scienceofintelligence.de

Nina Poth (Science of Intelligence), “Exploring the Prospects for a Prediction-Oriented View of Intelligence”
2023-04-13 10:00 - 11:00 (Europe/Berlin)
Abstract: It has recently been proposed that a minimal condition of intelligence is the ability to form accurate predictions (Tjøstheim & Stephens 2021). In this talk, I evaluate the promise of this view for integrating intelligence research across subdisciplines within the cognitive and life sciences. I argue that this view combines two desirable features: (1) it allows us to subsume key dimensions identifying intelligent behaviour, such as goal-directedness, adaptiveness, generality, and flexibility (Coelho Mollo 2021; Glock 2019), under a unifying conceptual framework; (2) it combines well with a non-symbolic approach to representation at various degrees of abstraction (Gärdenfors 2004). Thereby, the predictive view provides opportunities for sharing concepts and transforming problems across research on artificial and biological intelligence. However, an outstanding issue with this approach is its current lack of insight into the relevant mechanisms and functional interactions generating intelligent behavior. I respond to this challenge by discussing a set of available research heuristics for mechanism discovery (Poth 2022).

References
Coelho Mollo, D. (2022). Intelligent Behaviour. Erkenntnis, 1-17.
Gärdenfors, P. (2004). Conceptual spaces as a framework for knowledge representation. Mind and Matter, 2(2), 9-27.
Glock, H. J. (2019). Agency, intelligence and reasons in animals. Philosophy, 94(4), 645-671.
Poth, N. (2022). Schema-centred unity and process-centred pluralism of the predictive mind. Minds and Machines, 32(3), 433-459.
Tjøstheim, T. A., & Stephens, A. (2021). Intelligence as Accurate Prediction. Review of Philosophy and Psychology, 1-25.
This talk will take place in person at SCIoI.

Xing Li (Science of Intelligence), "Learning to Manipulate Articulated Objects From Human Demonstrations"
2023-04-06 10:00 - 11:00
Abstract:
Programming robots to manipulate articulated objects such as drawers, doors, or locks is a challenging task. One of the major reasons for this difficulty is that robots must physically interact with objects, and even minor errors during manipulation can result in significant internal forces that may cause damage.
While robots struggle with these manipulation tasks, humans can effortlessly operate complex mechanisms with great reliability. Moreover, humans can transfer their experience between objects of the same type, resulting in remarkable generalization. This raises the question of how we can transfer these robust and general manipulation skills from humans to robots.
In this presentation, we will introduce a viable solution to achieve this transfer in the context of manipulating articulated objects. Specifically, we will demonstrate that a robot can acquire a manipulation policy that reliably manipulates various instances of the same type based on a single demonstration of a human opening an articulated object.
Following the presentation, we invite those who are interested to participate in an interactive session where we can discuss and share our experiences with controlling the robot with the soft hand in the robotics lab on the second floor.
This talk will take place in person at SCIoI.

Marah Halawa (Science of Intelligence), "Contrastive Learning Approaches for Computer Vision Applications"
2023-03-30 10:00 - 11:00
Abstract:
The recent success in Computer Vision has been mostly attributed to improved results using deep learning models trained on large labeled datasets. Many of these datasets have been labeled by humans. The labeling process, however, can be time-consuming, and in many applications, it may require expertise that could be costly to acquire. In order to address this requirement, more research focus and effort have shifted toward unsupervised learning algorithms, in order to utilize the ever-increasing quantities of unlabeled data. Self-supervised learning (SSL), in particular, is a set of algorithms that specifically aim to learn rich data representations from unlabeled samples, and it achieves comparable results to fully supervised methods on common benchmarks for image classification and segmentation. The idea behind SSL methods is to learn broad features from the signals that exist in unlabeled data. In other words, to acquire more general information and knowledge, and store them as neural network features that will be useful as prior knowledge for subsequent downstream supervised tasks (classification, segmentation, regression, ..etc.).
There are two types of SSL methods. First, self-prediction methods, which predict some omitted parts (in purpose) of the data using the other existing part of the data, such as jigsaw puzzle solving. Second, contrastive learning methods, which utilize similarities and dissimilarities, or simply relations, amongst data samples to form a classification problem, such as SimCLR (simple contrastive learning of representations). Contrastive learning methods have proven effective as representation learners in applications of natural image classification. Nevertheless, extending such algorithms to multiple application domains comes with challenges, and we identify certain limitations in these approaches. Therefore, in this talk, we will focus on contrastive learning methods and how to apply them in several computer vision applications. We also discuss the challenges and limitations we identified and how to address them in project 29.
This talk will take place in person at SCIoI.

