Category robots in business

New AI Offers New Automation Opportunities for Writers

ChatGPT’s maker OpenAI has released new experimental software — dubbed ‘Operator’ — that enables users to create autonomous AI agents.

Theoretically, writers could use the software to program an AI agent that, for example, could research, write — and continuously update — any article on any subject by:

~Automatically engaging in initial research on the Web

~Scouting for quotes to go along with that research from blogs and press releases

~Auto-writing the article in a preferred writing style

~SEO-optimizing the article for easy discovery by search engines

~Periodically researching the Web for new developments in
the article’s story

~Continually rewriting the article as new developments in the article’s story occur

So far, the experimental software is only available to ChatGPT Pro users, who pay a cool $200/month for premium access to ChatGPT services and new features.

Key competitors to OpenAI — including Google, Microsoft and Anthropic — have already released similar agent-making software.

In other news and analysis on AI writing:

*U.S. Government Officials to Get Briefing on ‘Super Agents’ This Week: ChatGPT-maker CEO Sam Altman is slated to give top U.S. government officials a closed door briefing Jan. 30 on its PhD-level ‘super agents.’

Observes writer Duncan Riley: “That OpenAI’s offering is said to include Ph.D.-level super-agents might also suggest that OpenAI has taken the technology beyond being able to automate tasks through to something more.”

The meeting reflects the overall zeitgeist associated with the breakneck advancement of AI during the past two years, which according to many experts and casual observers, has been at once thrilling and terrifying.

*ChatGPT-Maker Frees-Up Microsoft’s Grip on Its Tech: OpenAI — which has been running ChatGPT on Microsoft servers — has cut a new deal with its partner, enabling it to use the servers of other companies to run its AI.

Essentially, the deal gives Microsoft first crack at providing server services for OpenAI — as long as Microsoft can handle the request.

Observes writer Sebastian Moss: “With OpenAI’s compute demands growing, that relationship has grown strained as the world’s second-largest cloud provider struggled to keep up.”

“Last year, OpenAI announced that it would also work with Oracle — albeit in partnership with Microsoft.”

*ChatGPT-Maker a Major Player in Trump-Championed $500 Billion Stargate Project: OpenAI has emerged as a key player in the Stargate Project — an initiative to designed to attract major investment for the rapid build-out of AI infrastructure in the U.S.

Observes writer Craig S. Smith: “Intended to soak-up global investment capital before China has a chance to do the same, the recently announced Stargate Project — with its ambitious $500 billion investment over four years — represents a seismic shift in the global AI race, not only in terms of scale but also in strategy and execution.

“The initiative – a joint venture between OpenAI, Oracle and Softbank announced by President Donald J. Trump — will be the biggest AI infrastructure project in the world.

“It underscores the United States’ intent to assert dominance in AI development, framing it as a contest not just of technology but of economic and geopolitical power.”

*China’s AI Looking to Eat ChatGPT’s Lunch for Pennies-on-the-Dollar: Chinese researchers have released a competitor to ChatGPT — dubbed DeepSeek — they say performs just as well as ChatGPT at a fraction of the cost.

Observes writer Radhika Rajkumar: “The cost differences it represents could shake up the industry.”

A similar inexpensive rival to ChatGPT was also recently created by researchers at UC Berkeley, according to Rajkumar.

*Look for More Research Help from AI in Popular Apps: Writers and others can expect more AI-powered research tools in their favorite apps, thanks to a tool for developers from Perplexity, dubbed ‘Sonar.’

Essentially, the new tool gives app makers access to the same AI search tech that has made the Perplexity chatbot an extremely powerful alternative for users looking to perform Web searches and generate Web search summaries.

Observes writer Michael Nunez: “Zoom has already integrated Sonar into its AI Companion 2.0 product, allowing users to access real-time information without leaving video conferences.”

*Apple Kills Its AI News Summary Service: Smarting from glaring mistakes made by its AI news summary service, Apple has pulled the plug on the AI — at least for now.

One of the highest profile news media outlets disenchanted with Apple’s service is the BBC.

