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DTSTART;TZID=Europe/Berlin:20210715T100000
DTEND;TZID=Europe/Berlin:20210715T110000
DTSTAMP:20260619T145541
CREATED:20210526T110411Z
LAST-MODIFIED:20250604T095521Z
UID:10233-1626343200-1626346800@www.scienceofintelligence.de
SUMMARY:Dimitri Coelho Mollo (SCIoI)\, “Modelling Intelligence: The Good\, the Bad\, and the Plural”
DESCRIPTION:Abstract:  I argue that artificial intelligence research has been both fuelled and hindered by the use of ‘model tasks’\, that is\, tasks the solution of which are taken to be sufficient for\, or at least indicative of intelligence. Before AI proper\, cybernetics explored model tasks involving basic real-time and world-involving action control aimed at the maintenance of homeostasis\, an approach echoed more recently by the embodied AI movement. Logicist AI\, in contrast\, took as model tasks for intelligence the solution of abstract problems\, such as theorem-proving and proficiency in combinatorially complex games\, chess having pride of place. Connectionist AI – including the current deep learning wave – despite privileging model tasks tied to learning from ‘experience’\, shares this focus on abstract\, disembodied behaviours as key to intelligence\, with particular effort being done in language processing\, categorisation\, and combinatorially complex games\, such as Go. Reliance on model tasks has led to considerable progress in solving those specific tasks\, but against expectation they did not lead to theoretical insights about the nature of intelligence in general\, and how to build it. This outcome\, I argue\, is in part due to the failure of recognising the limited scope of model tasks\, as well as the abstractions and idealisations of real-world intelligent behaviour that they embody. All mainstream frameworks in AI research\, in brief\, focus on circumscribed\, idealised models of intelligent behaviour\, those for which the respective approaches tend to generate cumulative progress and satisfactory solutions. Such models\, however\, abstract or idealise away important features of intelligence\, and\, if unchecked\, close off potentially rewarding paths of research. Bringing to the fore the limitations tied to such model task choices\, as well as the abstractions and idealisation involved in each\, I argue\, opens the way for a more integrative and plural approach to AI. \nThe Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions)
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-work-in-progress-dimitri-coelho-mollo-scioi-modelling-intelligence-the-good-the-bad-and-the-plural/
CATEGORIES:Thursday Morning Talk
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20210701T100000
DTEND;TZID=Europe/Berlin:20210701T110000
DTSTAMP:20260619T145541
CREATED:20210526T105942Z
LAST-MODIFIED:20250604T095539Z
UID:10226-1625133600-1625137200@www.scienceofintelligence.de
SUMMARY:Rasmus Rothe\, PhD (Merantix)\, “How To Build a (Deep Tech) Startup”
DESCRIPTION:Abstract: Rasmus Rothe is Co-Founder at Merantix\, the Artificial Intelligent Venture Studio. In this talk he will give insight into how a deep tech startup is built via ideation\, incubation and scaling\, and the specifics and challenges of working with technology AI in the process. \nBIO: Rasmus Rothe is the co-founder and CTO of Berlin-based Merantix\, the world’s first venture studio for AI\, co-initioator of the AI Campus Berlin\, the leading AI community hub in Berlin\, and a renowned deep learning researcher. He has published over 15 academic papers with more than 1000 citations on deep learning while attending Oxford\, Princeton\, and ETH Zurich\, where he received his Ph.D and launched a face recognition service with 150m+ users. In 2019\, he was featured on Forbes “30 under 30”. Rasmus is a founding board member of the German Association of AI\, devising and implementing the national AI strategy in close cooperation with the German government. \nThe Zoom Link will be sent the day before the lecture.