Dr. Arlena Jung, "Time Management & Resilience"
2023-03-23 10:00 - 12:00
Abstract:
In this talk, Dr. Jung will focus on the three key principles of good time management: defining priorities, managing expectations and developing routines that work. Following the lecture, the participants have the opportunity to discuss their time management challenges in an individual coaching session.
Defining Priorities: Dealing with high performance expectations in wide array of areas ranging from research to writing, presenting, networking and teaching is a key challenge for early-stage researches. In order to deal effectively with the in part conflicting expectations PhD students and early stage researchers need both the mindset and the self-confidence to define priorities. This means developing short and middle term goals that are both compatible with one’s own long-term goals and the expectations one’s “relevant other’s”. Without clear goals defining priorities and quality criteria become impossible tasks. That participants learn to understand the use of time management tools using the power of the 4 Zs to define SMART goals, and integrating a “definition of done” into work packages, milestones and at times even individual tasks. We also address the emotional challenge of dealing with in part conflicting goals, roles and expectations. Together we discuss how ambiguity tolerance and strategic thinking can be used as key strengths in dealing with the multifaceted challenges but also opportunities of this career phase.
Managing Expectations: Complex interdependencies are an inherent part of the qualification phase of early stage research. Without the ability to manage expectations. PhD students have a very limited ability to actually turn their priorities into actionable plans. In this section of the lecture the participants are acquainted with key stakeholder-management tools such as the stakeholder-matrix and the systemic portrait. We, however, also discuss key communication skills needed to manage expectations effectively such as “7 shades of no”, turning “yes” into a deliberate decision and creating solution oriented dialogues.
Developing routines that work: In order to use the limited resources available as effectively as possible early stage researchers need to learn to develop routines that work. This means figuring out what time management tools fit nicely both with their individual needs and their operational and conceptual tasks. In the last section of the lecture we present time management tools that help PhD students structure their working days and weeks ranging from the pomodoro and the ivy lee method to stimulus-response regulation practices and self-monitoring and self-evaluation methods.

This talk will take place in person at SCIoI.
Photo by freestocks on Unsplash

Judith L. Bronstein (University of Arizona), "Why Cooperate with Another Species? The Puzzles of Mutualism"
2023-03-09 10:00 - 11:00
Abstract:
The classic view of nature is one of a deathly struggle for existence. Yet, throughout nature, organisms cooperate with each other. Mutualisms – mutually beneficial interactions between species – are more than fascinating natural history stories: they are central to the diversity and the diversification of life on Earth. Charles Darwin, well aware of mutualisms, mused that if species could be shown to act exclusively for the good of others, “it would annihilate my theory”. The very young field of mutualism research attempts in part to address Darwin’s challenge. I will first briefly discuss the relationship between within-species cooperation and mutualism. I will then introduce two underlying concepts that are helping to guide our growing understanding: mutualism not only confers benefits but also exacts costs on the participants; and the immediate interests of mutualists commonly conflict. Then, I will review some of my group’s recent findings that help address two of the most vexing puzzles mutualism poses: if mutualisms are beneficial, why isn’t the world covered with them; and if mutualisms are costly, then why doesn’t everyone cheat their partners? Our understanding of mutualism has exploded in recent years, but this new focus has come at the cost of exploring connections between and mutualism and other forms of interaction – a situation I am working to mend during my Wiko fellowship. I will conclude by highlighting the interfaces that excite me the most.

This talk will take place in person at SCIoI.

Photo by Joseph Sharp on Unsplash

Winter School "Ethics of Neuroscience and AI" 2022
2023-02-28 - 2023-03-04
The 11th Winter School “Ethics of Neuroscience and AI” is taking place on Feb 28 – March 4, 2022. It is organized by the BCCN Berlin/ICCN, the Berlin School of Mind and Brain, and the Excellence Cluster “Science of Intelligence”. The event is tailored for MSc and PhD students, but covers a range of topics of potential interest to other researchers, reflecting on the ethical and societal consequences of modern neuroscience.
Theoretical foundations, as well as practical and ethical aspects are addressed. Participants will benefit from a combination of lectures with group work and discussions, where they will put the learned content into practice.
Scientific organizers: John-Dylan Haynes and Thomas Schmidt.
Keynote lecture: Kent Kiehl (University of New Mexico) will discuss the ethical issues involved with neuroprediction (Live stream available)
You are welcome to join the keynote which will be live-streamed on the Bernstein Network’s Vimeo channel.
Fees: The Winter School is free of cost but registration is necessary.
Venue: Due to the ongoing pandemic, the Winter School will be held online.

