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May 12, 2022 | Written by Zoi Kaoudi

A framework to efficiently create training data for optimizers

A demo paper co-authored by Robin van de Water, Francesco Ventura, Zoi Kaoudi, Jorge-Arnulfo Quiane-Ruiz, and Volker Markl on “Farming Your ML-based Query Optimizer’s Food” presented at the virtual conference ICDE 2022 has won the best paper award.

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More and more data will be generated by an exponentially increasing number of IoT devices.
Copyright: iStock
May 12, 2022 | Written by Volker Markl

Nebulastream aims to unify the Cloud, the Edge and the Sensors

NebulaStream, the novel, general-purpose, end-to-end data management system for the IoT and the Cloud, recently announced the release of NebulaStream 0.2.0., the closed-beta release. The System is developed and explored by a team of BIFOLD researchers led by Prof. Dr. Volker Markl. It addresses the unique challenges of the “Internet of Things” (IoT).

May 05, 2022 | Written by Volker Markl

ACM SIGMOD Research Highlight Award

The paper “Efficient Control Flow in Dataflow Systems: When Ease-of-Use Meets High Performance” of six BIFOLD researchers was honored with a 2021 ACM SIGMOD Research Highlights Award. 

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Prof. Wojciech Samek receives his certificate of appointment from the President of TU Berlin, Prof. Geraldine Rauch.
Copyright: private
Apr 28, 2022 | Written by Wojciech Samek

“I want to move beyond purely ‘Explaining’ AI”

BIFOLD researcher Dr. Wojciech Samek has been appointed Professor of Machine Learning and Communications at TU Berlin with effect from 1 May 2022. Professor Samek heads the Department of Artificial Intelligence at the Fraunhofer Heinrich-Hertz-Institute. His goal is to further develop three areas: explainability and trustworthiness of artificial intelligence, the compression of neural networks, and so-called federated leaning. He aims to focus on the practical, methodological, and theoretical aspects of machine learning at the interface to other areas of application.

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Mar 17, 2022 | Written by Volker Markl

The Art of Entanglement

The Berlin Institute for the Foundations of Learning and Data (BIFOLD), together with the Science Gallery at Technische Universität Berlin, has announced a new artist in residence program called “Art of Entanglement”. The goal of the program is to combine artistic and scientific perspectives of artificial intelligence.
The program is endowed with a gross total of 30,000 euros. The open call was published on sciencegallery.submittable.com. Applications are open to artists based in Berlin who are interested in working intensively with topics and scientists in the fields of Big Data Management and Machine Learning as well as their intersection.
The selected artist will have the opportunity to realize an artistic project of their choice at BIFOLD, the national Berlin Center of Excellence for Artificial Intelligence at TU Berlin, and the Science Gallery platform.

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The seed project established by Danh Le Phuoc focuses on IoT and Edge computing.
Copyright: pixabay
Mar 15, 2022 | Written by Danh Le Phuoc

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.

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Inverse molecular design reverses the structure-property relationship.
Copyright: Pixabay
Feb 21, 2022 | Written by Klaus-Robert Müller

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.

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Schematic representation of the different methods.
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Feb 17, 2022 | Written by Marina Marie-Claire Höhne (Née Vidovic)

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.

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The more features there are in the data, the more difficult machine learning tasks become.
Copyright: Unsplash
Dec 21, 2021 | Written by Robert Vandermeulen

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.

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Being able to predict and model the individual steps of a chemical reaction at the molecular or even atomic level is a long-held dream of many material scientists.
Copyright: istock.com/peterscheiber.media
Dec 15, 2021 | Written by Klaus-Robert Müller

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.