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Agent capabilities increase in direct proportion to demandAs the AI agents grow, they obtain new capabilities, with rising demand from other users on Cronos zkEVM unlocking new features. As an agent reaches the fixed market cap of $127,100, it becomes capable of engaging in Telegram chats. It cultivates a liquidity pool on Cronos’ DEX, alleviating trading and discoverability in the ecosystem.Additional features are unlocked as agents attain milestone-based

Imagen 3 – scheduled for release to all Vertex AI customers next week – promises unprecedented realism in generated images, with marked improvements in detail, lighting, and artifact reduction. The model includes new features for enterprise customers on an allowlist, including advanced editing capabilities and brand customisation options.Transforming operationsSeveral major firms have begun implementing these technologies into their operations.Mondelez Int

The expansion of Amazon Bedrock’s ecosystem reflects its growing popularity, with the service recording a 4.7x increase in its customer base over the last year. Industry leaders like Adobe, BMW, Zendesk, and Tenovos have all embraced AWS’s latest innovations to improve their generative AI capabilities. Most of the newly announced tools – such as inference management, Knowledge Bases with structured data retrieval, and GraphRAG – are currently in preview, w

if ( !emtpy($headline_subheadline ) ) : ?> Context size and data quality don’t get as much attention as GPU spend, but they’re driving up the same bills. Be sure the data you’re sending to models is worth paying to process. endif; ?> Credit: freedomnaruk / Shutterstock Artificial IntelligenceGenerative AIData ManagementSoftware Development

In recent years, much of the research interest in large language models (LLMs) such as OpenAI’s autoregressive GPT has shifted from what these models can do to how they do it. While LLMs have demonstrated impressive prediction consistency with factual knowledge, their computations remain opaque. Knowing how and where such factual associations are stored and retrieved and improving understanding of the mechanisms underlying autoregressive knowledge represen

Hot AIGC Track and High Cost AIGC has recently risen to be one of the hottest topics in AI. With the emergence of cross-model applications that allow textually-generated images (Stable Diffusion, Midjourney, NovelAI, DALL-E, etc.), AIGC has attracted the attention of computer scientists and the public. AIGC Generated Using Stable Diffusion AIGC is in high industry demand and has been labeled as one of the most prospective paths for the future of AI. The AI

Transformer architectures have demonstrated impressive performance improvements since their introduction in 2017 and are now the standard in the natural language processing and computer vision research fields. The wider real-world application of transformers is however limited by the massive computation and memory demands of their self-attention mechanisms when capturing diverse syntactic and semantic representations from long input sequences. In the new p

The Conference and Workshop on Neural Information Processing Systems (NeurIPS) is one of the most influential annual meetings for presenting and sharing research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. The NeurIPS 2022 organizing committee announced its coveted awards this week, recognizing thirteen Outstanding Papers, two in the Datasets & Benchmarks category, and a Test of Time p

Recent research has demonstrated that prompting language models to generate reasoning steps can improve performance on various natural language reasoning tasks. But how to supervise such models? Is it better to employ outcome-based approaches that supervise the final answer, or process-based approaches that supervise the reasoning process itself? In the new paper Solving Math Word Problems With Process- and Outcome-based Feedback, a DeepMind research team

AI-powered image synthesis has achieved results that would have been unimaginable just a few years ago — and there is much more to come. Introduced in a 2020 ECCV paper, Neural radiance fields (NeRF) use deep neural networks to model and render entire 3D scenes based on 2D images — a technique that will supercharge virtual and augmented reality applications. Current NeRF rendering processes via volumetric grids that store information on voxels however incu

Ten years ago, Geoffrey Hinton and his University of Toronto students published the paper ImageNet Classification with Deep Convolutional Neural Networks, presenting the first convolutional neural network to significantly surpass state-of-the-art results on the ImageNet database. The paper was recently honoured, in its first year of eligibility, with the NeurIPS 2022 Test of Time Award. In his NeurIPS keynote speech last week, Hinton offered his thoughts o

In-context learning has enabled pretrained language models to achieve impressive zero- and few-shot learning performance without parameter updates. Most conventional in-context learning techniques however restrict the number of training examples, which limits their effectiveness when a large number of examples are available. In the new paper Structured Prompting: Scaling In-Context Learning to 1,000 Examples, a Microsoft Research team proposes structured p

In the new paper Mu²SLAM: Multitask, Multilingual Speech and Language Models, the Google researchers present Mu2SLAM, a multilingual sequence-to-sequence pretraining method for speech and text models that covers arbitrary tasks in over 100 languages and achieves state-of-the-art translation performance on the CoVoST benchmark. Mu²SLAM is based on an encoder-decoder backbone model and is jointly pretrained on four different data types: speech-only, text-onl