Earlier this month, Apple’s AI news summary service mistakenly reported that alleged CEO killer Luigi Mangione had shot himself — wrongly citing the BBC as the source of its summary.

Observes writer Tripp Mickle: “In a note to developers, Apple said it was working to improve summaries of notifications for news and entertainment apps.

“It plans to make the feature available again in a future software update.”

*Google Doubles Down In Its Race to Catch ChatGPT: Frustrated by ChatGPT’s dominance in the AI market, Google’s CEO Sundar Pichai is hoping to dominate the AI writer/tool by the close of 2025 with its own chatbot, Google Gemini.

Observes writer Miles Kruppa: “Google hasn’t said how many people currently use Gemini. But market leader ChatGPT has about 300 million weekly users.

“The Gemini app was the 54th most downloaded free app on iPhones Wednesday.

“ChatGPT was No. 4.”

*Look for Multi-Modal Search at Your Workplace in 2025: Google is predicting that increasing numbers of businesses will be offering enhanced search in 2025, which will enable you to input images, audio and video into your company search engine when doing research.

Other predictions in Google’s “5 Ways AI Will Shape Businesses in 2025” include:

~The rise of AI agents capable of completing autonomous tasks

~More AI on Web sites

~AI-enhanced cybersecurity

*AI Big Picture: China Militarizes AI Developed by Facebook Parent, Meta: Chinese researchers have modified open source AI software from Meta so that it can be used in warfare, according to writer Efosa Udinmwen.

Meta’s AI software — dubbed Llama — is free to download from the Web and has already been downloaded thousands of times.

Observes Udinmwen: “Meta, like other tech companies, has licensed Llama with clear restrictions against its use in military applications.

“However, as with many open-source projects, enforcing such restrictions is practically impossible.”

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 Releases New AI Agent Creator appeared first on Robot Writers AI.

This month, Microsoft refined its Copilot pricing for Office 365, and you get a lot for your extra $30 a month. But this also reminds me of a product called Crosstalk that I followed back in the early 90s, right […]

The post Why I Think Copilot Means the End of Office as We Know It appeared first on TechSpective.

When designing a robot, such as Boston Dynamics' anthropomorphic robot Atlas, which appears exercising and sorting boxes, fiducial markers are the guides that help them move, detect objects and determine their exact location. It is a machine vision tool that is used to estimate objects' positions. At first glance they are flat, high-contrast black and white square codes, roughly resembling the QR marking system, but with an advantage: they can be detected at much greater distances.

Researchers at the Technical University of Darmstadt and the Helmholtz Center Dresden-Rossendorf have developed flexible robot wings that are moved by magnetic fields. Inspired by the efficiency and adaptability of the wings of the monarch butterfly, they enable precise movements without electronics or batteries.

This year, we dive deeper into the trends driving AIoT, showing how they are reshaping industries and fostering a more efficient, secure, and sustainable future.

Claire chatted to Didem Gurdur Broo from Uppsala University about how to shape the future of robotics, autonomous vehicles, and industrial automation.

Didem Gurdur Broo is an Assistant Professor and Associate Senior Lecturer at the Department of Information Technology at Uppsala University. She leads the Cyber-physical Systems Lab, directing research on intelligent systems like collaborative robots, autonomous vehicles, and smart cities. Didem is a computer scientist with a PhD in mechatronics, which can give you an idea about how much she loves to talk about the future and emerging technologies. She dreams a better world and actively works on improving inequalities regardless of their nature.

We humans excel at generalization. If you taught a toddler to identify the color red by showing her a red ball, a red truck and a red rose, she will most likely correctly identify the color of a tomato, even if it is the first time she sees one.

While we move through 2025, pen and paper remain as familiar on construction sites as ever, and many construction managers still rely on intuition and experience over the promises of technology or robotic innovation.

Recent advances in robotics and artificial intelligence (AI) are enabling the development of a wide range of systems with unique characteristics designed for varying real-world applications. These include robots that can engage in activities traditionally only completed by humans, such as sketching, painting and even hand-writing documents.

