3D generative AI enables rapid and accessible creation of 3D models from text or image inputs. However, translating these outputs into physical objects remains a challenge due to the constraints in the physical world. Recent studies have focused on improving the capabilities of 3D generative AI to produce fabricable outputs, with 3D printing as the main fabrication method. However, this workshop paper calls for a broader perspective by considering how fabrication methods align with the capabilities of 3D generative AI. As a case study, we present a novel system using discrete robotic assembly and 3D generative AI to make physical objects. Through this work, we identified five key aspects to consider in a physical making process based on the capabilities of 3D generative AI. 1) Fabrication Constraints: Current text-to-3D models can generate a wide range of 3D designs, requiring fabrication methods that can adapt to the variability of generative AI outputs. 2) Time: While generative AI can generate 3D models in seconds, fabricating physical objects can take hours or even days. Faster production could enable a closer iterative design loop between humans and AI in the making process. 3) Sustainability: Although text-to-3D models can generate thousands of models in the digital world, extending this capability to the real world would be resource-intensive, unsustainable and irresponsible. 4) Functionality: Unlike digital outputs from 3D generative AI models, the fabrication method plays a crucial role in the usability of physical objects. 5) Accessibility: While generative AI simplifies 3D model creation, the need for fabrication equipment can limit participation, making AI-assisted creation less inclusive. These five key aspects provide a framework for assessing how well a physical making process aligns with the capabilities of 3D generative AI and values in the world.

The concept of digital twin is not new. The technology has been around since the 1960s. NASA has been physically creating duplicate systems for its various space missions at ground level, to test its equipment in a virtual environment. An example of this is Apollo 13, for which a digital twin was developed by NASA to assess and simulate conditions on board. In the recent past, digital twin has become one of the most promising technological trends. It is estimated that the global Digital Twin technology will reach $48.2 billion by 2026, from $3.1 billion in 2020 and estimated to grow at a rate of 58% between 2021 and 2026 A digital twin is a digital representation of a physical object, process, or service.

The concept of digital twin is not new. The technology has been around since the 1960s. NASA has been physically creating duplicate systems for its various space missions at ground level, to test its equipment in a virtual environment. An example of this is Apollo 13, for which a digital twin was developed by NASA to assess and simulate conditions on board. In the recent past, digital twin has become one of the most promising technological trends.

A future beckons where typing may be redundant. Gorithm) we have argued that AL. needs a conscious and wisdom. For the purposes of this Blog essay, a conscious and wisdom are presented as images, allowing us to generalize within a mathematical morphology. A mirror metaphor is used simply to highlight the challenged faced at that moment in time when geometric neuron patterns of a conscious brain are transubstantiated onto a physical object. This Blog essay builds on earlier research[1].

Digital transformation is rapidly disrupting current industry models. The adoption of new technologies is particularly accelerating in the logistics and manufacturing sector due to the benefits it offers enterprises, and is resulting in wider implementation of smart industry solutions. As the manufacturing and logistics sectors undergo major transformation, digital twins, artificial intelligence, the industrial internet of things, and warehouse robotization rank among the leading smart industry trends for 2019 and the coming years. Digital transformation of industry continues to move forward. A study by the German branch of the company PwC indicates that 91% of the industrial companies that participated in the research are investing or plan to invest in digital factories in Europe.

A neural network looks at a picture of a turtle and sees a rifle. A self-driving car blows past a stop sign because a carefully crafted sticker bamboozled its computer vision. An eyeglass frame confuse facial recognition tech into thinking a random dude is actress Milla Jovovich. The hacking of artificial intelligence is an emerging security crisis. Pre-empting criminals attempting to hijack artificial intelligence by tampering with datasets or the physical environment, researchers have turned to adversarial machine learning.

The Web and its Semantic extension (i.e. Linked Open Data) contain open global-scale knowledge and make it available to potentially intelligent machines that want to benefit from it. Nevertheless, most of Linked Open Data lack ontological distinctions and have sparse axiomatisation. For example, distinctions such as whether an entity is inherently a class or an individual, or whether it is a physical object or not, are hardly expressed in the data, although they have been largely studied and formalised by foundational ontologies (e.g. DOLCE, SUMO). These distinctions belong to common sense too, which is relevant for many artificial intelligence tasks such as natural language understanding, scene recognition, and the like. There is a gap between foundational ontologies, that often formalise or are inspired by pre-existing philosophical theories and are developed with a top-down approach, and Linked Open Data that mostly derive from existing databases or crowd-based effort (e.g. DBpedia, Wikidata). We investigate whether machines can learn foundational distinctions over Linked Open Data entities, and if they match common sense. We want to answer questions such as "does the DBpedia entity for dog refer to a class or to an instance?". We report on a set of experiments based on machine learning and crowdsourcing that show promising results.

Misha Denil and her colleagues from the University of California, Berkeley announced that they have trained an AI to learn the "physical properties" of objects by interacting with them virtually. This includes numerous aspects of the world, including questions such as "Can I sit on this?" or "Is it squishy?" In their paper, the AI systems were experimented in two environments. The first involved introducing five blocks arranged in a tower. Others were stuck together to make larger blocks, while others did not.

This report describes a new version of the OntoSpec methodology for ontology building. Defined by the LaRIA Knowledge Engineering Team (University of Picardie Jules Verne, Amiens, France), OntoSpec aims at helping builders to model ontological knowledge (upstream of formal representation). The methodology relies on a set of rigorously-defined modelling primitives and principles. Its application leads to the elaboration of a semi-informal ontology, which is independent of knowledge representation languages. We recently enriched the OntoSpec methodology by endowing it with a new resource, the DOLCE top-level ontology defined at the LOA (IST-CNR, Trento, Italy). The goal of this integration is to provide modellers with additional help in structuring application ontologies, while maintaining independence vis-à-vis formal representation languages. In this report, we first provide an overview of the OntoSpec methodology's general principles and then describe the DOLCE re-engineering process. A complete version of DOLCE-OS (i.e. a specification of DOLCE in the semi-informal OntoSpec language) is presented in an appendix.