News

ChatGPT-Maker’s CEO Sam Altman just disclosed an eye-opening revelation in the Wall Street Journal: Most of the people using ChatGPT are students.

Given that 400 million people now visit the ChatGPT Web site every week, that means approximately 300-350 million of the people using ChatGPT are students (most).

The takeaway: The statistic explains that while ChatGPT can reduce writing time for simple tasks like email by as much as 90% or more, students are the people who have picked-up and run with that realization – not business pros.

That’s a problem for the lion’s share of business people who ‘get’ that AI writing is not simply coming – it’s here – but have yet to add AI to their toolbox.

Essentially: Colleges in the U.S. alone release 4 million new graduates each year into the U.S. workforce.

And you can bet that since 2023 — when ChatGPT became a force to be reckoned with across the globe — most U.S. college graduates walked into their first jobs already knowing how to automate their business writing with AI.

Something tells me their older brothers and sisters have gotten the memo, too.

In other news and analysis on AI writing:

*ChatGPT-Maker Experimenting With Turbo-Charged Creative Writer: OpenAI is currently experimenting with a new AI chatbot it believes produces its best, automated creative writing yet.

Observes OpenAI CEO Sam Altman: “This is the first time I have been really struck by something written by AI.

“It got the vibe of metafiction so right.”

So far, OpenAI has no plans to release the experimental AI writer to the public.

*Google Releases ‘Nearly as Good AI’ That Runs on a Single Chip: Google has released a new family of AI engines that are nearly as good as bleeding edge AI — but are able to run on a single chip.

The theory: By offering somewhat less than state-of-the-art performance, the stripped-down AI will not need a series of expensive chips to produce results.

Instead, the AI can run locally on a desktop, laptop, smartphone or similar hardware – representing major cost savings as compared to going to the cloud for computations.

*New Chinese AI Agent Takes On ChatGPT-Maker: A new, experimental AI agent – dubbed Manus – has gone viral, amazing writers and others with its ability to perform independent, automated tasks on a computer.

The new AI competes directly with a similar AI agent – Operator – which is offered by OpenAI under its $200/month ChatGPT Pro subscription.

Theoretically, writers using the agent-creating software will be able to:

~Automatically research an article on the Web
~Scout for quotes to go along with that research from blogs and press releases
~Auto-write the article in a preferred writing style
~SEO-optimize the article for easy discovery by search engines
~Periodically research the Web for new developments in
the article’s story
~Continually re-rewrite the article as new developments in the article’s story occur

*Local News Media Using AI to Monitor Public Meetings: Nonprofit education news service Chalkbeat is currently using AI to monitor public meetings in about 80 school districts across 30 states.

The tech, dubbed LocalLens, listens-in on the public meetings, then uses AI to transcribe, summarize and archive what’s said.

Observes Eric Gorski, a managing editor at Chalkbeat: “We are going to be in the rooms where we need to be, where the big decisions are being made, but we can’t be everywhere all the time.”

“The summaries are springboards for more reporting. It’s not a replacement for coverage. And we’re not trusting AI to get these things right. It’s more like a news tip.”

Back in the day, when I got my first job in journalism, covering local school district and local government meetings used to be the sole purview of human beings.

And one of those human beings happened to be me.

*Small Business Software Adds AI-Written Reports: Pipedrive, a maker of customer relationship management software, has made it easier for salespeople to auto-generate reports using AI.

Observes Viktoria Ruubel, CPO, Pipedrive: “With AI-powered report creation, sales teams can now shift their focus to what truly matters – analyzing trends, identifying opportunities and making informed decisions within seconds.”

Pipedrive took special care to ensure the reports can be easily generated by natural language prompts.

Plus, the new AI report-writer also comes with 14, prefabricated prompts to generate commonly needed reports.

*AI Fiction Writer Out With an Upgrade: AI creative writing pioneer Sudowrite has released a new update that promises to deliver prose that sounds more original – and eschews clichés.