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-rasmus-rothe-phd-merantix-how-to-build-a-deep-tech-startup/
LOCATION:On Zoom
CATEGORIES:Thursday Morning Talk
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20210617T100000
DTEND;TZID=Europe/Berlin:20210617T110000
DTSTAMP:20260619T145541
CREATED:20210429T080929Z
LAST-MODIFIED:20240813T093732Z
UID:10115-1623924000-1623927600@www.scienceofintelligence.de
SUMMARY:Jose Hernandez-Orallo (Valencia/Cambridge)\, "The Generality of Natural and Artificial Intelligence: Task Difficulty as the Elephant in the Room"
DESCRIPTION:Abstract: Understanding and recreating intelligence is possibly the biggest scientific challenge of our time. Evolution has produced organisms that are highly specialised for some cognitive tasks\, whereas others present what has been called general intelligence\, with humans identified as the paragon. Artificial intelligence (AI)\, despite decades of efforts to achieve generality\, is still specialised. It is a major research question to disentangle the notion of general intelligence\, by clearly determining what generality is and how it can be measured for individuals rather than populations. Under limited resources\, we must overhaul the classical yet misleading interpretation of general intelligence as ‘success in all sorts of situations’ and introduce a new view of generality as ‘comprehensive performance up to a level of difficulty’. The degree of generality then refers to the way an agent’s capability is distributed as a function of task difficulty\, according to environmental and cognitive pressures. This dissects the notion of general intelligence into two non-populational measures\, generality and capability. We interpret and apply these measures with humans\, non-human animals and AI systems. The choice of the difficulty function now plays a prominent role in this new conception of generality\, which brings a quantitative tool for shedding light on long-standing questions about the evolution of general intelligence and the evaluation of progress in Artificial General Intelligence. \nHosted by Dimitri Coelho Mollo \nThe Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions) \n 
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-jose-hernandez-orallo-valencia-cambridge-the-generality-of-natural-and-artificial-intelligence-task-difficulty-as-the-elephant-in-the-room/
LOCATION:On Zoom
CATEGORIES:Thursday Morning Talk
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20210211T100000
DTEND;TZID=Europe/Berlin:20210211T110000
DTSTAMP:20260619T145541
CREATED:20210126T093136Z
LAST-MODIFIED:20250604T095716Z
UID:9602-1613037600-1613041200@www.scienceofintelligence.de
SUMMARY:Alice Auersperg\, “COCKATOOLS: Innovative Tool Use and Manufacture in the Goffin’s Cockatoo”
DESCRIPTION:Finding flexible tool use and manufacture in non-specialized animals\, may contribute to our understanding of the origins of tool-related cognition. Goffin’s cockatoos are Indonesian parrots that originate from a small archipelago in the Moluccas. They are highly opportunist generalists that forage on a large number of different and often patchily distributed or seasonal resources. Accordingly\, they show flexibility and innovativeness during physical problem solving and extractive foraging tasks. Yet more unexpectedly\, in captivity and more recently also in the field we discovered highly flexible tool using and manufacturing abilities rivalling those of the great apes.\nNevertheless\, Goffin’s cockatoos are not dependent on tool obtained resources and lack two ecological predispositions (nest building and food caching) that have been proposed to promote the onset of tool use in birds.\nSo far\, our findings suggest that tool use in this species is associated to opportunism\, extreme extractive foraging and a strong psychological motivation to establish complex object combinations. \nThe Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions)
URL:https://www.scienceofintelligence.de/event/thursday-morning-lecture-with-alice-auersperg/
CATEGORIES:Thursday Morning Talk
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20210204T100000
DTEND;TZID=Europe/Berlin:20210204T230000
DTSTAMP:20260619T145541
CREATED:20210125T164206Z
LAST-MODIFIED:20250604T095741Z
UID:9553-1612432800-1612479600@www.scienceofintelligence.de
SUMMARY:Christa Thöne-Reinecke\, “Ethical Justification of Animal Experiments in Germany”
DESCRIPTION:All animal ethical positions are largely in agreement that animals – as beings capable of suffering – must be morally considered for their own sake and that certain consequences for one’s own actions must be derived from this.\nThis insight has been incorporated into animal protection legislation based on the EU Directive 2010/63.\nGerman legislation requires a reasonable justification of the pain\, suffering\, and harm inflicted on animals.\nFor this reason\, every scientist must demonstrate ethical justifiability of the intended experiment in accordance with the principle of proportionality within the framework of the approval procedure of animal experiments.\nMore specifically\, it must be demonstrated that no alternative method in reaching the project´s aims exists. Furthermore\, the project´s indispensability must be scientifically explained and it must be assigned to a permissible purpose. Study planning must be carried out by implementing statistical methods to reduce the number of animals and their burden to the indispensable level.\nAnimal keeping and medical care must be ensured by the permission to keep and breed animals in the context of a culture of care.\nUltimately\, the expected gain in knowledge must be set in relation to the burden inflicted on the animals and must be ethically justifiable or may even be considered an ethical imperative.\nThe scientist´s proposal and declarations are then revised by the animal welfare officer and\, if applicable\, by the ethics committee of respective institution.\nIt is then further examined by the local authorities and the §15 Commission\, in which ethics experts and animal welfare organizations are actively involved.\nAfter this revision process\, also involving the responsible scientist\, the final examination and approval is carried out by the local authorities.\nIt must be considered that ethical concepts and attitudes of society may be subject to change in the course of time. Hence\, a high degree of transparency is necessary in order to maintain public approval. \nThe Zoom Link will be sent the day before the lecture.