As part of the ongoing war in Ukraine, one night in late November, Russia sent a swarm of 188 drones to attack Ukrainian infrastructure like electrical utilities, as well as residential areas, according to news reports. Ukrainian forces said they shot down 76 drones, but the damage was still extensive. Those kinds of attacks are continuing almost daily now.

Generative AI holds incredible promise, but its potential is often blocked by poor app experiences. 

AI leaders aren’t just grappling with model performance — they’re contending with the practical realities of turning generative AI into user-friendly applications that deliver measurable enterprise value.

Infrastructure demands, unclear output expectations, and complex prototyping processes stall progress and frustrate teams.

The rapid pace of AI innovation has also introduced a growing patchwork of tools and processes, forcing teams to spend time on integration and basic functionality instead of delivering meaningful business solutions.

This blog explores why AI teams encounter these hurdles and offers actionable solutions to overcome them.

What stands in the way of effective generative AI apps?

While teams move quickly on technical advancements, they often face significant barriers to delivering usable, effective business applications: 

• Technology complexity: Building the infrastructure to support generative AI apps — from vector databases to Large Language Model (LLM) orchestration — requires deep technical expertise that most organizations lack. Choosing the right LLM for specific business needs adds another layer of complexity.

• Unclear objectives: Generative AI’s unpredictability makes it hard to define clear, business-aligned objectives. Teams often struggle to connect AI capabilities into solutions that meet real-world needs and expectations.

• Talent and expertise: Generative AI moves fast, but skilled talent to develop, manage, and govern these applications is in short supply. Many organizations rely on a patchwork of roles to fill gaps, increasing risk and slowing progress.

• Collaboration gaps: Misalignment between technical teams and business stakeholders often results in generative AI apps that miss expectations — both in what they deliver and how users consume them.

• Prototyping barriers: Prototyping generative AI apps is slow and resource-intensive. Teams struggle to test user interactions, refine interfaces, and validate outputs efficiently, delaying progress and limiting innovation.

• Hosting difficulties: High computational demands, integration complexities, and unpredictable outcomes often make deployment challenging. Success requires not only cross-functional collaboration but also robust orchestration and tools that can adapt to evolving needs. Without workflows that unite processes, teams are left managing disconnected systems, further delaying innovation.

The result? A fractured, inefficient development process that undermines generative AI’s transformative potential.

Despite these app experience hurdles, some organizations have navigated this landscape successfully. 

For example, after carefully evaluating its needs and capabilities, The New Zealand Post — a 180-year-old institution — integrated generative AI into its operations, reducing customer calls by 33%.

Their success highlights the importance of aligning generative AI initiatives with business goals and equipping teams with flexible tools to adapt quickly.

Turn generative AI challenges into opportunities

Generative AI success depends on more than just technology — it requires strategic alignment and robust execution. Even with the best intentions, organizations can easily misstep.

Overlook ethical considerations, mismanage model outputs, or rely on flawed data, and small mistakes quickly snowball into costly setbacks.

AI leaders must also contend with rapidly evolving technologies, skill gaps, and mounting demands from stakeholders, all while ensuring their models are secure, compliant, and reliably perform in real-world scenarios.

Here are six strategies to keep your initiatives on track:

1. Business alignment and needs assessment: Anchor your AI initiatives to your organization’s mission, vision, and strategic objectives to ensure meaningful impact.
   
   
2. AI technology readiness: Assess your infrastructure and tools. Does your organization have the tech, hardware, networking, and storage to support generative AI implementation? Do you have tools that enable seamless orchestration and collaboration, allowing teams to deploy and refine models quickly?
   
   
3. AI security and governance: Embed ethics, security, and compliance into your AI initiatives. Establish processes for ongoing monitoring, maintenance, and optimization to mitigate risks and ensure accountability.
   
   
4. Change management and training: Foster a culture of innovation by building skills, delivering targeted training, and assessing readiness across your organization.
   
