scienceofintelligence.de

Yuejiang Liu (EPFL University), "Learning Beyond the IID Setting with Robust and Adaptive Representations"
2022-02-17 10:00 - 11:00 (Europe/Berlin) | On Zoom
Abstract
Machine learning models have achieved stunning successes in the IID setting. Yet, beyond this setting, existing models still suffer from two grand challenges: brittle under covariate shift and inefficient for knowledge transfer. In this talk, I will introduce three approaches to tackle these challenges, namely self-supervised learning, causal representation learning, and test-time training. More specifically, I will share our recent findings on (i) incorporating prior knowledge of negative examples into representation learning, (ii) promoting causal invariance and structure by making use of data from multiple domains, (iii) exploiting extra information besides model parameters for effective test-time adaptation. I will show how these techniques enable deep neural networks to more robustly generalize and efficiently adapt to new environments in the motion or vision context. I will finally discuss the implications of these results on the design, training, and deployment of deep models for domain generalization and adaptation. Comments and feedback are more than welcome.

Paper Links
Social NCE: Contrastive Learning of Socially-Aware Motion Representations, ICCV’21
TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? NeurIPS’21
Collaborative Sampling in Generative Adversarial Networks, AAAI’20
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective, Preprint’21 (under review)

Bio
Yuejiang Liu is a PhD student at EPFL, advised by Alexandre Alahi. His research interests center around representation learning and its applications to autonomous agents. He is particularly excited about unsupervised learning for robust generalization and efficient adaptation.

The Zoom Link will be sent the day before the lecture.

Mathilde Caron, “Self-Supervised Learning: How To Learn From Images Without Human Annotations”
2022-02-10 10:00 - 11:00 | On Zoom
Abstract:
Self-supervised learning (SSL) consists in training neural network systems without using any human annotations. Typically, neural networks require large amounts of annotated data, which have limited their applications in fields where accessing these annotations is expensive or difficult. Moreover, manual annotations are biased towards a specific task and towards the annotator’s own biases, which can result in noisy and unreliable signals. Training systems without annotations could lead to better, more generic and robust representations. In this talk, I will present different contributions to the fast-growing field of SSL conducted during my PhD. I will finish by discussing open questions and challenges for the future of SSL.

The Zoom Link will be sent the day before the lecture.

Dimitri Coelho Mollo (Science of Intelligence), "The Concept of Intelligence - A progress report"
2022-01-27 10:00 - 11:00
In this presentation, I will report on the results of my work so far on the concept of intelligence, summarising some of the main points and proposals made, and opening the floor for open discussion about the topic.

The Zoom Link will be sent the day before the lecture.

Henning Sprekeler (Science of Intelligence), “Harnessing Machine Learning To Model Biological Systems”
2022-01-20 16:00 - 17:30
“Harnessing machine learning to model biological systems”
Abstract:
Classically, models of biological systems follow two different approaches. In bottom-up approaches, biological data are used to constrain a phenomenological model of the system in question, and the model is the studied to identify potential functions or potential consequences of the observations that flow into the model. Top-down approaches, on the other hand, start with a presumed function and ask how this question could be implemented in a biologically inspired architecture. Both approaches have been very successful, but both suffer from their own kind of problems. Bottom-up approaches often suffer from (potentially many) parameters that cannot be sufficiently constrained from data. Top-down approaches were in the past hard to combine with the complexities of the biological system in question. Recent advances in machine learning (ML) software now offer a promising hybrid approach, because they allow to optimize not only neural nets, but basically any dynamical system by gradient descent. I will offer a few examples how we have used ML to study biological systems, ranging from behavioral level (nature vs. nurture) down to the level of neural circuits (role of feedback for invariant sensory processing, and, time permitting, the function of different cell classes).
The Zoom Link will be sent the day before the lecture.

