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--- start [2020/11/23 08:52]
IIS Webadmin
+++ start [2025/10/23 06:25] (current)
IIS Webadmin
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 ===== Working With Us =====
   * Check our thesis topics for [[theses/start|Bachelor and Master students]].
-  * [[https://ellis.eu/|ELLIS]] PhD Program: [[https://ellis.eu/de/news/ellis-phd-program-call-for-applications|Call for Applications]]
+  * [[:jobs:#notice_for_non-eueea_prospective_master_students|Notice]] for non-[[https://en.wikipedia.org/wiki/EU|EU]]/[[https://en.wikipedia.org/wiki/European_Economic_Area|EEA]] prospective Master students
+  * We currently do not have any open PhD positions.
+<html><!--We currently have [[jobs|one open PhD position]].--></html>
 <html><!--
@@ Line 22: / Line 25: @@
 </html>
+{{ ::iis_retreat_2024.jpg?direct&900 |}}\\
-{{::dsc_0052.jpg?640|Group Picture}}\\
+<html>
-Group picture taken at our retreat in Obergurgl
+<div style="text-align: center;">
+  <p>Group picture taken at our 2024 retreat at Meissner Haus.</p>
+</div>
+</html>
 ===== News =====
-<HTML><table><tr valign="top"><td>2020-11-20</td><td>Simon Haller-Seeber and Patrick Lamprecht present a show <i>Explainable AI: A sneak peek into the Black-Box</i> at <a href="https://youtube.com/watch?v=4NVfPwdLnCg&amp;t=17m43s">Science Slam</a>, online.</td></tr><tr valign="top"><td>2020-06-22</td><td>Justus Piater gives an invited talk <i>Conditional Neural Movement Primitives</i> at <a href="http://www.gdr-isis.fr/index.php?page=reunion&amp;idreunion=424">GdR
+<HTML><table><tr valign="top"><td>2025-09-18</td><td>Samuele Tosatto gives an invited keynote <i>Where are all the intelligent robots? A quest for efficiency in reinforcement learning</i> at <a href="https://sarl-plus.github.io/RL-Bootcamp2025/">Reinforcement Learning Bootcamp 2025</a>, Salzburg. <span class="actions"><a href="javascript:void(0)" onclick="showHide('newsitem0', 'Abstract')">[Abstract]</a></span><blockquote id="newsitem0Abstract" style="display:none">As artificial intelligence reshapes our digital world at a breathtaking pace,
-    ISIS Réunion Apprentissage et Robotique</a>, online. <span class="actions"><a href="javascript:void(0)" onclick="showHide('newsitem1', 'Abstract')">[Abstract]</a></span><blockquote id="newsitem1Abstract" style="display:none">Conditional Neural Movement Primitives (CNMP) constitute
+    a curious question arises: Where are all the real-world, intelligent robots?
-    a novel framework for robot programming by demonstration based on
+    While we have mastered the generation of text, images, and video by leveraging vast web-scale datasets,
-    Conditional Neural Processes (CNP).  Like Bayesian methods such as
+    robotics still faces a fundamental data bottleneck. Reinforcement learning (RL) offers a compelling solution,
-    Gaussian Processes (GP), CNP learn how target distributions depend
+    enabling agents to learn autonomously by collecting their own experience.
-    on data, and can be conditioned on specific data points to infer
+    However, the path to autonomy is often blocked by the staggering inefficiency of current RL algorithms,
-    new target distributions at test time.  Unlike GP that are
+    which can require millions of trials to master simple tasks. This talk embarks on a quest to tackle this
-    expensive to train and scale poorly to high dimensions, CNP are
+    efficiency problem head-on. I will argue that a crucial step toward unlocking the potential of
-    neural networks and are trained by gradient descent.  CNMP
+    robot learning lies in a two-pronged approach: first, by developing statistically efficient
-    leverage CNP to represent motion trajectories that can be
+    algorithms that can reuse data by leveraging sound off-policy techniques, and second,
-    conditioned, at test time, on task paramters such as goal
+    by designing better action representations for physical, real-world agents.