Dubbed Muse, the new module’s emphasis on originality was engineered in collaboration with hundreds of authors, according to Sudowrite founder James Yu.

Sudowrite currently runs on a number of AI engines, including ChatGPT, Claude and DeepSeek.

*AI-Powered Search Engines Wrong 60% of the Time: AI companies looking to steal Google’s thunder by branching out into search have been hit with a hard dose of reality.

On average, the citations they use to certify their search results to users are wrong more than 60% of the time, according to a new study from the Tow Center – although some tools performed better than others.

Observes writer Andrew Deck: “Perplexity, which brands itself as a tool for research, had the lowest failure rate, answering incorrectly 37% of the time.

“Meanwhile, Grok-3 Search had the highest failure rate at 94%.”

*Microsoft Puts More Daylight Between Itself and ChatGPT-Maker: Microsoft and OpenAI – once the dynamic duo when it came to all things AI – continue to drift apart.

Specifically, Microsoft says its developing in-house AI engines – dubbed MAI — being designed to compete directly with the AI engines that power ChatGPT, Gemini, Claude and Grok.

The news comes on the heels of a report that ChatGPT-maker OpenAI just inked a $11.9 billion dollar deal with CoreWeave, which will provide servers and other infrastructure to help drive OpenAI’s software.

Used to be, Microsoft was the overwhelming, go-to trading partner for such infrastructure services.

*AI BIG PICTURE: AI and Our Future: ChatGPT Competitor Anthropic’s View: This one-hour video from the Council on Foreign Relations offers one of the most lucid perspectives on the anticipated impact of AI in recent memory.

It features an interview with Dario Amodei, CEO, Anthropic – the maker of Claude, a fierce competitor to market-leader ChatGPT.

Not to be missed.

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.

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Over the past decades, roboticists have introduced a wide range of systems with distinct body structures and varying capabilities. As the number of developed robots continuously grows, being able to easily learn about these many systems, their unique characteristics, differences and performance on specific tasks could prove highly valuable.

How does a robot perform as a boss at work? The results of research by Polish scientists published in Cognition, Technology & Work suggest that while robots can command obedience, they are not as proficient at it as humans. The level of obedience towards them is generally lower than towards human authority figures, and work efficiency under the supervision of a robot is lower.

How does a robot perform as a boss at work? The results of research by Polish scientists published in Cognition, Technology & Work suggest that while robots can command obedience, they are not as proficient at it as humans. The level of obedience towards them is generally lower than towards human authority figures, and work efficiency under the supervision of a robot is lower.

Claire chatted to Kaspar Althoefer from Queen Mary University of London about soft robotic manipulators for healthcare and manufacturing.

Kaspar Althoefer is Director of the Centre for Advanced Robotics at Queen Mary University of London (QMUL). His research focuses on soft robotics, tactile perception, intelligent manipulation, and machine learning techniques for sensor signal interpretation. His research advancements have significant applications in robot-assisted minimally invasive surgery, rehabilitation, assistive technologies, and human-robot interactions within a range of scenarios, including manufacturing. Before joining QMUL, he was a Professor at King’s College London, where he also earned his PhD.

From Düsseldorf to Houston, the technology event for autonomy will drive industry progress by addressing regulatory challenges, showcasing innovations, and accelerating global collaboration

An original photograph taken by Felice Frankel (left) and an AI-generated image of the same content. Credit: Felice Frankel. Image on right was generated with DALL-E

By Melanie M Kaufman

For over 30 years, science photographer Felice Frankel has helped MIT professors, researchers, and students communicate their work visually. Throughout that time, she has seen the development of various tools to support the creation of compelling images: some helpful, and some antithetical to the effort of producing a trustworthy and complete representation of the research. In a recent opinion piece published in Nature magazine, Frankel discusses the burgeoning use of generative artificial intelligence (GenAI) in images and the challenges and implications it has for communicating research. On a more personal note, she questions whether there will still be a place for a science photographer in the research community.