   
5. Scaling and continuous improvement: Identify new use cases, measure and communicate AI impact, and continually refine your AI strategy to maximize ROI. Focus on reducing time-to-value by adopting workflows that are adaptable to your specific business needs, ensuring that AI delivers real, measurable outcomes.
   

Generative AI isn’t an industry secret — it’s transforming businesses across sectors, driving innovation, efficiency, and creativity.

Yet, according to our Unmet AI Needs survey, 66% of respondents cited difficulties in implementing and hosting generative AI applications. But with the right strategy, businesses in virtually every industry can gain a competitive edge and tap into AI’s full potential. 

Lead the way to generative AI success

AI leaders hold the key to overcoming the challenges of implementing and hosting generative AI applications. By setting clear goals, streamlining workflows, fostering collaboration, and investing in scalable solutions, they can pave the way for success.

To achieve this, it’s critical to move beyond the chaos of disconnected tools and processes. AI leaders who unify their models, teams, and workflows gain a strategic advantage, enabling them to adapt quickly to changing demands while ensuring security and compliance.

Equipping teams with the right tools, targeted training, and a culture of experimentation transforms generative AI from a daunting initiative into a powerful competitive advantage.

Want to dive deeper into the gaps teams face with developing, delivering, and governing AI? Explore  our Unmet AI Needs report for actionable insights and strategies.

The post Solving the generative AI app experience challenge appeared first on DataRobot.

The culmination of the challenge will be in-person presentations to industry, investors, and community members at the Robotics Summit & Expo in Boston from April 30 to May 1st.

Experts highlight the need for a clear framework when it comes to AI research, given the rapid adoption of artificial intelligence by children and adolescents using digital devices to access the internet and social media.

Research in the International Journal of Computational Vision and Robotics could lead to faster and more accurate robots for high-precision tasks in factories.

DEEP Robotics develops a range of advanced robotic platforms, each designed for specific real-world applications across industrial, public service, and research sectors.

Lite3 is an agile quadruped platform ideal for education, research, and light industrial tasks, offering versatility for developing AI and robotics solutions.
Lite3 supports open source, users can develop advanced perception capabilities such as autonomous navigation, automatic obstacle avoidance, visual localization, environment reconstruction, customizable API for robotics development and AI training

Image credit: Deep Robotics – deeprobotics.cn

X30, a robust quadruped, is tailored for inspection, security, and autonomous navigation in hazardous environments such as industrial sites and power plants.
X30 robot dog conducts autonomous inspection day and night in any weather stably, the operating temperature range of X30 has been extended to between minus 20°C and plus 55°C, the load capacity can be up to 85KG

Image credit: Deep Robotics – deeprobotics.cn

DEEP Robotics Lynx, an off-road wheeled quadruped, is built for outdoor exploration, search-and-rescue missions, providing unmatched mobility on uneven terrain.
DEEP Robotics Lynx all-terrain robot features an agile design and powerful multi-terrain adaptability, combined with a distinctive wheel-legged movement system and AI driven, striking an ideal balance between speed and agility

Image credit: Deep Robotics – deeprobotics.cn

DR01 humanoid robot showcases advanced locomotion for dynamic human-like motion, contributing to research in human-robot interaction and service robotics for task automation.
DR01 humanoid robot boasts highly flexible movement capabilities, adapts to complex environments, and integrates sensing/perception abilities and powerful autonomous learning capabilities

Image credit: Deep Robotics – deeprobotics.cn

Various Challenges for the systems above: Enhancing autonomy, improving power efficiency, and refining adaptability to diverse environments.

Future Goals for the systems:
Integrating AI-driven perception systems for smarter navigation.
Expanding industrial, public safety, and service applications.
Developing robots for seamless, collaborative human-robot interactions with superior performance in real-world scenarios.

Videos:
https://www.youtube.com/watch?v=zxGwOEYYFVo
https://www.youtube.com/watch?v=NNkxlKLMoMM&t=19s
https://www.youtube.com/watch?v=iL833P0Vino
https://www.youtube.com/watch?v=lCFyfh3mLOQ

The content and media above is provided to us by Deep Robotics. www.deeprobotics.cn