Mengmi Zhang (Harvard Medical School), “A Peek Into How Brain Computations Inspire New Paths in AI and How AI Elucidate Brain Computations”
2022-01-20 10:00 - 11:00 | On Zoom
Abstract:
The fields of neuroscience and AI have a long and intertwined history. From the study of simple and complex cells in visual areas of the brain to the recent success of convolution neural networks in many real-world applications, experimental and theoretical neuroscience has contributed significantly to designing smarter machines. In turn, AI models help us better understand brain computations that underlie biological intelligence. In my talk, I will present several efforts of deciphering brain computations by building computational models and quantifying model behaviors with human benchmarks in visual search and object recognition. Specifically, I divide my presentation into two parts. First, I will present works on predicting eye movement behaviors during visual search tasks. An intriguing property of some classical search tasks is asymmetry such that finding a target A among distractors B can be easier than finding B among A. We elucidate the mechanisms responsible for asymmetry in visual search. Second, I will introduce two works on contextual reasoning in object recognition. We systematically investigated critical properties of where, when, and how context modulates recognition in humans and machines.
Please refer to the following list of papers for details.
CVPR 2020: https://arxiv.org/pdf/1911.07349.pdf
ICCV 2021: https://arxiv.org/pdf/2104.02215.pdf
Neurips 2021: https://arxiv.org/pdf/2106.02953.pdf
Nature Communications 2018: https://www.nature.com/articles/s41467-018-06217-x.pdf
Bio:
Mengmi Zhang is a research scientist and principal investigator in Agency for Science, Technology and Research (A*STAR), Singapore. Prior to this, Dr. Zhang is a postdoc with Gabriel Kreiman at the Harvard Medical School from 2019-2021. She obtained her PhD at the National University of Singapore (2015-2019) and was a visiting graduate student in KreimanLab at the Harvard Medical School (2017-2018). Her research background is multi-disciplinary at the intersection of artificial intelligence and computational neuroscience. She has published multiple papers in top-tier conferences (such as CVPR, ICCV, IROS and NeurIPS) and international science journals (TPAMI, Nature Communications, Nature Human Behaviors). Her papers often come with humorous titles like “Finding any Waldo with zero-shot invariant and efficient visual search” or “When Pigs Fly”. She will become an adjunct assistant professor in the Department of Electrical and Computer Engineering, National University of Singapore from August 2022.

The Zoom Link will be sent the day before the lecture.

Lars Lewejohann (Science of Intelligence), “What’s on a Mouse’s Mind? Behavioral Measures To Understand Experiences and Needs of an Animal”
2022-01-06 16:00 - 17:30
What’s on a mouse’s mind? Behavioral measures to understand experiences and needs of an animal
Lars Lewejohann, Freie Universität Berlin, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R)
Mice, as 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.
The Zoom Link will be sent the day before the lecture.

Ruben Arslan (MPI Berlin): “Bad Science vs. Open Science. The Replication Crisis and Possible Ways Out.”
2022-01-06 10:00 - 11:00 | On Zoom
Estimates from large-scale replication projects in psychology suggest that the majority of studies from top journals do not replicate. Using commonly accepted research methods, several academic fields amassed prolific, seemingly coherent literatures on phenomena that do not exist, such as extrasensory perception and depression candidate genes. Throughout the biomedical and life sciences, data detectives keep finding highly cited papers that are riddled with errors invalidating their conclusions. Our textbooks are full of findings that do not replicate or are otherwise in serious doubt.
Academia as a system has issues, but can we use the scientific method to understand and remedy them? A vibrant reform movement is seeking to do so, but it is hard to keep track of all the suggestions to do better and tell fads from truly beneficial reforms. I outline concrete plans and paths that could lead to lasting improvements, such as PCI Registered Reports, the Peer Reviewer’s Openness Initiative, post publication peer review, and guideline and incentive setting at the journal, hiring and funding level.

The Zoom Link will be sent the day before the lecture.

Elke Weber (Princeton University), "Personal and Social Information Search and Integration for Intelligent Decisions on Climate Action"
2021-12-23 10:00 - 11:00 | On Zoom
Abstract:
Some of my past and current research looks at “decisions from experience,” i.e., decisions based on the personally experienced outcomes of past choices, along the lines of reinforcement learning models and how such learning and updating is related to and differs from the way in which people and other intelligent agents use other sources of information, e.g., vicarious feedback (anecdotal/social and/or in the form of statistical distributions of outcomes) or science- or model-based outcome predictions. What happens when these different sources of forecasts of the consequences of choices disagree with each other? How do such conflicts get resolved? How do these different ways of learning and updating over time lie at the basis of the formation and/or modification of social norms? And how can answers to this complex of questions be put to use to motivate greater action on climate change?
The Zoom Link will be sent the day before the lecture.

