发布日期:2026-03-04
收录条目:20
1. Meet SymTorch: A PyTorch Library that Translates Deep Learning Models into Human-Readable Equations
- 来源:MarkTechPost
- 发布时间:2026-03-03 23:39 UTC
- 链接:https://www.marktechpost.com/2026/03/03/meet-symtorch-a-pytorch-library-that-translates-deep-learning-models-into-human-readable-equations/
摘要:Can symbolic regression be the key to transforming opaque deep learning models into interpretable, closed-form mathematical equations? or Say you have trained your deep learning model. It works. But do you know what it h
2. How to Build a Stable and Efficient QLoRA Fine-Tuning Pipeline Using Unsloth for Large Language Models
- 来源:MarkTechPost
- 发布时间:2026-03-03 22:29 UTC
- 链接:https://www.marktechpost.com/2026/03/03/how-to-build-a-stable-and-efficient-qlora-fine-tuning-pipeline-using-unsloth-for-large-language-models/
摘要:In this tutorial, we demonstrate how to efficiently fine-tune a large language model using Unsloth and QLoRA. We focus on building a stable, end-to-end supervised fine-tuning pipeline that handles common Colab issues suc
3. Google’s latest Pixel drop allows Gemini to order groceries for you and more
- 来源:The Verge AI
- 发布时间:2026-03-03 19:00 UTC
- 链接:https://www.theverge.com/tech/888295/google-gemini-pixel-drop-march-2026
摘要:Google is adding several new features to Pixel phones with its latest March update, including the ability for its Gemini AI assistant to do things for you, like order groceries or book a ride. This feature, which was fir
4. Google Drops Gemini 3.1 Flash-Lite: A Cost-efficient Powerhouse with Adjustable Thinking Levels Designed for High-Scale Production AI
- 来源:MarkTechPost
- 发布时间:2026-03-03 18:28 UTC
- 链接:https://www.marktechpost.com/2026/03/03/google-drops-gemini-3-1-flash-lite-a-cost-efficient-powerhouse-with-adjustable-thinking-levels-designed-for-high-scale-production-ai/
摘要:Google has released Gemini 3.1 Flash-Lite, the most cost-efficient entry in the Gemini 3 model series. Designed for ‘intelligence at scale,’ this model is optimized for high-volume tasks where low latency and cost-per-to
5. How the experts figure out what’s real in the age of deepfakes
- 来源:The Verge AI
- 发布时间:2026-03-03 18:22 UTC
- 链接:https://www.theverge.com/tech/888303/photo-video-fake-news-verification-nyt-bellingway
摘要:In the days that followed the US and Israel's joint military strike on Iran on Saturday, floods of images and videos that supposedly document the war have appeared online. Some are old or depict unrelated conflicts, are
6. Building a scalable virtual try-on solution using Amazon Nova on AWS: part 1
- 来源:AWS ML Blog
- 发布时间:2026-03-03 16:23 UTC
- 链接:https://aws.amazon.com/blogs/machine-learning/building-a-scalable-virtual-try-on-solution-using-amazon-nova-on-aws-part-1/
摘要:In this post, we explore the virtual try-on capability now available in Amazon Nova Canvas, including sample code to get started quickly and tips to help get the best outputs.
7. How Lendi revamped the refinance journey for its customers using agentic AI in 16 weeks using Amazon Bedrock
- 来源:AWS ML Blog
- 发布时间:2026-03-03 16:18 UTC
- 链接:https://aws.amazon.com/blogs/machine-learning/how-lendi-revamped-the-refinance-journey-for-its-customers-using-agentic-ai-in-12-weeks-using-amazon-bedrock/
摘要:This post details how Lendi Group built their AI-powered Home Loan Guardian using Amazon Bedrock, the challenges they faced, the architecture they implemented, and the significant business outcomes they’ve achieved. Thei
8. How Tines enhances security analysis with Amazon Quick Suite
- 来源:AWS ML Blog
- 发布时间:2026-03-03 16:15 UTC
- 链接:https://aws.amazon.com/blogs/machine-learning/how-tines-enhances-security-analysis-with-amazon-quick-suite/
摘要:In this post, we show you how to connect Quick Suite with Tines to securely retrieve, analyze, and visualize enterprise data from any security or IT system. We walk through an example that uses a MCP server in Tines to r
9. Xiaomi, unlike Google and Samsung, thinks camera hardware comes first
- 来源:The Verge AI
- 发布时间:2026-03-03 14:34 UTC
- 链接:https://www.theverge.com/tech/888082/xiaomi-unlike-google-and-samsung-thinks-camera-hardware-comes-first
摘要:When it launched the 17 and 17 Ultra in Europe on Saturday, Xiaomi bucked an industry trend: it didn't really talk about AI all that much. And it really didn't talk about AI when it showed off the two phones' cameras, in
