Genie 3 can generate dynamic worlds that you can navigate in real time at 24 frames per second, retaining consistency for a few minutes at a resolution of 720p.

Capital investments in advanced manufacturing environments require careful consideration, especially if that investment is for a new process or technology. If you are looking at investing in OLP here are some things to consider about the potential return on investment (ROI).

This is a test

Game Arena is a new, open-source platform for rigorous evaluation of AI models. It allows for head-to-head comparison of frontier systems in environments with clear winning conditions.

A POSTECH research team has developed a thin, flexible robotic actuator inspired by human muscle proteins. As thin as paper, yet capable of generating strong forces, this robot can maneuver through tight spaces and manipulate objects, making it suitable for a wide range of applications—from surgical robots to industrial equipment. The study has been published in Nature Communications.

When planning cycles stall, business outcomes suffer. Static forecasts and slow collaboration keeps teams from responding to change, leaving businesses a step behind. 

SAP provides a powerful foundation for enterprise planning and operations. But as market conditions shift faster than ever, teams need new ways to respond with speed, precision, and adaptability.

Agentic AI introduces intelligent, self-adaptive automation into the picture, enhancing existing planning processes so teams can move faster and make better decisions.

Now, with the SAP® Endorsed App from DataRobot, organizations can bring that intelligence directly into their SAP environment, extending the value of their investment and enabling more responsive, future-ready planning. 

What’s new, and why it matters

AI Apps and Platform by DataRobot are now SAP® Endorsed and available on the SAP Store.

This designation is more than a badge. It’s a mark of technical excellence and proven customer value. Earning it means DataRobot has met SAP’s premium certification standards, including security reviews, cloud integration tests, and static code analysis.

For customers, this opens a clear, low-risk path to adopting agentic AI with confidence, including:

• Avoiding workarounds or patchwork integrations
• Keeping your existing systems. No rip-and-replace required
• Running AI securely and natively inside your SAP environment, from day one
  

A smarter path to enterprise planning

Many SAP customers face similar planning roadblocks:

• Fragmented systems and rigid workflows slow down decisions and force manual workarounds
• Disconnected roles and teams struggle to align or respond quickly when conditions change
• Slow, complex data integration makes it hard to adapt plans in real time
• AI investments get stuck in pilots, never delivering value at scale
  

SAP customers are already managing complex data and planning processes across finance, operations, and supply chain. But as needs evolve, many teams are looking for new ways to respond faster to change, extract insight from growing data sets, and operationalize AI across business functions.

To close these gaps, SAP customers are turning to agentic AI apps that can adapt, automate, and scale alongside their existing workflows.

Agentic AI modernizes enterprise planning by:

• Empowering business users with intuitive agentic AI interfaces
• Delivering more accurate, self-adapting forecasts across use cases, from demand and headcount to resource allocation and beyond
• Reducing dependency on manual updates and isolated workflows, accelerating planning across functions

The agentic AI Apps and Platform that DataRobot provides are designed to complement SAP, layering intelligent decision support, automation, and learning into the tools your teams already rely on.

Agentic AI planning for finance teams

Finance leaders face increasing pressure to forecast, advise, and act faster than ever, with tighter margins and greater precision. Agentic AI helps them meet the moment.

With the SAP Endorsed App from DataRobot, finance teams can:

• Predict cash flow gaps early to free up working capital
• Catch financial anomalies before they impact the bottom line
• Automate invoice validation to reduce errors and accelerate approvals
• Confidently model revenue scenarios to guide strategic decisions
• Assess credit risk in real time to avoid delays and disruptions

This shifts finance from reactive to strategic, enabling faster insight, earlier action, and better decision-making that helps the business stay ahead.

Supply chain teams: prevent delays, reduce waste, and move faster

Modern supply chains can’t afford blind spots. From demand planning to delivery, even minor delays can ripple across the business.

SAP already plays a central role in planning and supply chain execution. With agentic AI, teams can go further, adapting in real time, anticipating disruptions, streamlining decisions, and simplifying operations.

With this combination, supply chain teams can:

• Forecast demand more accurately to reduce stockouts and excess inventory
• Use real-time signals to manage delays and minimize disruptions
• Optimize production schedules to maximize output and resource use
• Plan labor more efficiently to align staffing with operational needs
• Predict maintenance needs early to prevent costly downtime

With agentic AI layered into SAP, supply chain teams adapt faster without adding new systems or complexity.

Explore what’s possible: Agentic AI that integrates, accelerates, and delivers

This isn’t a bolt-on demo or shadow tool. It’s an enterprise-grade AI platform designed to work inside your SAP environment.

It connects directly to SAP data and workflows, delivering: 

• Agentic AI decisions embedded in your SAP data layer
• Prebuilt apps and customizable templates for real-world use cases
• End-to-end governance and control built for enterprise standards
• Expert support to help you build, deploy, and scale agentic AI safely

The result: faster outcomes, more resilient planning, and a practical way to run your business.

