Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 18

Anthropic CEO Dario Amodei believes today’s AI models hallucinate, or make things up and present them as if they’re true, at a lower rate than humans do, he said during a press briefing at Anthropic’s first developer event, Code with Claude, in San Francisco on Thursday.

Amodei said all this in the midst of a larger point he was making: that AI hallucinations are not a limitation on Anthropic’s path to AGI — AI systems with human-level intelligence or better.

“It really depends how you measure it, but I suspect that AI models probably hallucinate less than humans, but they hallucinate in more surprising ways,” Amodei said, responding to TechCrunch’s question.

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Classical biomedical data science models are trained on a single modality and aimed at one specific task. However, the exponential increase in the size and capabilities of the foundation models inside and outside medicine shows a shift toward task-agnostic models using large-scale, often internet-based, data. Recent research into smaller foundation models trained on specific literature, such as programming textbooks, demonstrated that they can display capabilities similar to or superior to large generalist models, suggesting a potential middle ground between small task-specific and large foundation models. This study attempts to introduce a domain-specific multimodal model, Congress of Neurological Surgeons (CNS)-Contrastive Language-Image Pretraining (CLIP), developed for neurosurgical applications, leveraging data exclusively from Neurosurgery Publications.

METHODS:

We constructed a multimodal data set of articles from Neurosurgery Publications through PDF data collection and figure-caption extraction using an artificial intelligence pipeline for quality control. Our final data set included 24 021 figure-caption pairs. We then developed a fine-tuning protocol for the OpenAI CLIP model. The model was evaluated on tasks including neurosurgical information retrieval, computed tomography imaging classification, and zero-shot ImageNet classification.

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Self-driving cars which eliminate traffic jams, getting a health care diagnosis instantly without leaving your home, or feeling the touch of loved ones based across the continent may sound like the stuff of science fiction.

But new research, led by the University of Bristol and published in the journal Nature Electronics, could make all this and more a step closer to reality thanks to a radical breakthrough in .

The futuristic concepts rely on the ability to communicate and transfer vast volumes of data much faster than existing networks. So physicists have developed an innovative way to accelerate this process between scores of users, potentially across the globe.

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The IBS-Yonsei research team introduces a novel Lp-Convolution method at ICLR 2025. A team of researchers from the Institute for Basic Science (IBS), Yonsei University, and the Max Planck Institute has developed a new artificial intelligence (AI) technique that brings machine vision closer to the

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Is artificial intelligence (AI) capable of suggesting appropriate behavior in emotionally charged situations? A team from the University of Geneva (UNIGE) and the University of Bern (UniBE) put six generative AIs—including ChatGPT—to the test using emotional intelligence (EI) assessments typically designed for humans.

The outcome: these AIs outperformed average human performance and were even able to generate new tests in record time. These findings open up new possibilities for AI in education, coaching, and . The study is published in Communications Psychology.

Large language models (LLMs) are (AI) systems capable of processing, interpreting and generating human language. The ChatGPT generative AI, for example, is based on this type of model. LLMs can answer questions and solve complex problems. But can they also suggest emotionally intelligent behavior?

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