Q: You’ve mentioned that as soon as a photo is taken, the image can be considered “manipulated.” There are ways you’ve manipulated your own images to create a visual that more successfully communicates the desired message. Where is the line between acceptable and unacceptable manipulation?

A: In the broadest sense, the decisions made on how to frame and structure the content of an image, along with which tools used to create the image, are already a manipulation of reality. We need to remember the image is merely a representation of the thing, and not the thing itself. Decisions have to be made when creating the image. The critical issue is not to manipulate the data, and in the case of most images, the data is the structure. For example, for an image I made some time ago, I digitally deleted the petri dish in which a yeast colony was growing, to bring attention to the stunning morphology of the colony. The data in the image is the morphology of the colony. I did not manipulate that data. However, I always indicate in the text if I have done something to an image. I discuss the idea of image enhancement in my handbook, “The Visual Elements, Photography”.

An image of a growing yeast colony where the petri dish has been digitally deleted. This type of manipulation could be acceptable because the actual data has not been manipulated, Frankel says. Image credit: Felice Frankel

Q: What can researchers do to make sure their research is communicated correctly and ethically?

A: With the advent of AI, I see three main issues concerning visual representation: the difference between illustration and documentation, the ethics around digital manipulation, and a continuing need for researchers to be trained in visual communication. For years, I have been trying to develop a visual literacy program for the present and upcoming classes of science and engineering researchers. MIT has a communication requirement which mostly addresses writing, but what about the visual, which is no longer tangential to a journal submission? I will bet that most readers of scientific articles go right to the figures, after they read the abstract.

We need to require students to learn how to critically look at a published graph or image and decide if there is something weird going on with it. We need to discuss the ethics of “nudging” an image to look a certain predetermined way. I describe in the article an incident when a student altered one of my images (without asking me) to match what the student wanted to visually communicate. I didn’t permit it, of course, and was disappointed that the ethics of such an alteration were not considered. We need to develop, at the very least, conversations on campus and, even better, create a visual literacy requirement along with the writing requirement.

Q: Generative AI is not going away. What do you see as the future for communicating science visually?

A: For the Nature article, I decided that a powerful way to question the use of AI in generating images was by example. I used one of the diffusion models to create an image using the following prompt:

“Create a photo of Moungi Bawendi’s nano crystals in vials against a black background, fluorescing at different wavelengths, depending on their size, when excited with UV light.”

The results of my AI experimentation were often cartoon-like images that could hardly pass as reality — let alone documentation — but there will be a time when they will be. In conversations with colleagues in research and computer-science communities, all agree that we should have clear standards on what is and is not allowed. And most importantly, a GenAI visual should never be allowed as documentation.

But AI-generated visuals will, in fact, be useful for illustration purposes. If an AI-generated visual is to be submitted to a journal (or, for that matter, be shown in a presentation), I believe the researcher MUST:

• clearly label if an image was created by an AI model;
• indicate what model was used;
• include what prompt was used; and
• include the image, if there is one, that was used to help the prompt.

In the ever-evolving world of artificial intelligence, Google has once again raised the bar with its latest update to the Gemini app. Announced on March 13, 2025, this update introduces groundbreaking personalization features that promise to make Gemini not just a tool, but a tailored extension of its users. With the ability to connect to...

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March 13, 2025 – The world of mobile technology is buzzing with excitement as Android rolls out a groundbreaking update: Auracast support. This cutting-edge feature promises to redefine how we interact with audio in public spaces, particularly for those with hearing aids. With Google and Samsung devices leading the charge, this free upgrade is set...

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A series of QUT research studies have shed light on the importance of involving individuals with intellectual disabilities in the development of assistive technologies.

Decision-making is complex, but getting the right insights shouldn’t be.

However, business leaders often face delays due to traditional analytics workflows and overwhelmed data teams. At the same time, AI leaders encounter lengthy deployment cycles and integration challenges.

In fact, 66% report lacking the right tools to deploy AI solutions that align with company goals. Integration challenges and long deployment cycles—often seven months or more—delay progress and make it harder to meet executive expectations.