Our SAP Endorsed App goes beyond technical alignment. They represent a new planning experience: one that’s intelligent, inclusive, and adaptive by design. It’s a shared vision for enabling intelligent enterprises, where planning isn’t just faster, but smarter, more adaptive, and more connected to real outcomes.

Explore our agentic AI apps on the SAP Store

The post Plans change. The SAP® Endorsed App from DataRobot keeps up. appeared first on DataRobot.

Precision robotics manufacturing is slowly becoming normalized because of its irrefutable positive impact on organizations. However, the journey to create error-free machinery never stops.

Two of the most critical success factors in the manufacturing industry are time to deployment and cycle time. Despite this, the design and deployment of robotic workcells has long remained a surprisingly manual and time-consuming process. Realtime Robotics is aiming to change that with Resolver, a cloud-based optimization engine that introduces industrial-scale automation into the earliest stages of robotic system planning.

At its core, Resolver addresses some of the most persistent engineering challenges in workcell design: motion planning, robot task allocation, target sequence optimization, and layout validation. Traditionally, these steps require iterative tweaking, deep domain expertise, and a significant investment of time and resources to get right and be able to deliver on time. Resolver replaces that trial-and-error approach with intelligent automation. As it runs, the engine explores thousands of potential options to deliver an increasingly optimized result; one that balances performance, accuracy, and feasibility – and does so within minutes.

This kind of computational efficiency opens new doors for how teams approach the design process. Rather than being limited by what’s manually achievable, engineers can let Resolver handle the mechanical complexity and instead focus on higher-level goals such as throughput, safety, or flexibility. Resolver adapts to a range of use cases, from greenfield line builds to individual cell retrofits, making it broadly applicable across industries and production scales. And it can do all this in mere minutes – faster than what’s humanly possible.

Recent integrations with leading 3D simulation platforms including Siemens Process Simulate, Visual Components, and Mitsubishi Electric’s MELSOFT Gemini, enable users to access Resolver’s capabilities directly within their preferred simulation environments. This embedded approach reflects a broader shift toward interoperability and hybrid workflows in advanced manufacturing, where simulation, design, and optimization are increasingly converging.

Early adopters, particularly in automotive manufacturing, have already reported cycle time improvements ranging from 15% to 40%, along with faster deployments and fewer errors. These outcomes suggest that Resolver is not just a point solution, but part of a larger movement toward AI-assisted engineering. A future where decision-making is augmented, not replaced, by automation.

Post provided by: Realtime Robotics – www.rtr.ai

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There may come a time when people stop throwing money at OpenAI, maker of ChatGPT, but that time is not now.

OpenAI’s latest haul: A cool $8.3 billion in new funding.

Observes lead writer Andrew Ross Sorkin: “DealBook hears that the company’s annual recurring revenue has soared to $13 billion, up from $10 billion in June — and is projected to surpass $20 billion by the end of the year.”

Key to fueling that growth are five million business users – up from three million from just a few months ago, according to Sorkin.

In other news and analysis on AI writing:

*ChatGPT-Maker Mulls a Discount Version: OpenAI is playing with the idea of offering a discount version of ChatGPT – at $10-$15/month — according to writer Irfan Ahmad.

The stripped-down version would still offer robust writing, but might not include other advanced features like AI agents, advanced customization or features for developers, according to Ahmad.

If the discount version emerges, it will most likely be dubbed ‘ChatGPT Go.’

*Google Search Enhances ‘AI Mode:’ Released earlier this summer, Google Search AI Mode has already nabbed a facelift.

With the enhanced version, you can now:

–Upload images and PDFs in AI Mode to give Google more context to your searches

–Use a ‘Canvas’ feature that enables you to build plans and organize searches over multiple sessions

–Show AI Mode video you see in the real world and ask questions about that video

*New York Times Licenses Its Content to Amazon: Writer Alexandra Bell reports Amazon will be paying The New York Times at least $20 million/year to use Times content on Amazon.

With the deal, expect Times content to start popping-up on Amazon’s product pages.

In addition, the Times has also given Amazon the right to train its AI using content from the paper.

*Writer Adds an AI Agent: Long-time AI pioneer Writer has expanded its feature mix to include an AI agent.

Observes Waseem Alshikh, chief technology officer, Writer: “Action Agent is a general-purpose autonomous agent that represents a fundamental leap in how we interact with technology.

“It can understand complex, multi-step requests, create a plan, and then autonomously use the same tools we do – browsers, terminals, file systems, code interpreters – to get the job done.”

*Google Reveals “Better than ChatGPT” Experimental Research Tool: Google is out with a new feature that reportedly offers next generation AI research capability for users looking to generate in-depth reports.

The new approach to AI research is “inspired by the iterative nature of human research through repeated cycles of searching, thinking and refining,” according to writer Sajjad Ansari.

Dubbed ‘Test-Time Diffusion Deep Researcher,’ the tool is still in experimental mode. But it’s still worth tracking by writers looking for the ultimate solution for in-depth AI research.