Winter School "Ethics of Neuroscience and AI" 2023
2023-02-27 - 2023-03-03
The 12th Winter School “Ethics of Neuroscience and AI” is taking place on Feb 27 – March 3, 2023. It is organized by the BCCN Berlin/ICCN, the Berlin School of Mind and Brain, and the Excellence Cluster “Science of Intelligence”. The event is tailored for MSc and PhD students, but covers a range of topics of potential interest to other researchers, reflecting on the ethical and societal consequences of modern neuroscience.
Theoretical foundations, as well as practical and ethical aspects are addressed. Participants will benefit from a combination of lectures with group work and discussions, where they will put the learned content into practice.
Scientific organizers: John-Dylan Haynes and Thomas Schmidt.
Keynote lecture: Christine Heim (Charité-Universitätsmedizin Berlin)
Fees: The Winter School is free of cost but registration is necessary.
Venue: The Winter School takes place at the Bernstein Center for Computational Neuroscience Berlin.

Ryan Burnell, "A Cognitive Approach to the Evaluation of AI Systems"
2023-02-23 10:00 - 11:00
Abstract:
The capabilities of AI systems are improving rapidly, and these systems are being deployed in increasingly complex and high-stakes contexts, from self-driving cars to the detection of medical conditions. As the importance of AI grows, so too does the need for robust evaluation. If we want to determine the extent to which systems are safe, effective, and unbiased, it is vital that we understand the cognitive capabilities of those systems. In this endeavour, psychological science has a lot to offer—scientists from cognitive, developmental, and comparative psychology have spent many decades developing theories and paradigms to understand the cognitive capabilities of adults, children, and animals. Drawing on these theories and paradigms, we are working to build a framework for evaluating the cognitive capabilities of AI systems that we hope can be used to better track and regulate AI progress. I will present an initial version of the framework and discuss the open questions and challenges of applying cognitive science to AI evaluation.
This talk will take place in person at SCIoI.
Photo by Michael Dziedzic on Unsplash.

Julten Abdelhalim (Science of Intelligence), "Tips and Guidelines for your grant application in Germany"
2023-02-16 10:00 - 11:00
Abstract:
This talk will be targeting junior postdocs and phd at their final stages. It will be a short and brief introduction to the major options for grants (those aiming at the stars or smaller ones). Julten will offer some quick tips on the application process. She will also share her own experience in applying to the DFG Sachbeihilfe and ERC Starting Grant. The talk is not a detailed workshop in which we get into details about the proposal writing but rather a summary and a call out for how you should ideally plan your grant application journey. Those interested in detailed consultation are invited to book appointments later.

This talk will take place in person at SCIoI.

Oliver Brock (Science of Intelligence), "About the Interplay of Embodiment and Learning in Intelligent Systems"
2023-02-09 16:00 - 17:30 | MAR 2.057
Abstract:
Biological intelligent systems manifest their intelligence in physical interactions with other agents and with their environment. Such interactions require embodiment. Intelligence, both artificial and biological, also requires some kind of learning. But what is the relationship between the two? How should the two interact? Do they even have to? What could be a common ground on which this relationship can be explored, negotiated, and ultimately designed? In this presentation, I will attempt to provide my personal answers to these questions. I will argue that one of the reasons (deep) machine learning has not yet been able to replicate its smashing successes in the context of robotics lies in the widespread disregard for the important capabilities provided by the body. Instead of considering embodiment, machine learning seems to be resorting to massive use of physical simulations. This seems to be unnecessarily complicated without being convincingly effective.