Lars Chittka (Queen Mary, University of London), “The Mind of a Bee”
2021-12-16 16:00 - 17:30 | TU Berlin
Abstract: Bees have a diverse instinctual repertoire that exceeds in complexity that of most vertebrates. This repertoire allows the social organisation of such feats as the construction of precisely hexagonal honeycombs, an exact climate control system inside their home, the provision of the hive with commodities that must be harvested over a large territory (nectar, pollen, resin, and water), as well as a symbolic communication system that allows them to inform hive members about the location of these commodities. However, the richness of bees’ instincts has traditionally been contrasted with the notion that bees’ small brains allow little behavioural flexibility and learning behaviour. This view has been entirely overturned in recent years, when it was discovered that bees display abilities such as counting, attention, simple tool use, learning by observation and metacognition (knowing their own knowledge). Thus, some scholars now discuss the possibility of consciousness-like phenomena in the bees. These observations raise the obvious question of how such capacities may be implemented at a neuronal level in the miniature brains of insects. We need to understand the neural circuits, not just the size of brain regions, which underlie these feats. Neural network analyses show that cognitive features found in insects, such as numerosity, attention and categorisation-like processes, may require only very limited neuron numbers. Using computational models of the bees’ visual and olfactory systems, we explore whether seemingly advanced cognitive capacities might ‘pop out’ of the properties of relatively basic neural processes in the insect brain’s visual processing area, and their connection with the mushroom bodies, higher order learning centres in the brains of insects.
The Zoom Link will be sent the day before the lecture.

Romain Couillet (University Grenoble-Alps, France), “Random Matrices Could Steer the Dangerous Path Taken by AI but Even That Is Likely Not Enough”
2021-12-16 10:00 - 11:00 | On Zoom
Abstract:
Like most of our technologies today, AI dramatically increases the world’s carbon footprint, thereby strengthening the severity of the coming downfall of life on the planet. In this talk, I propose that recent advances in large dimensional mathematics, and especially random matrices, could help AI engage in the future economic growth. This being said, even those mitigating solutions are only temporary in regards to the imminence of collapse, which calls for drastically more decisive changes in the whole research and industry world. I will discuss these aspects in a second part and hope to leave ample time for discussion.
Hosted by Pia Bideau
The Zoom Link will be sent the day before the lecture.

POSTPONED - Distinguished Speaker Series Talk, Iain Couzin (University of Konstanz)
2021-12-09 16:00 - 17:30
Postponed to a later date (to be assessed)
More info coming soon!

Eric J. Johnson (Columbia University, US), “Can We Improve Choices by Changing How Choices Are Posed?”
2021-12-09 10:00 - 2021-12-16 11:00 | On Zoom
Abstract:
Choice architecture suggests that much of what we decide is influenced by that options are presented. This means that the choice environment can encode intelligence that will help (or can hurt) the decision maker. The talk will start by reviewing some results from choice architecture and describe how the environment can affect choice through the choice of strategy and emphasize the role of memory. I will then turn toward developments in studying choice processes including online process tracing techniques and recent developments in the application of eye-tracking using web-based cameras. Finally, I will talk about applications to presenting consumers and policy makers with information to support sustainable decisions.
Hosted by Oliver Brock

The Zoom Link will be sent the day before the lecture.

Alice Von Auersperg (University of Veterinary Medicine Vienna), “Breaking New Ground: Innovation in Birds, Primates and Human Infants”
2021-12-02 16:00 - 17:30
Breaking new ground: innovation in birds, primates and human infants
Novel behaviors that suddenly appear either as a solution to a new problem or as an alternative way to solve an existing problem allow humans and animals to deal with environmental challenges and to create new opportunities. They are thus key ingredients for developing advanced problem-solving abilities.
In order to gain a more meaningful understanding of the evolution of such innovative behavior, it is important to study it comparatively across distantly related species that face variable selection pressures.
In this talk I would like to introduce the audience to a variety of different approaches used in comparative cognition research to study both innovation rate as well as strategies underlying innovative behavior in birds and primates.
Innovating a new way to manufacture a tool or innovatively creating new tool combinations (so-called associative tool use) are believed to be particularly relevant to the onset of complex technology. Humans show a striking developmental blockage for this particular type of innovation until they are about eight years old. Interestingly, this phenomenon was discovered by applying experimental setups that were originally used on birds to human infants. I will present current avian and human developmental research on tool innovations and discuss motivational systems as well as cognitive means underlying the performances.

