they have to leave a strong environmental impact after use. The topic of sustainability in soft robotics has been brought into focus by an international team of researchers from the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart (Germany), the Johannes Kepler University (JKU) in Linz (Austria), and the University of Colorado (CU Boulder), Boulder (USA). The scientists collaborated to design a fully biodegradable, high performance artificial muscle – based on gelatin, oil, and bioplastics.

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AlphaTensor was designed to perform matrix multiplications, but the same approach could be used to tackle other mathematical challenges.Credit: DeepMind Researchers at DeepMind in London have shown that artificial intelligence (AI) can find shortcuts in a fundamental type of mathematical calculation, by turning the problem into a game and then leveraging the machine-learning techniques that another of the company's AIs used to beat human players in games such as Go and chess. The AI discovered algorithms that break decades-old records for computational efficiency, and the team's findings, published on 5 October in Nature1, could open up new paths to faster computing in some fields. "It is very impressive," says Martina Seidl, a computer scientist at Johannes Kepler University in Linz, Austria. "This work demonstrates the potential of using machine learning for solving hard mathematical problems." Advances in machine learning have allowed researchers to develop AIs that generate language, predict the shapes of proteins2 or detect hackers.

With increased applications of collaborative robots (cobots) in industrial workplaces, behavioural effects of human-cobot interactions need to be further investigated. This is of particular importance as nonverbal behaviours of collaboration partners in human-robot teams significantly influence the experience of the human interaction partners and the success of the collaborative task. During the Ars Electronica 2020 Festival for Art, Technology and Society (Linz, Austria), we invited visitors to exploratively interact with an industrial robot, exhibiting restricted interaction capabilities: extending and retracting its arm, depending on the movements of the volunteer. The movements of the arm were pre-programmed and telecontrolled for safety reasons (which was not obvious to the participants). We recorded video data of these interactions and investigated general nonverbal behaviours of the humans interacting with the robot, as well as nonverbal behaviours of people in the audience. Our results showed that people were more interested in exploring the robot's action and perception capabilities than just reproducing the interaction game as introduced by the instructors. We also found that the majority of participants interacting with the robot approached it up to a distance which would be perceived as threatening or intimidating, if it were a human interaction partner. Regarding bystanders, we found examples where people made movements as if trying out variants of the current participant's behaviour.

Fabrizio Augusto Poltronieri is an award-winning computer artist working with Creative AI, exploring the relationship between technology and deep-rooted philosophical concepts, such as chance. His current artwork involves Artificial Intelligence, applying machine and deep learning techniques to create and design narratives, moving images and objects. He started his artistic career as the founder of the art group [ zero], in 2006, which for over 13 years has participated in international exhibitions and festivals such as the Ars Electronica, in Linz, Austria, presenting artworks and live performances that plays with the relationship man-machine. With his solo artistic practice, Poltronieri became an award-winning artist, with a long list of exhibitions. His artworks are in prominent collections such as that of the Victoria & Albert Museum in London.

Have a look at 2020's best robot vacuums -- all tried and tested in my office and home. A new project that forms a data visualization of brain signals in clothing has recently been showcased at the virtual Ars Electronica festival. The robotic dress is coupled to 1,024 channels of a BCI (Brain-Computer Interface) and has 64 outputs for light and movement. The Pangolin Scales' dress components function like animatronic elements that move and light up based on the recordings of the brain waves. The project originated at the Institute for integrated circuits at JKU (Johannes Kepler University, Linz, Austria), in collaboration with the Austrian Neurotechnology company G.tec.

A researcher at the Ars Electronica Futurelab research center in Austria used open-source artificial intelligence software to mimic classical symphonies. Ali Nikrang, who works at the Ars Electronica Futurelab research center in Austria, is using open-source artificial intelligence (AI) software to mimic classical symphonies. Nikrang debuted the program at the recent Ars Electronica Festival in Linz, Austria, which aims to highlight connections between science, art, and technology. During the festival, a traditional orchestra performed Gustav Mahler's unfinished Symphony No. 10, which was immediately followed by six minutes of "Mahleresque" music written by the MuseNet software. The software used the first ten notes from Mahler's Symphony No. 10 and produced four suggested segments.

Linz (Austria) (AFP) - Can artificial intelligence turn out symphonies to match one of the greats of classical music? That was the question posed by one unusual orchestra performance in the Austrian city of Linz on Friday, in which Gustav Mahler's unfinished Symphony No.10 was played -- immediately followed by six minutes of "Mahleresque" music written by software. The project's creator says that the two are clearly distinguishable but not everyone in the audience agreed. "I couldn't really feel the difference... I believe it was really well done," Maria Jose Sanchez Varela, 34, a science and philosophy researcher from Mexico, told AFP.