While large language models (LLMs) have demonstrated remarkable capabilities in extracting data and generating connected responses, there are real questions about how these artificial intelligence (AI) models reach their answers. At stake are the potential for unwanted bias or the generation of nonsensical or inaccurate “hallucinations,” both of which can lead to false data.
That’s why SMU researchers Corey Clark and Steph Buongiorno are presenting a paper at the upcoming IEEE Conference on Games, scheduled for August 5–8 in Milan, Italy. They will share their creation of a GAME-KG framework, which stands for “Gaming for Augmenting Metadata and Enhancing Knowledge Graphs.”
The research is published on the arXiv preprint server.
At my company, data.world, we’ve been building the foundation of our platform for AI since our founding in 2016. We knew back then that data would be the essential feedstock of AI, the oxygen of its metabolism. And in a world where data grows exponentially, data silos, data errors, missing context, and sheer data deluge are the bane of many companies and institutions. Our mission is to transform data into tools of institutional cognition, the most recent advance of which is our AI Context Engine™. The most important product we’ve ever launched, this tool makes corporate data now inaccessible to AI an essential part of companies’ strategic toolkit. The chat-with-your-data future has never been closer than it is right now, and our AI Context Engine is our fastest new product takeoff in our company’s history.
So back to the journey that we are all on. Let’s explore the essentials of Nearer together in summary.
Despite these challenges, the potential rewards of edge AI are driving innovation in model optimization, device management and security solutions. As these advancements continue, the barriers to edge AI deployment are gradually being lowered, paving the way for its widespread adoption across industries.
Ultimately, edge computing democratizes AI by removing it from complex, costly cloud execution and moving it to the local, accessible devices companies already own and use. This means that small and medium-sized businesses can gain access to tools previously reserved for much larger companies.
As we move forward, AI in business and edge computing are intertwined. The ebb and flow of progress is already noticeable in edge computing applications, and AI will continue this trajectory. As edge devices become more powerful, the proliferation of intelligent applications that operate seamlessly at the edge will transform industries.
Researchers at the University of Texas have developed an AI that predicted 70% of earthquakes during a trial in China, indicating potential for future quake risk mitigation.
The AI, trained on seismic data, also ranked first in an international competition, underscoring its effectiveness and opening doors for further enhancements in regions like California and Texas.
Summary: A new machine learning model, AutMedAI, can predict autism in children under two with nearly 80% accuracy, offering a promising tool for early detection and intervention.
The model analyzes 28 parameters available before 24 months, such as age of first smile and eating difficulties, to identify children likely to have autism. Early diagnosis is crucial for optimal development, and further validation of the model is underway.
