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Successful Seed Funding For IoT Projects
TU Berlin, Siemens AG (SAG) and University of Oxford (UoO) recently partnered in a trilateral seed fund to stimulate joint research project bids. One of the altogether five successful seed projects was initiated by BIFOLD Junior Fellow Dr. Danh Le Phuoc, a DFG principle investigator at TU Berlin, and focuses on IoT and Edge computing, in particular for smart factory, autonomous vehicle, smart city and smart energy network. The seed projects will run during 2022 and are aimed at developing large-scale public funding bids.
Function determines Form
An interdisciplinary research group has developed an algorithm which uses AI to implement inverse chemical design and thus generates targeted molecules based on their desired properties. The BIFOLD researchers expect that such algorithms, used in concert with other AI-driven approaches and quantum chemical methods, can greatly accelerate the search for new molecules and materials in many practical areas.
Shining a light into the Black Box of AI Systems
In the paper “NoiseGrad — Enhancing Explanations by Introducing Stochasticity to Model Weights,” to be presented at the 36th AAAI-22 Conference on Artificial Intelligence, a team of researchers, among them BIFOLD researchers Dr. Marina Höhne, Shinichi Nakajima, PhD, and Kirill Bykov, propose new methods to reduce visual diffusion of the different explanation methods, which have shown to make existing explanation methods more robust and reliable.
Lifting the curse of dimensionality for statistics in ML
The paper “Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation” by BIFOLD researcher Dr. Robert A. Vandermeulen and his colleague Dr. Antoine Ledent, Technical University Kaiserslautern, was presented at the Conference on Neural Information Processing Systems (NeurIPS 2021). Their paper provides the first solid theoretical foundations for applying low-rank methods to nonparametric density estimation.
Tracking spooky action at a distance
The use of AI in classical sciences such as chemistry, physics, or mathematics remains largely uncharted territory. Researchers from the Berlin Institute for the Foundation of Learning and Data (BIFOLD) at TU Berlin and Google Research have successfully developed an algorithm to precisely and efficiently predict the potential energy state of individual molecules using quantum mechanical data. Their findings, which offer entirely new opportunities for material scientists, have now been published in the paper “SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects” in Nature Communications.
Benchmarking Neural Network Explanations
Neural networks have found their way into many every day applications. During the past years they reached excellent performances on various largescale prediction tasks, ranging from computer vision, language processing or medical diagnosis. Even if in recent years AI research developed various techniques that uncover the decision-making process and detect so called “Clever Hans” predictors – there exists no ground truth-based evaluation framework for such explanation methods. BIFOLD researcher Dr. Wojciech Samek and his colleagues now established an Open Source ground truth framework, that provides a selective, controlled and realistic testbed for the evaluation of neural network explanations. The work will be published in Information Fusion.
Two BIFOLD papers ranked as ESI Highly Cited and Hot Papers
Two machine learning papers by BIFOLD researchers received the “Essential Science indicators” (ESI) “Highly Cited” and “Hot Papers” labels for their impact in the science community.
Learning about population health from Twitter texts
Is it possible to learn about the health status of a population and potential side effect of medicationsby analyzing social media conversations? BIFOLD researchers tackled the challenge of making social media posts of medical laypersons concerning diseases and medications understandable for machines. At the BioCreative VII Challenge Evaluation Workshop 2021, they recently explored how a combination of background knowledge and a language transformer model can increase the precision of medical information extraction from Twitter texts.
BIFOLD researchers honored with BBBAW membership
At the “Einsteintag 2021” event on November 26, which honored Albert Einstein – prominent member of a predecessor institution of the Berlin-Brandenburg Academy of Sciences and Humanities (BBAW) – both BIFOLD Co-Director Volker Markl and BIFOLD Fellow Frank Noé were announced as new BBAW members.
Machine Learning consultation
Machine learning (ML) and artificial intelligence (AI) have permeated the sciences and large parts of working life. Today many people use machine learning techniques without being a proven expert. Consequently, many questions and problems arise while using these techniques. BIFOLD accommodates distinguished machine learning experts from different areas and offers a weekly consultation on machine learning for students, but also for companies and institutions.