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This, more or less, is the line being taken by AIresearchers in a recent survey. Given that AGI is what AIdevelopers all claim to be their end game , it's safe to say that scaling is widely seen as a dead end. You can only throw so much money at a problem.
Last Updated on April 6, 2023 by Editorial Team Author(s): LucianoSphere Originally published on Towards AI. Hinton, a British-Canadian computerscientist and cognitive psychologist, is considered… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter.
Korotkiy ) 1951-present: Computerscientists consider whether a sufficiently powerful misaligned AI system will escape containment and end life on Earth. Foundational computerscientist Alan Turing in 1951. The message will arrive at its destination in 2029. Photo by S.
Dissenting Voices: The Debate Over AI's Potential Harm Contrarily, there exists a significant portion of the AI community that considers these warnings as overblown. Yann LeCun, NYU Professor and AIresearcher at Meta, famously expressed his exasperation with these ‘doomsday prophecies'.
The researchers suggest that instead of just fixing biased data or discarding it, we should use an “artifacts” approach. This means recognizing how social and historical factors influence data collection and clinical AIdevelopment. All Credit For This Research Goes To the Researchers on This Project.
However, if AGI development uses similar building blocks as narrow AI, some existing tools and technologies will likely be crucial for adoption. The exact nature of general intelligence in AGI remains a topic of debate among AIresearchers. The skills gap in gen AIdevelopment is a significant hurdle.
Announcing the launch of the Medical AIResearch Center (MedARC) Medical AIResearch Center (MedARC) announced a new open and collaborative research center dedicated to advancing the field of AI in healthcare.
Compute” regulation : Training advanced AI models requires a lot of computing, including actual math conducted by graphics processing units (GPUs) or other more specialized chips to train and fine-tune neural networks. Cut off access to advanced chips or large orders of ordinary chips and you slow AI progress.
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