Andreagiovanni Reina (Université Libre De Bruxelles), “The Power of Inhibition for Collective Decision Making in Minimalistic Robot Swarms”
2023-02-09 10:00 - 11:00
Abstract:
I investigate how large groups of simple robots can reach a consensus with decentralized minimalistic algorithms. Simple robots can be useful in nanorobotics and in scenarios with low-cost requirements. I show that through decentralized voting algorithms, swarms of minimalistic robots can make best-of-n decisions. In my research, I show that using a biologically-inspired voting model based on inhibitory signals, the swarm can collectively perform better and be more resilient against a minority of misbehaving robots than in models without inhibition. Our best-of-n decision algorithm can also be used for collective environmental monitoring. I will show that investigating these models can be very interesting and yield surprising results. As Anderson said in 1972, More is different. In our analysis, we found that limiting the communication range or the speed of the robots can improve collective performance in a range of relevant conditions. We explain the mechanisms of some of these phenomena with a combination of mathematical models and large-scale robot experiments.

This talk will take place in person at SCIoI.

Lars Lewejohann (Science of Intelligence), “What’s on a Mouse’s Mind? Behavioral Measures To Understand Experiences and Needs of an Animal”
2023-01-26 16:00 - 17:30 | MAR 2.057
Lars Lewejohann, Freie Universität Berlin, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R)
Mice, like all other living creatures, have adapted to specific living conditions in the course of evolution. From a human point of view, the behavior of animals is therefore not always easy to understand. This applies not only to the question of whether mice are actually capable of behaving intelligently, but also to the question of what is necessary for optimizing animal welfare of laboratory animals. In our work, we are interested in both questions and follow an animal-centered approach and are giving mice their say. Of course mice cannot fill out questionnaires, but we have developed a series of behavioral tests that allow to query the animals. In this lecture I will outline our approach with regard to improving housing and living conditions as well as the implications of using mice as a model species for the science of intelligence.

Scott Robins (Bonn University), "What Machines Shouldn't Do"
2023-01-26 10:00 - 11:00
Abstract:
From writing essays to evaluating potential hires, machines are doing a lot these days. In all spheres of life, it seems that machines are being delegated more and more decisions. Some of these machines are being delegated decisions that could have significant impact on human lives.Examples of such machines which have caused such impact are widespread and include machines evaluating loan applications, machines evaluating criminals for sentencing, autonomous weapon systems, driverless cars, digital assistants, etc. Considering that machines cannot be held morally accountable for their actions (Bryson, 2010; Johnson, 2006; van Wynsberghe & Robbins, 2018), the question that governments, NGOs, academics, and the general public should be asking themselves is: how do we keep meaningful human control (MHC) over these machines?

The literature thus far details what features the machine or the context must have in order for MHC to be realized. Should humans be in the loop or on the loop? Should we force machines to be explainable? Lastly, should we endow machines with moral reasoning capabilities? (Ekelhof, 2019; Floridi et al., 2018; Robbins, 2019a, 2019b; Santoni de Sio & van den Hoven, 2018; Wendall Wallach & Allen, 2010; Wendell Wallach, 2007). Rather than look to the machine itself or what part humans have to play in the context, I argue here that we should shine the spotlight on the decisions that machines are being delegated. Meaningful human control, then, will be about controlling what decisions get made by machines.

I argue that keeping meaningful human control over machines (especially AI which relies on opaque methods) means restricting machines to decisions that do not require a justifying explanation and can, in principle, be proven efficacious. Because contemporary methodologies in AI are opaque, many machines cannot offer explanations for their outputs. In many cases, decisions require justifying explanations, and we should therefore not use machines for such cases. It won’t be surprising that machines should be efficacious if they are to be used – especially in contexts that will have impacts on human beings. Increasingly, however, machines are being delegated decisions for which we are unable, in principle, to evaluate their efficacy. This should not happen.

These arguments lead to the conclusion that machines should be restricted to descriptive outputs. It must always be a human being deciding how to employ evaluative terms as these terms not only refer to specific states of affairs but also say something about how the world ought to be. Machines which are able to make decisions based on opaque considerations should not be telling humans how the world ought to be. This is a breakdown of human control in the most severe way. Not only would we be losing control over specific decisions in specific contexts, but we would be losing control over what descriptive content grounds evaluative classifications.

In this talk, I will first discuss what it means to say that a machine is ‘doing’ something. I then briefly discuss different proposals for MHC and why they fall short. I then argue that machines should not be delegated evaluative decisions as they require justifying explanations which machines cannot give and cannot be evaluated for efficacy. While this talk is framed negatively, it is my hope that this focuses research and development to design and build machines to help us realize our visions for how the world ought to be, rather than machines that tell us hour the world ought to be. Only humans can decide that.

