Thursday Morning Talk

Abstract: Rasmus Rothe is Co-Founder at Merantix, the Artificial Intelligent Venture Studio. In this talk he will give insight into how a deep tech startup is built via ideation, incubation and scaling, and the specifics and challenges of working with technology AI in the process. BIO: Rasmus Rothe is the co-founder and CTO of Berlin-based Merantix,

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

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

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

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

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,

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,

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,

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

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.

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

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