-    locations, via points, and/or force readings.  Moreover, CNMP are
+    By making our algorithms more efficient and refining their core hypotheses,
-    conditioned on sensor readings during execution, resulting in
+    we can accelerate the journey toward real embodied artificial intelligence.</blockquote></td></tr><tr valign="top"><td>2025-07-15</td><td>Justus Piater gives an invited keynote <i>Making robots learn to perceive and act with
-    robust, reactive behavior.  This talk will present an overview of
+    understanding</i> at <a href="https://www.uibk.ac.at/en/congress/multibody2025/">The
-    how CNMP work and how they can be used in various robot
+th ECCOMAS Thematic Conference on Multibody Dynamics</a>, Innsbruck. <span class="actions"><a href="javascript:void(0)" onclick="showHide('newsitem1', 'Abstract')">[Abstract]</a></span><blockquote id="newsitem1Abstract" style="display:none">The flexibility and robustness of current robots is
-    applications.</blockquote></td></tr><tr valign="top"><td>2020-06-03</td><td>Justus Piater appears in the media: Wie der Roboter denken lernt.</td></tr><tr valign="top"><td>2020-01-29</td><td>Justus Piater gives an invited talk <i>Digital Science</i> at <a href="https://www.graduateacademy.uni-heidelberg.de/karriere/veranstaltungsreihen.html">Vortragsreihe
+    limited by their lack of understanding of their environment. For
-    „Primers for Predocs – Strategien für eine erfolgreiche
+    this reason, most robots operate in controlled environments.
-    Promotion“</a>, Universität Heidelberg. <span class="actions"><a href="javascript:void(0)" onclick="showHide('newsitem4', 'Abstract')">[Abstract]</a></span><blockquote id="newsitem4Abstract" style="display:none">Massive availability of data and computing power are
+    Machine learning can circumvent modeling problems but introduces
-    promoting data-driven methods in all areas of science and
+    new problems of generalizing from examples. How can robots acquire
-    technology.  I will describe how the University of Innsbruck
+    understanding (of structure, function, causality, etc.) that
-    supports this via its new Digital Science Center, and will give a
+    allows them to generalize from sparse experience? Motivated by
-    flavor of machine learning for data analysis.</blockquote></td></tr><tr valign="top"><td>2020-01-20</td><td>Joanna Chimiak-Opoka, Carina König, and Justus Piater appear in the media: <a href="https://www.uibk.ac.at/newsroom/ergaenzung-digital-science-erfolgreich-gestartet.html.de">Ergänzung Digital Science erfolgreich gestartet – UIBK Newsroom</a>.</td></tr><tr valign="top"><td>2020-01-03</td><td>Justus Piater gives an invited talk <i>Künstliche Intelligenz: Grundlagen, Erfolge,
+    shortcomings of current machine-learning methods, I will argue
-    Herausforderungen</i> at 47. Tagung des Innsbrucker Kreises von MoraltheologInnen
+    that &quot;understanding&quot; is a meaningful notion in AI that reaches
-    und SozialethikerInnen, Innsbruck.</td></tr><tr valign="top"><td>2019-12-19</td><td>Justus Piater appears in the media: <a href="https://tvthek.orf.at/profile/Tirol-heute/70023/Tirol-heute/14035670/Alexa-Siri-Co-Spione-im-eigenen-Haus/14610743">TV interview by ORF 2 Tirol Heute RedHaus (in German)</a>.</td></tr><tr valign="top"><td>2019-12-12</td><td>Justus Piater gives an invited lecture <i>Too Smart to Be Trusted – Do I Even Want to
+    beyond prediction and control. I will discuss examples of our
-    Understand My Robot?</i> at <a href="http://www.trustrobots.eu/">TrustRobots Lecture series
+    recent work on learning visual relational concepts, extrapolation
-    Trust in Robots</a>, TU Vienna.</td></tr><tr valign="top"><td>2019-12-05</td><td>IIS guest <span style="font-weight:bold">Heiko Neumann</span>, University of Ulm,  gives an invited colloquium <i>Biologically inspired visual-auditory processing – from
+    of learned movements beyond the training distribution, learning of
