New audio-processing technology is making entertainment more accessible for millions of viewers.
Reasoning models can generate seven to 10 times as many tokens as necessary on simple tasks, creating unsustainable costs at scale. Amazon's vision for metacognitive AI could fundamentally shift how models allocate computational resources.
"Reinforcement learning gyms" train agents on the many low-level tasks that they must chain together to execute customer requests.
A new hybrid optimization approach allows edge devices to fine-tune vision-language models using only forward passes, achieving up to 7% higher accuracy than existing techniques.
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January 13, 20267 min readLeveraging existing environment simulators and reward functions based on verifiable ground truth boosts task success rate, even with small models and small training datasets. -
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December 29, 20259 min read
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Hosted by Dr. Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Meet the 63 Amazon Research Award (ARA) recipients, who represent 41 universities in 8 countries.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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2026Vectorized High-Definition (HD) maps offer rich and precise environmental information about driving scenes, playing a crucial role in improving driver safety by supporting autonomous driving and advanced driver-assistance systems (ADAS). Processing individual camera images creates fragmented view of the world requiring complex and error-prone merging. Existing multi-view camera methods train deep neural
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2026We study streaming data with categorical features where the vocabulary of categorical feature values is changing and can even grow unboundedly over time. Feature hashing is commonly used as a pre-processing step to map these categorical values into a feature space of fixed size before learning their embeddings (Coleman et al. 2024; Desai, Chou, and Shrivastava 2022). While these methods have been developed
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2026Workflow automation is critical for reducing manual efforts in industries, yet existing pipelines fail to handle generative tasks like summarization and extraction without pre-built tools, forcing human intervention. While LLM-based agents offer solutions, their creation depends heavily on prompt engineering—a resource-intensive process often yielding sub-optimal results. Current automated approaches face
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Given an unfamiliar dataset without ground truth annotations or established taxonomies, how do we systematically discover meaningful patterns? Even with large language models providing initial categorization suggestions, it remains challenging to capture patterns and standardize them into consistent representations across unstructured data. This persistent challenge highlights the need for systematic discovery
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AAAI 2026 Workshop on the Bridge between Artificial Intelligence and Law2026Given the constant flux in the world of geopolitics, staying up to date and compliant with international trade issues is challenging. But exploring if LLMs can aid this task is a frontier hitherto unexplored in the LLM evaluation literature - primarily due to the lack of a dataset for benchmarking the capabilities of LLMs on questions regarding international trade subjects. To address this gap, we introduce
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