This talk will take place in person at SCIoI.
Photo by David Levêque on Unsplash

Ingmar Posner (University of Oxford), "Learning to Perceive and to Act - Disentangling Tales from (Structured) Latent Space"
2023-01-19 16:00 - 17:30
Abstract:
Unsupervised learning is experiencing a renaissance. Driven by an abundance of unlabelled data and the advent of deep generative models, machines are now able to synthesise complex images, videos and sounds. In robotics, one of the most promising features of these models – the ability to learn structured latent spaces – is gradually gaining traction. The ability of a deep generative model to disentangle semantic information into individual latent-space dimensions seems naturally suited to state-space estimation. Combining this information with generative world-models, models which are able to predict the likely sequence of future states given an initial observation, is widely recognised to be a promising research direction with applications in perception, planning and control. Yet, to date, designing generative models capable of decomposing and synthesising scenes based on higher-level concepts such as objects remains elusive in all but simple cases. In this talk I will motivate and describe our recent work using deep generative models for unsupervised object-centric scene inference and generation. Furthermore, I will make the case that exploiting correlations encoded in latent space, and learnt through experience, lead to a powerful and intuitive way to disentangle and manipulate task-relevant factors of variation. I will show that this not only casts a novel light on affordance learning, but also that the same framework is capable of generating plans executable on complex real-world robot platforms.
Photo courtesy by Ingmar Posner.
This talk will take place in person at SCIoI.

David Garzón Ramos (Université Libre De Bruxelles), “Automatic Design of Robot Swarms: Context and Experiments”
2023-01-19 10:00 - 11:30
Abstract:

Swarm robotics is a promising approach to the coordination of large groups of robots. Traditionally, the design of collective behaviors for robot swarms has been an iterative manual process: a human designer manually refines the control software of the individual robots until the desired collective behavior emerges.

In this talk, I discuss automatic design as an alternative approach to manual design. In automatic methods, the design process is cast into an optimization problem: given a task to be performed by the swarm, an optimization process designs a collective behavior to perform the task and produces appropriate control software for the robots. I focus on experiments that highlight the various aspects of the automatic design of robot swarms: classes of collective behaviors, control architectures, and the optimization process. In particular, I present a case study on the design of shepherding behaviors for groups of robots. The results presented in this talk are outcomes of the project DEMIURGE; an ERC funded project devoted to the study of the automatic design of robot swarms (PI Mauro Birattari).
This talk will take place in person at SCIoI.
Photo by Omar Flores on Unsplash.

Dustin Lehmann, Fritz Francisco, Jorg Raisch, Pawel Romanczuk (Science of Intelligence), “Dynamical Adaptation and Learning: Knowledge Transfer and Cooperative Learning in Groups of Heterogeneous Agents”
2023-01-12 10:00 - 11:00
Abstract:
In groups of agents learning how to solve a common task, interaction and knowledge transfer between agents is important and can vary depending on network topology. Heterogeneity is one of the key principles that influences the type and quality of interaction between learning agents. Different learning strategies and behaviors can be a driving factor for the learning success at the group and individual level, whereas differences in dynamics (or capabilities, behaviors, internal states, etc.) can impede the direct transferability of knowledge and may require dynamic adaption of the agents.
In this talk, we show how to infer behavioral heterogeneity in learning groups of fish and how this affects future learning capabilities. Prior knowledge of social partners affects the outcome of learning processes and timing of information uptake. We further investigate behavioral heterogeneity from the perspective of synthetic dynamic systems and how to transfer knowledge between dissimilar agents to enable cooperative learning of how to solve a common task. First results show how to exploit heterogeneity for learning in synthetic agents and which information gradient is beneficial when dealing with novel tasks in a social context.

This talk will take place in person at SCIoI.

