My name is Gero Hillebrandt, I live in Germany and hold an B.Sc. in Physics and an M.Sc. in Business Mathematics from the MLU Halle-Wittenberg. I am mainly interested in the various challenges of software development, ML/AI and quantitative finance.
This blog is a fun project where I automated daily summaries about the field of AI. Any future manual posts will be tagged with “by gh”. See this post for more details.
Notice: This post has been automatically generated and does not reflect the views of the site owner, nor does it claim to be accurate. Possible consequences of current developments The quality of AAAI reviews is atrocious Benefits: Improving the quality of AAAI reviews could lead to a more rigorous evaluation of AI research. Enhanced reviews foster higher standards in submissions, promoting the dissemination of high-quality, reliable findings. Academics and practitioners would benefit from insights that emerge from well-evaluated papers, contributing to more effective AI systems and applications....
Notice: This post has been automatically generated and does not reflect the views of the site owner, nor does it claim to be accurate. Possible consequences of current developments No Google or Meta at EMNLP 2025? Benefits: The absence of major tech companies like Google and Meta at an event like EMNLP could open doors for smaller, independent researchers and startups. This could foster a more diverse range of ideas and innovations, as these organizations often set the research agenda, sometimes overshadowing independent voices....
Notice: This post has been automatically generated and does not reflect the views of the site owner, nor does it claim to be accurate. Possible consequences of current developments Which papers HAVEN’T stood the test of time? Benefits: Identifying papers that have not stood the test of time can help researchers and practitioners avoid outdated methodologies or theories. This allows for the allocation of resources to more fruitful research directions....
Notice: This post has been automatically generated and does not reflect the views of the site owner, nor does it claim to be accurate. Possible consequences of current developments Larry Ellison: Inference is where the money is going to be made. Benefits: The focus on inference allows businesses to leverage machine learning models efficiently. With advancements in inference capabilities, companies can quickly process data and generate insights, leading to faster decision-making and improved services....
Notice: This post has been automatically generated and does not reflect the views of the site owner, nor does it claim to be accurate. Possible consequences of current developments Math Foundations to Understand Convergence Proofs Benefits: Understanding convergence proofs provides a solid foundation for evaluating algorithms’ performance and reliability. It aids in ensuring that methods like gradient descent reliably find optimal solutions in machine learning and optimization tasks. This understanding can foster the development of more efficient computational techniques, enhancing the capabilities of various technologies that rely on iterative algorithms....
Notice: This post has been automatically generated and does not reflect the views of the site owner, nor does it claim to be accurate. Possible consequences of current developments NVIDIA Blackwell Ultra crushes MLPerf Benefits: The Blackwell Ultra architecture represents significant advancements in GPU technology, enabling faster and more efficient processing for machine learning tasks. This increase in computational power allows developers and researchers to complete complex simulations and training of large models in shorter timeframes....
Notice: This post has been automatically generated and does not reflect the views of the site owner, nor does it claim to be accurate. Possible consequences of current developments IJCNLP-AACL 2025: Paper Reviews (ARR July 2025 Cycle) Benefits: The review process for the IJCNLP-AACL conference could lead to improved research standards, fostering innovation in natural language processing (NLP). The public availability of papers can facilitate collaboration and cross-pollination of ideas among researchers, ultimately driving forward advancements in AI technologies that could benefit various industries....
Notice: This post has been automatically generated and does not reflect the views of the site owner, nor does it claim to be accurate. Possible consequences of current developments LLMs Play a Cooperative Card Game, Coordination Without Communication Benefits: The ability for large language models (LLMs) to coordinate in a cooperative card game without direct communication showcases their potential for enhancing artificial intelligence’s understanding of teamwork and strategy. This could lead to advancements in collaborative robotics, where machines can make decisions based on mutual understanding rather than explicit instructions, potentially increasing efficiency in teamwork scenarios, from manufacturing to healthcare....
Notice: This post has been automatically generated and does not reflect the views of the site owner, nor does it claim to be accurate. Possible consequences of current developments Why Language Models Hallucinate - OpenAI Pseudo Paper Benefits: Understanding why language models hallucinate can improve AI reliability, leading to more accurate and contextually appropriate responses. This research can enhance user trust, facilitate informed decision-making, and improve the overall user experience in applications like customer support and content generation....
Notice: This post has been automatically generated and does not reflect the views of the site owner, nor does it claim to be accurate. Possible consequences of current developments The apparent randomness of residual block design Benefits: The random nature of residual block design can foster innovation by allowing a diverse range of architectures that potentially improve deep learning model performance. By accommodating various connections and pathways within the network, this randomness can lead to improved feature learning and better generalization....