URL:https://www.scienceofintelligence.de/event/thursday-morning-lecture-christa-thone-reinecke/
CATEGORIES:Thursday Morning Talk
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20201203T100000
DTEND;TZID=Europe/Berlin:20201203T110000
DTSTAMP:20260619T145541
CREATED:20201130T132146Z
LAST-MODIFIED:20240813T105558Z
UID:9231-1606989600-1606993200@www.scienceofintelligence.de
SUMMARY:Michael Pauen
DESCRIPTION:BIO: Michael Pauen is a philosopher with a focus on the philosophy of mind. As the academic director of an interdisciplinary graduate school\, he has extensive experience in interdisciplinary research and training. Having a specific interest in philosophical and psychological aspects of human sociality\, he will focus on social intelligence both in humans and in artificial systems. \nThe Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions)
URL:https://www.scienceofintelligence.de/event/thursday-mornng-lecture-michael-pauen/
LOCATION:On ZOOM (Contact communication@scioi.de for link)
CATEGORIES:Thursday Morning Talk
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20201112T100000
DTEND;TZID=Europe/Berlin:20201112T113000
DTSTAMP:20260619T145541
CREATED:20201102T113930Z
LAST-MODIFIED:20240813T105714Z
UID:9084-1605175200-1605180600@www.scienceofintelligence.de
SUMMARY:Heiko Hamann\, Minimize Surprise in Robots: An Innate Motivation for Collective Behavior
DESCRIPTION:Minimize Surprise in Robots: An Innate Motivation for Collective Behavior \nAfter a quick overview of other related research projects in my lab (bio-hybrid systems\, swarm performance\, collective decision-making)\, I will present our work on minimize surprise for multi-robot systems. Each robot has two artificial neural networks\, a world model (“prediction machine”) and a behavioral module (“action selection network”)\, that are trained concurrently. There is no predefined task\, instead the swarm is rewarded for making correct predictions about future sensory input. As an effect\, robots discover behaviors introducing predictable spatiotemporal sensor patterns. I will present simulated results for flocking\, aggregation\, self-assembly\, construction\, and first results using real-world mobile robots. \nThe Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions)
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-heiko-hamann-minimize-surprise-in-robots-an-innate-motivation-for-collective-behavior/
CATEGORIES:Thursday Morning Talk
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20201105T100000
DTEND;TZID=Europe/Berlin:20201105T110000
DTSTAMP:20260619T145541
CREATED:20201102T111116Z
LAST-MODIFIED:20250604T095834Z
UID:9081-1604570400-1604574000@www.scienceofintelligence.de
SUMMARY:Robert Lange (SCIoI): “Learning Not To Learn\, Nature Versus Nurture In Silico”
DESCRIPTION:Abstract: Animals are equipped with a rich innate repertoire of sensory\, behavioral and motor skills\, which allows them to interact with the world immediately after birth. At the same time\, many behaviors are highly adaptive and can be tailored to specific environments by means of learning and exploration. In this work\, we use mathematical analysis and the framework of meta-learning (or ‘learning to learn’) to answer when it is beneficial to learn such an adaptive strategy and when to hard-code a heuristic behavior. We find that the interplay of ecological uncertainty\, task complexity and the agents’ lifetime has crucial effects on the meta-learned amortized Bayesian inference performed by an agent. There exist two regimes: One in which meta- learning yields a learning algorithm that implements task-dependent exploration and a second regime in which meta-learning imprints a purely exploitative and ‘hard-coded’ behavior. Further analysis reveals that non-adaptive behaviors are not only optimal for aspects of the environment that are stable across individuals\, but also in situations where an adaptation to the environment would in fact be highly beneficial\, but could not be done quickly enough to be exploited within the remaining lifetime. Hard-coded behaviors should hence not only be those that always work\, but also those that are too complex to be learned within a reasonable time frame.