AAAI survey finds that most respondents are sceptical that the technology underpinning large-language models is sufficient for artificial general intelligence.
>"More than three-quarters of respondents said that enlarging current AI systems ― an approach that has been hugely successful in enhancing their performance over the past few years ― is unlikely to lead to what is known as artificial general intelligence (AGI). An even higher proportion said that neural networks, the fundamental technology behind generative AI, alone probably cannot match or surpass human intelligence. And the very pursuit of these capabilities also provokes scepticism: less than one-quarter of respondents said that achieving AGI should be the core mission of the AI research community.
>"More than three-quarters of respondents said that enlarging current AI systems ― an approach that has been hugely successful in enhancing their performance over the past few years ― is unlikely to lead to what is known as artificial general intelligence (AGI). An even higher proportion said that neural networks, the fundamental technology behind generative AI, alone probably cannot match or surpass human intelligence. And the very pursuit of these capabilities also provokes scepticism: less than one-quarter of respondents said that achieving AGI should be the core mission of the AI research community.
Scientists are exploring the capabilities of the DeepSeek-R1 AI model, released by a Chinese firm. This open and cost-effective model performs comparably to industry leaders in solving mathematical and scientific problems. Researchers are leveraging its accessibility to create custom models for specific disciplines, although it still struggles with some tasks.
A detailed explanation of the Transformer model, a key architecture in modern deep learning for tasks like neural machine translation, focusing on components like self-attention, encoder and decoder stacks, positional encoding, and training.
The article discusses the resurgence of programming languages designed specifically for AI development, highlighting Mojo as a promising example. It explores the historical context of AI-focused languages, the limitations of Python for AI, and the features and benefits of Mojo and other emerging AI languages.
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