AI Modeling AI Research Computer Scientist 
MARCH 18, 2025
This, more or less, is the line being taken by AI researchers in a recent survey. Given that AGI is what AI developers all claim to be their end game , it's safe to say that scaling is widely seen as a dead end. The premise that AI could be indefinitely improved by scaling was always on shaky ground.
Unite.AI
DECEMBER 20, 2023
Recent advancements in AI emphasize the need for improved reproducibility due to the rapid pace of innovation and the complexity of AI models. Thus, reproducibility becomes a shared responsibility among researchers to ensure that accurate findings are accessible to a diverse audience.
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Unite.AI
OCTOBER 25, 2023
When pitted against established models, such as those underlying popular chatbots, this new neural network displayed a superior ability to fold newly learned words into its existing lexicon and use them in unfamiliar contexts. For nearly four decades, this question has seen AI researchers at loggerheads.
AI Impacts
DECEMBER 18, 2023
Korotkiy ) 1951-present: Computer scientists consider whether a sufficiently powerful misaligned AI system will escape containment and end life on Earth. Foundational computer scientist Alan Turing in 1951. The message will arrive at its destination in 2029. Photo by S.
Marktechpost
SEPTEMBER 14, 2023
Machine learning models have become indispensable tools in various professional fields, driving applications in smartphones, software packages, and online services. However, the complexity of these models has rendered their underlying processes and predictions increasingly opaque, even to seasoned computer scientists.
IBM Journey to AI blog
APRIL 18, 2024
The exact nature of general intelligence in AGI remains a topic of debate among AI researchers. Most experts categorize it as a powerful, but narrow AI model. Current AI advancements demonstrate impressive capabilities in specific areas. A key trend is the adoption of multiple models in production.
Marktechpost
SEPTEMBER 16, 2023
This means recognizing how social and historical factors influence data collection and clinical AI development. Computer scientists may not fully grasp the social and historical aspects behind the data they use, so collaboration is essential to make AI models work well for all groups in healthcare.
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