Peter Neri (Laboratoire Des Systèmes Perceptifs, CNRS, Paris), “The Unreasonable Recalcitrance of Human Vision to Theoretical Domestication”
2023-01-05 16:00
Abstract:
We can view cortex from two fundamentally different perspectives: a powerful device for performing optimal inference, or an assembly of biological components not built for achieving statistical optimality. The former approach is attractive thanks to its elegance and potentially wide applicability, however the basic facts of human pattern vision do not support it. Instead, they indicate that the idiosyncratic behaviour produced by visual cortex is largely dictated by its hardware components. The output of these components can be steered towards optimality by our cognitive apparatus, but only to a marginal extent. We conclude that current theories of visually-guided behaviour are at best inadequate, and we turn to neural networks in an attempt to establish whether the idiosyncratic character of human vision may be learnt from a larger repertoire of functional constraints, such as the statistics of the natural environment. We challenge deep convolutional networks with the same stimuli/tasks used with human observers and apply equivalent characterization of the stimulus–response coupling. For shallow depth of behavioural characterization, some variants of network-architecture/training-protocol produce human-like trends; however, more articulate empirical descriptors expose glaring discrepancies. Our results urge caution in assessing whether neural networks do or do not capture human behavior: ultimately, our ability to assess ‘’success’’ in this area can only be as good as afforded by the depth of behavioral characterization against which the network is evaluated. More generally, our results provide a compelling demonstration of how far we still are from securing an adequate computational account of even the most basic operations carried out by human vision.
Photo by Mathew Schwartz on Unsplash
This talk will take place in person at SCIoI.

John Dylan Haynes (Science of Intelligence), “Intelligence in Humans Versus Machines”
2022-12-15 16:00 - 17:30
Many claims have been made that machine intelligence could make humans superfluous in the near future. Today this claim is largely seen as overstated, but it is still important to assess the relative strengths of human versus machine cognition.

This talk will take place in person at SCIoI.

Robert Lange and Luis Gomez (Science of Intelligence), “Quantifying and Modelling Collective Behavior Across Ecological Contexts”
2022-12-15 10:00 - 11:00
Abstract:
A central challenge in understanding the concept of swarm intelligence is the relation between the behavior of a swarm of agents and its ecological niche. In order to interpret such collective concept, we have been using analytical and synthetic approaches to get more insights using mainly one particular biological system of Sulphur mollies as study system. We have combined analytical behavioral characterizations of schools of these fish with synthetic state-of-the-art machine learning methods to understand the functionality of the behavior in real life. In this talk, we will show our main findings related to the collective behavior. We will show i) that the highly synchronized diving behavior of the school is close to criticality, ii) how this can be functionally related to effective communication about predator attacks, and iii) how to study the heterogeneity in collectives by inferring the parameters of models using machine learning algorithms.
This talk will take place in person at SCIoI.

Erik Rodner “Please Label Me: Challenges and Efficient Strategies for Data Annotation and Selection”
2022-12-08 10:00 - 11:00
Abstract:
Lack of data and annotations has been the showstopper for machine learning projects when I started my PhD and 15 years later it still is. In my talk, I will give a brief overview of recent models we developed for weakly- and semi supervised as well as for active learning.
In addition, we will analyze the relevance of these algorithms from an industrial perspective, which often contradicts with the usual story line in traditional computer vision publications.
This talk will take place in person at SCIoI.

Photo by vackground.com on Unsplash

Klaus Obermayer (Science of Intelligence), "Computational Models of Electric Field Effects and Optimal Control of Neurons and Neural Populations"
2022-12-01 16:00 - 17:30
Abstract:
The brain is a complex dynamical system with processes operating on different spatial scales. At the macroscopic end one observes global dynamical phenomena, which are called „brain states“ and which are often acompanied by oscillations in different frequency bands or by specific functional connectivity patterns between populations of neuron. A common hypothesis states, that the global dynamics establishes a task-dependent operating point, which is required by individual neurons and local networks to perform information processing tasks. Perturbation experiments are performed, on the one hand, to perform causal analyses into the consequences of this and related hypotheses and, on the other hand, to restore a brain’s operating point in case of dysfunction.
In my talk I will summarize some of our recent modelling work to better understand the interaction between the neural dynamics and external control inputs, taking non-invasive electrical stimulation of neural tissue as an example. I will first present some results on the biophysics of (microscopic) neuron-field interactions and our modelling attempts to propagate these effects to the macroscopic level. In the second part of my presentation I will show, how techniques from Optimal Control Theory can be used to probe controllability aspects of neural systems and to help design efficient ways of steering the neural dynamics.

This talk will take place in person at SCIoI.

