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Google Releases Gemini Omni 1.2 Flash: 15-Second AI Video at $0.07 per Second
Concepts & ImplementationsMarkTechPost2 hours ago

Google Releases Gemini Omni 1.2 Flash: 15-Second AI Video at $0.07 per Second

Google has released Gemini Omni 1.2 Flash, the newest version of its video generation and editing model. Gemini Omni 1.2 Flash generates and edits clips up to 15 seconds natively. It also cuts the price by 30% compared with Omni 1.1 Flash. The model is generally available on Google Cloud and the Gemini API. For developers, the takeaways are longer clips, fewer re-renders and a lower bill per second.

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Thinking Machines Releases Inkling 2 Small, a 594B MoE With 17B Active
Concepts & ImplementationsMarkTechPost2 hours ago

Thinking Machines Releases Inkling 2 Small, a 594B MoE With 17B Active

Thinking Machines Lab has released Inkling 2 Small, the first model of its second Inkling generation. The Inkling 2 Small model is a sparse Mixture-of-Experts (MoE) with 594B total and 17B active parameters. It accepts text and images, and supports variable reasoning effort. On the Artificial Analysis Intelligence Index, it scores 31. Its predecessor, Inkling Small, scores 13 on the same index. The model is live on Tinker for fine-tuning and inference.

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Zyphra ZYFA: FP8 FlashAttention for AMD MI355X, Up to 1.76x Faster
Concepts & ImplementationsMarkTechPost2 hours ago

Zyphra ZYFA: FP8 FlashAttention for AMD MI355X, Up to 1.76x Faster

Zyphra has released ZYFA (Zyphra FlashAttention on AMD), a set of FP8 and BF16 prefill attention kernels for AMD Instinct MI355X GPUs. ZYFA is Zyphra’s FP8 attention engine for long-context prefill, and the code is open source under Apache 2.0. On real model activations, its FP8 kernels run 1.16-1.76x faster than AITER, AMD’s default kernel library. At head dimension 128, ZYFA’s FP8 error is 62-78% lower than AITER’s.

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Buoyant Releases Kinference: An Open Source Inference Control Plane for Kubernetes and vLLM
Concepts & ImplementationsMarkTechPost2 hours ago

Buoyant Releases Kinference: An Open Source Inference Control Plane for Kubernetes and vLLM

Buoyant, the company behind Linkerd, has released Kinference, an open source project for building inference platforms on Kubernetes. Kinference is an inference control plane for Kubernetes and vLLM. It sits in front of a fleet of vLLM workers and decides where each request goes. It applies routing policies, token rate limits, KV-cache-aware load balancing and per-tenant metrics in one layer. For dev teams serving many open-weight models to many internal clients, it targets the layer above the model server. The project is Apache 2.0 licensed on GitHub. The current release is v0.1.0-alpha.7.

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Systems Thinking Is Vital in the Age of AI
Industry NewsIEEE Spectrum - AI2 hours ago

Systems Thinking Is Vital in the Age of AI

Modern engineering projects increasingly combine human judgment with machine capability. An engineer can define a problem, then ask an artificial intelligence tool to search relevant research and technical standards, generate design options, test them in simulation, and compare the results. The engineer can then bring the results to specialists who understand the parts AI cannot judge on its own, such as whether a design can be built safely, whether it makes sense in a particular setting, or how it might affect its users. The final decision about whether and how to move forward remains a human responsibility.

Read original on IEEE Spectrum - AI ↗
Consumer Appetite for Personal AI Goes Mainstream
Industry NewsIEEE Spectrum - AI2 hours ago

Consumer Appetite for Personal AI Goes Mainstream

For many consumers, much of the promise and appeal of AI is personal. Consumers are looking past generic tools and want to know how these technologies will help them in their daily lives. These trends coincide with the advent of new classes of wearable devices that, in addition to being able to leverage powerful frontier models, feature built-in intelligence that understand the context of a user. Where are they? What are they seeing and hearing? What are their habits, their preferences, their schedules?

Read original on IEEE Spectrum - AI ↗
Guarding the Gates of Knowledge
Industry NewsIEEE Spectrum - AI2 hours ago

Guarding the Gates of Knowledge

A recent scroll through social media got me thinking about trust. About half my feed was filled with AI slop that astute users would call out as being machine-generated, often to the embarrassment of the rube who unknowingly spread it and sometimes to the delight of the poster who shared it deliberately because they found it amusing or useful for sowing confusion.

Read original on IEEE Spectrum - AI ↗
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Import AI 476: Creativity and RSI; worse-than-China open weight models; AI goes to space
ResearchImport AI2 hours ago

Import AI 476: Creativity and RSI; worse-than-China open weight models; AI goes to space

New benchmark shows how AI models lack intuitive visual reasoning:…Humanity’s Sixth Sense highlights some shortcomings of current AI systems…AI startups Elorian and Scale AI have built and released Humanity’s Sixth Sense, a benchmark that “measures how a model interprets and reasons through information a visual scene implies but doesn’t explicitly show.” Some examples of the types of task in HSS are a picture of a bookcase and being asked whether it can fit more books, or a photo of horses pulling a plough and a question of whether the chains bear the plough’s drag weight. HSS contains 522 questions that cover 288 images and 234 short video clips (totalling 17.6 hours of video). The questions fall into four categories:

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Import AI 477: In which I tell the IMF the Singularity has begun
ResearchImport AI2 hours ago

