Eine Einführung in das Hacking von Neuronalen Netzen
jate
Ich will den Zuhörerinnen einen Überblick über die aktuellen Möglichkeiten geben, wie Neuronale Netze angegriffen und manipuliert werden können. Das Ziel des Vortrags ist es, verschiedene Angriffe zu erklären und anhand von Beispielen zu veranschaulichen. Dies dient auch dazu, die Funktionsweise neuronaler Netze besser zu verstehen und ihre Limitierungen aufzuzeigen. Abschließend zeige ich, welche Maßnahmen ergriffen werden können, um diese Angriffe zu erkennen oder zu verhindern.
Can Artificial Intelligence become conscious?
Joscha
Despite the rapid progress of AI capabilities, the core question of Artificial Intelligence seems to be still unanswered: What does it take to create a mind? Let us explore the boundaries of AI: sentience, self awareness, and the possibility of machine consciousness.
Estimating the costs of algorithms for attacks and defense applications
Alessandro Luongo
In in this talk we explore the potential ramifications of quantum computing in the field of cybersecurity We'll delve into two critical aspects: the application of quantum machine learning algorithms for defence and the impact of quantum attacks on cryptography and post-quantum cryptography for offence. We'll present insights on the theoretical advantages of quantum algorithms, improvements in factoring large numbers, and the impacts of post-quantum crypto attacks. While the hype around quantum technologies is growing, the estimates in the resources needed to run a quantum algorithm and the current number of qubits pose caution in the enthusiasm. The limitations in terms of available qubits, error rates, and scalability are critical factors that need to be considered when assessing the real-world applicability of quantum computing.
Getting first-hand insights into the impact of machine learning on life science
Jan Gebauer
In 2020, the scientific community was shaken when the results of a special contest for protein prediction, known as the Critical Assessment of Protein Structure Prediction (CASP), were revealed. A relatively new competitor emerged as the champion, surpassing all other teams that had been participating in the game for decades. This new competitor was Google and their predictor was a neuronal network called "AlphaFold". Their new approach caused significant waves in the field of structural biology, even capturing the attention of the mainstream media. Several news channels featured reports on AlphaFold, with one German magazine, "Der Spiegel," declaring that "The year 2020 will be known [...] as the year when machines began to outstrip us in research." Join me as we explore the background behind this transformative development and assess the magnitude of machine learning's impact on science, with a particular focus on structural biology.