Hosted by ztudium Group, the Businessabc AI Global Summit convenes over 1,300 global policymakers, industry executives, and academics to feature LeoAI and AdaAI—sophisticated AI agents modeled on Leonardo da Vinci and Ada Lovelace. Trained on their original writings, these agents deliver keynote insights into creativity, ethical frameworks, and human-centric AI innovation.
Key points
LeoAI and AdaAI are 3D spatial computing agents trained on original writings of da Vinci and Lovelace, enabling immersive, historically grounded AI keynotes.
Desdemona humanoid robot concert leverages SingularityNET’s decentralized intelligence to stream a transatlantic performance, showcasing real-time human-AI collaboration.
Businessabc AI Global Index provides a live, interactive platform tracking AI’s evolution across business, society, governance, and ethics with real-time data visualizations.
Why it matters:
This summit demonstrates how ethically engineered AI agents integrate historical creativity with modern technology to shape future governance and innovation frameworks.
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Academy
Artificial Intelligence
Artificial intelligence (AI) refers to computational systems designed to perform tasks that normally require human intelligence, such as learning, reasoning, perception, and language understanding. AI encompasses a range of technologies that enable machines to analyze data, recognize patterns, and make predictions or decisions with minimal human intervention.
AI can be broadly categorized into:
- Machine Learning: Algorithms that improve performance by learning from data; includes supervised, unsupervised, and reinforcement learning.
- Deep Learning: A subset of machine learning using neural networks with many layers to model complex patterns in large datasets.
- Natural Language Processing (NLP): Techniques that allow machines to understand, interpret, and generate human language.
- Computer Vision: Methods for interpreting visual information from images or videos.
Key Components:
- Data: High-quality, representative datasets are essential for training accurate AI models.
- Algorithms: Mathematical procedures that process data to learn patterns and make predictions.
- Computing Power: Specialized hardware (e.g., GPUs) accelerates model training and inference.
- Evaluation Metrics: Measures such as accuracy, precision, recall, and F1 score assess model performance.
Applications in Longevity Science: AI accelerates drug discovery by analyzing molecular structures and predicting biological effects, helps identify biomarkers of aging, and optimizes personalized health interventions. By processing large-scale genomic and clinical data, AI supports the development of therapies to extend healthspan and monitor age-related disease progression.
Benefits and Challenges: AI offers rapid insights and automation, reducing research time and costs. However, challenges include data privacy, algorithmic bias, and the need for multidisciplinary collaboration to ensure ethical and safe deployment.
For longevity enthusiasts, understanding AI empowers you to appreciate how computational tools drive breakthroughs in aging research, enabling tailored interventions and accelerating the journey toward healthier, longer lives.