The Zoom Link will be sent the day before the lecture.

Kate Storrs (Justus Liebig University, Giessen), “Modelling Mid-Level Vision With Unsupervised Learning”
2021-12-02 10:00 - 11:00 | On Zoom
Abstract:
Models of vision have come far in the past 10 years. Deep neural networks can recognise objects with near-human accuracy, and predict brain activity in high-level visual regions. However, most networks require supervised training using ground-truth labels for millions of images, whereas brains must somehow learn from sensory experience alone. We have been using unsupervised deep learning, combined with computer-rendered artificial environments, as a framework to understand how brains learn rich scene representations without ground-truth information about the world. I will show how an unsupervised deep neural network trained on an artificial environment of surfaces that have different shapes, materials and lighting, spontaneously comes to encode those factors in its internal representations. Most strikingly, the model makes patterns of errors in its perception of material that follow, on an image-by-image basis, the patterns of errors made by human observers. Unsupervised deep learning may provide a coherent framework for how many perceptual dimensions form, in mid-level vision and beyond.
Hosted by Martin Rolfs
The Zoom Link will be sent the day before the lecture.

What Does the Swarm Know? Collective Intelligence Explained at the HU Lab’s MitWissenschaft Event
2021-11-25 07:00 - 20:30 | Humboldt Forum
As part of the events linked to the exhibition Nach der Natur at the Humboldt Forum, on 25 November at 7pm cluster members Jens Krause, Lea Musiolek, and Pawel Romanczuk will take part in an exciting discussion on the meaning, the study, and the challenges of collective intelligence, bringing great examples and fascinating materials. How do schools of fish move together, and how do they avoid danger? How do groups make decisions that are efficient for everyone? And how does this apply to artificial agents such as robots? Do not miss this moderated, in-person talk at the Humboldt Forum!
More information and registration instructions here

Berlin Science Week Talk, Fritz Francisco: Why Behaviour? Why We Behave the Way We Do
2021-11-10 18:00 - 19:00 | On Zoom
From French photographers, Austrian naturalists and English adventurers – all have been equally fascinated by the way we are affected by, and interact with our surroundings. It is the intricate interplay between self and others, known and unknown, present and future that makes behaviour so hard to grasp and exciting to study. Ethology, the study of animal behaviour tackled this for ages. However, it has recently undergone a change, with digital assistance, such as cameras, as well as computers and their algorithms. Yet, the underlying object of interest – behaviour – has not changed! Fritz Francisco will discuss what behaviour actually is and wish to make us see our own behaviour in a slightly different light.
Please register on the BSW website here

Berlin Science Week Talk, Ralf Kurvers and Pawel Romanczuk: Collective Intelligence or Collective Stupidity? From Fish Schools to Human Groups. Exploring swarm intelligence in fish and humans
2021-11-08 18:00 - 19:00 | On Zoom
Scientists Pawel Romanczuk and Ralf Kurvers will explore swarm intelligence in fish and humans, investigating the role of single individuals and social interactions in collective decisions, also exploring when collectives make good decisions, and when they go wrong.
In this talk, David Bierbach will present results from fascinating experiments with a clonal fish, the Amazon molly (Poecilia formosa), to test whether near-identical rearing conditions can dampen individual differences in behavior. In sharp contrast to predictions, researchers found substantial individual variation in behavior among genetically identical individuals raised under highly standardized environments.
But how does this individuality allow swarms, herds and shoals of animals to perform highly coordinated collective behaviors like synchronized movements or rapid consensus decision-making? Using a robotic fish, Bierbach and his team also explored the effects of individual differences in behavior on collective movement, migration and the emergence of group-specific behavioral patterns, and showed that individual behavior plays a crucial role in shaping collective patterns, with some animal personalities having greater impact on the group’s performance than others.
Watch the interview of David Bierbach for the BSW here.
Please register on the BSW website here

Berlin Science Week Talk, Alex Kacelnik and Marc Toussaint: Unsolved Problems in the Study of Intelligence. Learning About Intelligence, From Crows to Robots
2021-11-07 18:00 - 19:00 | On Zoom
A live discussion of Alex Kacelnik (Biologist, U Oxford) and Marc Toussaint (AI Roboticist, TU Berlin) on key problems in studying intelligence in both animals and AI systems.