-    brain-like computation to neuromorphic algorithms</i> at <a href="https://www.uibk.ac.at/informatik/forschung/lunchtime-seminar/index.html.en">IFI Lunchtime Seminar</a>. <span class="actions"><a href="javascript:void(0)" onclick="showHide('newsitem9', 'Abstract')">[Abstract]</a></span><blockquote id="newsitem9Abstract" style="display:none">A fundamental task of sensory processing is to detect
+    symbolic concepts and rules, and structure-driven skill learning
-    and integrate feature items to group them into perceptual units
+    from sensorimotor experience. Our long-term objective is to
-    segregating them from other objects and the background. A
+    improve abstraction, generalization, robustness, and ultimately
-    framework is discussed which explains how perceptual grouping at
+    explainability of robot perception and action.</blockquote></td></tr><tr valign="top"><td>2025-07-11</td><td>Justus Piater and Alejandro Agostini give an invited talk <i>Learning Symbols and Abstractions in Robot Planning</i> at <a href="https://www.unibz.it/en/events/abstraction-language-science-engineering">Abstraction:
-    early as well as higher-level cognitive stages may be implemented
+    Language - Science - Engineering</a>, Bolzano. (International Workshop)</td></tr><tr valign="top"><td>2025-04-25</td><td>Simon Haller-Seeber gives an invited talk <i>AI-powered tools for real-time transcription and translation in action: A self-hosted open-source framework for digital spaces.</i> at <a href="https://www.uibk.ac.at/de/medien/veranstaltungen/tagungen/medien-wissen-bildung-2025/">Medien - Wissen - Bildung 2025: Streifzüge an den Nahtstellen von Medien, Bildung und Philosophie</a>, Universität Innsbruck.</td></tr><tr valign="top"><td>2025-04-10</td><td>Simon Haller-Seeber gives an invited keynote <i>Hands-on Approaches to Software, Robotics, and AI: Exploring Experiments and Science in Action</i> at <a href="https://iis.uibk.ac.at/public/simon/ECER-2025/">European Conference on Educational Robotics (ECER 2025)</a>, HTL Anichstrasse.</td></tr><tr valign="top"><td>2025-04-10</td><td>Simon Haller-Seeber and Christopher Kelter teach a tutorial <i>Was sind Roboter, was macht eine KI? Entwickle deine eigene KI und programmiere unsere Minibots</i> at Campustag BG/BRG Sillgasse, Universität Innsbruck.</td></tr><tr valign="top"><td>2025-04-03</td><td>Justus Piater gives an invited keynote <i>What is AI really?</i> at <a href="https://www.uibk.ac.at/events/2025/04/03/ai-in-law-and-practice-regional-perspectives-on-european-rules">Cross-Border
-    in cortex. Different grouping mechanisms are implemented which are
+    Seminar: AI in Law and Practice: Regional Perspectives on European
-    attuned to basic features and feature combinations and mainly
+    Rules</a>, Universität Innsbruck.</td></tr><tr valign="top"><td>2025-03-06</td><td>Justus Piater teaches a tutorial <i>Funktionsweise, Möglichkeiten und Grenzen von KI</i> at <a href="https://www.tirol.gv.at/fileadmin/themen/bildung/medienzentrum/downloads/07_Magazin_Co/2025_01.pdf">Lieber
-    evaluated along the forward sweep of stimulus processing. However,
+    gleich berechtigt als später: Hallo KI, hilfst du uns bei der
-    due to limitations of local feature detection mechanisms and
+    Gleichstellung?!</a>, Tiroler Bildungsinstitut Grillhof Vill. <span class="actions"><a href="javascript:void(0)" onclick="showHide('newsitem7', 'Abstract')">[Abstract]</a></span><blockquote id="newsitem7Abstract" style="display:none">Künstliche Intelligenz&quot; bezeichnet derzeit Systeme, die
-    inherent ambiguities, top-down feedback is required to deliver
+    auf maschinellem Lernen (ML) basieren. Dieser Workshop führt in
-    contextual information helping to disambiguate initial
+    die Grundlagen des ML ein, mit besonderem Augenmerk auf neuronale
-    measurements. Feedback of contextual information is demonstrated
+    Netze, der derzeit populärsten ML-Technologie. Darauf aufbauend
-    to improve object recognition performance, stabilize learning of
+    vermittelt er ein Grundverständnis für prinzipielle Möglichkeiten
-    object categories, and integrate multi-sensory representations.