*ChatGPT-Maker Wants a Cut From In-App Sales: ChatGPT’s maker OpenAI is putting together an interesting offer to online retailers: We’ll promote your products in ChatGPT, but we want a taste.

Observes Medium: “People familiar with the plans state that OpenAI is actively working on an in-chat checkout experience.

“This would let users complete purchases inside the platform, and merchants would then pay OpenAI a commission on each sale.”

*Microsoft Updates CoPilot, Its AI Assistant: Writers using Microsoft 365 Copilot to generate supplemental images should find those can be more photorealistic now, thanks to a recent overhaul of the AI tool.

Other perks include tighter integration with the Microsoft Edge browser, Microsoft Teams and availability of Copilot as an app for MacOS.

Memory fans will also like enhancements that enable Copilot Memory to recall key facts about you – such as your preferences, working style and your favorite topics.

*Shopify Drops AI Blog Optimizer for Online Retailers: Digital merchants looking to get their blogs picked up by the search engines will want to check-out a new SEO optimizer from Shopify.

Dubbed ‘AI Rewrite App,’ the tool instantly rewrites and SEO-optimizes retail blogs.

Observes Fredy Dellis, CEO, TheGenieLab: “With the AI Rewrite App, merchants can now refresh their blog library in seconds, drive more traffic — and keep content aligned with evolving SEO strategies.”

*AI BIG PICTURE: Snapshot: Increasing Numbers of CEOs Warn of AI-Driven Job Loss: Investors Business Daily offers an excellent wrap-up on the growing AI take-over of white collar jobs in this 11-minute video.

Once a taboo subject, unvarnished predictions of job loss due to AI are becoming increasingly common among CEOs.

Included is a report on Microsoft, which has credited AI for racking-up $500 million in savings – as the company continues to slash jobs.

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post ChatGPT-Maker Snags Another $8.3 Billion appeared first on Robot Writers AI.

Weed that grow among crops is a problem in many ways since humanity started agriculture. Weeds compete with crops for water, soil nutrients, sunlight. They can host pets, harbor diseases. They cost labor to remove them, either manually or chemically. This also increases overall costs and chemical removal may mean environmental impacts. Overall, weeds cause lower crop yield, decrease in quality, and higher costs. Considering all these, it is a very critical task, to remove them as efficiently as possible, which is where robots can be very useful and bring the costs down.

A robot that is developed in Spain, which is called “The GreenBot” aims to undertake such task. The robot is still in development stage but according to the press release provided by the team, it completed its successful field trials. The robot is developed by GMV (www.gmv.com), and a consortium made of University of Seville’s AGR-278 “Smart Biosystems Laboratory” research group, GMV, TEPRO, PIONEER HiBred Spain SL, and Cooperativas Agroalimentarias de Andalucía, where each participant undertook tasks belonging to different disciplines. The collaboration was initially scheduled to continue for 21 months, which concluded end of June, 2025.

The robot is basically a robotic vehicle and a robotic arm, equipped with AI, autonomous navigation and machine vision technologies, which are all essential to accurately identify and treat weeds such as the ones that grow near almond, citrus and olive trees.

During field tests, the robot effectively completed its tasks under different light, soil and plant combinations. Detection of smaller weeds under shade however, still remains a challenge, which the team plans to tackle by training the model with further data. The robot operates in real time, with an inference frequency of 1 second per image. This eliminated the need of using external servers, and enabled seamless integration between perception, navigation and application. The robot runs with the popular open source operating system ROS2 (Robot Operating System).

The robot basically works by approaching the tree, encircling the trunk by its robotic arm, and while further movement of the robot body (basically the vehicle) still continues, the half circular arm sprays precisely targeted chemicals on identified weeds. This not only automates weed treatment but also significantly reduces the use of chemicals, and hence, the environmental impact. The weed detection core, which was developed by the University of Seville, can identify position, species and dimensions of weeds within a tolerance of 2 cm.

The project was funded by grants for European Innovation Partnership (EIP) Operational Groups, within the framework of Rural Development Program of Andalusia, which operates under Spanish Ministry of Agriculture.

The project specific details in this post were obtained from a press release shared by Ariadne Comunicación (www.ariadne.es), who handles press communications for GMV (www.gmv.com), the maker of the robot.

Post By: A. Tuter

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Robots excel at many things, but having a good sense of touch is not among them. Whether dropping items or pinching them too tightly, which crushes the object, many robots struggle with these basic skills that humans have mastered.

Deep Think utilizes extended, parallel thinking and novel reinforcement learning techniques for significantly improved problem-solving.

Wheeled robots like ours are more efficient for the same reason cycling is more efficient than running, which includes less of a fight with gravity. The efficiency, as well as the sheer complexity of humanoid robots adds up to something that may be very much higher cost.

Wheeled robots like ours are more efficient for the same reason cycling is more efficient than running, which includes less of a fight with gravity. The efficiency, as well as the sheer complexity of humanoid robots adds up to something that may be very much higher cost.

A Los Angeles-area startup is using artificial intelligence and robotics in an unlikely way: making sashimi and other fish dishes taste better, last longer and be more humane.