10. Why is SpaceX going public?
- 来源:The Verge AI
- 发布时间:2026-03-03 14:30 UTC
- 链接:https://www.theverge.com/tech/887899/spacex-ipo-risks-ai
摘要:I am excited about the SpaceX IPO for all the reasons investors shouldn't be. Maybe it'll be a real marquee moment for Silicon Valley, but I see the potential for a shitshow. After all, more than a decade ago, Musk said
11. GPT-5.3 Instant System Card
- 来源:OpenAI News
- 发布时间:2026-03-03 10:00 UTC
- 链接:https://openai.com/index/gpt-5-3-instant-system-card
摘要:暂无摘要。
12. GPT-5.3 Instant: Smoother, more useful everyday conversations
- 来源:OpenAI News
- 发布时间:2026-03-03 10:00 UTC
- 链接:https://openai.com/index/gpt-5-3-instant
摘要:暂无摘要。
13. Alibaba Releases OpenSandbox to Provide Software Developers with a Unified, Secure, and Scalable API for Autonomous AI Agent Execution
- 来源:MarkTechPost
- 发布时间:2026-03-03 08:32 UTC
- 链接:https://www.marktechpost.com/2026/03/03/alibaba-releases-opensandbox-to-provide-software-developers-with-a-unified-secure-and-scalable-api-for-autonomous-ai-agent-execution/
摘要:Alibaba has released OpenSandbox, an open-source tool designed to provide AI agents with secure, isolated environments for code execution, web browsing, and model training. Released under the Apache 2.0 license, the prop
14. Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking
- 来源:arXiv cs.AI
- 发布时间:2026-03-03 05:00 UTC
- 链接:https://arxiv.org/abs/2603.00267
摘要:arXiv:2603.00267v1 Announce Type: new Abstract: Misinformation spreading over the Internet poses a significant threat to both societies and individuals, necessitating robust and scalable fact-checking that relies on retr
15. TraderBench: How Robust Are AI Agents in Adversarial Capital Markets?
- 来源:arXiv cs.AI
- 发布时间:2026-03-03 05:00 UTC
- 链接:https://arxiv.org/abs/2603.00285
摘要:arXiv:2603.00285v1 Announce Type: new Abstract: Evaluating AI agents in finance faces two key challenges: static benchmarks require costly expert annotation yet miss the dynamic decision-making central to real-world trad
16. DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths
- 来源:arXiv cs.AI
- 发布时间:2026-03-03 05:00 UTC
- 链接:https://arxiv.org/abs/2603.00309
摘要:arXiv:2603.00309v1 Announce Type: new Abstract: The increasingly popular agentic AI paradigm promises to harness the power of multiple, general-purpose large language model (LLM) agents to collaboratively complete comple
17. How Well Do Multimodal Models Reason on ECG Signals?
- 来源:arXiv cs.AI
- 发布时间:2026-03-03 05:00 UTC
- 链接:https://arxiv.org/abs/2603.00312
摘要:arXiv:2603.00312v1 Announce Type: new Abstract: While multimodal large language models offer a promising solution to the "black box" nature of health AI by generating interpretable reasoning traces, verifying the validit
18. EmCoop: A Framework and Benchmark for Embodied Cooperation Among LLM Agents
- 来源:arXiv cs.AI
- 发布时间:2026-03-03 05:00 UTC
- 链接:https://arxiv.org/abs/2603.00349
摘要:arXiv:2603.00349v1 Announce Type: new Abstract: Real-world scenarios increasingly require multiple embodied agents to collaborate in dynamic environments under embodied constraints, as many tasks exceed the capabilities
19. Monotropic Artificial Intelligence: Toward a Cognitive Taxonomy of Domain-Specialized Language Models
- 来源:arXiv cs.AI
- 发布时间:2026-03-03 05:00 UTC
- 链接:https://arxiv.org/abs/2603.00350
摘要:arXiv:2603.00350v1 Announce Type: new Abstract: The prevailing paradigm in artificial intelligence research equates progress with scale: larger models trained on broader datasets are presumed to yield superior capabiliti
20. Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning
- 来源:arXiv cs.AI
- 发布时间:2026-03-03 05:00 UTC
- 链接:https://arxiv.org/abs/2603.00374
摘要:arXiv:2603.00374v1 Announce Type: new Abstract: Offline learning of strategies takes data efficiency to its extreme by restricting algorithms to a fixed dataset of state-action trajectories. We consider the problem in a