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Study: Sycophantic AI can undermine human judgment

Study: Sycophantic AI can undermine human judgment

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We all need a little validation now and then from friends or family, but sometimes too much validation can backfire—and the same is true of AI chatbots. There have been several recent cases of overly sycophantic AI tools leading to negative outcomes, including users harming themselves and/or others. But the harm might not be limited to these extreme cases, according to a new paper published in the journal Science. As more people rely on AI tools for everyday […]

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Beyond identifiability: Learning causal representations with few environments and finite samples

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We provide explicit, finite-sample guarantees for learning causal representations from data with a sublinear number of environments. Causal representation learning seeks to provide a rigourous foundation for the general representation learning problem by bridging causal models with latent factor models in order to learn interpretable representations with causal semantics. Despite a blossoming theory of identifiability in causal representation learning, estimation and finite-sample bounds are less well understood. We show that causal representations can be learned with only a […]

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No Hard Negatives Required: Concept Centric Learning Leads to Compositionality without Degrading Zero-shot Capabilities of Contrastive Models

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Contrastive vision-language (V&L) models remain a popular choice for various applications. However, several limitations have emerged, most notably the limited ability of V&L models to learn compositional representations. Prior methods often addressed this limitation by generating custom training data to obtain hard negative samples. Hard negatives have been shown to improve performance on compositionality tasks, but are often specific to a single benchmark, do not generalize, and can cause substantial degradation of basic V&L capabilities such as zero-shot […]

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Neural Network Conversion of Machine Learning Pipelines

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Transfer learning and knowledge distillation has recently gained a lot of attention in the deep learning community. One transfer approach, the student-teacher learning, has been shown to successfully create “small” student neural networks that mimic the performance of a much bigger and more complex “teacher” networks. In this paper, we investigate an extension to this approach and transfer from a non-neural-based machine learning pipeline as teacher to a neural network (NN) student, which would allow for joint optimization […]

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The debut of Gemini 3.1 Flash Live could make it harder to know if you're talking to a robot

The debut of Gemini 3.1 Flash Live could make it harder to know if you’re talking to a robot

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Text generated by artificial intelligence often has a particular vibe that gives it away as machine-generated, but it has become harder to pick out those idiosyncrasies as the tech has improved. We may be seeing a similar evolution of generative AI audio. Google has announced a new AI audio model called Gemini 3.1 Flash Live—as the name implies, it’s designed for real-time conversation. It’s rolling out in some Google products starting today, and developers will be able to […]

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Building age-responsive, context-aware AI with Amazon Bedrock Guardrails

Building age-responsive, context-aware AI with Amazon Bedrock Guardrails

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As you deploy generative AI applications to diverse user groups, you might face a significant challenge that impacts user safety and application reliability: verifying each AI response is appropriate, accurate, and safe for the specific user receiving it. Content suitable for adults might be inappropriate or confusing for children, while explanations designed for beginners might be insufficient for domain experts. As AI adoption accelerates across industries, the need to match responses to user age, role, and domain knowledge […]

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Accelerating LLM fine-tuning with unstructured data using SageMaker Unified Studio and S3

Accelerating LLM fine-tuning with unstructured data using SageMaker Unified Studio and S3

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Last year, AWS announced an integration between Amazon SageMaker Unified Studio and Amazon S3 general purpose buckets. This integration makes it straightforward for teams to use unstructured data stored in Amazon Simple Storage Service (Amazon S3) for machine learning (ML) and data analytics use cases. In this post, we show how to integrate S3 general purpose buckets with Amazon SageMaker Catalog to fine-tune Llama 3.2 11B Vision Instruct for visual question answering (VQA) using Amazon SageMaker Unified Studio. […]

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Introducing Amazon Polly Bidirectional Streaming: Real-time speech synthesis for conversational AI

Introducing Amazon Polly Bidirectional Streaming: Real-time speech synthesis for conversational AI

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Building natural conversational experiences requires speech synthesis that keeps pace with real-time interactions. Today, we’re excited to announce the new Bidirectional Streaming API for Amazon Polly, enabling streamlined real-time text-to-speech (TTS) synthesis where you can start sending text and receiving audio simultaneously. This new API is built for conversational AI applications that generate text or audio incrementally, like responses from large language models (LLMs), where users must begin synthesizing audio before the full text is available. Amazon Polly […]

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Intel Core Ultra 270K and 250K Plus review: Conditionally great CPUs

Intel Core Ultra 270K and 250K Plus review: Conditionally great CPUs

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Many of our graphics card reviews early last year and in the early 2020s focused on the difficulties of reviewing and recommending graphics cards when the manufacturer-suggested price points effectively didn’t exist. Now, reviews of any new PC component have to contend with the much more broadly awful market for consumer PC parts as AI data center-fueled demand for RAM and flash memory chips drives up prices for DDR5 kits, SSDs, and GPUs. In our August 2025 system guide, […]

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Social Hippocampus Memory Learning

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Social learning highlights that learning agents improve not in isolation, but through interaction and structured knowledge exchange with others. When introduced into machine learning, this principle gives rise to social machine learning (SML), where multiple agents collaboratively learn by sharing abstracted knowledge. Federated learning (FL) provides a natural collaboration substrate for this paradigm, yet existing heterogeneous FL approaches often rely on sharing model parameters or intermediate representations, which may expose sensitive information and incur additional overhead. In this […]

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