Generative AI and agentic AI promise a way forward, but adoption remains difficult. 77% of business leaders fear they are already falling behind and are pushing their teams to accelerate implementation.

The answer isn’t more complex tooling—it’s pre-built, configurable agentic AI apps. 

These agentic AI apps empower AI leaders to scale AI faster while giving business leaders the instant, intuitive, and reliable AI solutions they seek.

Roadblocks to AI-driven answers

While AI holds the promise of transforming decision-making, several entrenched obstacles continue to hinder its effective implementation:

•  Overwhelmed data and AI teams:  The increasing demand for AI-powered insights is stretching teams thin. Time-sensitive requests pile up faster than they can be addressed, leading to bottlenecks and burnout. In addition, AI teams face challenges in scaling solutions efficiently, hindering timely adoption and impact.
  
  
• Slow AI deployment and orchestration: Even when AI solutions are available, moving them from concept to production is a significant challenge. Integrating with enterprise systems, ensuring data is AI-ready, and aligning with governance policies can take months — far too long for today’s fast-paced business environment.
  
  
• Limited self-service, complex queries: Traditional Business Intelligence (BI) dashboards provide visibility, but real-time ad-hoc analysis with AI recommendations and insights still requires SQL, custom queries, or advanced analytics — making business users reliant on technical teams. Instead of acting on insights, they find themselves waiting for data analysts to generate reports.
  
  
• Security and compliance hurdles: Strict data privacy regulations like GDPR and HIPAA, along with internal security controls, are essential for protecting sensitive information. However, each data request requires approvals, permissions, and secure handling, adding friction that slows down access to critical business insights.
  

These persistent challenges underscore the need for a transformative approach to getting businesses the insights they want as fast as they need — one that streamlines processes and empowers both business and AI teams to achieve faster, more reliable outcomes.

Move from data to decisions instantly with agentic AI

Business leaders need a faster, more intuitive way to get AI-powered insights without overburdening technical teams or waiting on complex reports. 

This is why the Talk to My Data agentic AI app was developed.

Unlike traditional BI dashboards that require constant human input, the Talk to My Data agentic AI app actively retrieves and synthesizes data, using a chain-of-thought prompting to deliver business-ready answers in real time.

For business leaders, this means:

• No more navigating BI dashboards, submitting insight requests, or relying on SQL queries.  
  
• The ability to ask questions in plain language and getting instant, contextual responses.
  

For AI leaders, this means: 

• Removing manual query bottlenecks.
  
• Accelerating AI adoption while maintaining governance and scalability.
  

With schema intelligence, enterprise data integration, and built-in compliance, Talk to My Data enables AI teams to deploy faster, reduce operational overhead, and align AI with business goals.

A GPS for business decisions

Think of the Talk to My Data agentic app as a GPS for your business decisions. Instead of mapping the route yourself, just ask where you need to go—and the right path appears instantly.

But just like a GPS doesn’t suggest random routes, Talk to My Data factors in business context, historical trends, and predictive insights to deliver the most relevant answers.

• Versatile applications: Whether you’re optimizing sales performance, tracking financial health, or identifying operational bottlenecks, the AI dynamically retrieves, interprets, and refines queries—ensuring both speed and accuracy.
  
  
• Comprehensive outputs: Talk to My Data provides visual summaries, tables, and even source code for deeper exploration, allowing AI teams to customize or extend analytics as needed.
  
  
• Empowered decision-making: By eliminating delays in data access, leaders at all levels can identify high-value opportunities, pivot quickly, and maximize ROI, all while reducing dependence on technical teams for routine analytics.

