Category: responsible-ai
-
Multi-Agent Arena: Insights from London Great Agent Hack 2025
Multi-Agent Arena: Insights from London Great Agent Hack 2025 What mattered: robust agents, glass-box reasoning, and red-team resilience The post Multi-Agent Arena: Insights from London Great Agent Hack 2025 appeared first on Towards Data Science. Erika G. Gonçalves Go to original source
-
The Shadow Side of AutoML: When No-Code Tools Hurt More Than Help
The Shadow Side of AutoML: When No-Code Tools Hurt More Than Help Automl has become the gateway drug to machine learning for many organizations. It promises exactly what teams under pressure want to hear: you bring the data, and we’ll handle the modeling. There are no pipelines to manage, no hyperparameters to tune, and no…
-
Uh-Uh, Not Guilty
Uh-Uh, Not Guilty When the six merry murderesses of the Cook County Jail climbed the stage in the Chicago musical, they were aligned on the message: They had it coming, they had it coming all along. I didn’t do it. But if I’d done it, how could you tell me that I was wrong? And the part of…
-
We Need a Fourth Law of Robotics in the Age of AI
We Need a Fourth Law of Robotics in the Age of AI Artificial Intelligence has become a mainstay of our daily lives, revolutionizing industries, accelerating scientific discoveries, and reshaping how we communicate. Yet, alongside its undeniable benefits, AI has also ignited a range of ethical and social dilemmas that our existing regulatory frameworks have struggled…
-
The Urgent Need for Intrinsic Alignment Technologies for Responsible Agentic AI
The Urgent Need for Intrinsic Alignment Technologies for Responsible Agentic AI Advancements in agentic artificial intelligence (AI) promise to bring significant opportunities to individuals and businesses in all sectors. However, as AI agents become more autonomous, they may use scheming behavior or break rules to achieve their functional goals. This can lead to the machine…
-
Fighting Fraud Fairly: Upgrade Your AI Toolkit
Fighting Fraud Fairly: Upgrade Your AI Toolkit A practical approach to address bias in AI systems Photo by the author As sophisticated AI systems are increasingly used in decision-making, ensuring fairness has become a priority, with a growing need to prevent algorithms from disproportionately affecting vulnerable groups in sensitive areas like the justice or educational system. One…
-
Learning from Machine Learning | Sebastian Raschka: Mastering ML and Pushing AI Forward Responsibly
Learning from Machine Learning | Sebastian Raschka: Mastering ML and Pushing AI Forward Responsibly Sebastian Raschka has helped demystify deep learning for thousands through his books, tutorials and teachings Sebastian Raschka has helped shape how thousands of data scientists and machine learning engineers learn their craft. As a passionate coder and proponent of open-source software,…
-
A New Approach to AI Safety: Layer Enhanced Classification (LEC)
A New Approach to AI Safety: Layer Enhanced Classification (LEC) LEC surpasses best in class models, like GPT-4o, by combining the efficiency of a ML classifier with the language understanding of an LLM Imagine sitting in a boardroom, discussing the most transformative technology of our time — artificial intelligence — and realizing we’re riding a rocket with no reliable safety…
-
Smaller is smarter
Smaller is smarter Concerns about the environmental impacts of Large Language Models (LLMs) are growing. Although detailed information about the actual costs of LLMs can be difficult to find, let’s attempt to gather some facts to understand the scale. Generated with ChatGPT-4o Since comprehensive data on ChatGPT-4 is not readily available, we can consider Llama 3.1…