David Bierbach (Science of Intelligence), "Anticipation in social interactions among live and artificial agents"
2022-12-01 10:00 - 11:00
Abstract:
The aim of SCIoI’s P10 is to investigate how anticipation and prediction shapes social interactions among live and artificial agents using for example the Robofish system. We will outline our research showing the sophisticated anticipation abilities of live fish, as well as how we integrated prediction and anticipation into Robofish’s social interaction behaviors. We will furthermore show how experiments with robotic animals can help to promote animal welfare and what is necessary to build biomimetic robots that will be accepted by live animals as conspecifics (see also these articles: https://www.frontiersin.org/articles/10.3389/fbioe.2020.00441/full, https://www.annualreviews.org/doi/10.1146/annurev-control-061920-103228, https://link.springer.com/chapter/10.1007/978-3-030-64313-3_26 ). Finally we will dive into our public outreach activities that include the Robofish exhibition in the Humboldt Labor at Stadtschloss Berlin with more than 100,000 visitors since 2021.
This talk will take place in person at SCIoI.

Jan De Houwer (Ghent University), "Learning in Individual Organisms, Genes, Machines, and Groups: A New Way of Defining and Relating Learning in Different Systems"
2022-11-24 16:00 - 17:30 | MAR 2.057
Abstract:
Learning is a central concept in many scientific disciplines. Communication about research on learning is, however, hampered by the fact that different researchers define learning in different ways. In this talk, we introduce the extended functional definition of learning that can be used across scientific disciplines. We provide examples of how the definition can be applied to individual organisms, genes, machines, and groups. The extended functional definition (a) reveals a heuristic framework for research that can be applied across scientific disciplines, (b) allows researchers to engage in intersystem analyses that relate the behavior and learning of different systems, and (c) clarifies how learning differs from other phenomena such as (changes in) behavior, damaging systems, and programming systems.
Photo by DeepMind on Unsplash
This talk will take place in person at SCIoI.

Thursday morning talk: Nicolas Mandel, "Kangaroos & Quadcopters"
2022-11-24 11:30 - 13:00
Abstract:
The contents of this presentation will be twofold. In the first part the Centre for Robotics of the Queensland University of Technology (QUT) and its research directions and facilities will be introduced. The research on semantics for the benefit of UAVs, specifically quadcopters, will be highlighted. The second part will contain the personal experiences of the presenter of undertaking a PhD in Australia, highlighting differences, challenges and lessons learnt along the way.Disclaimer: The views and opinions in this talk are the presenters and do not necessarily reflect the opinions of any of the employers or affiliates.
This talk will take place in person at SCIoI.

Photo by Indy Bruhin on Unsplash

What are futures made of? Collactive Materials, a joint SCIoI/MoA project
2022-11-24 10:00 - 11:00
Abstract:
The BUA-funded experimental knowledge transfer project CollActive Materials, a collaboration between the Clusters of Excellence Science of Intelligence and Matters of Activity, encourages speculation on what the future has in store.
Which intelligent materials will pave our tomorrows? How can substances and materials change our world in an intelligent way? What will the world look like in the coming decades, and how can we turn our speculations into something tangible? Finally, what kinds of relationships could we create with intelligent materials?
In this Thursday Morning Talk the audience will learn more about the CollActive Materials project and all the exciting interactions between the two clusters, and most importantly, they will get a chance to dive into the project themselves by taking part in a mini speculative design exercise.
SPEAKERS:
Léa Perraudin is a media theorist and speculative material scholar and works as postdoctoral research associate at the Cluster of Excellence »Matters of Activity. Image Space Material«. Léa currently works on a habilitation project, bringing forth a media theory of phase transitions by investigating the ties of material and metaphor in contemporary technocapitalist media environments through transience, dispersal, abundance and solidification.
Furthermore, Léa is the co-leader of the experimental laboratory »CollActive Materials«, a joint project of the Clusters of Excellence »Matters of Activity« and »Science of Intelligence«, that intends to gather multiple publics to jointly tackle possible material futures through the method of speculative design.
Martin Müller researches at the intersection of cultural history and theory, media studies, history of knowledge and science, and design theory. He is a postdoctoral research associate at the Cluster of Excellence »Matters of Activity. Image Space Material« – in the projects »Symbolic Material« and »Material Form Function«. Since 2015 he has been teaching at the Department of Cultural History and Theory at Humboldt-Universität zu Berlin. Martin is the co-leader of the experimental laboratory for knowledge exchange and speculative design »CollActive Materials«. Recently published: »The Will to Engineer. Synthetic Biology and the Escalation of Zoëpolitics«, in: P. Ribault (Ed.): Design, Gestaltung, Formatività, 2022
This talk will take place in person at SCIoI.