Please register on the BSW website here

Berlin Science Week Talk, David Bierbach: Just Be Yourself! How Individual Differences Shape Collective Behavior. Using a Robotic Fish To Understand Collective Behavior
2021-11-06 12:00 - 13:30 | Naturkundemuseum
Do genetically identical individuals living in the same environment develop identical behaviors? And what role does individuality play in shaping the collective behavior of a flock of birds or a school of fish?
In this talk, David Bierbach will present results from fascinating experiments with a clonal fish, the Amazon molly (Poecilia formosa), to test whether near-identical rearing conditions can dampen individual differences in behavior. In sharp contrast to predictions, researchers found substantial individual variation in behavior among genetically identical individuals raised under highly standardized environments.
But how does this individuality allow swarms, herds and shoals of animals to perform highly coordinated collective behaviors like synchronized movements or rapid consensus decision-making? Using a robotic fish, Bierbach and his team also explored the effects of individual differences in behavior on collective movement, migration and the emergence of group-specific behavioral patterns, and showed that individual behavior plays a crucial role in shaping collective patterns, with some animal personalities having greater impact on the group’s performance than others.
Watch the interview of David Bierbach for the BSW here
Please register on the BSW website here

Berlin Science Week Talk, Guillermo Gallego: Giving Robots T-Rex-Inspired Eyes
2021-11-02 06:00 - 19:00 | On Zoom
Ever heard of neuromorphic cameras? They are bio-inspired sensors that mimic the transient visual pathway. These cameras do not acquire pictures or full images as a normal video camera. Instead, they sense only intensity changes at every pixel as they occur with microsecond resolution (called “events” or “spikes”). These cameras offer many advantages compared to traditional image-based cameras, such as high speed, a very high dynamic range, and low power consumption, and are slowly finding their way into computer vision and robotics applications. Come to learn more about these novel cameras!
Please register on the BSW website here

Cameron Buckner (Univ. of Houston), Imagination and the Prospects for Empiricist Artificial Intelligence
2021-10-28 16:00 - 17:30 | On Zoom
Abstract: In current debates over deep-neural-network-based AI, deep learning researchers have adopted the mantle of philosophical empiricism and associationism, and its critics have taken up the side of philosophical rationalism and nativism. These rationalist critics, however, often interpret associationism and empiricism in a way which is too caricatured to fit the views of any significant thinker in the empiricist tradition. In particular, most empiricists were faculty theorists; while they generally eschewed innate knowledge, they appealed to a variety of domain-general innate faculties like memory, imagination, and attention to explain how the mind abstracts knowledge from experience. This dynamic is vividly illustrated in a centuries-displaced debate between David Hume and Jerry Fodor over the role of imagination in cognitive architecture. Fodor famously claimed that the ability to synthesize novel ideas and create new compositional representations is required for cognition. Fodor applauds Hume for agreeing on these points, but criticizes Hume’s use of the imagination to discharge these burdens. Fodor claims that such an appeal for an associationist is “cheating”, and notes that Hume never explains how the empiricist imagination actually works, merely assigning it a variety of essential functions to perform “as if by magic”.
More recently, deep learning researchers have claimed to create generative deep neural network models that perform one or more of the roles ascribed to the imagination by cognitive psychology and neuroscience. In this talk, I canvass these models and their achievements (especially Generative Adversarial Networks, Variational Autoencoders, and Generative Transformers) to arbitrate this dispute between Humean empiricism and Fodorian rationalism. Of particular interest will be various methods of latent space vector interpolation which appear to allow these models to create novel compositional representations, whether these methods still count as associationist in nature, and whether the purportedly crucial distinction between interpolation and extrapolation remains viable in the higher-order dimensional spaces over which these models operate.
Click here for Cameron Buckner’s Bio.
The Zoom Link will be sent the day before the lecture.