+    und Grenzen von ML und warum es mit derzeitigen Mitteln extrem
+    schwierig ist, Diskriminierung in KI-Systemen zu
-    The canonical principles of neural computation define a set of
+    verhindern.</blockquote></td></tr><tr valign="top"><td>2025-02-26</td><td>Simon Haller-Seeber and Christopher Kelter teach a tutorial <i>Programmieren eines autonomen Roboters anhand einer selbstentwickelten KI</i> at STAIR-Lab INNALP Workshop für das Gymnasium Ursulinenen Innsbruck, Universität Innsbruck.</td></tr><tr valign="top"><td>2025-02-26</td><td>Marko Zarić teaches a tutorial <i>Einführung in das Programmieren mit Microcontrollern</i> at STAIR-Lab INNALP Workshop für das Gymnasium Ursulinenen Innsbruck, Universität Innsbruck.</td></tr></table></HTML>
-    core operations to implement above-mentioned mechanisms of
-    perceptual and cognitive inference. These operations can be
-    mapped, in a simplified form, onto neuromorphic platforms to
-    emulate brain-like computation. It is demonstrated that an
-    architecture composed of canonical circuit mechanisms can be
-    mapped onto neuromorphic chip technology facilitating low-energy
-    non-von Neumann computation.</blockquote></td></tr><tr valign="top"><td>2019-11-26</td><td>IIS guest <span style="font-weight:bold">Tamim Asfour</span>, Karlsruhe Institute of Technology,  gives an invited keynote <i>Engineering Humanoids with Motion Intelligence</i> at <a href="https://www.uibk.ac.at/informatik/studium/inday-students/index.html.en">inday students</a>. <span class="actions"><a href="javascript:void(0)" onclick="showHide('newsitem10', 'Abstract')">[Abstract]</a></span><blockquote id="newsitem10Abstract" style="display:none">Humanoid robotics plays a central role in robotics
-    research as well as in understanding intelligence. Engineering
-    humanoid robots that are able to learn from humans and
-    sensorimotor experience, to predict the consequences of actions
-    and exploit the interaction with the world to extend their
-    cognitive horizon remains a research grand challenge. Currently,
-    we are experiencing AI systems with superhuman performance in
-    games, image and speech processing. However, the generation of
-    robot behaviors with human-like motion intelligence and
-    performance has yet to be achieved. In this talk, I will present
-    recent progress towards engineering 24/7 humanoid robots that link
-    perception and action to generate intelligent behavior. I will
-    show the ARMAR humanoid robots performing complex grasping and
-    manipulation tasks in kitchen and industrial environments,
-    learning actions from human observation and experience as well as
-    reasoning about object-action relations.</blockquote></td></tr></table></HTML>
 [[news|Older News]]
@@ Line 104: / Line 87: @@
 Austria
-**How to find us:** See the [[http://informatik.uibk.ac.at/how-to-reach-us/|directions]].
+**How to find us:** See the [[https://www.uibk.ac.at/informatik/kontakt/anfahrt.html.en|directions]].
 **Legal Notice:** See the [[:impressum|Impress and Privacy Notice]].

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