“With Talk to My Data agentic AI app, business leaders and their teams can confidently make informed decisions without waiting on technical support. AI leaders can drive faster AI adoption and ensure scalability, while empowering business users to ask questions, get trusted answers, and visualize insights instantly—all on their terms.”
– Justin Swansburg, VP Applied AI & Technical Field Leads

How Talk to My Data agentic AI app empowers AI and business teams drive impact

The Talk to My Data app offers several features designed to enhance efficiency and effectiveness:

• Built-in AI, security, and app logic
  Deploying AI solutions often requires extensive customization and integration. However, with built-in AI logic, security logic, and app logic, AI teams can quickly customize the app to their organization’s unique business needs. This approach enables business users to immediately leverage AI for reporting and insights, minimizing the need for extensive adjustments. 
  
  
• Seamless data integration
  Working with data from various systems—such as Databricks, Snowflake, Google BigQuery, and even local files—often presents challenges due to manual integration processes.
  
  The Talk to My Data app addresses this by incorporating a built-in schema layer that automates data alignment, reducing the need for manual reconciliation across diverse data tables and sources. This automation minimizes the time data and AI teams spend resolving data issues, enabling business leaders to access faster, more reliable insights.
  

• Cost-effective system optimization
  Selecting appropriate AI systems is crucial for balancing performance and cost. By offering a library of LLMs tailored to specific business needs, AI teams can choose underlying components that optimize expenses while maintaining effectiveness. This flexibility ensures that AI initiatives remain both efficient and economical.
  
  
• Natural language interaction
  Accessing data insights shouldn’t require technical expertise. By enabling natural language queries, users can explore data, uncover insights, and make decisions faster, without the need for SQL or waiting on analysts for routine queries.
  
  For technical teams, the availability of underlying Python or SQL code allows for review, modification, and reuse, offering deeper analytical capabilities when needed.
  
  
• Simplified advanced analytics
  Leveraging AI-powered insights and Python-based analytics tools without coding can democratize data analysis. Users can generate charts, tables, and source code to answer questions effortlessly, making advanced analytics accessible to a broader audience.
  
  
• Built-in security and compliance
  Ensuring compliance with standards such as GDPR and HIPAA is essential in today’s data-driven environment. Built-in security features ensure that data access is secure, allowing decision-making processes to proceed without compromising compliance.
  
  
• Industry-specific adaptability
  Different industries face unique challenges. By offering real-time visualizations and analyses tailored to specific industry needs, users can gain precise, context-aware insights.
  
  Customizable prompts and visualizations—including charts, graphs, and tailored recommendations—enable deeper analysis and informed decision-making, aligning with the specific priorities of each industry.
  

The right answers from your data right when you need them

​
​Imagine your AI team delivering a powerful agentic AI experience that your business leaders rely on daily to obtain precise answers by simply querying your extensive data sources.

That’s what’s possible with the Talk to My Data agentic AI app.

Seamlessly integrate generative and agentic AI into your organization’s decision-making process, eliminating delays, complexities, and technical dependencies.

No more enduring lengthy AI development cycles, integration challenges, or concerns over security and governance. 

Discover how it works, and invite your team to experience it firsthand.

The post Talk to My Data: Instant, explainable answers with agentic AI appeared first on DataRobot.

For the first time ever, AGILOX will showcase four different AMR models operating simultaneously in a live tradeshow environment. Attendees will experience real-world automation in action just like in an actual manufacturing facility.

Locomotion, the ability to move from one place to another, is an essential survival strategy for virtually every organism. Adapting to the unpredictable terrain they run into, cells, fungi and microorganisms autonomously move and change shape to explore their environments, while animals run, crawl, slither, roll and jump.

In an unexpected move, Apple has rolled out iOS 18.3.2, a crucial security update for iPhone users worldwide. Released on March 12, 2025, this update addresses a significant vulnerability in the WebKit engine powering Safari, which could have allowed hackers to exploit malicious web content and gain remote access to your device. If you haven’t...

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March 12, 2025 – The mobile gaming world is buzzing with seismic news: Niantic, the innovative force behind Pokémon GO, has sold its gaming division to Scopely for a staggering $3.5 billion. This blockbuster deal, announced today, marks a pivotal shift for both companies and raises big questions about the future of augmented reality (AR)...

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