Mental Health in PhD Students. The Role of Graduate Schools, the Integration of International Students and Impostor Syndrome
2021-10-19 10:00 - 18:00 | On Zoom
Scholar Minds, in collaboration with Science of Intelligence and the Berlin School of Mind and Brain, would like to invite you to a special conference on the topic “Mental Health in PhD students: The role of graduate schools, the integration of international students and impostor syndrome”. The event will take place online on October 19th from 10AM-6PM. The event will be held in English and is free of charge.
The event will incorporate keynotes, workshops, and hackathons to work on improving the current situations for early career researchers. The conference was initiated by Scholar Minds & the Berlin Cluster of Excellence “Science of Intelligence” and is a collaborative project of several mental health initiatives across Germany.
We are pleased to present a keynote lecture by Gordon Feld who will give an overview of how Germany needs to address the structural challenges of the academic system to best promote early career researchers.
Further, we offer workshops on a selection of recurrent topics for early career researchers such as impostor syndrome (Mental Health Collective), stress management (Innerminder) or how to coordinate your projects (Scholar Minds).
Another highlight of the conference will be several hackathons tackling current challenges of the academic world such as how to improve the situation for international early career researchers in Germany (MATH+), bridging mental health and academic (N2 Network) or how the perfect graduate school (Dragonfly Mental Health) could look like.
Last but not least, there will be a panel discussion on the topic “Towards sunnier days: How to overcome the mental health crisis in academia?” opening the stage for different players of the academic system such as Jule Specht (Humboldt-Universität zu Berlin), Martin Grund (Max Planck Institute Leipzig), Ralf Kurvers (Science of Intelligence), and Aite Kashef (Lise-Meitner-Gesellschaft).
Please use this link to register: https://bit.ly/mh-event2021
If you have any questions, please contact scholar-minds@charite.de.
Homepage: http://www.scholar-minds.net/
E-Mail: scholar-minds@charite.de
Twitter: @BerlinMinds

Tim Landgraf (Science of Intelligence), “The Hidden Shallows of Explaining Deep Models”
2021-10-14 16:00 - 17:30
Abstract:
In the cognitive-, behavioral- or neuro-sciences we often match a computational model to observations and then, analyzing the model, hope to find results that generalize to the underlying system. With deep neural networks (DNNs) quite powerful function approximators are available that can be fitted to huge data sets, accelerated by cheap hardware and elaborate software stacks. It seems tempting to use DNNs as a default model but how do we analyze their behavior? DNNs are essentially black boxes: although we can write down the network function, it does not tell us anything about the features it extracts or about the rules animals employ when interacting with one another. In recent years, a new field has emerged and proposed a variety of methods to explain deep neural networks. In my talk, I will (1) introduce you to some ideas that explanation algorithms are based on, (2) show how quantifying their performance on proxy tasks can be misleading, (3) provide an intuition why some popular proponents of these algorithms won’t work in deep networks, (4) will introduce you to a new dataset generator that enables us to create challenging problems to test and evaluate explanation methods and (5) discuss why we need extensive (and expensive) user studies to investigate whether explanation methods actually provide additional information that would be available from the model’s outputs alone. I hope to stimulate a discussion about the use-cases for which “explain a DNN to discover the hidden rules of my study system” may work, and in which it may not.
The Zoom Link will be sent the day before the lecture.

