One of the main challenges facing large language models (LLMs) is the ability to handle complex tasks that require reasoning and planning. While LLMs excel at answering simple questions quickly, they often struggle with tasks that involve deliberate thinking and problem-solving. This limitation is often attributed to the fact that LLMs are more akin to
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In a recent discussion at VentureBeat’s Transform 2024, Elastic CEO Ash Kulkarni and DocuSign CPO Dmitri Krakovsky shed light on the transformational power of generative AI in enterprise search and contract management. The conversation emphasized the increasing relevance of AI-driven search capabilities for organizations grappling with extensive data repositories and intricate contractual relationships. Elastic’s enterprise
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Large language models (LLMs) have revolutionized natural language processing in recent years, but they come with challenges related to computational costs. Mixture-of-Experts (MoE) has emerged as a popular technique for scaling LLMs while keeping inference costs manageable. However, traditional MoE architectures have limitations in terms of the number of experts they can accommodate. In response
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The field of machine learning and artificial intelligence is rapidly expanding, with applications becoming more complex and demanding. However, the increasing size of neural networks has raised concerns about the sustainability of these technologies due to their growing energy consumption and training times. In response to these challenges, scientists at the Max Planck Institute for
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The European Union recently issued a warning to Company X regarding its blue checkmark verification system, citing violations of the bloc’s Digital Services Act (DSA). The EU found that Company X’s verification system was deceptive and against established industry practices, sparking concerns about transparency and user authenticity. Investigation Findings Following an investigation into Company X,
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