arXiv:2608.09998v1 Announce Type: new Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have become powerful tools for supporting and automating complex human tasks. Despite their benefits, growing attention has been directed toward their environmental implications, primarily due to their high energy demands and associated carbon emissions. This concern is particularly relevant in light of the increasing deployment of large-scale models, especially Deep Learning (DL) architectures, which provide advanced predictive capabilities but require substantial computational resources. This paper presents a systematic review of research on Green AI, Green DL, and optimization techniques aimed at reducing the environmental impact of AI models. In addition, we examine and compare sever…

arXiv:2608.09986v1 Announce Type: new Abstract: Most existing multimodal sentiment analysis approaches assume access to complete multimodal inputs. However, real-world applications frequently encounter incomplete or corrupted modalities, posing a critical challenge. Although several methods have been proposed to tackle this issue, they mainly rely on data imputation and heuristic coordination constraints, which fail to effectively extract and leverage task-relevant information from the incomplete multimodal data. To address this challenge, we propose a unified framework termed Mutual Information Disentanglement with uncertainty-Aware fuSion (MIDAS), which effectively restructures multimodal representations under incomplete conditions. MIDAS adopts a variational modeling strategy to repres…

arXiv:2608.09967v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) agents achieve strong performance in complex environments, yet their decision-making processes remain difficult to interpret. We introduce SPOT (Sampling Policy Observation Tree), a novel model-agnostic, sampling-based framework for interpreting DRL policies. Given access to the policy and an environment simulator, SPOT constructs an interpretable finite-horizon tree by sampling actions and recursively simulating the resulting successor states. The tree provides an empirical representation of the policy's action preferences and their possible downstream evolution. We provide formal guarantees establishing SPOT's asymptotic recovery of the policy's unique most probable action and characterizing its disagreeme…

arXiv:2608.09949v1 Announce Type: new Abstract: This study evaluates the application of Large Language Models (LLMs) in complex biological systems, evolving from data analysis to autonomous, AI-guided experimentation. The framework is driven by data from a 49-channel phytosensor network, encompassing multispectral, electrochemical, and dielectric modalities. To enhance accessibility, the system provides real-time natural-language interpretation for both specialists and non-experts. However, its core advantage lies in the transition from human-in-the-loop analysis to autonomous control. Processing biophysical data, the LLM evaluates plant physiology and triggers hardware actuators to optimize microclimates, execute phenotyping protocols, or induce controlled stress scenarios. This closed-l…

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Lethe — your work, understood Free beta · no card One identity. Every AI. Every AI you use is quietly building its own half-right picture of you. Lethe is the one place your identity actually lives — and every AI reads…

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Five experts weigh in. The mania had started with something real. The safety bicycle: equal wheels, pneumatic tires, a chain drive. The first bicycle model where the rider’s feet could actually touch the road if they ne…

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AI can do a lot, but it can't replace human taste and judgment. Coinbase CTO Rob Witoff told Business Insider that those are the traits that make engineering candidates stand out nowadays. As AI takes over routine tasks…

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Getting a billion people to do anything is a major accomplishment, but Google does have some notable advantages. Virtually every Android phone in the world right now ships with the Gemini app and multiple features that…

After a former lead writer claimed Saber "replaced me with ChatGPT," CEO Matthew Karch now claims, "Neither Saber nor Unigine have replaced any writers with AI," for the Rideshare "Stimulator" game announced last month, developed by Unigine. The writer, Stella Sacco, says differently, however, posting on Bluesky that "I was lead writer on this one! And Saber replaced me with ChatGPT midway through development. All the passenger voices were AI too. Either they changed direction at some point or they're not disclosing it on Steam." Saber describes Rideshare "Stimulator" as an "immersive driving simulation game that puts you behind the wheel … Read the full story at The Verge.

We announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent financing platforms designed to mobilize over $500 billion of third-party capital to support the buildout of AI infrastructure over time. This is a major milestone for NVIDIA and the AI industry. We have moved from an era in which companies […]

Trendingnow 1 U.S. Treasury is paying $3 billion a day in interest on national debt, says the CBO—having spent $10 billion to prop up the currency of its top lender 2 Steve and Connie Ballmer are splitting their $8 bill…

Diabetes charity capitalises on AI technology The Bermuda Diabetes Association is partnering with a United States-based company to use AI technology in a free screening exercise for chronic kidney disease this month. Th…

Quick start One install command. Pick a model. Start coding. No accounts, no configuration files, no setup guides. Step 01 Go offline in one command Type /offline-mode — Ante installs a local inference engine. No API ke…

Qwen3.8-2.4T-A95B by Alibaba, with a 1M token context window, is now available via Modal Auto Endpoints.

See how RingCentral uses ChatGPT Work and Codex to accelerate AI product development and centralize operational intelligence across engineering and operations.

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Not a brain as complex as the one behind your eyes, of course, but a glob of gray matter nonetheless, with a few-million-odd neurons that can send and receive electrical signals. Biologists call these strange creations…

There are no lossless transformations of natural-language text Sophie Alpert shares her "internal policy on acceptable use of AI writing by engineers". It's a short read (supporting its own recommendations) and really good. If you chose to have LLMs help massage your writing the following rule seems crucial to me: You must stand behind every idea and every sentence in your docs. It is your responsibility to make sure that the entire document is representative of your own thoughts before you share it. If a reviewer asks, “What did you mean by this line?”, it’s not acceptable to reply with “Oh sorry, AI wrote that, just ignore it.” You will confuse your readers (and waste their time) if you present them things that are not genuinely representative of your thoughts. The "no lossless transfor…

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We use prototyping only for storytelling | Documents by Kadhir Mani (1.1 minutes) Early Prototyping Experiments Prototyping (through tools like Lovable, V0, Replit, etc) went through two failed framings for us. First, w…

Video gamers are in demand to help patrol the nation's skies.U.S. Secretary of Transportation Sean Duffy said in a recent social media post that the federal agency's recruitment campaign to recruit gamers as air traffic…

Change data capture is one of the most common things data engineers build on Spark...

River AI Inc., a startup that helps enterprises customize open-source artificial intelligence models, has raised $1.1 billion in early-stage funding. The company stated in today’s announcement that it received the capital over two rounds, a seed and a Series A. General Catalyst and AMP PBC were the lead investors. They were joined by Nvidia Corp., […] The post Personalized AI startup River AI raises $1.1B from consortium backed by Nvidia, AMD appeared first on SiliconANGLE.