Tina Klüwer (Science of Intelligence), AI Director Science & Startups
2021-09-23 10:00 - 11:00 | On Zoom
Through a talk followed by a discussion and Q&A, AI Director at Science & Startups Tina Klüwer will explore the joint programmes and resources offered by Berlin’s universities to those wishing to successfully start and develop a company, also explaining what support is available.
BIO:
Dr. Tina Klüwer is a recognized expert, manager and technical ambassador for the topic of Artificial Intelligence and its implementation in business.
Currently, she leads the AI project of Science & Startups, the network of startup services of the Berlin universities and the Charité Universitätsmedizin Berlin.
Before starting the project, she was founder and CEO of parlamind GmbH, a company for automation in customer service through AI and language processing. After the successful exit of the business, she led its four sister companies as technical director. Previously she worked as a researcher at the German Research Center for Artificial Intelligence (DFKI), the Bonn University and Freie Universität Berlin, Germany for over ten years. She received her PhD thesis in computational linguistics from Saarland University.
Tina Klüwer is board member of the German KI Bundesverband (Federal Association for AI), Chairwoman of the Technological Sovereignty Advisory Council of the Federal Ministry of Education and Research, as well as part of the coordination group for AI Standardization by Federal Ministry for Economic Affairs and Energy (BMWI) and DIN. She was also an expert member of the German Bundestag’s two-year Enquete Commission on Artificial Intelligence.

The Zoom Link will be sent the day before the lecture.

SCIoI Open Day
2021-09-16 14:00 - 17:00
Interested in SCIoI’s activities? Want to know more about how to apply for a research position at SCIoI?
At this all-digital event, prospective applicants as well as other interested persons can visit the cluster, have a (virtual) look around the spaces and facilities, meet researchers and staff, and get a general feel for the place.
Click here to attend, Thursday 16 September between 2 and 5pm!

Workshop for Early-Career Researchers: “How To Review in Interdisciplinary Research”
2021-08-31 14:00 - 17:00
One major part of the scientific workflow is the publication of scientific results. An essential step on the way to the published item is the peer-review process, in which other scientists evaluate submitted work. Their views, recommendations and critique often strongly influence the decision of whether a submitted item is published or not. We assume that these peers are themselves experts on the topic they are reviewing, and while this is most often true for reviews made on items that lay within a certain discipline, more and more research is done across the boundaries of scientific disciplines, which produces the publication of research items, that may contain parts from vastly different scientific areas.
In addition, many disciplines have their own “styles” when it comes to the review process.
So, what to do as a reviewer when asked to assess a scientific work, that lies only partially within your own area of expertise? This workshop will tackle this question through presenting experts’ views from different scientific areas, a general discussion stage as well as a DIY part in which we will work on a guideline for interdisciplinary peer-reviews.
Registration deadline: 16 August 2021
Organizers: Pia Bideau, David Bierbach, Andrzej Banburski

Opening of the Humboldt Labs Exhibition "After Nature"
2021-07-20 10:00 - 18:00
Come see our videos and materials at the exhibition “After Nature” at the brand-new Humboldt Forum!
Entry is free for everyone, so we hope to see you all there on 20 July! Tickets for the exhibitions will be available from 13 July 2021. You can book your ticket online or via +49 30 99 211 89 89, or visit www.humboldtforum.org.
Address: Schlossplatz, 10178 Berlin

Heiko Hamann (Science of Intelligence), “Group Performance and Scalability in Collective Systems”
2021-07-15 16:00 - 17:30
Abstract:
Scalability can be challenging in groups of collaborating agents, such as animals, robots, or computers. While a small group may work efficiently together, a bigger group may be slowed down due to increased needs to communicate and synchronize or due to other scarce shared resources. We go through a number of examples for observed system performance over system size and find common features. Based on these findings, we define a simple mathematical model that catches these main features and can generically be applied to different domains, such as robotics, computing, and sensor networks or possibly even human groups. We end by speculating a bit of what might be common to all of these systems and what might be the underlying drivers for the limits of scalability.
The Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions)

Dimitri Coelho Mollo (SCIoI), “Modelling Intelligence: The Good, the Bad, and the Plural”
2021-07-15 10:00 - 11:00
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.
The Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions)

Guillermo Gallego (Science of Intelligence), “Current Status of Event-Based Vision Research”
2021-07-01 16:00 - 17:30
Abstract:
Event-based cameras, also called neuromorphic cameras or silicon retinas, are novel vision sensors that mimic functions from the human retina and offer potential advantages over traditional cameras (low latency, high speed, high dynamic range, bandwidth savings, low power, etc.). My previous talk was about event-based cameras for Spatial AI. In this talk I will provide an overview of how event-based cameras are becoming more and more widely spread in multiple applications (monitoring, tracking, counting, recognition, etc.).
The Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions)