Import AI 477: In which I tell the IMF the Singularity has begun

Here’s a speech I gave on Saturday the 17th of October at the IMF meeting in Bangkok. This speech is inspired by the megatrend readers have seen me cover this year of AI beginning to operate at the frontier of human science, sometimes autonomously. While we all may quibble over the details, I think it’s evident we’re in the early stages of the singularity, and I wanted to use my privileged position of giving the Per Jacobsson lecture to try and tell the assembled audience of economists, bankers, and policymakers, that they should expect significant, rapid change in the future. In this speech, I focused primarily on how humanity can share in the benefits of AI if we exercise proper…

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Import AI 479: Spacewar; low-latency tasks versus LLMs; Genie 4
ResearchImport AI2 hours ago

Import AI 479: Spacewar; low-latency tasks versus LLMs; Genie 4

LLMs are very bad at low-latency tasks:…Realtime-Bench says it’ll be a while till AI systems can play games or do online auctions for you…Researchers have built Realtime-Bench, “a suite of carefully curated dynamic tasks where delayed actions directly affect success”. The test helps evaluate how well modern, general-purpose AI systems can perform tasks where decisions need to be made in real-time. The benchmark consists of 50 interactive game and web tasks, split across 30 web tasks in four categories (travel, e-commerce, service, and security), and 20 game tasks (arcade, puzzle, strategy, and simulation).Who did the research: The benchmark was built by people at the University of Texas at Austin, Ohio State University, University of Illinois at Urbana-Champaign, NVIDIA, Northwestern University,…

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Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 1
Tools & Open SourceAWS Machine Learning Blog2 hours ago

Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 1

An AI agent that updates a ticket or reads a customer record acts for a person, inside applications that person can reach. Your identity team then has to answer a question that per-app OAuth consent and shared service accounts leave open: which agent might act for which user in which application, and how you stop it. Okta Cross App Access (XAA) answers it in your identity provider (IdP). You register the agent in Okta, an administrator approves the applications it might reach, and Okta evaluates policy each time the agent asks to act for a user. In our tests, after an administrator removed a user’s assignment to the agent, Okta refused the agent’s new requests for that user within about…

Read original on AWS Machine Learning Blog ↗
Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 2
Tools & Open SourceAWS Machine Learning Blog2 hours ago

Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 2

In Part 1, you built a task agent on Amazon Bedrock AgentCore, a platform to build, connect, and optimize agents at scale, with any framework or model. The agent runs on Amazon Bedrock AgentCore runtime, a capability of Amazon Bedrock AgentCore, and acts for signed-in users through Okta Cross App Access (XAA). Okta decided which agent might act for which user, and Amazon Bedrock AgentCore Identity, a capability of Amazon Bedrock AgentCore, redeemed Okta’s grant inside the agent. That proves the governance mechanism, and it runs on a deliberately small topology: one agent, two Model Context Protocol (MCP) servers, and the authorization check written into each server.

Read original on AWS Machine Learning Blog ↗
Serverless MCP server for AI agent data lake access on Amazon ECS Fargate
Tools & Open SourceAWS Machine Learning Blog2 hours ago

Serverless MCP server for AI agent data lake access on Amazon ECS Fargate

Organizations building applications powered by artificial intelligence (AI) face a recurring challenge: giving large language model (LLM) agents access to enterprise data lakes without duplicating integration logic across every agent and application. Most teams begin by wiring agents directly to databases and APIs, with no shared gateway such as a Model Context Protocol (MCP) server in between. Each new dataset demands custom code, and each agent reimplements its own access control. As the number of datasets or agents grows, permission enforcement drifts, and onboarding a new data source becomes a multi-day engineering effort.

Read original on AWS Machine Learning Blog ↗
Taxonomy of agentic primitives in Amazon Quick
Tools & Open SourceAWS Machine Learning Blog2 hours ago

Taxonomy of agentic primitives in Amazon Quick

Over the past year, as we built Amazon Quick, our agents went from answering questions to taking actions: scheduling meetings, running research, generating reports. As the system evolved, one problem kept resurfacing: there was no shared definition of the primitives that agentic work is built from. The same few ideas kept getting rebuilt under different names: an actor that does the work, a signal that starts it, a packaged procedure, and a memory that carries lessons forward. This repetition leaked into product design and onto the user.

Read original on AWS Machine Learning Blog ↗
Five Ways NVIDIA Co-Design Maximizes the Value of Every Byte in the AI Factory
Tools & Open SourceNVIDIA Technical Blog2 hours ago

Five Ways NVIDIA Co-Design Maximizes the Value of Every Byte in the AI Factory

In an AI factory, high bandwidth memory (HBM)is the fastest, most expensive, and most contested memory tier. Every byte spent on a redundant weight copy, an idle session’s KV cache, or a recomputed prefix is a byte that is not being used to produce tokens. The question is no longer how much memory a GPU has, but how much useful work each byte delivers.

Read original on NVIDIA Technical Blog ↗
Accelerating Reinforcement Learning Weight Refit with the Reshard Collective
Tools & Open SourceNVIDIA Technical Blog2 hours ago

Accelerating Reinforcement Learning Weight Refit with the Reshard Collective

During reinforcement learning (RL) post-training, training trajectories are generated on the fly by running inference with the current policy model. For training efficiency, it is common to run inference and training in parallel on separate GPU resources in a disaggregated setup. Although the policy model is updated only on the training GPUs, the system must periodically refresh the weights on the inference GPUs with the latest parameters generated on the training GPUs. This transfer of model weights between training and inference GPUs is called refit.

Read original on NVIDIA Technical Blog ↗

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