\nLink: https://arxiv.org/abs/2010.04466 \nThe Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions)
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-robert-lange-title-learning-not-to-learn-nature-versus-nurture-in-silico/
CATEGORIES:Thursday Morning Talk
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200618T100000
DTEND;TZID=Europe/Berlin:20200618T110000
DTSTAMP:20260619T145541
CREATED:20200528T092941Z
LAST-MODIFIED:20240417T125547Z
UID:7990-1592474400-1592478000@www.scienceofintelligence.de
SUMMARY:Manuel Lopes (hosted by Marc Toussaint): Optimal Behavior Without Optimal Rewards : Artificial Vs Natural
DESCRIPTION:Abstract:\nResearch in robotics and A.I. aims at optimizing very specific task rewards. Intelligent animals have a high degree of curiosity\, and recent\nresults have shown that instrumental reward optimization is a poor explanation for their behavior. We can show that to explain empirical\nresults from animals\, we need to have the drive to optimize reward\, a drive to reduce uncertainty\, and a drive for positive cues. We then show examples in robotics where a more complex reward system provides benefits in learning.\n\nReferences:\nDaddaoua\, N.\, Lopes\, . & Gottlieb\, J. Intrinsically motivated oculomotor exploration guided by uncertainty reduction and conditioned\nreinforcement in non-human primates. Sci Rep 6\, 20202 (2016). https://doi.org/10.1038/srep20202\nLopes\, M.\, Lang\, T.\, Toussaint\, M.\, & Oudeyer\, P. Y. (2012). Exploration in model-based reinforcement learning by empirically estimating learning progress. In Advances in neural information processing systems (pp. 206-214).\n\n***Want to know more about this lecture? Contact us at communication@scioi.de\n\n(Photo by Franck V. on Unsplash)
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-manuel-lopes-hosted-by-marc-toussaint/
LOCATION:On ZOOM (Contact communication@scioi.de for link)
CATEGORIES:Thursday Morning Talk
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200528T100000
DTEND;TZID=Europe/Berlin:20200528T110000
DTSTAMP:20260619T145541
CREATED:20200513T133418Z
LAST-MODIFIED:20240813T105459Z
UID:7930-1590660000-1590663600@www.scienceofintelligence.de
SUMMARY:Alan Akbik (SCIoI): Automatically Understanding Human Language: Challenges and Applications
DESCRIPTION:With research in machine learning (ML) and natural language processing (NLP)\, we aim to give machines the ability to understand and use human language. In this talk\, I give a high level introduction of some of the challenges of the field and give an overview of basic NLP tasks (and show some demos). I also introduce the Flair framework – developed by my group together with the open source community – that allows you to use state-of-the-art NLP methods in your research or applications. Time permitting\, I’ll also briefly cover research aspects of the framework\, such as learning word and sentence representations with neural language modeling\, and discuss future directions.
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-with-alan-akbik-scioi-automatically-understanding-human-language-challenges-and-applications/
LOCATION:On ZOOM (Contact us for Link)
CATEGORIES:Thursday Morning Talk
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