Heiner Spiess (Science of Intelligence), “Tools To Study the Generality of Deep Neural Network Representations”
2022-11-17 10:00 - 11:00
Abstract:
As many of us know by now, Deep Learning has enabled tackling very challenging problems and applications that were previously almost impossible to solve with machine learning. However, for most of the tasks we want to solve with Deep Learning, we need large, if not huge, amounts of data and computing power. This is very limiting for many applications for which we do not have the necessary amounts of data or for practitioners who do not have access to enough computation power to train well-performing Deep Networks for their desired tasks.We hope to overcome these two limitations by leveraging the generality of already trained models through Transfer Learning or combining the information from multiple, perhaps relatively small, datasets with Multi-Task-Learning.In this project, we are investigating the generality of representations learned by Deep Networks. Today I would like to introduce one of the families of tools we use in this effort: Representational Similarity Analysis (RSA).I will present the methodology behind these tools and provide some insights into Deep Networks gained through their use. However, I would highlight some concerns to be aware of when using these tools and present some challenges that arise in practice. Considering these concerns, I will present a variant of these tools that solves some of the existing problems.Furthermore, I will shortly present a tool that we have developed to synthesize realistic image data, allowing us to systematically analyse which properties of the data are represented in Deep Networks.Finally, I want to mention our SCIoI cooperation with project 01 on “Scanpath Prediction in Dynamic Scenes using an end-to-end Deep Learning approach”.
Photo by Nina Ž. on Unsplash
This talk will take place in person at SCIoI.

Berlin Science Week 2022 - The Science Slam of the Berlin Clusters of Excellence, "Clear the stage for science"
2022-11-10 19:30 - 22:00
At our cluster science slam, scientists try everything to entertain their audience, regardless of whether the subject is e.g. mathematics, neuroscience or active material. The sky is the limit when it comes to what’s possible. Costumes, props, movies, power-point presentations or other experimental setups – it is all allowed. Only time sets the limits – every slammer will have ten minutes at most. And the audience will decide which presentation is best!
Constitutional Hardball in Action (Robert Benson)
How to make the U-Bahn go brrr (Enrico Bortoletto)
Tuning in to the sound of fish (Dr. Antonia Groneberg)
Searching for Intelligence (Benjamin Lang)
In Search of Lost Chaos (Dr. Guillermo Olicón Méndez)
Knowing the Nature’s messengers (Dr. Ramprasad Misra)
Nature knows best (Dr. Alina Pushkarev)
Visit the BSW website for more info
Photo taken from Berlin Science Week
The Zoom Link will be sent the day before the lecture.

Jan De Bruyne (Leiden University), "Liability for Damage Involving AI – Some Regulatory Challenges and Priorities"
2022-11-10 10:00 - 11:00
More details to follow.
Photo by DeepMind on Unsplash
This talk will take place in person at SCIoI.

Berlin Science Week 2022 - Collective Materials Workshop, "What is the Future Made Of?"
2022-11-05 09:30 - 11:00
Nature is a great designer. Through billions of years of evolution – of design trial and error (or re-route) – it has come up with uniquely functional and beautiful materials. It uses simple materials in clever ways. Natural materials are often sophisticated in structure and function, yet they are made from simple, abundant resources. More than that, they are designed to be part of a natural cycle of making and breaking down – no material is wasted. CollActive Materials, together with the speculative designer Emilia Tikka, invites you to a hands-on speculation workshop. Find out how researchers from Matters of Activity and Science of Intelligence use bio-design in their work – and make up your very own version of a bio-inspired future.
Visit the BSW website page for more info
Photo taken from Berlin Science Week
The Zoom Link will be sent the day before the lecture.

Berlin Science Week 2022 - Panel Discussion with Dafna Burema, Mattis Jacobs, and Jonas Frenkel, "Artificial Intelligence: Examples of AI gone wrong and Ethical Questions"
2022-11-05 09:30 - 11:00
In this lively debate, our researchers Dafna Burema, Mattis Jacobs and Jonas Frenkel from Science of Intelligence will talk about Artificial Intelligence and its ethical implications including examples of AI gone wrong. How do we imagine sustainable futures with robots? What are the open questions scientists face every day when dealing with Artificial Intelligence?
Visit the BSW website for more info
Photo taken from Berlin Science Week
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The Zoom Link will be sent the day before the lecture.
